mbanerjeepalmer+listennotes 's Listen Later: Recent Episodes
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Podcast: The David McWilliams Podcast (LS 54 · TOP 0.5% what is this?)
Episode: The Trump Epoch: Entering a New Supercyle
Pub date: 2024-11-12
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Live from Kilkenomics, we unpack the ‘Trump Epoch,’ a transformative shift that’s reshaping America and reverberating across the globe. From disrupting political dynasties to setting the stage for a new supercycle, we dive into how Trumpism is redefining the world’s economic and political landscapes. Expect a deep dive into the rise of disaffected voices, shifting priorities like immigration and cost of living, and the lasting impact of this new era. If you’re curious about where America—and by extension, the world—is headed, this episode maps out the road ahead. Join the gang! https://plus.acast.com/s/the-david-mcwilliams-podcast.
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The podcast and artwork embedded on this page are from David McWilliams & John Davis, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The David McWilliams Podcast (LS 54 · TOP 0.5% what is this?)
Episode: The Trumpquake: Power, Divisions, and a New World Order
Pub date: 2024-11-07
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The unthinkable has happened: Donald Trump has swept the Presidency, Congress, the Senate, the popular vote—even the Supreme Court stands behind him. In today’s episode, we dive into the five emotional stages America seems to be racing through, from denial to acceptance, as a the Trumpquake sends shockwaves across the world. With Bitcoin soaring and alliances shifting, what does this all mean for the globe—from an empowered Netanyahu in Israel to an isolated Europe led by a wary Berlin? We’ll explore the billionaire backers pulling strings, the curious coalition of Bitcoiners and evangelicals in Trump’s camp, and the simmering question of whether this mandate signals peak plutocracy. Tune in as we confront the realities of this bold new era—where old rules don’t apply, and power takes on a whole new meaning. Join the gang! https://plus.acast.com/s/the-david-mcwilliams-podcast.
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The podcast and artwork embedded on this page are from David McWilliams & John Davis, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The David McWilliams Podcast (LS 54 · TOP 0.5% what is this?)
Episode: Are Strong Institutions Enough? Unpacking the Nobel Prize in Economics
Pub date: 2024-10-17
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In this episode, we break down the Nobel Prize in Economics awarded to Darren Acemoglu, Simon Johnson, and James Robinson for their work on development economics. Their central claim? Strong institutions are the key to national success. But is that the whole story? With the richest 20% of countries now 30 times wealthier than the poorest 20%, we ask if focusing solely on institutions ignores deeper issues like geography, culture, and historical legacy. From South Korea’s meteoric rise to Jamaica’s recent turnaround, we explore whether stable institutions are enough to solve the world’s growing inequality. Are these Nobel winners missing something critical, or is this a blueprint for global prosperity? Join the gang! https://plus.acast.com/s/the-david-mcwilliams-podcast.
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The podcast and artwork embedded on this page are from David McWilliams & John Davis, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The David McWilliams Podcast (LS 54 · TOP 0.5% what is this?)
Episode: Is Your City Dying?
Pub date: 2024-10-24
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In this episode, we dive into the decline of Dublin’s inner city, a reflection of urban decay across the English-speaking world. Drawing on Jane Jacobs’ urban theories, we explore how cities like Kreuzberg in Berlin and Hackney in London turned around from dereliction to thriving hubs—and ask whether Dublin can do the same. We discuss the importance of repopulating cities with residents, not just tourists, and why mixed-use spaces are crucial for community vibrancy. Can initiatives like “meanwhile use” transform dead zones into lively areas again? Or is Dublin—and cities like it—stuck in a cycle of neglect and decay? Join us as we explore the future of urban living and what it will take to revive dying cities. Join the gang! https://plus.acast.com/s/the-david-mcwilliams-podcast.
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The podcast and artwork embedded on this page are from David McWilliams & John Davis, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The David McWilliams Podcast (LS 54 · TOP 0.5% what is this?)
Episode: Scotland The Brave
Pub date: 2024-10-29
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I'm up in Glasgow and we're devoting this podcast to all things Scottish, kkicking off with the amazing Scottish Enlightenment. Why did 18th-century Scotland emerge as a crucible for radical ideas, drawing intellectuals, inventors, and innovators alike? The Scottish Enlightenment marked an era where thinkers like David Hume and Adam Smith thrived amidst newfound economic growth, sparked by an influx of wealth from trade routes and ventures (like Scotland's ill-fated attempt to build a canal in Panama). But unlike France, where revolutionary fervor overthrew established order, Scotland’s intellectual revolution developed under the stability of the British Empire, with Scots integrating into its growing power. As thinkers flocked to Glasgow's and Edinburgh’s salons and Masonic lodges, they fostered advancements in empiricism, economics, and even steam technology, laying foundations for the industrial age. With Ireland facing a similar boom today, we explore whether prosperity will again inspire an era of transformative thinking.
Buy the new book here: https://linktr.ee/moneydavidmcwilliams
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The podcast and artwork embedded on this page are from David McWilliams & John Davis, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The David McWilliams Podcast (LS 54 · TOP 0.5% what is this?)
Episode: What's Happening in Germany?
Pub date: 2024-11-14
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This week, we’re diving into Germany's political shake-up as the country heads for a snap election this February. Beyond coalition drama, there’s a deeper story here: Germany’s cultural obsession with saving and fiscal restraint—a "fetish for frugality" that hasn’t always served them well. Seen as both a national virtue and a strict economic doctrine, balanced budgets are gospel, and spending is often viewed with suspicion. How will this election impact Germany's economic direction? And could a new leadership shift the country's stance on growth, flexibility, and spending? Join us this week to find out. Join the gang! https://plus.acast.com/s/the-david-mcwilliams-podcast.
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The podcast and artwork embedded on this page are from David McWilliams & John Davis, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Monocle on Design (LS 44 · TOP 1% what is this?)
Episode: Mobile Phone Museum
Pub date: 2024-10-17
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We meet the co-founder of the virtual Mobile Phone Museum to discuss the rich history and design diversity of these portable devices.
See omnystudio.com/listener for privacy information.
The podcast and artwork embedded on this page are from Monocle, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Ma parole (LS 41 · TOP 1.5% what is this?)
Episode: Comment s'indigner, avec Salomé Saqué : l'intégrale
Pub date: 2024-08-21
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durée : 00:58:49 - Ma parole ! - par : Bertrand Périer, Marie Dosé - - réalisation : François Teste
The podcast and artwork embedded on this page are from France Culture, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Piquantes, by Alexandra Guerain et Camille Farrugia
Episode: Ce que nos ruptures nous ont appris
Pub date: 2024-10-09
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Salut les petits piments ! Ça y est, la rupture est digérée ? Dans ce cinquième épisode de Piquantes, Alexandra Guerain et Camille Farrugia parlent de l'après rupture : comment ça se passe, de se remettre à dater ? À draguer ? Quelles leçons tire-t-on de nos histoires précédentes, et de la façon dont elles se sont terminées ? Nouveaux green flags, nouveaux red flags, nouveaux besoins et nouvelles envies : après la fin d'une histoire, tout peut changer pour la suite.
Piquantes, un podcast de @holycamille et @alexandraguerain.
Produit et réalisé par Benjamin Saeptem Hours / TDA Prod.
Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.
The podcast and artwork embedded on this page are from Alexandra Guerain et Camille Farrugia, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Liste de lecture (LS 53 · TOP 0.5% what is this?)
Episode: La place d’Annie Ernaux
Pub date: 2024-10-02
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Aujourd’hui, on parle de La place, de Annie Ernaux !
Un des plus beaux livres jamais écrit par une fille sur son père ; une réflexion sur les classes sociales, qui a inspiré plein d’auteurs contemporains (coucou Edouard Louis et Nicolas Mathieu) ; et surtout un style d’écriture inédit qui a renouvelé la Littérature, et qui a gagné un Prix Nobel.
Découvrez le plus court des grands livres, dans ce nouvel épisode de Liste de lecture.
Un podcast fabriqué par Ambre Chalumeau et Alice Martinot-Lagarde
Montage : Juliette Paris
Lecture : Jenna Castetbon
Musique : Adrien Houssier
Contact : pierreyvespietri@bangumi.fr
The podcast and artwork embedded on this page are from Ambre Chalumeau, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Le Phil d'Actu - Philosophie et Actualité
Episode: Israël / Palestine : une philosophie de la légitime défense
Pub date: 2024-10-09
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Et si la légitime défense était un concept philosophique... problématique ?
Cette semaine marque un triste anniversaire : la commémoration des épouvantables massacres du 7 octobre par le Hamas, et une année de représailles sanglantes par Israël. Une année marquée par un déchaînement de souffrance, de fureur, de destruction.
L’argument employé depuis 1 an pour justifier cette folie meurtrière, c’est qu’Israël aurait un droit légitime à se défendre. Mais jusqu’où la défense est-elle légitime ?
Aujourd’hui, je vous propose une philosophie de la légitime défense. On va parler droit, violence, justice, du conflit israélo-palestinien, de la philosophie d’Elsa Dorlin, et bien sûr, de légitime défense.
🚨 ALERTE : LE PHIL D'ACTU EST NOMME AU PARIS PODCAST FESTIVAL ! 🚨
Il est donc en lice pour le prix du public. J'ai besoin de vous !
Pour voter pour le Phil d'Actu, rdv sur ce lien et choisissez le podcast n°3 !
Ca prend 1mn, et vous pouvez voter jusqu'au 12 octobre.
Merci pour votre soutien ! 💜
Le Phil d'Actu, c'est le podcast engagé qui met la philosophie au cœur de l'actualité !
Ce podcast est 100% indépendant, gratuit, sans publicité. Il ne survit que grâce à vos dons.
🙏 Pour me soutenir, vous pouvez faire un don, ponctuel ou régulier, sur cette page.
💜 Merci pour votre soutien !
⭐Si vous aimez l'épisode, n'oubliez pas de vous abonner, de mettre 5 étoiles, et de le partager sur les réseaux sociaux.
Pour ne rien manquer du Phil d'Actu, suivez-moi sur Instagram !
Hébergé par Ausha. Visitez ausha.co/politique-de-confidentialite pour plus d'informations.
The podcast and artwork embedded on this page are from Alice de Rochechouart, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Guns for Hire
Episode: Mercenary meatgrinder: The price of Bakhmut
Pub date: 2024-06-26
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Send Alia a Text Message
Alia is joined by the Russian reporter and photojournalist, David Frenkel, to consider the staggering toll of the “Bakhmut meatgrinder”– Russia’s bloodiest battle since the Second World War. They talk through his latest report from independent Russian news platform Mediazona and the BBC Russian Service, “The Price of Bakhmut”, and David’s methodology for determining which prisoners were recruited and from where. David also examines the numbers that were killed, the question marks over whether promised death payments were made, and why the Wagner Group turned to prisons for manpower in the first place.
“This is a long tradition from Soviet times when inmates were used for building factories and deforestation in Siberia and so basically, they came back to that old idea to use inmates as soldiers.”
The Guns for Hire podcast is written, produced and hosted by Dr Alia Brahimi.
The podcast and artwork embedded on this page are from Atlantic Council, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Guns for Hire
Episode: What makes Colombian mercenaries so interesting?
Pub date: 2024-05-29
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Send Alia a Text Message
Host Alia Brahimi speaks with Dr Andrés Macías, a Bogota-based expert on Colombian mercenaries. They begin by discussing the explosive case of 26 Colombian nationals arrested for their part in the 2021 assassination of Haitian president Jovenel Moïse. Andrés goes on to look at the thousands of Colombians who enlist under the banner of the UAE, what’s driving hundreds of Colombians to sign up to fight Russia in Ukraine, and what makes former Colombian soldiers so interesting to the international private security market.
“What is the Colombian government doing with the huge number of veterans that we have had throughout the years? Because actually, we don’t know so much about what they are doing, what activities they are performing, and what is their status.”
The Guns for Hire podcast is written, produced and hosted by Dr Alia Brahimi.
The podcast and artwork embedded on this page are from Atlantic Council, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: People I (Mostly) Admire (LS 62 · TOP 0.1% what is this?)
Episode: 142. What’s Impacting American Workers?
Pub date: 2024-10-12
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David Autor took his first economics class at 29 years old. Now he’s one of the central academics studying the labor market. The M.I.T. economist and Steve dissect the impact of technology on labor, spar on A.I., and discuss why economists can sometimes be oblivious.
- SOURCES:
- David Autor, professor of economics at the Massachusetts Institute of Technology.
- RESOURCES:
- "Does Automation Replace Experts or Augment Expertise? The Answer Is Yes," by David Autor (Joseph Schumpeter Lecture at the European Economic Association Annual Meeting, 2024).
- “Applying AI to Rebuild Middle Class Jobs,” by David Autor (NBER Working Paper, 2024).
- “New Frontiers: The Origins and Content of New Work, 1940–2018,” by David Autor, Caroline Chin, Anna Salomons, and Bryan Seegmiller (The Quarterly Journal of Economics, 2024).
- “Bottlenecks: Sectoral Imbalances and the US Productivity Slowdown,” by Daron Acemoglu, David Autor, and Christina Patterson (NBER Macroeconomics Annual, 2024).
- "Good News: There’s a Labor Shortage," by David Autor (The New York Times, 2021).
- "David Autor, the Academic Voice of the American Worker," (The Economist, 2019).
- “Why Are There Still So Many Jobs? The History and Future of Workplace Automation,” by David Autor (The Journal of Economic Perspectives, 2015).
- “The Growth of Low-Skill Service Jobs and the Polarization of the US Labor Market,” by David Autor and David Dorn (The American Economic Review, 2013).
- “The China Syndrome: Local Labor Market Effects of Import Competition in the United States,” by David Autor, David Dorn, and Gordon H. Hanson (The American Economic Review, 2013).
- EXTRAS:
- "What Do People Do All Day?" by Freakonomics Radio (2024).
- "Daron Acemoglu on Economics, Politics, and Power," by People I (Mostly) Admire (2024).
- "You Make Me Feel Like a Natural Experiment," by People I (Mostly) Admire (2022).
- "In Search of the Real Adam Smith," series by Freakonomics Radio (2022).
- "Max Tegmark on Why Superhuman Artificial Intelligence Won’t be Our Slave," by People I (Mostly) Admire (2021).
- "Automation," by Last Week Tonight With John Oliver (2019).
The podcast and artwork embedded on this page are from Freakonomics Radio + Stitcher, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Guns for Hire
Episode: Mercenaries and Gaza
Pub date: 2024-01-09
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In Episode 10 of the Guns for Hire podcast, host Alia Brahimi is joined by Renad Mansour, an expert on Iraq, Iran and allied groups. As the region convulses from the war in Gaza, they begin by assessing reports that the Wagner Group has been tasked with transferring a Russian air defence system from Syria to Hizballah in Lebanon.
They go on to discuss the rivalry between Russia and Iran in the Middle East, what is motivating the non-state actors leading the bid to avenge Gazans, and whether elements of the Iran-backed Shi’a militia that have fanned out across the region can be classed as mercenaries. Alia and Renad also consider parallels between the campaign in Gaza and the violence and futility of the 2003 Iraq war.
“There will be within these networks and this massive web of [Shi’a] armed groups those that are economically inclined, those that do see economic opportunity in trade or in taking advantage of conflict”
The Guns for Hire podcast is written, produced and hosted by Dr Alia Brahimi.
The podcast and artwork embedded on this page are from Atlantic Council, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Syntax - Tasty Web Development Treats (LS 61 · TOP 0.1% what is this?)
Episode: 829: 14 Web Development Tips I Wish I Knew Sooner
Pub date: 2024-10-02
Scott and Wes share 14 game-changing tips they wish they’d known earlier in their web dev careers. From embracing tools to learning by doing, these insights will help you level up your skills faster!
Show Notes * 00:00 Welcome to Syntax! * 02:09 Brought to you by Sentry.io. * 03:45 13 Web Development Tips I Wish I Knew Sooner. * 03:49 Number 1 - No one is all-knowing. * 05:39 Number 2 - People with extremely strong opinions. * 11:13 Number 3 - Using tools to help you isn’t a bad thing. * 12:34 Number 4 - Approach new technology with a mixed dose of skepticism and open-mindedness. * 16:05 Number 5 - Things make more sense the more you actually use them. * 18:40 Number 6 - Willingness to change your mind is a strong skill. * 20:06 Number 7 - Doing is better than reading or watching. * 22:29 Number 8 - Asking questions is good. * 26:45 Number 9 - Everyone doesn’t learn the same. * 30:41 Number 10 - You don’t need a SAAS for everything. * 35:53 Number 11 - You don’t need to worry about scale. * 37:49 Number 12 - Learning the fundamentals will always pay off. * 40:07 Number 13 - Working in public will make you more hireable. * 42:48 Number 14 - You can interview without accepting a job offer. * 45:29 Sick Picks & Shameless Plugs.
Sick Picks * Scott: Glasses. * Wes: Oxo Whisk, Danish Whisk.
Shameless Plugs * Scott: Syntax on YouTube.
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Scott: X Instagram Tiktok LinkedIn Threads
Randy: X Instagram YouTube Threads
The podcast and artwork embedded on this page are from Wes Bos & Scott Tolinski - Full Stack JavaScript Web Developers, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Syntax - Tasty Web Development Treats (LS 61 · TOP 0.1% what is this?)
Episode: 816: Why Your CSS Sucks
Pub date: 2024-09-02
Scott and Wes break down why your CSS might suck—from misusing specificity to not leveraging CSS variables. Tune in as they dive into common pitfalls that are making your stylesheets a hot mess.
Show Notes * 00:00 Welcome to Syntax! * 00:33 Breakdancing in the Olympics. * 05:29 Brought to you by Sentry.io. * 05:44 Why your CSS sucks. * 07:01 You’re styling the wrong element. * 11:01 Nesting too deep. * 12:37 You don’t understand specificity. * 14:56 Your classes don’t use a system. * 16:24 You’re using values instead of CSS vars. * 20:16 You don’t understand block vs inline vs inline-block. + CSS Logical Properties * 21:16 You aren’t using the right tool for the job. + CSS Flexbox, CSS Grid. * 24:15 You’re setting the value in too many places. * 24:31 You’re scoping to tightly or not tightly enough.
Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads
Wes: X Instagram Tiktok LinkedIn Threads
Scott: X Instagram Tiktok LinkedIn Threads
Randy: X Instagram YouTube Threads
The podcast and artwork embedded on this page are from Wes Bos & Scott Tolinski - Full Stack JavaScript Web Developers, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Syntax - Tasty Web Development Treats (LS 61 · TOP 0.1% what is this?)
Episode: 821: Is Tauri the Electron Killer?
Pub date: 2024-09-13
In this episode of Syntax, Wes and Scott talk with Daniel Thompson-Yvetot about Tauri. They dive into what Tauri is, the motivations behind its development, its open-source ecosystem, use cases, and more.
Show Notes * 00:00 Welcome to Syntax! * 02:01 What is Tauri? * 02:59 What’s new in Tauri 2.0? * 06:41 The benefits of Tauri over Electron * 11:28 Can you use Node? * 14:21 Mac, Linux, and Windows + Verso + Servo * 25:05 How does Tauri make money? + CrabNebula * 30:05 Brought to you by Sentry.io * 30:30 Accessing Swift from JavaScript * 31:44 What’s the hardest part of a project like this? + Haptics Plugin * 37:00 Some of the apps that have shipped with Tauri + Cody + GitButler + Tauri Discord + Awesome Tauri * 43:18 The future of Tauri * 50:23 Sick Picks & Shameless Plugs
Links * Rustlings * Cassidy Williams
Sick Picks * Daniel: 5secondfilms
Shameless Plugs * Guest: Manufacturing European Software (Coming Soon)
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Wes: X Instagram Tiktok LinkedIn Threads
Scott: X Instagram Tiktok LinkedIn Threads
Randy: X Instagram YouTube Threads
The podcast and artwork embedded on this page are from Wes Bos & Scott Tolinski - Full Stack JavaScript Web Developers, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Syntax - Tasty Web Development Treats (LS 61 · TOP 0.1% what is this?)
Episode: 823: Is Cursor AI the VS Code Killer?
Pub date: 2024-09-18
Scott and Wes serve up a discussion on AI coding assistants with a deep look at Cursor AI, exploring its unique features like multi-line auto-complete and Smart Rewrites. They also discuss why Cursor’s intuitive UI stands out and tackle the big question: is it worth the investment?
Show Notes * 00:00 Welcome to Syntax! * 01:16 Brought to you by Sentry.io. * 01:48 Handling objections around AI assistants. * 02:55 Context windows and how they’re improving functionality. + Syntax.fm Episode 728 with Kevin Hou of Codeium * 04:08 Cursor’s UI. * 04:51 This is cool, why is it not a plugin? * 08:12 What makes the UI interesting. * 09:13 Smart Rewrites. * 11:44 It can create multiple files. * 13:05 Using the chat interface. * 16:32 Another chat example. * 20:22 The main features of Cursor. * 21:55 Multi-line auto-complete. * 23:55 Using docs for additional context. * 27:26 AI is here to help you, not replace you. * 33:27 Is it worth it? * 33:55 The pricing. * 44:10 Sick Picks & Shameless Plugs.
Sick Picks * Scott: The Iron Historian, Scott’s Salt & Pepper Mills. * Wes: Oxo Salt & Pepper Mills.
Shameless Plugs * Scott: Syntax.fm Zed Theme.
Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads
Wes: X Instagram Tiktok LinkedIn Threads
Scott: X Instagram Tiktok LinkedIn Threads
Randy: X Instagram YouTube Threads
The podcast and artwork embedded on this page are from Wes Bos & Scott Tolinski - Full Stack JavaScript Web Developers, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Syntax - Tasty Web Development Treats (LS 61 · TOP 0.1% what is this?)
Episode: 828: Logging in Verification: Magic Links, 2FA, SMS Codes
Pub date: 2024-09-30
Scott and Wes serve up the pros and cons of modern authentication methods like magic links, 2FA, and SMS codes. Learn how each technique works, the security trade-offs, and what might be the best approach for your apps.
Show Notes * 00:00 Welcome to Syntax! * 00:11 Brought to you by Sentry.io. * 00:34 Logging in Verification. * 01:09 Magic Links. + 01:24 Pros of magic links. + 03:50 How magic links work. + 04:25 Cons to magic links. * 06:21 Magic Sessions. + 06:37 Using email verification. + 07:12 Using code verification. + 07:55 Previously trusted device verification. + 08:14 Classic email and verification process. + 09:54 Email Code. * 10:51 Gmail verification options. * 12:01 OAuth.
Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads
Wes: X Instagram Tiktok LinkedIn Threads
Scott: X Instagram Tiktok LinkedIn Threads
Randy: X Instagram YouTube Threads
The podcast and artwork embedded on this page are from Wes Bos & Scott Tolinski - Full Stack JavaScript Web Developers, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Recsperts - Recommender Systems Experts
Episode: #24: Video Recommendations at Facebook with Amey Dharwadker
Pub date: 2024-10-01
In episode 24 of Recsperts, I sit down with Amey Dharwadker, Machine Learning Engineering Manager at Facebook, to dive into the complexities of large-scale video recommendations. Amey, who leads the Video Recommendations Quality Ranking team at Facebook, sheds light on the intricate challenges of delivering personalized video feeds at scale. Our conversation covers content understanding, user interaction data, real-time signals, exploration, and evaluation techniques.
We kick off the episode by reflecting on the inaugural VideoRecSys workshop at RecSys 2023, setting the stage for a deeper discussion on Facebook’s approach to video recommendations. Amey walks us through the critical challenges they face, such as gathering reliable user feedback signals to avoid pitfalls like watchbait. With a vast and ever-growing corpus of billions of videos—millions of which are added each month—the cold start problem looms large. We explore how content understanding, user feedback aggregation, and exploration techniques help address this issue. Amey explains how engagement metrics like watch time, comments, and reactions are used to rank content, ensuring users receive meaningful and diverse video feeds.
A key highlight of the conversation is the importance of real-time personalization in fast-paced environments, such as short-form video platforms, where user preferences change quickly. Amey also emphasizes the value of cross-domain data in enriching user profiles and improving recommendations.
Towards the end, Amey shares his insights on leadership in machine learning teams, pointing out the characteristics of a great ML team.
Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.
Don't forget to follow the podcast and please leave a review
- (00:00) - Introduction
- (02:32) - About Amey Dharwadker
- (08:39) - Video Recommendation Use Cases on Facebook
- (16:18) - Recommendation Teams and Collaboration
- (25:04) - Challenges of Video Recommendations
- (31:07) - Video Content Understanding and Metadata
- (33:18) - Multi-Stage RecSys and Models
- (42:42) - Goals and Objectives
- (49:04) - User Behavior Signals
- (59:38) - Evaluation
- (01:06:33) - Cross-Domain User Representation
- (01:08:49) - Leadership and What Makes a Great Recommendation Team
- (01:13:01) - Closing Remarks
Links from the Episode:* Amey Dharwadker on LinkedIn * Amey's Website * RecSys Challenge 2021 * VideoRecSys Workshop 2023 * VideoRecSys + LargeRecSys 2024
Papers:
- Mahajan et al. (2023): CAViaR: Context Aware Video Recommendations
- Mahajan et al. (2023): PIE: Personalized Interest Exploration for Large-Scale Recommender Systems
- Raul et al. (2023): CAM2: Conformity-Aware Multi-Task Ranking Model for Large-Scale Recommender Systems
- Zhai et al. (2024): Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations
- Saket et al. (2023): Formulating Video Watch Success Signals for Recommendations on Short Video Platforms
- Wang et al. (2022): Surrogate for Long-Term User Experience in Recommender Systems
- Su et al. (2024): Long-Term Value of Exploration: Measurements, Findings and Algorithms
General Links:
- Follow me on LinkedIn
- Follow me on X
- Send me your comments, questions and suggestions to marcel.kurovski@gmail.com
- Recsperts Website
The podcast and artwork embedded on this page are from Marcel Kurovski, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Software Misadventures (LS 28 · TOP 10% what is this?)
Episode: LLMs are like your weird, over-confident intern | Simon Willison (Datasette)
Pub date: 2024-09-10
Known for co-creating Django and Datasette, as well as his thoughtful writing on LLMs, Simon Willison joins the show to chat about blogging as an accountability mechanism, how to build intuition with LLMs, building a startup with his partner on their honeymoon, and more.
Segments:
(00:00:00) The weird intern
(00:01:50) The early days of LLMs
(00:04:59) Blogging as an accountability mechanism
(00:09:24) The low-pressure approach to blogging
(00:11:47) GitHub issues as a system of records
(00:16:15) Temporal documentation and design docs
(00:18:19) GitHub issues for team collaboration
(00:21:53) Copy-paste as an API
(00:26:54) Observable notebooks
(00:28:50) pip install LLM
(00:32:26) The evolution of using LLMs daily
(00:34:47) Building intuition with LLMs
(00:43:24) Democratizing access to automation
(00:47:45) Alternative interfaces for language models
(00:53:39) Is prompt engineering really engineering?
(00:58:39) The frustrations of working with LLMs
(01:01:59) Structured data extraction with LLMs
(01:06:08) How Simon would go about building a LLM app
(01:09:49) LLMs making developers more ambitious
(01:13:32) Typical workflow with LLMs
(01:19:58) Vibes-based evaluation
(01:23:25) Staying up-to-date with LLMs
(01:27:49) The impact of LLMs on new programmers
(01:29:37) The rise of 'Goop' and the future of software development
(01:40:20) Being an independent developer
(01:42:26) Staying focused and accountable
(01:47:30) Building a startup with your partner on the honeymoon
(01:51:30) The responsibility of AI practitioners
(01:53:07) The hidden dangers of prompt injection
(01:53:44) “Artificial intelligence” is really “imitation intelligence”
Show Notes:
Simon’s blog: https://simonwillison.net/
Natalie’s post on them building a startup together: https://blog.natbat.net/post/61658401806/lanyrd-from-idea-to-exit
Simon’s talk from DjangoCon: https://www.youtube.com/watch?v=GLkRK2rJGB0
Simon on twitter: https://x.com/simonw
Datasette: https://github.com/simonw/datasette
Stay in touch:
👋 Make Ronak’s day by leaving us a review and let us know who we should talk to next! hello@softwaremisadventures.com
Music: Vlad Gluschenko — Forest License: Creative Commons Attribution 3.0 Unported: https://creativecommons.org/licenses/by/3.0/deed.en
The podcast and artwork embedded on this page are from Ronak Nathani, Guang Yang, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Invest Like the Best with Patrick O'Shaughnessy (LS 67 · TOP 0.05% what is this?)
Episode: Bret Taylor - The Agent Era - [Invest Like the Best, EP.386]
Pub date: 2024-09-03
My guest today is Bret Taylor. His resume is absurd. He built google maps--famously rewriting the whole thing in a weekend. He was the CTO of Facebook in critical years. He founded Quip. He was the chair of the board at Twitter. He was the co-CEO of Salesforce...the incredible list goes on. Now, Bret is the co-founder of Sierra, a conversational AI platform for businesses, and he is the chairman of the board at OpenAI. We discuss the past, present, and future of AI agents: new programs that will begin doing incredible amounts of work for us humans in astonishing ways that are a thrill to talk about. Bret believes agents will become a meaningful part of the future and transform the ways in which we interact with technology. We discuss a strategic approach to AI integration, the different categories of agents and their scopes, and the essentials of craftsmanship. Please enjoy this discussion with Bret Taylor.
For the full show notes, transcript, and links to mentioned content, check out the episode page here.
This episode is brought to you by Ridgeline. Ridgeline has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. I think this platform will become the standard for investment managers, and if you run an investing firm, I highly recommend you find time to speak with them. Head to ridgelineapps.com to learn more about the platform.
—
This episode is brought to you by Tegus, where we're changing the game in investment research. Step away from outdated, inefficient methods and into the future with our platform, proudly hosting over 100,000 transcripts – with over 25,000 transcripts added just this year alone. Our platform grows eight times faster and adds twice as much monthly content as our competitors, putting us at the forefront of the industry. Plus, with 75% of private market transcripts available exclusively on Tegus, we offer insights you simply can't find elsewhere. See the difference a vast, quality-driven transcript library makes. Unlock your free trial at tegus.com/patrick.
Invest Like the Best is a property of Colossus, LLC. For more episodes of Invest Like the Best, visit joincolossus.com/episodes.
Past guests include Tobi Lutke, Kevin Systrom, Mike Krieger, John Collison, Kat Cole, Marc Andreessen, Matthew Ball, Bill Gurley, Anu Hariharan, Ben Thompson, and many more.
Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here.
Follow us on Twitter: @patrick_oshag | @JoinColossus
Editing and post-production work for this episode was provided by The Podcast Consultant (https://thepodcastconsultant.com).
Show Notes:
(00:00:00) Welcome to Invest Like the Best
(00:04:27) The Dynamics of Small Teams in Software Development
(00:05:46) Challenges of Large Teams and Bureaucracy
(00:06:27) The Google Maps Legendary Rewrite Story
(00:13:59) Introduction to AI Agents
(00:16:48) Types of AI Agents and Their Applications
(00:22:15) Building Robust AI Agents for Customer Experience
(00:33:28) The Future of AI Agents and Customer Interaction
(00:45:12) Impact of AI on Productivity and Inequality
(00:51:05) Technological Evolution and Societal Changes
(00:56:25) The Role of Multimodal Models in AI
(00:57:19) The Future of Human-Computer Interaction
(01:00:15) Building Companies in the AI Era
(01:05:36) OpenAI's Unique Structure and Mission
(01:11:22) Insights on Sales and Customer Success
(01:20:06) Balancing Ambition and Personal Life
(01:21:35) Preparing for the Agent Era
(01:26:20) The Kindest Thing Anyone Has Ever Done for Bret
The podcast and artwork embedded on this page are from Colossus | Investing & Business Podcasts, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: ACQ2 by Acquired (LS 44 · TOP 1% what is this?)
Episode: Retool CEO David Hsu on Finding Product-Market Fit via Sales
Pub date: 2023-04-13
David Hsu has one of the most interesting founders journeys in tech today. After growing up in Silicon Valley, he left to study both philosophy and computer science at Oxford in the UK, then returned immediately afterward to found an internal enterprise tools company. Fast forward to today, and Retool is a multi-billion dollar valuation juggernaut that — almost uniquely for this era — operates at roughly cashflow breakeven while still growing rapidly. On this episode David shares his thoughts on finding product-market fit through sales, the dangers of product-led growth, how to get $1-5 million in ARR with just 5-10 people on the team. Tune in!
Sponsors:
- Vanta
Links:
- Retool!
- Follow David H. on Twitter
The podcast and artwork embedded on this page are from Ben Gilbert and David Rosenthal, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: ACQ2 by Acquired (LS 44 · TOP 1% what is this?)
Episode: Netflix's Journey, Building TCV, and Investing Through Downturns (with TCV co-founder Jay Hoag)
Pub date: 2022-11-10
We had the rare opportunity to interview Jay Hoag, cofounder of the first tech crossover investing firm, TCV, at TCV’s Engage Summit in Half Moon Bay earlier this fall. Jay and Rick Kimball started TCV back in 1995 and have been part of the private-to-public journeys of storied companies like Netflix (which Jay shares some great war stories about on this episode), Spotify, Zillow, Expedia, Facebook, Airbnb, Peloton and many others. Jay and TCV were kind enough to let us release the conversation as an Acquired LP episode, and we’re excited to share it with all of you. We cover the firm’s history, how companies should calibrate the magnitude of their future-looking product investments (a topic we didn’t realize would end up being so timely) and perhaps most importantly, pivotal moments where now seemingly unstoppable companies almost died amidst big macroeconomic changes. We hope you enjoy!
Links:
- The Acquired Merch Store
- The Acquired Slack
Sponsors:
- Vanta
Note: Acquired hosts and guests may hold assets discussed in this episode. This podcast is not investment advice, and is intended for informational and entertainment purposes only. You should do your own research and make your own independent decisions when considering any financial transactions.
The podcast and artwork embedded on this page are from Ben Gilbert and David Rosenthal, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: ACQ2 by Acquired (LS 44 · TOP 1% what is this?)
Episode: Consumer Investing in 2022 (with Brian O'Malley of Forerunner Ventures)
Pub date: 2022-10-31
We sit down with Brian O’Malley of Forerunner Ventures to talk about where in the cycle we are right now for consumer investing. We touch on the macro environment (obviously!), but also how to navigate between and around the current generation of platform incumbents, and where the next breakthrough consumer technology companies might come from. And in true Acquired Playbook fashion we talk about the benefits of focusing on niches — and how on the internet they can expand ever bigger than you might initially imagine!
Links:
- Acquired Qualcomm Live Show at Breakpoint 2022!
- The 2022 Acquired Survey!
- The Acquired Merch Store
- The Acquired Slack
Sponsors:
- Vanta
Note: Acquired hosts and guests may hold assets discussed in this episode. This podcast is not investment advice, and is intended for informational and entertainment purposes only. You should do your own research and make your own independent decisions when considering any financial transactions.
The podcast and artwork embedded on this page are from Ben Gilbert and David Rosenthal, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: ACQ2 by Acquired (LS 44 · TOP 1% what is this?)
Episode: Generative AI in Video and the Future of Storytelling (with Runway CEO Cristobal Valenzuela)
Pub date: 2023-08-30
We sit down with RunwayML’s CEO Cristobal Valenzuela to discuss the incredible tools they’re bringing to film and video creators (including last year’s Best Picture “Everything Everywhere All at Once” from A24), and the history + current state of the “visual” branch of generative AI. We cover how they’ve gone to market with both creators and enterprises, the potential for much more radical future use cases, and the company’s recent $141m strategic raise from Google, Nvidia + Salesforce and the context of the current AI fundraising landscape. Tune in!
Links:
- Runway
- Follow Cris on Twitter
Sponsors:
- Vanta
The podcast and artwork embedded on this page are from Ben Gilbert and David Rosenthal, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Lenny's Podcast: Product | Growth | Career (LS 59 · TOP 0.5% what is this?)
Episode: Building product at Stripe: craft, metrics, and customer obsession | Jeff Weinstein (Product lead)
Pub date: 2024-07-11
Jeff Weinstein is a product lead at Stripe, where he helped grow their payment APIs to hundreds of billions in volume and transformed the way founders start companies into a few simple clicks with Atlas. Prior to Stripe, Jeff led several startups and sold companies to Groupon and Box. He’s known for his customer obsession, craft, quality, and building beloved products businesses rely on. In our conversation, we discuss:
• The power of customer obsession and how to operationalize it in the product development process
• How to pick the right metrics and use them to drive impact
• Techniques for getting things done at big companies
• A group practice Jeff started to uplevel product craft, called Study Group
• The story behind Stripe Atlas and its mission to increase entrepreneurship globally
• Lessons from working with the founders of Stripe
—
Brought to you by:
• Pendo—The all-in-one platform for product-led companies building breakthrough digital experiences
• Cycle—Your feedback hub, on autopilot
• Anvil—The fastest way to build software for documents
—
Find the transcript at: https://www.lennysnewsletter.com/p/creating-a-culture-of-excellence
—
Where to find Jeff Weinstein:
• X: https://x.com/jeff_weinstein
• LinkedIn: https://www.linkedin.com/in/jeffwweinstein/
• Email: jweinstein@gmail.com
—
Where to find Lenny:
• Newsletter: https://www.lennysnewsletter.com
• X: https://twitter.com/lennysan
• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/
—
In this episode, we cover:
(00:00) Jeff’s background
(10:16) The “go, go, go ASAP + optimistic, long-term compounding” approach
(15:38) The importance of craft and quality
(24:15) Effective customer communication strategies
(28:57) The importance of speed in customer interactions
(33:19) Narrowing your focus
(36:53) Why you should pay attention only to paying-customer feedback
(40:24) Practicing silence when communicating
(45:33) The role of metrics in product success
(54:08) Empowering teams with a single metric
(58:23) Picking the right metric for your audience
(01:05:10) The importance of metric hygiene
(01:11:33) How Stripe uses “study groups” for product improvement
(01:37:20) Stripe’s Atlas: simplifying company formation
(01:50:38) Automation and operational efficiency
(01:55:13) Diversity and team building
(02:03:09) Building new products within a large company
(02:21:10) Lightning round
—
Referenced:
• Atlas: https://stripe.com/atlas
• Stripe: https://stripe.com/
• SQL: https://en.wikipedia.org/wiki/SQL
• GitHub: https://github.com/
• Linear: https://linear.app/
• Figma: https://www.figma.com/
• Jeff’s tweet about Stripe’s bug-finder program: https://x.com/jeff_weinstein/status/1777487507934040300
• The “Collison installation”: https://news.ycombinator.com/item?id=18400504
• How we use friction logs to improve products at Stripe: https://dev.to/stripe/how-we-use-friction-logs-to-improve-products-at-stripe-i6p
• Fidelity: https://www.fidelity.com/
• 83(b) election: https://docs.stripe.com/atlas/83b-election
• Jeff’s tweet about Atlas’s NPS score: https://x.com/jeff_weinstein/status/1788644576330469638
• What is a Delaware corporation? Here’s what makes this state so attractive to businesses: https://stripe.com/resources/more/what-is-a-delaware-corporation
• Incorporating in Delaware explained: Why it’s such a popular option for businesses: https://stripe.com/resources/more/incorporating-in-delaware-explained
• 7 of Pixar’s Best Storyboard Examples and the Stories Behind Them: https://boords.com/blog/7-of-pixars-best-storyboard-examples-and-the-stories-behind-them
• Alex Kehayias on LinkedIn: https://www.linkedin.com/in/alexkehayias/
• Patrick McKenzie on LinkedIn: https://www.linkedin.com/in/patrickmckenzie/
• AngelList: https://www.angellist.com/
• Dan Hightower on LinkedIn: https://www.linkedin.com/in/danhighto/
• Stripe Atlas perks partners: https://support.stripe.com/questions/stripe-atlas-perks-partners
• Vision, conviction, and hype: How to build 0 to 1 inside a company | Mihika Kapoor (Product at Figma): https://www.lennysnewsletter.com/p/vision-conviction-hype-mihika-kapoor
• High Output Management: https://www.amazon.com/High-Output-Management-Andrew-Grove/dp/0679762884
• Orbiting the Giant Hairball: A Corporate Fool’s Guide to Surviving with Grace: https://www.amazon.com/Orbiting-Giant-Hairball-Corporate-Surviving/dp/0670879835
• 7 Powers: The Foundations of Business Strategy: https://www.amazon.com/7-Powers-Foundations-Business-Strategy/dp/0998116319
• Business strategy with Hamilton Helmer (author of 7 Powers): https://www.lennysnewsletter.com/p/business-strategy-with-hamilton-helmer
• Box: https://www.box.com/
• Patrick Collison on X: https://x.com/patrickc
• How to with John Wilson on HBO: https://www.hbo.com/how-to-with-john-wilson
• The Quiet Girl on Hulu: https://www.hulu.com/movie/the-quiet-girl-b50a4b8e-d3ff-4635-b806-5e7dbd292ca4
• Raycast: https://www.raycast.com/
• Quicksilver: https://qsapp.com/
• Alfred: https://www.alfredapp.com/help/workflows/automations/
• CleanShot: https://cleanshot.com/
• John Collison on X: https://x.com/collision
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.
—
Lenny may be an investor in the companies discussed.
Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe
The podcast and artwork embedded on this page are from Lenny Rachitsky, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Lenny's Podcast: Product | Growth | Career (LS 59 · TOP 0.5% what is this?)
Episode: What most people miss about marketing | Rory Sutherland (Vice Chairman of Ogilvy UK, author)
Pub date: 2024-07-21
Rory Sutherland is widely regarded as one of the most influential (and most entertaining) thinkers in marketing and behavioral science.He’s the vice chairman of Ogilvy UK, the author of Alchemy: The Dark Art and Curious Science of Creating Magic in Brands, Business, and Life, and the founder of Nudgestock, the world’s biggest festival of behavioral science and creativity. He champions thinking from first principles and using human psychology—what he calls “thinking psycho-logically”—over mere logic. In our conversation, we cover:
• Why good products don’t always succeed, and bad ones don’t necessarily fail
• Why less functionality can sometimes be more valuable
• The importance of fame in building successful brands
• The importance of timing in product success
• The concept of “most advanced, yet acceptable”
• Why metrics-driven workplaces can be demotivating
• Lots of real-world case studies
• Much more
—
Brought to you by:
• Pendo—The only all-in-one product experience platform for any type of application
• Cycle—Your feedback hub, on autopilot
• Coda—The all-in-one collaborative workspace
—
Find the transcript at: https://www.lennysnewsletter.com/p/what-most-people-miss-about-marketing
—
Where to find Rory Sutherland:
• X: https://x.com/rorysutherland
• LinkedIn: https://www.linkedin.com/in/rorysutherland
• Book: Alchemy: The Dark Art and Curious Science of Creating Magic in Brands, Business, and Life: https://www.amazon.com/Alchemy-Curious-Science-Creating-Business/dp/006238841X
—
Where to find Lenny:
• Newsletter: https://www.lennysnewsletter.com
• X: https://twitter.com/lennysan
• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/
—
In this episode, we cover:
(00:00) Rory’s background
(02:37) The success and failure of products
(04:08) Why the urge to appear serious can be a disaster in marketing
(08:05) The role of distinctiveness in product design
(12:29) The MAYA principle
(15:50) How thinking irrationally can be advantageous
(17:40) The fault of multiple-choice tests
(21:31) Companies that have successfully implemented out-of-the-box thinking
(30:31) “Psycho-logical” thinking
(31:45) The hare and the dog metaphor
(38:51) Marketing’s crucial role in product adoption
(49:21) The quirks of Google Glass
(55:44) Survivorship bias
(56:09) Balancing rational ideas with irrational ideas
(01:06:19) The rise and fall of tech innovations
(01:09:54) Consistency, distinctiveness, and clarity
(01:21:12) Considering psychological, technological, and economic factors in parallel
(01:23:35) Where to find Rory
—
Referenced:
• Google Glass: https://en.wikipedia.org/wiki/Google_Glass
• Meta Portal TV: https://www.meta.com/portal/products/portal-tv/
• Rory’s quote in a LinkedIn post: https://www.linkedin.com/posts/brad-jackson-04766642_the-urge-to-appear-serious-is-a-disaster-activity-7093497742710210560-1LYN/
• The MAYA Principle: Design for the Future, but Balance It with Your Users’ Present: https://www.interaction-design.org/literature/article/design-for-the-future-but-balance-it-with-your-users-present
• Ogilvy: https://www.ogilvy.com/
• MCI: https://www.mci.world/
• Veuve Clicquot: https://en.wikipedia.org/wiki/Veuve_Clicquot
• Why do the French call the British ‘the roast beefs’?: http://news.bbc.co.uk/2/hi/2913151.stm
• The Killing on Hulu: https://www.hulu.com/series/the-killing-f5da5c2d-4626-4ba9-bcf3-ff5f891771fb
• Original The Killing on BBC: https://www.bbc.co.uk/programmes/b017h7m1
• The Mandarin Oriental, Hong Kong: https://www.mandarinoriental.com/en/hong-kong/victoria-harbour
• SAT: https://satsuite.collegeboard.org/sat
• The Widening Racial Scoring Gap on the SAT College Admissions Test: https://www.jbhe.com/features/49_college_admissions-test.html
• What is the age of the captain?: https://www.icopilots.com/what-is-the-age-of-the-captain/
• Octopus Energy: https://octopus.energy/
• Kraken: https://octopusenergy.group/kraken-technologies
• Toby Shannan: https://theorg.com/org/shopify/org-chart/toby-shannan
• Dunbar’s number: Why we can only maintain 150 relationships: https://www.bbc.com/future/article/20191001-dunbars-number-why-we-can-only-maintain-150-relationships
• AO: https://ao.com/
• Zappos: https://www.zappos.com/
• Joe Cano on LinkedIn: https://www.linkedin.com/in/joeycano/
• John Ralston Saul’s website: https://www.johnralstonsaul.com/
• Voltaire’s B*s: The Dictatorship of Reason in the West*: https://www.amazon.com/Voltaires-B****s-Dictatorship-Reason-West/dp/0679748199
• Psycho-Logic: Why Too Much Logic Deters Magic: https://coffeeandjunk.com/psycho-logic/
• Herbert Simon’s Decision-Making Approach: https://bura.brunel.ac.uk/bitstream/2438/4995/1/Fulltext.pdf
• Robert Trivers’s website: https://roberttrivers.com/Welcome.html
• Crazy Ivan: https://jollycontrarian.com/index.php?title=Crazy_Ivan
• The Joys of Being a Late Tech Adopter: https://www.nytimes.com/2019/08/28/technology/personaltech/joys-late-tech-adopter.html
• Jean-Claude Van Damme: https://en.wikipedia.org/wiki/Jean-Claude_Van_Damme
• Tim Berners-Lee: https://en.wikipedia.org/wiki/Tim_Berners-Lee
• Edward Jenner and the history of smallpox and vaccination: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1200696/
• The real story behind penicillin: https://www.pbs.org/newshour/health/the-real-story-behind-the-worlds-first-antibiotic
• What Are Japanese Toilets?: https://www.bigbathroomshop.co.uk/info/blog/japanese-toilets/
• reMarkable: https://remarkable.com/
• Chumby: https://en.wikipedia.org/wiki/Chumby
• Survivorship bias: https://en.wikipedia.org/wiki/Survivorship_bias
• Jony Ive: https://en.wikipedia.org/wiki/Jony_Ive
• Marc Newson’s website: https://marc-newson.com/
• Designing Men: https://www.vanityfair.com/news/business/2013/11/jony-ive-marc-newson-design-auction
• Qantas A330: https://marc-newson.com/qantas-a330/
• Herodotus: https://en.wikipedia.org/wiki/Herodotus
• Big Decision? Consider It Both Drunk and Sober: https://www.forbes.com/sites/chunkamui/2016/03/22/wine-and-sleep-make-for-better-decisions/?sh=5c97fdc524b1
• How Henry Ford and Thomas Edison killed the electric car: https://www.speakev.com/threads/how-henry-ford-and-thomas-edison-killed-the-electric-car.4270/
• Watch Jay Leno get nostalgic and swoon over this 1909 EV: https://thenextweb.com/news/jay-leno-talk-about-electric-car-1909-baker
• Jay Leno’s Garage: https://www.youtube.com/@jaylenosgarage
• Nudgestock: https://nudgestock.com/
• Akio Morita: https://en.wikipedia.org/wiki/Akio_Morita
• Don Norman on LinkedIn: https://www.linkedin.com/in/donnorman/
• What Makes Tesla’s Business Model Different: https://www.investopedia.com/articles/active-trading/072115/what-makes-teslas-business-model-different.asp
• Monica Lewinsky on X: https://x.com/MonicaLewinsky
• Blindsight: The (Mostly) Hidden Ways Marketing Reshapes Our Brains: azon.com/Blindsight-Mostly-Hidden-Marketing-Reshapes-ebook/dp/B07ZKZ5DWF
• Branding That Means Business: https://www.amazon.com/Branding-that-Means-Business-Economist-ebook/dp/B09QBCCH9N
• PwC: https://www.pwc.com
• Ryanair: https://www.ryanair.com
• British Airways: https://www.britishairways.com/
• Wrigley’s began as a soap business: know when to pivot: https://theamericangenius.com/entrepreneur/wrigleys-began-as-soap-know-when-to-pivot/
• Transport for Humans: https://www.amazon.com/Transport-Humans-Perspectives-Pete-Dyson/dp/1913019357
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.
—
Lenny may be an investor in the companies discussed.
Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe
The podcast and artwork embedded on this page are from Lenny Rachitsky, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Guns for Hire
Episode: I was a Blackwater mercenary in Iraq
Pub date: 2024-07-27
Send Alia a Text Message
Alia is joined by former Blackwater contractor Morgan Lerette. In a wide-ranging conversation about Morgan's experiences, they look at Blackwater’s lax vetting procedures, the tense relationship on the ground between private contractors and the US military, the absence of long-term benefits or a support system for former Blackwater employees, and the prevalence of substance abuse and suicides. Morgan likens private military contractors to a single-serve coffee cup, to be used and discarded at will so that corporations can profit, and governments can avoid committing boots on the ground.
“I still don’t know what legal rules and regulations govern private military contractors. And I think there’s a reason for that”
The Guns for Hire podcast is written, produced and hosted by Dr Alia Brahimi.
The podcast and artwork embedded on this page are from Atlantic Council, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Syntax - Tasty Web Development Treats (LS 61 · TOP 0.1% what is this?)
Episode: 796: Do We Need JS Frameworks × Are You Over-Engineering? × Webview vs Native
Pub date: 2024-07-17
Scott and Wes tackle a variety of audience questions, from the nuances of over-engineering to the energy consumption of AI LLMs. They also discuss the pros and cons of monorepos, frameworks, and the ever-important question: Do you really need to learn all the developer tooling?
Show Notes * 00:00 Welcome to Syntax! * 00:41 Brought to you by Sentry.io. * 01:07 Challenges around a resume playback function. * 05:56 Why use Google Forms for Potluck questions? * 07:57 What constitutes over-engineering and how to avoid it. * 13:28 Webview vs native component based mobile apps. * 18:06 Running and managing monorepos. * 20:59 Energy consumption of AI LLMs vs static web content. + A guide to LLM inference and performance. + From Words to Watts: Benchmarking the Energy Costs of LLM Inference. * 25:19 Why do we need frameworks? + Frank M Taylor Blog Post. * 33:05 Handling ad-blockers blocking Sentry and other tools. + Syntax GitHub. * 38:25 Creating sites without JavaScript. * 42:49 Do I really have to learn all the various developer tooling? + Wes Bos Tweet. * 44:47 What are the best ways to network and meet other developers? * 50:16 Sick Picks & Shameless Plugs.
Sick Picks * Scott: Tweek App/ * Wes: Rain-X Waterless Car Wash.
Shameless Plugs * Scott: Swag Store. * Wes: Audio Player Updates.
Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads
Wes: X Instagram Tiktok LinkedIn Threads
Scott: X Instagram Tiktok LinkedIn Threads
Randy: X Instagram YouTube Threads
The podcast and artwork embedded on this page are from Wes Bos & Scott Tolinski - Full Stack JavaScript Web Developers, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Syntax - Tasty Web Development Treats (LS 61 · TOP 0.1% what is this?)
Episode: 798: Self Hosting: Reverse Proxy Servers
Pub date: 2024-07-22
Scott and Wes serve up an episode on reverse proxy servers. They discuss popular options like CF Tunnels, Caddy, Nginx, Apache, and more, explaining why you might need one for load balancing, SSL certificates, security, and managing multiple servers.
Show Notes * 00:00 Welcome to Syntax! * 01:30 Brought to you by Sentry.io. * 02:25 What is reverse proxy? * 03:16 Some examples of reverse proxies. * 05:04 Why do you need a reverse proxy? + 05:09 Combining multiple servers. + 06:51 Load balancing. + 07:23 SSL certificates. + 10:30 Security. - 10:37 Conceal your true IP. - 11:24 Access management. + 12:31 Routing static assets. + 13:31 CDN / local. * 15:55 Caddy × websocket support.
Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads
Wes: X Instagram Tiktok LinkedIn Threads
Scott: X Instagram Tiktok LinkedIn Threads
Randy: X Instagram YouTube Threads
The podcast and artwork embedded on this page are from Wes Bos & Scott Tolinski - Full Stack JavaScript Web Developers, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Recsperts - Recommender Systems Experts
Episode: #17: Microsoft Recommenders and LLM-based RecSys with Miguel Fierro
Pub date: 2023-06-15
In episode 17 of Recsperts, we meet Miguel Fierro who is a Principal Data Science Manager at Microsoft and holds a PhD in robotics. We talk about the Microsoft recommenders repository with over 15k stars on GitHub and discuss the impact of LLMs on RecSys. Miguel also shares his view of the T-shaped data scientist.
In our interview, Miguel shares how he transitioned from robotics into personalization as well as how the Microsoft recommenders repository started. We learn more about the three key components: examples, library, and tests. With more than 900 tests and more than 30 different algorithms, this library demonstrates a huge effort of open-source contribution and maintenance. We hear more about the principles that made this effort possible and successful. Therefore, Miguels also shares the reasoning behind evidence-based design to put the users of microsoft-recommenders and their expectations first. We also discuss the impact that recent LLM-related innovations have on RecSys.
At the end of the episode, Miguel explains the T-shaped data professional as an advice to stay competitive and build a champion data team. We conclude with some remarks regarding the adoption and ethical challenges recommender systems pose and which need further attention.
Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.
Don't forget to follow the podcast and please leave a review
- (00:00) - Episode Overview
- (03:34) - Introduction Miguel Fierro
- (16:19) - Microsoft Recommenders Repository
- (30:04) - Structure of MS Recommenders
- (34:16) - Contributors to MS Recommenders
- (37:10) - Scalability of MS Recommenders
- (39:32) - Impact of LLMs on RecSys
- (48:26) - T-shaped Data Professionals
- (53:29) - Further RecSys Challenges
- (59:28) - Closing Remarks
Links from the Episode:* Miguel Fierro on LinkedIn * Miguel Fierro on Twitter * Miguel's Website * Microsoft Recommenders * McKinsey (2013): How retailers can keep up with consumers * Fortune (2012): Amazon's recommendation secret * RecSys 2021 Keynote by Max Welling: Graph Neural Networks for Knowledge Representation and Recommendation
Papers:
- Geng et al. (2022): Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)
General Links:
- Follow me on LinkedIn
- Follow me on Twitter
- Send me your comments, questions and suggestions to marcel@recsperts.com
- Recsperts Website
The podcast and artwork embedded on this page are from Marcel Kurovski, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Recsperts - Recommender Systems Experts
Episode: #22: Pinterest Homefeed and Ads Ranking with Prabhat Agarwal and Aayush Mudgal
Pub date: 2024-06-06
In episode 22 of Recsperts, we welcome Prabhat Agarwal, Senior ML Engineer, and Aayush Mudgal, Staff ML Engineer, both from Pinterest, to the show. Prabhat works on recommendations and search systems at Pinterest, leading representation learning efforts. Aayush is responsible for ads ranking and privacy-aware conversion modeling. We discuss user and content modeling, short- vs. long-term objectives, evaluation as well as multi-task learning and touch on counterfactual evaluation as well.
In our interview, Prabhat guides us through the journey of continuous improvements of Pinterest's Homefeed personalization starting with techniques such as gradient boosting over two-tower models to DCN and transformers. We discuss how to capture users' short- and long-term preferences through multiple embeddings and the role of candidate generators for content diversification. Prabhat shares some details about position debiasing and the challenges to facilitate exploration.
With Aayush we get the chance to dive into the specifics of ads ranking at Pinterest and he helps us to better understand how multifaceted ads can be. We learn more about the pain of having too many models and the Pinterest's efforts to consolidate the model landscape to improve infrastructural costs, maintainability, and efficiency. Aayush also shares some insights about exploration and corresponding randomization in the context of ads and how user behavior is very different between different kinds of ads.
Both guests highlight the role of counterfactual evaluation and its impact for faster experimentation.
Towards the end of the episode, we also touch a bit on learnings from last year's RecSys challenge.
Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.
Don't forget to follow the podcast and please leave a review
- (00:00) - Introduction
- (03:51) - Guest Introductions
- (09:57) - Pinterest Introduction
- (21:57) - Homefeed Personalization
- (47:27) - Ads Ranking
- (01:14:58) - RecSys Challenge 2023
- (01:20:26) - Closing Remarks
Links from the Episode:* Prabhat Agarwal on LinkedIn * Aayush Mudgal on LinkedIn * RecSys Challenge 2023 * Pinterest Engineering Blog * Pinterest Labs * Prabhat's Talk at GTC 2022: Evolution of web-scale engagement modeling at Pinterest * Blogpost: How we use AutoML, Multi-task learning and Multi-tower models for Pinterest Ads * Blogpost: Pinterest Home Feed Unified Lightweight Scoring: A Two-tower Approach * Blogpost: Experiment without the wait: Speeding up the iteration cycle with Offline Replay Experimentation * Blogpost: MLEnv: Standardizing ML at Pinterest Under One ML Engine to Accelerate Innovation * Blogpost: Handling Online-Offline Discrepancy in Pinterest Ads Ranking System
Papers:
- Eksombatchai et al. (2018): Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-Time
- Ying et al. (2018): Graph Convolutional Neural Networks for Web-Scale Recommender Systems
- Pal et al. (2020): PinnerSage: Multi-Modal User Embedding Framework for Recommendations at Pinterest
- Pancha et al. (2022): PinnerFormer: Sequence Modeling for User Representation at Pinterest
- Zhao et al. (2019): Recommending what video to watch next: a multitask ranking system
General Links:
- Follow me on LinkedIn
- Follow me on X
- Send me your comments, questions and suggestions to marcel.kurovski@gmail.com
- Recsperts Website
The podcast and artwork embedded on this page are from Marcel Kurovski, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Recsperts - Recommender Systems Experts
Episode: #21: User-Centric Evaluation and Interactive Recommender Systems with Martijn Willemsen
Pub date: 2024-04-08
In episode 21 of Recsperts, we welcome Martijn Willemsen, Associate Professor at the Jheronimus Academy of Data Science and Eindhoven University of Technology. Martijn's researches on interactive recommender systems which includes aspects of decision psychology and user-centric evaluation. We discuss how users gain control over recommendations, how to support their goals and needs as well as how the user-centric evaluation framework fits into all of this.
In our interview, Martijn outlines the reasons for providing users control over recommendations and how to holistically evaluate the satisfaction and usefulness of recommendations for users goals and needs. We discuss the psychology of decision making with respect to how well or not recommender systems support it. We also dive into music recommender systems and discuss how nudging users to explore new genres can work as well as how longitudinal studies in recommender systems research can advance insights.
Towards the end of the episode, Martijn and I also discuss some examples and the usefulness of enabling users to provide negative explicit feedback to the system.
Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.
Don't forget to follow the podcast and please leave a review
- (00:00) - Introduction
- (03:03) - About Martijn Willemsen
- (15:14) - Waves of User-Centric Evaluation in RecSys
- (19:35) - Behaviorism is not Enough
- (46:21) - User-Centric Evaluation Framework
- (01:05:38) - Genre Exploration and Longitudinal Studies in Music RecSys
- (01:20:59) - User Control and Negative Explicit Feedback
- (01:31:50) - Closing Remarks
Links from the Episode:* Martijn Willemsen on LinkedIn * Martijn Willemsen's Website * User-centric Evaluation Framework * Behaviorism is not Enough (Talk at RecSys 2016) * Neil Hunt: Quantifying the Value of Better Recommendations (Keynote at RecSys 2014) * What recommender systems can learn from decision psychology about preference elicitation and behavioral change (Talk at Boise State (Idaho) and Grouplens at University of Minnesota) * Eric J. Johnson: The Elements of Choice * Rasch Model * Spotify Web API
Papers:
- Ekstrand et al. (2016): Behaviorism is not Enough: Better Recommendations Through Listening to Users
- Knijenburg et al. (2012): Explaining the user experience of recommender systems
- Ekstrand et al. (2014): User perception of differences in recommender algorithms
- Liang et al. (2022): Exploring the longitudinal effects of nudging on users’ music genre exploration behavior and listening preferences
- McNee et al. (2006): Being accurate is not enough: how accuracy metrics have hurt recommender systems
General Links:
- Follow me on LinkedIn
- Follow me on X
- Send me your comments, questions and suggestions to marcel.kurovski@gmail.com
- Recsperts Website
The podcast and artwork embedded on this page are from Marcel Kurovski, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Recsperts - Recommender Systems Experts
Episode: #14: User Modeling and Superlinked with Daniel Svonava
Pub date: 2023-03-15
In episode number 14 of Recsperts we talk to Daniel Svonava, CEO and Co-Founder of Superlinked, delivering user modeling infrastructure. In his former role he was a senior software engineer and tech lead at YouTube working on ad performance prediction and pricing.
We discuss the crucial role of user modeling for recommendations and discovery. Daniel presents two examples from YouTube’s ad performance forecasting to demonstrate the bandwidth of use cases for user modeling. We also discuss sources of information that fuel user models and additional personlization tasks that benefit from it like user onboarding. We learn that the tight combination of user modeling with (near) real-time updates is key to a sound personalized user experience.
Daniel also shares with us how Superlinked provides personalization as a service beyond ecommerce-centricity. Offering personalized recommendations of items and people across various industries and use cases is what sets Superlinked apart. In the end, we also touch on the major general challenge of the RecSys community which is rebranding in order to establish a more positive image of the field.
Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.
Chapters:
- (03:35) - Introduction Daniel Svonava
- (10:18) - Introduction to User Modeling
- (17:52) - User Modeling for YouTube Ads
- (35:43) - Real-Time Personalization
- (57:29) - ML Tooling for User Modeling and Real-Time Personalization
- (01:07:41) - Superlinked as a User Modeling Infrastructure
- (01:31:22) - Rebranding RecSys as Major Challenge
- (01:37:40) - Final Remarks
Links from the Episode:* Daniel Svonava on LinkedIn * Daniel Svonava on Twitter * Superlinked - User Modeling Infrastructure * The 2023 MAD (Machine Learning, Artificial Intelligence, Data Science) Landscape * Eric Ries: The Lean Startup * Rob Fitzpatrick: The Mom Test
Papers:
- Liu et al. (2022): Monolith: Real Time Recommendation System With Collisionless Embedding Table
- RSPapers Collection
General Links:
- Follow me on Twitter: https://twitter.com/MarcelKurovski
- Send me your comments, questions and suggestions to marcel@recsperts.com
- Podcast Website: https://www.recsperts.com/
The podcast and artwork embedded on this page are from Marcel Kurovski, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Recsperts - Recommender Systems Experts
Episode: #1: Practical Recommender Systems with Kim Falk
Pub date: 2021-10-08
In this first interview we talk to Kim Falk, Senior Data Scientist, multiple RecSys Industry Chair and author of the book "Practical Recommender Systems". We introduce into recommenders from a practical perspective discussing the fundamental difference between content-based and collaborative filtering as well as the cold-start problem - no mathematical deep-dive yet, but expect it to follow. In addition, we reason what constitutes good recommendations and briefly touch on a couple of ways of finding that out.
Looking a bit into the history of the recommender systems community, we touch on the Netflix Prize that was running from 2006 to 2009 as well as on the RecSys - the leading conference in recommender systems, where we also met for the first time.
In the end, we discuss a couple of challenges the field faces, in particular associated with approaches based on deep learning. Besides that, Spiderman will accompany our conversation at certain times. Plus many practical recommendations included on how to get started. Stay tuned!
Links from this Episode:
- Kim Falk on LinkedIn and Twitter
- Book: Practical Recommender Systems (Manning) (get 37% discount with the code podrecsperts37 during checkout)
- GitHub Repository for PRS Book
- ACM Conference on Recommender Systems 2021 (Amsterdam)
- Recommender Systems Specialization at Coursera
- Amazon.com Recommendations: Item-to-Item Collaborative Filtering
- Netflix Prize
- Netflix Prize dataset on Kaggle
- New York Times: A $1 Million Research Bargain for Netflix, and Maybe a Model for Others
- Evaluation Measures for Information Retrieval
- Paper by Dacrema et al. (2019): Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches (best paper award at RecSys 2019)
- Recommending music on Spotify with Deep Learning
- MovieLens Recommenders
General Links:
- Follow me on Twitter: https://twitter.com/LivesInAnalogia
- Send me your comments, questions and suggestions to marcel@recsperts.com
- Podcast Website: https://www.recsperts.com/
Twitter and LinkedIn posts for sharing:
The podcast and artwork embedded on this page are from Marcel Kurovski, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: UI Breakfast: UI/UX Design and Product Strategy (LS 48 · TOP 1% what is this?)
Episode: Episode 279: Managing Personal Knowledge with Jorge Arango
Pub date: 2024-02-09
What are the three principles of taking and managing notes? Our guest today is Jorge Arango, an information architect, author, and educator. You’ll learn about the evolution of knowledge management tools, how digital note-taking systems have extended our capabilities, why you should choose the right tools for different types of notes, and more.
Podcast feed: subscribe to https://feeds.simplecast.com/4MvgQ73R in your favorite podcast app, and follow us on iTunes, Stitcher, or Google Podcasts.
Show Notes* Duly Noted — Jorge’s new book * What is a Mind Map? * Figure It Out — a book by Steven Anderson and Karl Fast * Obsidian, Roam Research — popular tools for connected note taking * Drafts — a tool for temporary thinking * NotebookLM — a project by Google * Check out Jorge’s website * The Informed Life — Jorge’s podcast
This episode is brought to you by UC San Diego. Thinking about diving into the dynamic world of UX design, but not sure where to start? Explore UC San Diego Extended Studies’ UX Design Certificate. Master essential skills to build a standout portfolio that will help you land your dream job. Enroll today in Principles of UX and get 10% off as our listener. Head over to DiscoverUX.ucsd.edu and use code DISCOVERUX to apply the discount.
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The podcast and artwork embedded on this page are from Jane Portman, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: UI Breakfast: UI/UX Design and Product Strategy (LS 48 · TOP 1% what is this?)
Episode: Episode 280: User Interviewing Techniques with Steve Portigal
Pub date: 2024-03-08
Ready to learn tricks of the trade from professional user researchers? Our guest today is Steve Portigal, user research consultant and author of Interviewing Users. You’ll learn how to prepare for a user interview, what your interview guide should contain, behavior cues to look out for, and more.
Podcast feed: subscribe to https://feeds.simplecast.com/4MvgQ73R in your favorite podcast app, and follow us on iTunes, Stitcher, or Google Podcasts.
Show Notes* Interviewing Users — Steve’s book * Check out Steve’s website * Dollars to Donuts — Steve’s podcast * Connect with Steve on LinkedIn * Use the code UIBREAKFAST to get 20% off the book from Rosenfeld Media
This episode is brought to you by UC San Diego. Thinking about diving into the dynamic world of UX design, but not sure where to start? Explore UC San Diego Extended Studies’ UX Design Certificate. Master essential skills to build a standout portfolio that will help you land your dream job. Enroll today in Principles of UX and get 10% off as our listener. Head over to DiscoverUX.ucsd.edu and use code DISCOVERUX to apply the discount.
Interested in sponsoring an episode? Learn more here.
Leave a ReviewReviews are hugely important because they help new people discover this podcast. If you enjoyed listening to this episode, please leave a review on iTunes. Here’s how.
The podcast and artwork embedded on this page are from Jane Portman, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Rabbit Hole
Episode: #115 How Superhuman Built an Engine to Find Product Market Fit by Rahul Vohra
Pub date: 2024-05-02
Full article How Superhuman Built an Engine to Find Product Market Fit here
To support:
- Rate and share this podcast
- Sign up for The Rabbit Hole newsletter
- Become a patron
- Join The Latticework
To connect:
Sponsor:
The Rabbit Hole podcast is brought to you by The Latticework.
The Latticework curates, explains and interconnects hundreds of mental models so that you can become a better multidisciplinary thinker. Every partner applies and is vetted, ensuring a high-quality, curious, and low-ego community.
Join hundreds of partners from around the world by applying here, or learn more at LTCWRK.com
Become Multidisciplinary. Think Better
The podcast and artwork embedded on this page are from Blas Moros, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Rabbit Hole
Episode: #118 Soul Mining by Daniel Lanois
Pub date: 2024-06-10
"Bob Dylan, Willie Nelson, Emmylou Harris, U2, Peter Gabriel, and the Neville Brothers all have something in common: some of their best albums were produced by Daniel Lanois. Part technological treatise, part philosophical manifesto on the nature of artistic excellence and the overwhelming need for music, Soul Mining brings the reader viscerally inside the recording studio, where the surrounding forces have always been just as important as the resulting albums."
Full notes on Soul Mininghere
Buy the book here
To support:
- Rate and share this podcast
- Sign up for The Rabbit Hole newsletter
- Become a patron
- Join The Latticework
To connect:
Sponsor:
The Rabbit Hole podcast is brought to you by The Latticework.
The Latticework curates, explains and interconnects hundreds of mental models so that you can become a better multidisciplinary thinker. Every partner applies and is vetted, ensuring a high-quality, curious, and low-ego community.
Join hundreds of partners from around the world by applying here, or learn more at LTCWRK.com
Become Multidisciplinary. Think Better
The podcast and artwork embedded on this page are from Blas Moros, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Clef de voûte (LS 44 · TOP 1% what is this?)
Episode: #26 - Feedly - Mesurer le Product Market Fit d'une fonctionnalité avant son lancement (Olivia Malterre, senior PM)
Pub date: 2022-04-12
👉 Donne ton avis sur cet épisode en 30 secondes 💜
Olivia Malterre est la 1st Product Manager de Feedly.
Dans cet épisode, elle revient sur un challenge produit vécu chez Feedly où elle a appliqué une méthodologie efficace pour mesurer le PMF avant la sortie d'une fonctionnalité.
On y aborde :
🔨 Comment mesurer le PMF via la méthodologie développée par le CEO de Superhuman
🔨 Comment nourrir cette méthodologie en menant de la recherche quali et quanti
🔨 Comment modifier une fonctionnalité pour optimiser les résultats de cette méthodologie
💥 Pour apporter ton soutien au podcast :
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Abonne-toi pour ne pas louper les prochains épisodes 🔔
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Laisse un super avis sur Apple podcast ❤️
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Inscris-toi sur clefdevoute.pm pour recevoir les ressources de mes invités et être tenu·e au courant de la sortie des prochains épisodes 👈
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Design sonore et conseiller de luxe : Samuel Rozenbaum
Hébergé par Ausha. Visitez ausha.co/politique-de-confidentialite pour plus d'informations.
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Podcast: A Product Market Fit Show | Startups & Founders (LS 40 · TOP 1.5% what is this?)
Episode: 5 months in, Snapchat had only 127 users. Here's how Evan Spiegel found product market fit w/ Jeremy Liew (Partner at Lightspeed and Seed Investor in Snapchat)
Pub date: 2024-04-22
Snapchat got 0 downloads the day it launched. 5 months in, it had only 127 users. Today Snapchat is an $18B company with 400 million daily active users. Evan Spiegel noticed what even Zuck missed: daily communication is meant to be ephemeral, not recorded for all time.
In this episode, we dive deep into how Snapchat went from idea to product-market fit.
Our guest is Jeremy Liew, a Partner at Lightspeed and the first investor in Snapchat. He led Lightspeed's seed round in 2012 at a $5M valuation (!!).
He shares his four-part B2C framework that helped him understand why Evan and Snap were special before anyone else.
If you want to understand why Snapchat took off when so many other consumer startups fail to do so, check this episode out.
The podcast and artwork embedded on this page are from Mistral.vc, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: A Product Market Fit Show | Startups & Founders (LS 40 · TOP 1.5% what is this?)
Episode: Reddit CTO & Founding Engineer Chris Slowe | How Reddit Found Product Market Fit
Pub date: 2024-06-10
Reddit. You know the name, you've used the site. It's a ~$10B company, with nearly $1B in revenue. Their 1B+ monthly active users are so powerful they can move markets. This is the story of how it all began.
On this episode, we interview Chris Slowe, Reddit's current CTO and Founding Engineer. Chris was in YC's first-ever batch with Steve and Alexis. He was their roommate. When Chris's own startup failed, he moved over and joined them to build Reddit. This was almost 20 years ago, in 2005.
It only took them a year to hit 1 million monthly active users. But it took them well over 5 years to hit $1M in revenue. Here's the story of how they hit product-market fit and built the world's most powerful online community.
Why you should listen:- Learn how Reddit got started in 2005 and why it took them many years to monetize
- See how word of mouth & organic growth are the key to building a community like Reddit
- Hear Chris's perspective on scaling teams and organizations, preserving culture, and signs of clear product-market fit
- Why applying lessons from your first startup to your second one is not as easy as you think it might be
Keywords
Reddit, Y Combinator, growth, startups, founders, acquisition, Conde Nast, community-driven platform, culture, word of mouth, Google, organic growth, Hipmunk, monetization, product-market fit, scaling, startup advice
Timestamps:
(00:00:00) Intro
(00:01:55) The Start of Reddit
(00:07:13) Joining Reddit
(00:12:56) Building Communities
(00:20:37) The First Year of Reddit
(00:22:56) The Acquisition
(00:26:50) Staying Lean
(00:28:58) Leaving Reddit to do Hipmunk
(00:37:14) Hiring for Hipmunk
(00:41:08) Coming Back to Reddit
(00:45:05) Making the Mobile App
(00:47:13) Monetization
(00:50:05) Finding True Product Market Fit
(00:50:41) One Piece of Advice
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Podcast: Le 18/20 : le téléphone sonne (LS 44 · TOP 1% what is this?)
Episode: Après les élections, comment se réconcilier ?
Pub date: 2024-07-10
durée : 00:40:48 - Le 18/20 · Le téléphone sonne - Les élections européennes puis législatives ont remis en lumière l’existence des fractures sociales qui traversent la société française. Comment réconcilier les citoyens et apaiser les tensions après des mois de campagnes clivantes ?
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Podcast: The Athletic FC Podcast (LS 56 · TOP 0.5% what is this?)
Episode: Is Lamine Yamal the heir to Messi's throne?
Pub date: 2024-07-12
Lamine Yamal turns 17 the day before Spain’s Euro 2024 final against England and he’s already produced the moment of the tournament with his wonder goal in the semi-final win over France.
So how did he get so good so young?
And will his club side Barcelona be able to keep hold of him in the long term?
Adam Leventhal is joined by The Athletic's Pol Ballus, who is out in Germany covering Spain at Euro 2024 as well as La Liga writer Dermot Corrigan is analyse the rise of Lamine Yamal and look ahead to what could await him on his return to Barcelona.
Host: Adam Leventhal
With: Pol Ballus and Dermot Corrigan
Executive Producer: Adey Moorhead
Producer: Guy Clarke
Related articles:
Perfection, by Lamine Yamal - The Athletic
The story behind the viral photos of Lionel Messi and a baby Lamine Yamal - The Athletic
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The podcast and artwork embedded on this page are from The Athletic, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Athletic FC Podcast (LS 56 · TOP 0.5% what is this?)
Episode: Are England good or just lucky?
Pub date: 2024-07-11
England left it late, but thanks for Ollie Watkins' last minute winner are through to the final at Euro 2024.
So are they now good, or just really lucky having relied on a number of big moments throughout the tournament?
Ayo Akinwolere is joined by The Athletic's Oli Kay who was in Dortmund for the 2-1 win over the Netherlands as well as Jack Pitt-Brooke and Tim Spiers to analyse England's route to the final and the role of manager Gareth Southgate.
Host: Ayo Akinwolere
With: Oli Kay, Jack Pitt-Brooke and Tim Spiers
Executive Producer: Adey Moorhead
Producer: Guy Clarke
Related articles:
England, a team of comebacks, late winners and finals. These are extraordinary times - The Athletic
Ollie Watkins, Cole Palmer and an England goal that was worth the wait - The Athletic
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The podcast and artwork embedded on this page are from The Athletic, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Second Time Founders (LS 28 · TOP 10% what is this?)
Episode: E54 w/ the founder of Intro Raad Mobrem sharing why he started it, how it works and why Nikita Bier makes so much money on it
Pub date: 2024-02-06
Experienced venture founders talking about topics that can help others.
Guest
@Raadmobrem (Intro, Lettuce) intro.co/Raadmobrem
Hosts
@sm (Winnie) intro.co/SaraMauskopf
@kevingibbon (Shyp, Airhouse) intro.co/KevinGibbon
The podcast and artwork embedded on this page are from Second Time Founders, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Startup Ideas Podcast (LS 45 · TOP 1% what is this?)
Episode: Will Meta Bounce Back? (with Nikita Bier)
Pub date: 2022-02-15
When Meta lost more than $230 billion almost overnight many people questioned -- is Meta done? On this episode of Where It Happens, former Meta employee Nikita Bier (@nikitabier) joins hosts Sahil Bloom (@sahilbloom) and Greg Isenberg (@gregisenberg) to explore if Meta can recover and how it might happen. They also explore decentralized mobile networks, the future of music, and how Web2 and Web3 can co-exist.
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SPECIAL THANKS TO OUR SPONSORS
Copy: This episode is brought to you by Beam. We can’t get enough of their Dream Powder. We both personally take this before bed a few times a week and wake up refreshed in the morning. Their nano-CBD helps improve your body’s ability to absorb CBD, making their product the perfect supplement before you go to bed. And now Beam is offering $20 off any order of $75 or more with the code ROOM at beamorganics.com/room.
This episode is also brought to you by OpenPhone. OpenPhone is an all-in-one business phone system that can help your startup look more credible—and it works right from your existing smartphone or computer. Each phone number comes with its own inbox for managing calls, texts, and voicemails together—making it easy to keep track of every conversation. Sign up and start using your new business number in minutes. Visit OpenPhone.co/room to save 20% on your first six months.
The podcast and artwork embedded on this page are from Greg Isenberg, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Lenny's Podcast: Product | Growth | Career (LS 59 · TOP 0.5% what is this?)
Episode: Twitter’s former Head of Product opens up: being fired, meeting Elon, changing stagnant culture, building consumer product, more | Kayvon Beykpour
Pub date: 2024-04-28
Kayvon Beykpour was the longest-serving head of product at Twitter and was GM of Twitter’s consumer division until the platform was acquired by Elon Musk. He originally joined Twitter in 2015 through the acquisition of his company, Periscope, the largest live video streaming platform at the time. Periscope pioneered technology that inspired Instagram Live, TikTok Live, Facebook Live, and other social networks’ expansion into video streaming. In our conversation, we discuss:
• The story of being let go from Twitter after Elon’s acquisition
• How he turned Twitter’s stagnant culture around
• Kayvon’s thoughts on the limitations of frameworks like Jobs to Be Done
• Why Periscope failed
• Advice for building consumer products
• When to copy, when to innovate
—
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Find the transcript at: https://www.lennysnewsletter.com/p/twitters-former-head-of-product-kayvon-beykpour
—
Where to find Kayvon Beykpour:
• X: https://twitter.com/kayvz
• LinkedIn: https://www.linkedin.com/in/kayvz/
—
Where to find Lenny:
• Newsletter: https://www.lennysnewsletter.com
• X: https://twitter.com/lennysan
• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/
—
In this episode, we cover:
(00:00) Kayvon’s background
(04:31) Getting Elon up to speed at Twitter
(11:34) The story of being let go from Twitter after Elon’s acquisition
(21:09) Changing the product culture at Twitter
(29:44) Building the “hide replies” feature
(32:02) Sacred crows, taking bold bets, and reigniting growth
(34:28) Aquihires and their impact
(42:40) Tips for successful acquisitions and staffing
(47:00) The limitations of frameworks like JTBD
(53:20) Signs you’ve gone too far with a framework
(57:44) Lessons from building Periscope
(01:00:41) Reasons why Periscope failed
(01:07:24) The challenges of implementing video at Twitter
(01:12:05) Copying ideas in good taste
(01:17:58) How to get better at building consumer products
(01:19:51) What Kayvon is building
(01:20:31) Lightning round
—
Referenced:
• Lessons on building product sense, navigating AI, optimizing the first mile, and making it through the messy middle | Scott Belsky (Adobe, Behance): https://www.lennyspodcast.com/lessons-on-building-product-sense-navigating-ai-optimizing-the-first-mile-and-making-it-through-t/
• What it’s like to sell your startup for ~$120 million before it’s even launched: Meet Twitter’s new prized possession, Periscope: https://www.businessinsider.com/what-is-periscope-and-why-twitter-bought-it-2015-3
• Walter Isaacson on LinkedIn: https://www.linkedin.com/in/walter-isaacson-b8b81520/
• Elon Musk on X: https://twitter.com/elonmusk
• Parag Agrawal on LinkedIn: https://www.linkedin.com/in/parag-agrawal-5a14742a/
• Jack Dorsey on LinkedIn: https://www.linkedin.com/in/jack-dorsey-a43b07242/
• Blackboard: https://en.wikipedia.org/wiki/Blackboard_Inc.
• Keith Coleman on X: https://twitter.com/kcoleman
• Esther Crawford on LinkedIn: https://www.linkedin.com/in/esthercrawford/
• Twitter acquires Chroma Labs: https://tech.hindustantimes.com/tech/news/twitter-acquires-chroma-labs-story-aqvcRPAoYXqXJuAbefA6cN.html
• John Barnett on LinkedIn: https://www.linkedin.com/in/johnbarnettt/
• Jobs to Be Done framework: https://jobs-to-be-done.com/jobs-to-be-done-a-framework-for-customer-needs-c883cbf61c90
• Hot takes and techno-optimism from tech’s top power couple: https://www.lennyspodcast.com/hot-takes-and-techno-optimism-from-techs-top-power-couple-sriram-and-aarthi/
• Nike Is Unveiling the Kobe 11 Tomorrow Using Periscope: https://sneakernews.com/2015/12/13/nike-is-unveiling-the-kobe-11-tomorrow-using-periscope/
• Chris Sacca’s website: https://chrissacca.com/
• Facebook Live: https://www.facebook.com/formedia/tools/facebook-live
• Kevin Hart on X: https://twitter.com/KevinHart4real
• Clubhouse: https://www.clubhouse.com/
• Vine: https://en.wikipedia.org/wiki/Vine_(service)
• Paul Davison on LinkedIn: https://www.linkedin.com/in/davison/
• Rohan Seth on LinkedIn: https://www.linkedin.com/in/rohanseth/
• Cryptonomicon: https://www.amazon.com/Cryptonomicon-Neal-Stephenson/dp/0380788624
• Reamde: https://www.amazon.com/Reamde-Novel-Neal-Stephenson-ebook/dp/B004XVN0WW
• The Name of the Wind: https://www.amazon.com/Name-Wind-Kingkiller-Chronicle-Book-ebook/dp/B0010SKUYM
• Star Trek official site: https://www.startrek.com/
• Dune: part 2: https://www.dunemovie.com/
• Oppenheimer on Peacock: https://www.peacocktv.com/stream-movies/oppenheimer
• Tokyo Vice on Max: https://www.max.com/shows/tokyo-vice/e7d93204-7f98-4e62-ab52-6c1da053f942
• Devs on Hulu: https://www.fxnetworks.com/shows/devs
• Nick Offerman on X: https://twitter.com/nick_offerman
• 3 Body Problem on Netflix: https://www.netflix.com/title/81024821
• Perplexity AI: https://www.perplexity.ai/
• Particle: https://www.particle.news/
• Crokinole board game: https://boardgamegeek.com/boardgame/521/crokinole
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.
—
Lenny may be an investor in the companies discussed.
Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe
The podcast and artwork embedded on this page are from Lenny Rachitsky, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Lenny's Podcast: Product | Growth | Career (LS 59 · TOP 0.5% what is this?)
Episode: Pattern Breakers: How to find a breakthrough startup idea | Mike Maples, Jr. (Founding Partner at Floodgate, ex-Product at Silicon Graphics)
Pub date: 2024-07-07
Mike Maples, Jr. is a legendary early-stage startup investor and a co-founder and partner at Floodgate. He’s made early bets on transformative companies like Twitter, Lyft, Twitch, Okta, Rappi, and Applied Intuition and is one of the pioneers of seed-stage investing as a category. He’s been on the Forbes Midas List eight times and enjoys sharing the lessons he’s learned from his years studying iconic companies. In his new book, Pattern Breakers: Why Some Start-Ups Change the Future, co-authored with Peter Ziebelman,he discusses what he’s found separates startups and founders that break through and change the world from those that don’t. After spending years reviewing the notes and decks from the thousands of startups he’s known over the past two decades, he’s uncovered three ways that breakthrough founders think and act differently. In our conversation, Mike talks about:
• The three elements of breakthrough startup ideas
• Why you need to both think and act differently
• How to avoid the “comparison trap” and “conformity trap”
• The importance of movements, storytelling, and healthy disagreeableness in startup success
• How to apply pattern-breaking principles within large companies
• Mike’s one piece of advice for founders
• Much more
Pre-order Mike’s book here and get a second signed copy for free. Limited copies are available, so order ASAP: patternbreakers.com/lenny.
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—
Find the transcript at: https://www.lennysnewsletter.com/p/how-to-find-a-great-startup-idea-mike-maples-jr
—
Where to find Mike Maples, Jr.:
• X: https://x.com/m2jr
• LinkedIn: https://www.linkedin.com/in/maples/
• Substack: https://greatness.substack.com/
• Website: https://www.floodgate.com/
—
Where to find Lenny:
• Newsletter: https://www.lennysnewsletter.com
• X: https://twitter.com/lennysan
• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/
—
In this episode, we cover:
(00:00) Mike’s background
(03:10) The inspiration behind Pattern Breakers
(08:09) Uncovering startup insights
(11:37) A quick summary of Pattern Breakers
(13:52) Coming up with an idea
(15:30) Inflections
(17:09) Examples of inflections
(28:10) Insights
(36:58) The power of surprises
(47:36) Founder-future fit
(55:33) Advice for aspiring founders
(56:41) Living in the future: valid opinions
(55:34) Case study: Maddie Hall and Living Carbon
(58:40) Identifying lighthouse customers
(01:00:53) The importance of desperation in customer needs
(01:03:57) Creating movements and storytelling
(01:24:22) The role of disagreeableness in startups
(01:34:42) Applying these principles within a company
(01:40:43) Lightning round
—
Referenced:
• Pattern Breakers: Why Some Start-Ups Change the Future: https://www.amazon.com/Pattern-Breakers-Start-Ups-Change-Future/dp/1541704355
• Justin.tv: https://en.wikipedia.org/wiki/Justin.tv
• Airbnb’s CEO says a $40 cereal box changed the course of the multibillion-dollar company: https://fortune.com/2023/04/19/airbnb-ceo-cereal-box-investors-changed-everything-billion-dollar-company/
• Brian Chesky’s new playbook: https://www.lennysnewsletter.com/p/brian-cheskys-contrarian-approach
• The Unconventional Exit: How Justin Kan Sold His First Startup on eBay: https://medium.datadriveninvestor.com/the-unconventional-exit-how-justin-kan-sold-his-first-startup-on-ebay-4d705afe1354
• Kyle Vogt on LinkedIn: https://www.linkedin.com/in/kylevogt/
• The State of Telehealth Before and After the COVID-19 Pandemic: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9035352/
• The Craigslist Killers: https://www.gq.com/story/craigslist-killers
• The social radar: Y Combinator’s secret weapon | Jessica Livingston (co-founder of Y Combinator, author, podcast host): https://www.lennysnewsletter.com/p/the-social-radar-jessica-livingston
• Michael Seibel on LinkedIn: https://www.linkedin.com/in/mwseibel/
• The Airbnb Story: How Three Ordinary Guys Disrupted an Industry, Made Billions ... and Created Plenty of Controversy: https://www.amazon.com/Airbnb-Story-Ordinary-Disrupted-Controversy/dp/0544952669
• Scott Cook: https://www.forbes.com/profile/scott-cook/
• Chegg: https://www.chegg.com/
• Aayush Phumbhra on LinkedIn: https://www.linkedin.com/in/aayush/
• Osman Rashid on LinkedIn: https://www.linkedin.com/in/osmanrashid/
• Okta: https://www.okta.com/
• The Man Who Makes the Future: Wired Icon Marc Andreessen: https://www.wired.com/2012/04/ff-andreessen/
• Peter Ludwig on LinkedIn: https://www.linkedin.com/in/peterwludwig/
• Qasar Younis on LinkedIn: https://www.linkedin.com/in/qasar/
• Paul Allen’s website: https://paulallen.com/
• Louis Pasteur quote: https://www.forbes.com/quotes/6145/
• What was Atrium and why did it fail? https://www.failory.com/cemetery/atrium
• Patrick Collison on LinkedIn: https://www.linkedin.com/in/patrickcollison/
• Drew Houston on LinkedIn: https://www.linkedin.com/in/drewhouston/
• William Gibson’s quote: https://www.goodreads.com/quotes/681-the-future-is-already-here-it-s-just-not-evenly
• Maddie Hall on LinkedIn: https://www.linkedin.com/in/maddie-hall-76293135/
• Living Carbon: https://www.livingcarbon.com
• Zenefits (now Trinet): https://connect.trinet.com/
• Sam Altman on X: https://x.com/sama
• Steve Wozniak on LinkedIn: https://www.linkedin.com/in/wozniaksteve/
• Horsley Bridge Partners: https://www.horsleybridge.com/
• David Swensen: https://en.wikipedia.org/wiki/David_F._Swensen
• Judith Elsea on LinkedIn: https://www.linkedin.com/in/judithelsea/
• 7 Powers: The Foundations of Business Strategy: https://www.amazon.com/7-Powers-Foundations-Business-Strategy/dp/0998116319
• Business strategy with Hamilton Helmer (author of 7 Powers): https://www.lennysnewsletter.com/p/business-strategy-with-hamilton-helmer
• Lyft’s Focus on Community and the Story Behind the Pink Mustache: https://techcrunch.com/2012/09/17/lyfts-focus-on-community-and-the-story-behind-the-pink-mustache/
• Logan Green on LinkedIn: https://www.linkedin.com/in/logangreen/
• John Zimmer on LinkedIn: https://www.linkedin.com/in/johnzimmer11/
• Storytelling with Nancy Duarte: How to craft compelling presentations and tell a story that sticks: https://www.lennysnewsletter.com/p/storytelling-with-nancy-duarte-how
• Steve Jobs Introducing the iPhone at MacWorld 2007: https://www.youtube.com/watch?v=x7qPAY9JqE4
• Jonathan Livingston Seagull: https://www.amazon.com/Jonathan-Livingston-Seagull-Richard-Bach/dp/0743278909
• The paths to power: How to grow your influence and advance your career | Jeffrey Pfeffer (author of 7 Rules of Power, professor at Stanford GSB): https://www.lennysnewsletter.com/p/the-paths-to-power-jeffrey-pfeffer
• Robin Roberts on LinkedIn: https://www.linkedin.com/in/robin-roberts-393a934b/
• Skunkworks: https://www.lockheedmartin.com/en-us/who-we-are/business-areas/aeronautics/skunkworks.html
• Vision, conviction, and hype: How to build 0 to 1 inside a company | Mihika Kapoor (Product at Figma): https://www.lennysnewsletter.com/p/vision-conviction-hype-mihika-kapoor
• Hard-won lessons building 0 to 1 inside Atlassian | Tanguy Crusson (Head of Jira Product Discovery): https://www.lennysnewsletter.com/p/building-0-to-1-inside-atlassian-tanguy-crusson
• Figma: https://www.figma.com/
• Atlassian: https://www.atlassian.com/
• Vinod Khosla: https://www.khoslaventures.com/team/vinod-khosla/
• Top Five Regrets of the Dying: A Life Transformed by the Dearly Departing: https://www.amazon.com/Top-Five-Regrets-Dying-Transformed-ebook/dp/B07KNRLY1L
• Chase, Chance, and Creativity: The Lucky Art of Novelty: https://www.amazon.com/Chase-Chance-Creativity-Lucky-Novelty/dp/0262511355
• Clay Christensen’s books: https://www.amazon.com/stores/Clayton-M.-Christensen/author/B000APPD3Y
• Resonate: Present Visual Stories That Transform: https://www.amazon.com/Resonate-Present-Stories-Transform-Audiences/dp/0470632011
• Ferrari on Prime: https://www.amazon.com/Ferrari-Adam-Driver/dp/B0CNDBN672
• Montblanc fountain pens: https://www.montblanc.com/en-us
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.
—
Lenny may be an investor in the companies discussed.
Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe
The podcast and artwork embedded on this page are from Lenny Rachitsky, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Rocketship.fm (LS 49 · TOP 0.5% what is this?)
Episode: The Sprig Journey to Product Market Fit plus Tech Industry Insights
Pub date: 2024-04-01
In this episode, Michael Sacca and Michael Belsito drop some major news before diving into the latest developments in the AI space. They discuss the tempered expectations surrounding generative AI and its impact on tech giants and consumers alike. Amidst the AI hype, companies are cautious about investing in new AI services due to high costs and questionable productivity gains.
Later, they explore the product-market fit journey of Sprig, a product research platform, with CEO Ryan Glasglow. Glasglow shares his insights into identifying and validating critical problems faced by users, emphasizing the importance of understanding customer willingness to pay and the value proposition within existing software budgets. The episode also delves into Sprig's differentiation in a crowded market and their early customer acquisition strategy. Tune in for a deep dive into AI realities and product validation strategies in the tech world.
This podcast is brought to you by:
Go to http://porkbun.com/RocketshipFM24 to get $1 off your next desired domain name at Porkbun!
Miro: Go to Miro.com/podcast and get your first three Miro boards free forever.
Hubspot: Listen to The Science of Scaling wherever you get your podcasts.
Gigantic: Learn more about Gigantic's Product Leadership course, Generative AI, Product Management course, Executive Leadership course & Web3 for Marketing course, AI Product Management Course, and Customer Research and Discovery Course at Gigantic.is
Leadpages: The Easiest Way to Grow Your Sales. Check out their landing page templates today.
Rocketship is brought to you by The Podglomerate.
Previous Guests include Seth Goden, Christian Idioti, Ash Maurya, Dan Shapiro of Glowforge, Lolita Taub, Amy Hood of Hoodzpah, Amanda Goetz, Helen Tran, Ben Parr, Mac Conwell, Charli Marie Prangley of ConvertKit, Kandis O'Brian, Laura Roeder, Brenna Loury of Doist, Lopa van der Mersch of Rasa, Ken Norton, Randy Silver, Sanjiv Kalevar of OpenView Venture Partners, Dan Olsen, Jay Clouse, Melissa Perri, Dheerja Kaur of Robinhood, Rahul Vohra of Superhuman, Rich Mironov, Ben Foster, ChatGPT, Ron Weiner of Earth Class Mail.
This show is a part of the Podglomerate network, a company that produces, distributes, and monetizes podcasts. We encourage you to visit the website and sign up for our newsletter for more information about our shows, launches, and events. For more information on how The Podglomerate treats data, please see our Privacy Policy.
Since you're listening to Rocketship, we'd like to suggest you also try other Podglomerate shows surrounding entrepreneurship, business, and careers like Creative Elements and Freelance to Founder.
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The podcast and artwork embedded on this page are from Rocketship.FM, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Athletic FC Podcast (LS 56 · TOP 0.5% what is this?)
Episode: Should England drop Kane?
Pub date: 2024-07-08
England are through to the semi-final at Euro 2024 after seeing off Switzerland on penalties. Yet for Gareth Southgate’s side there were familiar problems in Dusseldorf with Harry Kane again struggling to make an impact.
No doubt he’ll start Wednesday’s semi-final against the Netherlands but should he?
Ayo Akinwolere is joined by The Athletic's senior football writer Oli Kay as well as Jacob Whitehead to discuss Kane's role ahead of Wednesday's meeting in Dortmund.
Football tactics writer Ahmed Walid also drops in to join in the conversation around the rest of the semi-finalists leading forwards while Pol Ballus explains the importance of Alvaro Morata to Spain both on and off the pitch.
Host: Ayo Akinwolere
With: Oli Kay, Jacob Whitehead and Ahmed Walid
Executive Producer: Adey Moorhead
Producer: Guy Clarke
Related articles
Is Harry Kane in danger of becoming England’s Cristiano Ronaldo? - The Athletic
Memphis Depay vs Wout Weghorst: The striker debate which cuts to the heart of Dutch football - The Athletic
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The podcast and artwork embedded on this page are from The Athletic, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Forcing Function Hour (LS 24 · TOP 10% what is this?)
Episode: Minimum Viable Testing for Startup Founders with Gagan Biyani
Pub date: 2021-12-10
Gagan Biyani is the co-founder of Maven, which empowers experts to offer cohort-based courses directly to their audience. Gagan is a serial entrepreneur; he previously co-founded Udemy and Sprig, a food delivery company. Gagan is an investor and advisor to over thirty companies.
Gagan joins Chris to share how founders can accelerate their path to acquiring product market fit. Gagan’s extensive startup experience taught him that creating a “minimum viable product” or MVP—widely considered startup dogma—leads teams astray by over-building and over-developing too early in the process. If you are building your company around outdated paradigms like Lean Startup, you’re going to have a bad time.
Instead, through Minimum Viable Testing, teams can save time, avoid hiring developers too early, and have higher accuracy on their eventual initial product.
For the video, transcript, and show notes, visit forcingfunctionhour.com/gagan-biyani.
The podcast and artwork embedded on this page are from Chris Sparks, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Le 18/20 : le téléphone sonne (LS 44 · TOP 1% what is this?)
Episode: Elections législatives : les leçons du premier tour
Pub date: 2024-07-01
durée : 01:14:47 - Le 18/20 · Le téléphone sonne - Au lendemain des résultats du premier tour des élections législatives édition spéciale du 18/20 ce soir, avec un Téléphone sonne grand format.
The podcast and artwork embedded on this page are from France Inter, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Athletic FC Podcast (LS 56 · TOP 0.5% what is this?)
Episode: Does it matter how England play if they win?
Pub date: 2024-07-01
England have squeezed through to the quarter-finals of Euro 2024 thanks in part to a moment of genius from who else but Jude Bellingham.
It was an underwhelming performance, but post match the England midfielder silenced critics.
So, does it matter how England play as long as they win?
Host: Ayo Akinwolere
With: Jack Pitt-Brooke, Jacob Whitehead
Executive Producer: Adey Moorhead
Producer: Jay Beale
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The podcast and artwork embedded on this page are from The Athletic, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Off Menu with Ed Gamble and James Acaster (LS 77 · TOP 0.01% what is this?)
Episode: Ep 240: Killer Mike
Pub date: 2024-04-10
Grammy Award-winning rapper (and half of Run The Jewels) Killer Mike joins us in the Dream Restaurant. And he’s impressed with Ed’s fishing skills.
Killer Mike’s Grammy-winning album ‘Michael’ is out now. Listen here.
Killer Mike is on tour this year. For dates and tickets go to killermike.com.
Follow Killer Mike on Instagram and Twitter @killermike
Recorded and edited by Ben Williams for Plosive.
Artwork by Paul Gilbey (photography and design).
Follow Off Menu on Twitter and Instagram: @offmenuofficial.
And go to our website www.offmenupodcast.co.uk for a list of restaurants recommended on the show.
Watch Ed and James's YouTube series 'Just Puddings'. Watch here.
Hosted on Acast. See acast.com/privacy for more information.
The podcast and artwork embedded on this page are from Plosive, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Machine Learning Street Talk (MLST) (LS 43 · TOP 1% what is this?)
Episode: New 50% ARC result and current winners interviewed
Pub date: 2024-06-18
The ARC Challenge, created by Francois Chollet, tests how well AI systems can generalize from a few examples in a grid-based intelligence test. We interview the current winners of the ARC Challenge—Jack Cole, Mohammed Osman and their collaborator Michael Hodel. They discuss how they tackled the ARC (Abstraction and Reasoning Corpus) Challenge using language models and neural networks. We also discuss the new "50%" approach announced today from Redwood Research.
Jack and Mohammed explain their approach, which involves fine-tuning a language model on a large, specially-generated dataset and then doing additional fine-tuning at test-time, a technique known in this context as "active inference". They use various strategies to represent the data for the language model and believe that with further improvements, the accuracy could reach above 50%. Michael talks about his work on generating new ARC-like tasks to help train the models.
Tim and the guests also debate whether their methods stay true to the spirit of measuring intelligence as intended by ARC's creator Francois Chollet. Despite some concerns, they agree that their solutions are promising and adaptable for other similar problems. The conversation wraps up with the guests encouraging others to explore the ARC tasks and share their creative solutions.
Jack Cole:
https://x.com/Jcole75Cole
https://lab42.global/community-interview-jack-cole/
Mohamed Osman:
Mohamed is looking to do a PhD in AI/ML, can you help him?
Email: mothman198@outlook.com
https://www.linkedin.com/in/mohamedosman1905/
Michael Hodel:
https://arxiv.org/pdf/2404.07353v1
https://www.linkedin.com/in/michael-hodel/
https://x.com/bayesilicon
https://github.com/michaelhodel
Getting 50% (SoTA) on ARC-AGI with GPT-4o - Ryan Greenblatt
https://redwoodresearch.substack.com/p/getting-50-sota-on-arc-agi-with-gpt
The podcast and artwork embedded on this page are from Machine Learning Street Talk (MLST), which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: ThursdAI - The top AI news from the past week (LS 25 · TOP 10% what is this?)
Episode: 📅 ThursdAI - Gemma 2, AI Engineer 24', AI Wearables, New LLM leaderboard
Pub date: 2024-06-27
Hey everyone, sending a quick one today, no deep dive, as I'm still in the middle of AI Engineer World's Fair 2024 in San Francisco (in fact, I'm writing this from the incredible floor 32 presidential suite, that the team here got for interviews, media and podcasting, and hey to all new folks who I’ve just met during the last two days!)
It's been an incredible few days meeting so many ThursdAI community members, listeners and folks who came on the pod! The list honestly is too long but I've got to meet friends of the pod Maxime Labonne, Wing Lian, Joao Morra (crew AI), Vik from Moondream, Stefania Druga not to mention the countless folks who came up and gave high fives, introduced themselves, it was honestly a LOT of fun. (and it's still not over, if you're here, please come and say hi, and let's take a LLM judge selfie together!)
On today's show, we recorded extra early because I had to run and play dress up, and boy am I relieved now that both the show and the talk are behind me, and I can go an enjoy the rest of the conference 🔥 (which I will bring you here in full once I get the recording!)
On today's show, we had the awesome pleasure to have Surya Bhupatiraju who's a research engineer at Google DeepMind, talk to us about their newly released amazing Gemma 2 models! It was very technical, and a super great conversation to check out!
Gemma 2 came out with 2 sizes, a 9B and a 27B parameter models, with 8K context (we addressed this on the show) and this 27B model incredible performance is beating LLama-3 70B on several benchmarks and is even beating Nemotron 340B from NVIDIA!
This model is also now available on the Google AI studio to play with, but also on the hub!
We also covered the renewal of the HuggingFace open LLM leaderboard with their new benchmarks in the mix and normalization of scores, and how Qwen 2 is again the best model that's tested!
It's was a very insightful conversation, that's worth listening to if you're interested in benchmarks, definitely give it a listen.
Last but not least, we had a conversation with Ethan Sutin, the co-founder of Bee Computer. At the AI Engineer speakers dinner, all the speakers received a wearable AI device as a gift, and I onboarded (cause Swyx asked me) and kinda forgot about it. On the way back to my hotel I walked with a friend and chatted about my life.
When I got back to my hotel, the app prompted me with "hey, I now know 7 new facts about you" and it was incredible to see how much of the conversation it was able to pick up, and extract facts and eve TODO's!
So I had to have Ethan on the show to try and dig a little bit into the privacy and the use-cases of these hardware AI devices, and it was a great chat!
Sorry for the quick one today, if this is the first newsletter after you just met me and register, usually there’s a deeper dive here, expect a more in depth write-ups in the next sessions, as now I have to run down and enjoy the rest of the conference!
Here's the TL;DR and my RAW show notes for the full show, in case it's helpful!
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AI Engineer is happening right now in SF
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Tracks include Multimodality, Open Models, RAG & LLM Frameworks, Agents, Al Leadership, Evals & LLM Ops, CodeGen & Dev Tools, Al in the Fortune 500, GPUs & Inference
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Open Source LLMs
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HuggingFace - LLM Leaderboard v2 - (Blog)
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Old Benchmarks sucked and it's time to renew
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New Benchmarks
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MMLU-Pro (Massive Multitask Language Understanding - Pro version, paper)
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GPQA (Google-Proof Q&A Benchmark, paper). GPQA is an extremely hard knowledge dataset
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MuSR (Multistep Soft Reasoning, paper).
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MATH (Mathematics Aptitude Test of Heuristics, Level 5 subset, paper)
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IFEval (Instruction Following Evaluation, paper)
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🤝 BBH (Big Bench Hard, paper). BBH is a subset of 23 challenging tasks from the BigBench dataset
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The community will be able to vote for models, and we will prioritize running models with the most votes first
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Mozilla announces Builders Accelerator @ AI Engineer (X)
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Theme: Local AI
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100K non dilutive funding
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Google releases Gemma 2 (X, Blog)
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Big CO LLMs + APIs
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UMG, Sony, Warner sue Udio and Suno for copyright (X)
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were able to recreate some songs
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sue both companies
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have 10 unnamed individuals who are also on the suit
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Google Chrome Canary has Gemini nano (X)
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Super easy to use window.ai.createTextSession()
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Nano 1 and 2, at a 4bit quantized 1.8B and 3.25B parameters has decent performance relative to Gemini Pro
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Behind a feature flag
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Most text gen under 500ms
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Unclear re: hardware requirements
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Someone already built extensions
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someone already posted this on HuggingFace
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Anthropic Claude share-able projects (X)
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Snapshots of Claude conversations shared with your team
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Can share custom instructions
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Anthropic has released new "Projects" feature for Claude AI to enable collaboration and enhanced workflows
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Projects allow users to ground Claude's outputs in their own internal knowledge and documents
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Projects can be customized with instructions to tailor Claude's responses for specific tasks or perspectives
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"Artifacts" feature allows users to see and interact with content generated by Claude alongside the conversation
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Claude Team users can share their best conversations with Claude to inspire and uplevel the whole team
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North Highland consultancy has seen 5x faster content creation and analysis using Claude
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Anthropic is committed to user privacy and will not use shared data to train models without consent
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Future plans include more integrations to bring in external knowledge sources for Claude
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OpenAI voice mode update - not until Fall
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AI Art & Diffusion & 3D
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Fal open sourced AuraSR - a 600M upscaler based on GigaGAN (X, Fal)
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Interview with Ethan Sutin from Bee Computer
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We all got Bees as a gifts
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AI Wearable that extracts TODOs, knows facts, etc'
This is a public episode. If you’d like to discuss this with other subscribers or get access to bonus episodes, visit sub.thursdai.news/subscribe
The podcast and artwork embedded on this page are from From Weights & Biases, Join AI Evangelist Alex Volkov and a panel of experts to cover everything important that happened in the world of AI from the past week, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Weaviate Podcast
Episode: DSPy and ColBERT with Omar Khattab! - Weaviate Podcast #85
Pub date: 2024-01-15
Hey everyone! I am beyond excited to present our interview with Omar Khattab from Stanford University! Omar is one of the world's leading scientists on AI and NLP. I highly recommend you check out Omar's remarkable list of publications linked below! This interview completely transformed my understanding of building RAG and LLM applications! I believe that DSPy will be one of the most impactful software project in LLM development because of the abstractions around program optimization. Here is my TLDR of this concept of LLM programs and program optimization with DSPy, I of course encourage you to view the podcast and listen to Omar's explanation haha.RAG is one of the most popular LLM programs we have seen. RAG typically consists of two components of retrieve and then generate. Within the generate component we have a prompt like "please ground your answer based on the search results {search_results}". DSPy gives us a framework to optimize this prompt, bootstrap few-shot examples, or even fine-tune the model if needed. This works by compiling the program based on some evaluation criteria we give DSPy. Now let's say we add a query re-writer that takes the query and writes a new query before sending it to the retrieval system, and a reranker that takes the search results and re-orders them before handing them to the answer generator. Now we have 4 components of query writer, retrieve, rerank, answer. The 3 components of query writer, rerank, and answer all have a prompt that can be optimized with DSPy to enhance the description of the task or add examples! This optimization is done with DSPy's Teleprompters.There are a few other really interesting components to DSPy as well -- such as the formatting of prompts with the docstrings and Signature abstraction, which in my view is quite similar to instructor or LMQL. DSPy also comes with built-in prompts like Chain-of-Thought that offer a really quick way to add this reasoning step and follow a structured output format. I am having so much fun learning about DSPy and I highly recommend you join me in viewing the GitHub repository linked below (with new examples!!):Omar also discusses ColBERT and late interaction retrieval! Omar describes how this achieves the contextualized attention of cross encoders but in a much more scalable system with the maximum similarity between vectors! Stay tuned for more updates from Weaviate as we are diving into multi vector representations to hopefully support systems like this soon!
Chapters
0:00 Weaviate at NeurIPS 2023!
0:38 Omar Khattab
0:57 What is the state of AI?
2:35 DSPy
10:37 Pipelines
14:24 Prompt Tuning and Optimization
18:12 Models for Specific Tasks
21:44 LLM Compiler
23:32 Colbert or ColBERT?
24:02 ColBERT
The podcast and artwork embedded on this page are from Weaviate, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Weaviate Podcast
Episode: Self-Discover DSPy with Chris Dossman - Weaviate Podcast #90!
Pub date: 2024-03-06
One of the core values of DSPy is the ability to add “reasoning modules” such as Chain-of-Thought to your LLM programs!
For example, Chain-of-Thought describes prompting the LLM with “Let’s think step by step …”. Interestingly, this meta-prompt around asking the LLM to think this way dramatically improves performance in tasks like question answering or document summarization.
Self-Discover is a meta-prompting technique that searches for the optimal thinking primitives to integrate into your program! For example, you could “Let’s think out of the box to arrive at a creative solution” or “Please explain your answer in 4 levels of abstraction: as if you are talking to a five year old, a high school student, a college student studying Computer Science, and a software engineer with years of experience in the topic”.
I am SUPER excited to be publishing our 90th Weaviate Podcast with Chris Dossman! Chris has implemented Self-Discover in DSPy, one of the most fascinating examples so far of what the DSPy framework is capable of!
Chris is also one of the most talented entrepreneurs I have met during my time at Weaviate thanks to introductions from Bob van Luijt and Byron Voorbach. Chris built one of the earliest RAG systems for government information and is now working on LLM opportunities in marketing with his new startup, Dicer.ai!
I hope you enjoy the podcast, it was such a fun one and I learned so much!
The podcast and artwork embedded on this page are from Weaviate, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Stadio: A Football Podcast (LS 57 · TOP 0.5% what is this?)
Episode: Georgia and Turkey Light Up the Final Day of the Group Stage
Pub date: 2024-06-27
The Euro 2024 group stages are done, so Musa and Ryan recap some of the games from the brilliant final round, beginning with Georgia's and Turkey's progression from Group F (09:58). They look back at the other games, look ahead to the knockouts (26:00) and, finally, read out some more of your Euro XIs.
Hosts: Ryan Hunn and Musa Okwonga
Producer: Ryan Hunn
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The podcast and artwork embedded on this page are from The Ringer, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Athletic FC Podcast (LS 56 · TOP 0.5% what is this?)
Episode: Why have France been so boring?
Pub date: 2024-06-28
After three lacklustre displays - France failed to top their group at a major tournament for the first time in 12 years.
Their two goals so far at Euro 2024 have consisted of an own goal and a penalty, so what is going wrong for Didier Deschamps’ side?
And how much good will is left in the bank from their 2018 World Cup triumph?
Host: Adam Leventhal
With: Tom Williams, Pierre-Etienne Minonzio
Executive Producer: Adey Moorhead
Producer: Mike Stavrou
Learn more about your ad choices. Visit megaphone.fm/adchoices
The podcast and artwork embedded on this page are from The Athletic, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Syntax - Tasty Web Development Treats (LS 61 · TOP 0.1% what is this?)
Episode: 760: Pro VSCode Setups
Pub date: 2024-04-24
Join Scott and Wes as they dish out the juiciest VSCode secrets for coding like a boss (or a Tolinski)! From speedy navigation to must-have extensions and the sickest themes, get ready to level up your coding game.
Show Notes * 00:00 Welcome to Syntax! * 00:47 Brought to you by Sentry.io. * 01:44 A recording bug. * 03:18 VSCode versions. * 05:59 Tabs or no tabs. * 10:32 Navigation tips. * 11:35 Mouse and trackpad input. * 13:43 Move, select and expand by * 19:07 Interface tips. * 19:17 Sidebars. * 24:23 Sticky headers. * 26:21 Activity bar. * 30:30 Show or hide? * 31:35 Profiles. * 32:43 Keyboard Shortcuts. * 32:49 Renaming. * 34:32 Extensions. + 34:45 Text pastry. + 36:43 Better comments. + 39:03 Auto rename tag. + 40:02 Change case. + 40:25 Permute lines. + 41:26 File utils. + 43:20 Sort JSON objects. + 43:50 SQLite viewer. + 44:29 Spell checker. + 45:42 APC. * 49:19 Themes. * Syntax Theme * 53:05 Final tricks. * Log Wrapper * 57:44 What about the AI stuff? * 01:00:10 Sick Picks & Shameless Plugs.
Sick Picks * Scott: Flicker Free Ultra Definition Phillips Bulbs. * Wes: Clear Shoe Box Organizers.
Shameless Plugs * Scott: Syntax Newsletter.
Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads
Wes: X Instagram Tiktok LinkedIn Threads
Scott:X Instagram Tiktok LinkedIn Threads
Randy: X Instagram YouTube Threads
The podcast and artwork embedded on this page are from Wes Bos & Scott Tolinski - Full Stack JavaScript Web Developers, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Syntax - Tasty Web Development Treats (LS 61 · TOP 0.1% what is this?)
Episode: 780: Cloud Storage: Bandwidth, Storage and BIG ZIPS
Pub date: 2024-06-10
Today, Scott and Wes dive into cloud storage solutions—why you might need them, how they use them, and what you need to know about the big players, fees, and more.
Show Notes * 00:00 Welcome to Syntax! * 01:14 Brought to you by Sentry.io. * 02:05 Why you might need a cloud storage provider. * 03:07 How we use cloud storage. + Dropshare. * 08:16 Why you may need larger storage. * 09:49 The big players in this space. + Amazon S3. + Cloudflare R2. + Backblaze B2. + Synology C2. + Google Cloud Storage. + Microsoft Azure. + Digital Ocean Spaces. + Oracle. + Bunny.net. + Amazon S3 Glacier. * 14:34 Storage fees. * 18:31 Why so cheap? * 20:49 Bandwidth (egress). + Cloudflare Bandwidth Alliance. * 26:46 Operation fees - costs money.
Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads
Wes: X Instagram Tiktok LinkedIn Threads
Scott:X Instagram Tiktok LinkedIn Threads
Randy: X Instagram YouTube Threads
The podcast and artwork embedded on this page are from Wes Bos & Scott Tolinski - Full Stack JavaScript Web Developers, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Syntax - Tasty Web Development Treats (LS 61 · TOP 0.1% what is this?)
Episode: 781: Potluck - The Value of TypeScript × Vue vs Svelte × Leetcode
Pub date: 2024-06-12
In this potluck episode of Syntax, Scott and CJ serve up a variety of community questions, from the nuances of beginner vs. advanced TypeScript to the pros and cons of SvelteKit. They also discuss falling out of love with React, shipping private packages via NPM, and the eternal struggle of always starting but never finishing projects.
Show Notes * 00:00 Welcome to Syntax! * 01:06 Brought to you by Sentry.io. * 01:49 Today’s format. * 02:23 Beginner vs advanced TypeScript. + DHH Tweet. * 09:23 Does Sveltekit replace Svelte with Astro? * 13:09 Handling multiple languages. * 19:52 Falling out of love with React. * 25:53 Shipping private packages via NPM. + npm-install. + Working with the npm registry. * 29:00 How do you feel about importing packages from a URL? * 30:36 VueJS vs Svelte. * 36:15 Leetcode type interview questions. * 41:58 Learning a new language for personal growth. * 46:21 Always starting, never finishing. + Scott’s Fluid Type Calculator. * 50:23 Code quality vs tackling tickets. * 55:36 Sick Picks + Shameless Plugs.
Sick Picks * Scott: Quick Look Plugins. * CJ: Sony WFC700n-b.
Shameless Plugs * Scott: Syntax on YouTube
Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads
Wes: X Instagram Tiktok LinkedIn Threads
Scott:X Instagram Tiktok LinkedIn Threads
Randy: X Instagram YouTube Threads
The podcast and artwork embedded on this page are from Wes Bos & Scott Tolinski - Full Stack JavaScript Web Developers, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Invest Like the Best with Patrick O'Shaughnessy (LS 67 · TOP 0.05% what is this?)
Episode: Tim Ferriss - Curating Curiosities - [Invest Like the Best, EP.369]
Pub date: 2024-04-16
My guest today is Tim Ferriss. Tim is a writer, podcaster, and investor. He has written five best-selling books, has been an early-stage investor in Facebook, Uber, Shopify, & other household names, and is the host of one of the biggest podcasts in the world. He started The Tim Ferriss Show as an experiment in April 2014 and has deconstructed world-class performers ever since. Last year, his show crossed 1 billion downloads. Together, we deconstruct his podcast and approach to life. We talk about the art of interviewing, the business behind his podcast, and what motivates Tim to keep teaching through his writing and podcast. Please enjoy this great conversation with Tim Ferriss.
Check Out Invest America 2024
Listen to Founders Podcast
For the full show notes, transcript, and links to mentioned content, check out the episode page here.
This episode is brought to you by Tegus, the only investment research platform built for the investor. With traditional research vendors, the diligence process is slow, fragmented, and expensive. That leaves investors competing on how well they can aggregate data — not on their unique ability to analyze insights and make great investment decisions. Tegus offers an end-to-end platform with all the data you need to get up to speed on a company or market: up-to-the-minute financials, customizable models, management and culture checks, and, of course, our vast and growing library of expert call transcripts. Tegus is changing the world of expert research. Learn more and get your free trial at tegus.com/patrick.
Invest Like the Best is a property of Colossus, LLC. For more episodes of Invest Like the Best, visit joincolossus.com/episodes.
Past guests include Tobi Lutke, Kevin Systrom, Mike Krieger, John Collison, Kat Cole, Marc Andreessen, Matthew Ball, Bill Gurley, Anu Hariharan, Ben Thompson, and many more.
Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here.
Follow us on Twitter: @patrick_oshag | @JoinColossus
Editing and post-production work for this episode was provided by The Podcast Consultant (https://thepodcastconsultant.com).
Show Notes:
(00:00:00) Welcome to Invest Like the Best
(00:03:32) The Evolution of Podcasting with Tim Ferriss
(00:09:56) Crafting Meaningful Conversations
(00:13:26) What Makes Tim Feel The Most Alive
(00:24:06) Who Tim Considers To Be His Mentors
(00:29:06) The Ingredients Of A World Class Performance
(00:31:06) The Business Side of Podcasting
(00:43:15) Identifying Emerging Trends
(00:50:12) Lessons From Building a Small, Efficient Team
(00:52:32) The Power of Constraints in Personal and Professional Growth
(00:53:10) Innovating Against the Grain (Anti-Video Experiment)
(00:54:54) Navigating Fame, Money, and Power
(01:02:00) The Impact of Sharing Difficult Stories
(01:06:18) Meta-Learning: A Framework for Fast, Effective Learning
(01:12:32) Reflecting on a Decade of Podcasting & What’s In Store
(01:24:41) The Kindest Thing Anyone Has Ever Done For Tim
The podcast and artwork embedded on this page are from Colossus | Investing & Business Podcasts, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Invest Like the Best with Patrick O'Shaughnessy (LS 67 · TOP 0.05% what is this?)
Episode: Adam Sandow - The Power of Print Media - [Invest Like the Best, EP.376]
Pub date: 2024-06-04
My guest today is Adam Sandow. Adam is the chairman and CEO of SANDOW Companies and the executive chairman and founder of Material Bank. He has built an entire ecosystem of businesses and brands that have brought him into the game of media, materials, and beyond. From creating the beauty product subscription model to getting magazines in the hands of billionaires to transforming the design industry with overnight access to samples, when Adam starts a business he writes his own rulebook. We discuss the founding stories of his most interesting companies, his obsession with targeting pain points, and his philosophies for when to go all in and betting on himself. Please enjoy this great discussion with Adam Sandow.
Listen to Founders Podcast
For the full show notes, transcript, and links to mentioned content, check out the episode page here.
This episode is brought to you by Tegus, where we're changing the game in investment research. Step away from outdated, inefficient methods and into the future with our platform, proudly hosting over 100,000 transcripts – with over 25,000 transcripts added just this year alone. Our platform grows eight times faster and adds twice as much monthly content as our competitors, putting us at the forefront of the industry. Plus, with 75% of private market transcripts available exclusively on Tegus, we offer insights you simply can't find elsewhere. See the difference a vast, quality-driven transcript library makes. Unlock your free trial at tegus.com/patrick.
Invest Like the Best is a property of Colossus, LLC. For more episodes of Invest Like the Best, visit joincolossus.com/episodes.
Past guests include Tobi Lutke, Kevin Systrom, Mike Krieger, John Collison, Kat Cole, Marc Andreessen, Matthew Ball, Bill Gurley, Anu Hariharan, Ben Thompson, and many more.
Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here.
Follow us on Twitter: @patrick_oshag | @JoinColossus
Editing and post-production work for this episode was provided by The Podcast Consultant (https://thepodcastconsultant.com).
Show Notes:
(00:00:00) Welcome to Invest Like the Best
(00:04:12) Building a Media Empire
(00:06:01) The Birth of the Beauty Subscription Model
(00:09:56) Revolutionizing Magazine Circulation
(00:14:46) The Contrarian Approach to Media
(00:16:08) The Origin of MediaJet
(00:18:35) The Future of Print and Digital Media
(00:27:25) The Genesis of Material Bank
(00:35:23) Building a Compelling Model for Manufacturers
(00:37:26) Innovative Logistics and Partnership with FedEx
(00:40:32) The Importance of High-Quality Content
(00:43:49) Building and Buying Media Properties
(00:46:01) Creating Unique Value Propositions
(00:54:22) The Role of Print in the Digital Age
(00:58:41) Nurturing an Ecosystem of Businesses
(01:03:37) The Kindest Thing Anyone Has Ever Done for Adam
The podcast and artwork embedded on this page are from Colossus | Investing & Business Podcasts, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Svelte Radio (LS 31 · TOP 5% what is this?)
Episode: Slicing Svelte with Sam Littlefair and Prismic
Pub date: 2023-12-06
In this episode of Svelte Radio, join hosts Brittany and Kev as they dive into the world of web development with their guest Sam, a technical writer at Prismic. Brittany shares her latest experiences battling with Tailwind, Rails, and Svelte, emphasizing the learning journey that comes with navigating the complexities of modern web development. She also offers a valuable tip about CSS line height, advising against the use of rems and ems due to their unpredictable inheritance and suggesting more reliable alternatives.
Recorded on October 11th.
Description
- Who is Sam?
- Svelte Starter Template
Unpopular Opinions
- Brittney: Meetings are not work.
- Sam: By 2030 we will reach 2.0
- Kevin: -
Picks
- Kevin
- Coolify
- CommitAI
- Sam
- OpenProps
- Adam Argyle
- Brittney:
- Github CLI
- Netlify CLI
The podcast and artwork embedded on this page are from Kevin Åberg Kultalahti, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Svelte Radio (LS 31 · TOP 5% what is this?)
Episode: SvelteKit-superforms with Andreas Söderlund
Pub date: 2023-04-28
SponsorVercel is the platform for frontend developers, providing the speed and reliability innovators need to create at the moment of inspiration. Founded by the creators of Next.js, Vercel has zero configuration support for 35+ frontend frameworks, including SvelteKit. We enable the world’s largest brands like Under Armour, eBay, and Nintendo, to iterate faster and create quality software. Try out Vercel today to experience the easiest way to use Svelte.
NOTE: RECORDED April 19th.
Description
Forms in SvelteKit are already top notch. But Superforms make them even better. We talk to Andreas Söderlund, the creator of the library. Enjoy!
MusicIntro music by Braden Wiggins a.k.a. Fractal (braden@fractal-hq.com)
Discussion topics
- Haxe: https://haxe.org
- Form Actions: https://kit.svelte.dev/docs/form-actions
- sveltekit-superforms: https://github.com/ciscoheat/sveltekit-superforms
- Documentation: https://superforms.vercel.app
- Zod: https://github.com/colinhacks/zod
- Timers: https://superforms.vercel.app/concepts/timers
- Response Time Discussion: https://www.nngroup.com/articles/response-times-3-important-limits/
- Multiple Forms: https://superforms.vercel.app/concepts/multiple-forms
Unpopular Opinions
- Brittney: Don’t use libraries that don’t have first class support for esm or Vite.
- Antony: AI is not going to steal our jobs
- Andreas: NPM and package managers should be more restrictive with what they allow on their registries.
Picks
- Kev: Sauna
- Antony: These USB-C Cable - https://amzn.to/3ophxql
- Andreas: DCI - https://blog.encodeart.dev/dci-tutorial-for-typescript-part-1
The podcast and artwork embedded on this page are from Kevin Åberg Kultalahti, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Svelte Radio (LS 31 · TOP 5% what is this?)
Episode: View Transitions in SvelteKit and beyond with Geoff Rich
Pub date: 2023-11-22
Today we chat with Geoff Rich about View Transitions. We explore what you can do with them, how to use them and much more. Can it be used with SvelteKit? Yes! Dive in and get all the tips!
Recorded on September 27st.
Description
- Who is Geoff?
- Website
- What are View Transitions?
- MDN
- Chrome for Developers
- Jake Archibald's Article on the Chrome Blog
- How do they work in SvelteKit?
- onNavigate
- svelte-view-transition
- Issue discussed
- Geoff's Svelte Summit Spring 2023 Talk
- jh3ey's twitter
Unpopular Opinions
- Kevin: Limitations are good!
- Brittney: -
- Geoff: ARIA attributes
- NVDA
Picks
- Brittney: Flowbite GitHub
- Kevin: The Winter King
- Geoff: The Wheel of Time S2 is so good!
The podcast and artwork embedded on this page are from Kevin Åberg Kultalahti, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Svelte Radio (LS 31 · TOP 5% what is this?)
Episode: Melting UIs with Thomas G. Lopes
Pub date: 2023-11-29
Join us as we dive into an engaging conversation with Thomas G. Lopez, the brains behind the popular UI library in the Svelte ecosystem, MeltUI. Thomas shares his journey in web development, moving from Vue to React, and ultimately finding his passion in Svelte. Discover the unique challenges and triumphs he experienced while creating MeltUI and becoming a Svelte ambassador.
Recorded on October 4th.
Description
- Who is Thomas?
- Website
- MeltUI
- Radix Svelte
- Svelte Sirens Stream
- Builder from Scratch London Meetup
- Search element
- TreeStyle Tabs / Panorama Tab Groups
- Practical Accessibility
- Markdoc
- Appwrite SvelteKit website re-launch
- Website
Picks
- Brittney: Invasion
- Kevin: The Continental
- Thomas: Berserk manga
The podcast and artwork embedded on this page are from Kevin Åberg Kultalahti, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Svelte Radio (LS 31 · TOP 5% what is this?)
Episode: Svelte in dynamic e-commerce widgets with Jacob Stordahl
Pub date: 2023-12-14
Sponsored by Contentful
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Summary
In this episode of Svelte Radio, Jacob Stordahl and the hosts discuss Jacob's transition from WordPress to Svelte and the practical aspects of developing third-party JavaScript widgets. They also touches on the role of AI in content management. Tune in!
Recorded on October 18, 2024
Discussion
- Who is Jacob Stordahl
- Website
- Stylitics
- Svelte Summit Talk Spring 2023
Unpopular Opinions
- Jacob: you shouldn’t use a git ui! git is poorly designed
- freeCodeCamp Course
- Kevin: you shouldn’t use fetch() on the client
- Antony: MacOS is not that great!
Picks
- Jacob: Staff Engineer by Will Larson
- Kevin: Satori
- Antony: BunnyCDN
The podcast and artwork embedded on this page are from Kevin Åberg Kultalahti, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Syntax - Tasty Web Development Treats (LS 61 · TOP 0.1% what is this?)
Episode: 762: What to Steal. Finding Inspiration in Web Development
Pub date: 2024-04-29
Scott and Wes discuss the delicate balance of what’s acceptable to borrow or be inspired by in web development and what crosses into territory that’s off-limits. Tune in as they share personal experiences, discuss where to find ethical inspiration, and offer tips on how to effectively capture and utilize it.
Show Notes * 00:00 Welcome to Syntax! * 01:57 Brought to you by Sentry.io. * 02:26 What is okay to steal? + 02:57 Color palettes. + 03:14 Font stacks. + 06:26 Type scales. - Warp’s CSS Gradient Border. - Sentry’s Date Picker. + 08:52 General layout patterns. + 10:39 General vibes. * 11:20 What is NOT okay to steal? + 11:26 Whole site designs. + 13:32 Taking too many things from ‘what to steal’ list. + 16:30 Text copy. - Wes’ Parity Purchasing Power. * 18:48 What we’ve had stolen + how it feels. * 21:45 Where to find inspiration. + 21:56 Code inspiration. - CSS Scan Buttons. - CSS Scan Box Shadows. - Codrops. - CodePen + 25:18 Design inspiration. - Bentro Grids. - Dribble. - Site Inspire. - SaaS Landing Pages. - One Page Love. - Type Wolf. - Mobbin. - Syntax Newsletter. - Hoverstat.es. - Internet Gems. + 32:48 UX inspiration. - Good UI. - The Component Gallery. - Open UI. - Nicely Done. * 35:25 How to capture inspiration.
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The podcast and artwork embedded on this page are from Wes Bos & Scott Tolinski - Full Stack JavaScript Web Developers, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Syntax - Tasty Web Development Treats (LS 61 · TOP 0.1% what is this?)
Episode: 765: JS Promises Fundamentals - Part 1
Pub date: 2024-05-06
In this 3-course series, Scott and Wes serve up some JavaScript Promises treats. In part 1, they unravel the concept of promises and delve into common examples of their usage, from creating and waiting on promises, to database queries and user permissions.
Show Notes * 00:00 Welcome to Syntax! * 01:27 Brought to you by Sentry.io. * 02:32 What is a promise? * Promise mdn web docs. * 03:27 Common examples of promises. + 03:45 A fetch call to an API returns a promise. + 05:54 A database query or Insert command + 07:18 A request for user permissions. + 07:37 A wait function. * 08:08 Resolve or rejecting promises. * 09:33 Creating promises. + 09:46 New promise. + 11:09 Promise.withResolvers(). + 11:37 An async function. * 14:34 Waiting on a promise. + 15:09 .then(). + 16:50 Await. + 17:44 Why use one wait method over the other?
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Wes: X Instagram Tiktok LinkedIn Threads
Scott:X Instagram Tiktok LinkedIn Threads
Randy: X Instagram YouTube Threads
The podcast and artwork embedded on this page are from Wes Bos & Scott Tolinski - Full Stack JavaScript Web Developers, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Syntax - Tasty Web Development Treats (LS 61 · TOP 0.1% what is this?)
Episode: 771: Promises: Error Handling, Aborts, and Helper Methods - Part 2
Pub date: 2024-05-20
We’re diving into part 2 of our 3-part series on Promises, focusing on error handling, aborts, and essential helper methods. We’ll explore how to manage errors effectively and improve performance with abort signals. Let’s get into it!
Show Notes * 00:00 Welcome to Syntax! * 00:41 Brought to you by Sentry.io. * 02:00 Cancelling promises. * 05:16 Why would you reach for an abort signal? * 06:26 Promise helpers. + 07:04 Promise.all() vs Promise.allSettled(). + 09:12 promiseInstance.finally() + 09:26 Promise.any() and Promise.race() * 12:08 Error handling strategies. + Tuple await-to-js. + Youtube - 5 Async + Await Error Handling Strategies. * 17:30 Promise.race() example. * 18:54 Static Promise.reject() and .resolve() methods.
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Wes: X Instagram Tiktok LinkedIn Threads
Scott:X Instagram Tiktok LinkedIn Threads
Randy: X Instagram YouTube Threads
The podcast and artwork embedded on this page are from Wes Bos & Scott Tolinski - Full Stack JavaScript Web Developers, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Syntax - Tasty Web Development Treats (LS 61 · TOP 0.1% what is this?)
Episode: 775: Components We Need on Every Project
Pub date: 2024-05-29
In today’s episode, Scott and Wes dive into the essential components they need on every web project, discussing whether to build them from scratch or leverage existing libraries—everything from navigation bars and modals to toast messages and icons.
Show Notes * 00:00 Welcome to Syntax! * 01:48 Brought to you by Sentry.io. * 02:53 Nav / Mobile Nav. * 08:43 Header. * 10:41 Toast message. + Wes’ TikTok Demo. * 18:51 Portal. * 21:02 Drawer. * 22:56 Auth forms. * 28:49 Dialog / Modal. * 31:30 Whats the difference between popover and dialog? * 33:48 Confirm. + Scott’s YouSureAboutThat. * 35:46 Bonus tip on becoming a better developer. * 36:29 Admin menu. + Level Up Tutorials - Side Menu Demo. * 37:51 Scott’s package directory rant. * 40:26 Mobile only / Desktop only. * 40:41 Client only. * 40:57 Admin table. * 41:23 The dump. * 43:39 Share / Social links. + Syntax ShareWindow. * 45:44 Markdown renderer. * 45:58 Tabs. * 46:08 User menu. * 46:18 Icon. * 48:07 Loading. * 49:21 Drop-down menu. * 49:31 Accordion. + CSS Tricks - How to Animate the Details Element. * 52:13 Sick Picks + Shameless Plugs.
Sick Picks * Scott: Supercommunicators. * Wes: Klack, Mech Vibes.
Shameless Plugs * Scott: Syntax on YouTube.
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Wes: X Instagram Tiktok LinkedIn Threads
Scott:X Instagram Tiktok LinkedIn Threads
Randy: X Instagram YouTube Threads
The podcast and artwork embedded on this page are from Wes Bos & Scott Tolinski - Full Stack JavaScript Web Developers, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Syntax - Tasty Web Development Treats (LS 61 · TOP 0.1% what is this?)
Episode: 778: 11 Habits of Highly Effective Developers
Pub date: 2024-06-05
Today, Scott and Wes dive into the 11 habits of effective web developers, from understanding stakeholder goals to maintaining a work-life balance. We’ll explore the importance of continuous learning, having a problem-solver mentality, and being empathetic towards coworkers and users—let’s get into it!
Show Notes * 00:00 Welcome to Syntax! + Previous Episode: 754. * 00:50 Brought to you by Sentry.io. + 01:24 Denver weather is something else. * 02:15 Habit #1: You understand stakeholder and business goals. * 05:34 Habit #2: You’re curious and always learning. * 07:43 Habit #3: You have an open mind about new technology. * 11:29 Habit #4: You ask for help. * 13:43 Habit #5: You help others. + 16:51 Chicken drumsticks. * 17:35 Habit #6: You have a “problem solver” mentality. + 24:44 Hose repair. * 26:02 Habit #7: You have fun with what you do. * 29:56 Habit #8: You understand work-life balance. * 33:18 Habit #9: You are empathetic to your co-workers and users. * 37:19 Habit #10: You pay attention to detail. * 41:18 Habit #11: You’re part of the community. * 45:55 Sick Picks + Shameless Plugs.
Sick Picks * Scott: Solar Lanterns. * Wes: Ninja Obstacle Course.
Shameless Plugs * Scott: Syntax on YouTube.
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Wes: X Instagram Tiktok LinkedIn Threads
Scott: X Instagram Tiktok LinkedIn Threads
Randy: X Instagram YouTube Threads
The podcast and artwork embedded on this page are from Wes Bos & Scott Tolinski - Full Stack JavaScript Web Developers, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Syntax - Tasty Web Development Treats (LS 61 · TOP 0.1% what is this?)
Episode: 779: Why SQLite is Taking Over with Brian Holt & Marco Bambini
Pub date: 2024-06-07
Scott and CJ dive into the world of SQLite Cloud with special guests Brian Holt and Marco Bambini. They explore why SQLite is gaining traction, its unique features, and the misconceptions surrounding its use—let’s get into it!
Show Notes * 00:00 Welcome to Syntax! * 01:20 Who is Brian Holt? * 02:26 Who is Marco Bambini? * 05:12 Why are people starting to talk so much about SQLite now? * 08:47 What makes SQLite special or interesting? * 09:46 What is a big misconception about SQLite? * 11:13 Installed by default in operating systems. * 12:03 A perception that SQLite is intended for single users. * 13:36 Convincing developers it’s a full-featured solution. * 15:11 What does SQLite do better than Postgres or MySQL? * 17:30 SQLite Cloud & local first features. * 20:38 Where does SQLite store the offline information? * 23:08 Are you typically reaching for ORMs? * 25:00 What is SQLite Cloud? * 27:29 What makes for an approachable software? * 29:18 What make SQLite cloud different from other hosted SQLite options? * 32:13 Is SQLite still evolving? * 34:40 What about branching? * 37:37 What is the GA timeline? * 40:04 How does SQLite actually work? * 41:19 Questions about security. * 44:28 But does it scale? * 45:52 Sick Picks + Shameless Plugs.
Sick Picks Brian: Trainer Road Marco: Tennis
Shameless Plugs * Brian: SQLite Cloud, Frontend Masters - Containers.
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Wes: X Instagram Tiktok LinkedIn Threads
Scott:X Instagram Tiktok LinkedIn Threads
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The podcast and artwork embedded on this page are from Wes Bos & Scott Tolinski - Full Stack JavaScript Web Developers, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: 30 Bach: The Goldberg Variations Podcast (LS 40 · TOP 2% what is this?)
Episode: "Bach would have been a good programmer, a good engineer"
Pub date: 2021-04-02
Variations 16, 17, 18. Bach was a consummate craftsmen -- he knew not just how to write music, but how to build it. In this Interview, we speak with pianist Jeffrey LaDeur and his student, Ken Kocienda. Kocienda was lead software engineer behind the Apple iPhone and developed a strong affinity for the Goldbergs. Kocienda and Ladeur discuss parallels between music and design, and how constraints can actually enhance creativity.
Interviews recorded in San Jose, California on January 2, 2018 and in San Francisco, California on July 2, 2019.
Photo credits: Jiyang Chen (LaDeur)
Musical recording credits available at https://www.thirtybach.com/podcast-episodes/bach-would-have-been-a-good-programmer
The podcast and artwork embedded on this page are from Lowry Yankwich, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Founders (LS 60 · TOP 0.1% what is this?)
Episode: #349 How Steve Jobs Kept Things Simple
Pub date: 2024-05-20
What I learned from reading Insanely Simple: The Obsession That Drives Apple's Success by Ken Segall.
Come build relationships at the Founders Conference on July 29th-July 31st in Scotts Valley, California
Learning from history is a form of leverage. —Charlie Munger. Founders Notes gives you the super power to learn from history's greatest entrepreneurs on demand.
Get access to the World’s Most Valuable Notebook for Founders
You can search all my notes and highlights from every book I've ever read for the podcast.
You can also ask SAGE any question and SAGE will read all my notes, highlights, and every transcript from every episode for you.
A few questions I've asked SAGE recently:
What are the most important leadership lessons from history's greatest entrepreneurs?
Can you give me a summary of Warren Buffett's best ideas? (Substitute any founder covered on the podcast and you'll get a comprehensive and easy to read summary of their ideas)
How did Edwin Land find new employees to hire? Any unusual sources to find talent?
What are some strategies that Cornelius Vanderbilt used against his competitors?
Get access to Founders Notes here.
(1:30) Steve wanted Apple to make a product that was simply amazing and amazingly simple.
(3:00) If you don’t zero in on your bureaucracy every so often, you will naturally build in layers. You never set out to add bureaucracy. You just get it. Period. Without even knowing it. So you always have to be looking to eliminate it. — Sam Walton: Made In America by Sam Walton. (Founders #234)
(5:00) Steve was always easy to understand. He would either approve a demo, or he would request to see something different next time. Whenever Steve reviewed a demo, he would say, often with highly detailed specificity, what he wanted to happen next. — Creative Selection: Inside Apple's Design Process During the Golden Age of Steve Jobs by Ken Kocienda. (Founders #281)
(7:00) Watch this video. Andy Miller tells GREAT Steve Jobs stories.
(10:00) Many are familiar with the re-emergence of Apple. They may not be as familiar with the fact that it has few, if any parallels.
When did a founder ever return to the company from which he had been rudely rejected to engineer a turnaround as complete and spectacular as Apple's? While turnarounds are difficult in any circumstances they are doubly difficult in a technology company. It is not too much of a stretch to say that Steve founded Apple not once but twice. And the second time he was alone.
— Return to the Little Kingdom: Steve Jobs and the Creation of Appleby Michael Moritz.
(15:00) If the ultimate decision maker is involved every step of the way the quality of the work increases.
(20:00) "You asked the question, What was your process like?' I kind of laugh because process is an organized way of doing things. I have to remind you, during the 'Walt Period' of designing Disneyland, we didn't have processes. We just did the work. Processes came later. All of these things had never been done before. Walt had gathered up all these people who had never designed a theme park, a Disneyland. So we're in the same boat at one time, and we figure out what to do and how to do it on the fly as we go along with it and not even discuss plans, timing, or anything. We just worked and Walt just walked around and had suggestions." — Disney's Land: Walt Disney and the Invention of the Amusement Park That Changed the World by Richard Snow. (Founders #347)
(23:00) The further you get away from 1 the more complexity you invite in.
(25:00) Your goal: A single idea expressed clearly.
(26:00) Jony Ive: Steve was the most focused person I’ve met in my life
(28:00) Editing your thinking is an act of service.
Learning from history is a form of leverage. —Charlie Munger. Founders Notes gives you the super power to learn from history's greatest entrepreneurs on demand.
Get access to the World’s Most Valuable Notebook for Founders
You can search all my notes and highlights from every book I've ever read for the podcast.
You can also ask SAGE any question and SAGE will read all my notes, highlights, and every transcript from every episode for you.
A few questions I've asked SAGE recently:
What are the most important leadership lessons from history's greatest entrepreneurs?
Can you give me a summary of Warren Buffett's best ideas? (Substitute any founder covered on the podcast and you'll get a comprehensive and easy to read summary of their ideas)
How did Edwin Land find new employees to hire? Any unusual sources to find talent?
What are some strategies that Cornelius Vanderbilt used against his competitors?
Get access to Founders Notes here.
“I have listened to every episode released and look forward to every episode that comes out. The only criticism I would have is that after each podcast I usually want to buy the book because I am interested so my poor wallet suffers. ” — Gareth
Be like Gareth. Buy a book: All the books featured on Founders Podcast
The podcast and artwork embedded on this page are from David Senra , which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Rabbit Hole
Episode: #63 Creative Selection by Ken Kocienda
Pub date: 2023-07-10
Hundreds of millions of people use Apple products every day; several thousand work on Apple's campus in Cupertino, California; but only a handful sit at the drawing board. Creative Selection recounts the life of one of the few who worked behind the scenes, a highly-respected software engineer who worked in the final years of the Steve Jobs era―the Golden Age of Apple.
Full notes on Creative Selection here
Buy the book here
To support:
- Rate and share this podcast
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- Join The Latticework
To connect:
Sponsor:
The Rabbit Hole podcast is brought to you by The Latticework.
The Latticework curates, explains and interconnects hundreds of mental models so that you can become a better multidisciplinary thinker. Every partner applies and is vetted, ensuring a high-quality, curious, and low-ego community.
Join hundreds of partners from around the world by applying here, or learn more at LTCWRK.com
Become Multidisciplinary. Think Better
The podcast and artwork embedded on this page are from Blas Moros, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Talk Show With John Gruber (LS 63 · TOP 0.1% what is this?)
Episode: 339: ‘2006: Hard Work’, With Ken Kocienda
Pub date: 2022-03-01
Special guest Ken Kocienda, author of Creative Selection, joins the show to talk about his years at Apple and the creation of the original iPhone.
Sponsored by:
- Remote: Global HR Solutions for distributed teams.
- Retool: Build internal tools 10× faster.
- Squarespace: Make your next move. Use code talkshow for 10% off your first order.
Links:
- Up Spell — Ken’s iOS word game.
- “Wallaby” iPhone prototype hardware.
- Steve Jobs’s quote on the wall outside Town Hall at Infinite Loop: “If you do something and it turns out pretty good, then you should go do something else wonderful, not dwell on for it too long. Just figure out what’s next.”
- Kim Scott’s books: Radical Candor and Just Work.
- DF’s 15-year anniversary post, with word counts.
- Stephen King’s On Writing.
- Andy Hertzfeld’s history of the original Mac:
- Folklore.org
- Revolution in the Valley
- Humane — The totally secret startup where Ken is now working.
- Job openings at Humane.
This episode of The Talk Show was edited by Caleb Sexton.
The podcast and artwork embedded on this page are from Daring Fireball / John Gruber, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Flip (LS 36 · TOP 2.5% what is this?)
Episode: Why Are Cross-Border Payments So Hard?
Pub date: 2024-05-16
NALA's Co-founder & CEO, Benjamin Fernandes, likes to say that payments are just 1% built in Africa.
Why are cross-border payments so hard?
In this episode, we're joined in conversation with Benjamin Fernandes and Dan Kleinbaum, a co-founder of Beyonic, which sold to Onafriq, and now the Founder of the FX platform GTXN.
This episode was recorded live from the FT Partners Fintech in Africa Summit in New York City. Download their FinTech in Africa research report, published in March 2024.
00:00 - Intro
01:29 - Payments are 1% built in Africa
06:32 - How to solve problems in Cross-Border payments
08:28 - Do we need more payment apps?
15:51 - Navigating regulatory challenges
17:03 - Why are Benji & Dan solving these problems?
20:30 - What's the cross-border payments pitch to investors?
25:39 - Benji & Dan turn the tables on Justin
This episode was the second in our series of interviews recorded live from the Fintech in Africa Summit. Our first episode was with the Nigerian Neobanks: https://theflip.africa/podcast/nigerian-neobank-roundtable-moniepoint-kuda-fairmoney
Episode Links:
Follow Benji on Twitter
Follow Dan on Twitter
Read Benji's Medium post: Are African Remittances Finished?
Our Links -
🎥 YouTube - https://youtube.com/@theflipafrica
💻 Website - https://theflip.africa
🐦 Twitter - https://twitter.com/theflipafrica
👥 LinkedIn - https://www.linkedin.com/company/theflipafrica/
📸 Instagram - https://instagram.com/theflipafrica
The podcast and artwork embedded on this page are from The Flip Media, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Deep Transformation (LS 38 · TOP 2% what is this?)
Episode: Daniel Schmachtenberger (Part 2) – Developing a Deeper Understanding of Life: Opening to the Complexity, Wholeness, and Beauty of Reality
Pub date: 2023-04-20
Ep. 74 (Part 2 of 2) | Daniel Schmachtenberger, one of the most brilliant and integrative thinkers of our time, expresses here his deep love and appreciation for reality itself. Daniel’s inquiries have led him to perceive the intrinsic beauty of the wholeness of reality and to the realization that everything is interesting—just like when you love someone, everything about them becomes fascinating. Along with this deep appreciation comes the desire to serve and protect, and Daniel is focused on investigating the drivers of the metacrisis and how best to meet the difficult challenges it presents, a subject interwoven in this conversation with Daniel’s findings and ideas about reality, human psychology, education, and the future of the planet.
Daniel is a wonderful testament to the far reaching effects of the right kind of education. He relates how he was homeschooled by parents who set him on the path towards goodness, meaning, and beauty right from the start, and who were dedicated to facilitating his interest wherever it led, to include systems theory and how to create a better world. This is a beautiful, rich conversation filled with gems of knowledge and insight—about our human family (actually, the lack of one), the horrible deficit of fathering in modern culture, how we can orient to the sacred and the meaningful, the fact that we actually didn’t evolve to deal with the crises we face now but to negotiate successfully as members of a tribe of around 150 people, and much more. Recorded January 10, 2023.
“I cannot imagine a context in which one’s choices matter more.”
(For Apple Podcast users, click here to view the complete show notes on the episode page.)
Topics & Time Stamps – Part 2 Does Daniel believe in God? (01:48) * In Daniel’s “The Dance of the Tao and the 10,000 Things,” he asks, “Do atoms exist? Kind of! (08:23) * The traps of reductionism in facing the metacrisis and how Daniel transcends them (10:49) * The relation of physical crises to crises of consciousness: co-informing and co-arising facets of an integrative reality (13:11) * Omni determinism, omni influence (16:31) * Marvin Harris’ framework for understanding civilization: infrastructure, social structure, and superstructure (17:19) * What in the interiority of human psyches, experiences & cultures are key drivers of the problems of the world? And how does our changed human genome, microbiome, and neurochemistry fit in? (19:00) * We evolved to have attachments to 150 people—our tribe—so everything about bonding, attachment theory, the ideas of co-dependence & interdependence evolved in a tribal setting, in fact, we did not evolve to deal with what is going on now (21:22) * The psychological generator function of the metacrisis results from perceiving the world as fragmented or made up of parts: conflict theory & mistake theory (23:34) * The Realpolitik assessment of humans: we are dumb and nasty (25:54) * It’s all based on a trade-off—we’re either trying to benefit ourselves now at the expense of our future selves, individually or collectively, or we just don’t realize the harm that is caused by what we do (27:26) * Can we survive the current unprecedented metacrisis? No chance can we make it through without the catastrophes intensifying (30:08) * The human family is not a real thing right now: there is no “we” (34:59) * How to live a meaningful life? Deeply appreciate and honor the beauty of life, be in service to the beauty of reality, and deepen the capacity for both (37:33) * Dharma inquiry and the vow of the bodhisattva (41:47) * Where is the vow of the bodhisattva missing something? (44:09) * It’s not just what is my unique calling but also what needs doing that no one else wants to do? (46:19) * One beautiful peak experience is worth all the pain (50:00)*
Resources & References – Part 2 Learning theory describes how people receive, process, and retain information * Tao Te Ching* (new English version translated by Stephen Mitchell)* * Daniel Schmachtenberger, The Dance of the Tao and the 10,000 Things * Werner Heisenberg*, theoretical physicist and one of the main pioneers of the theory of quantum mechanics* * Erwin Schrödinger*, Nobel Prize-winning physicist who developed a number of fundamental results in quantum theory, including the* Schrödinger equation * Buddhist Śūnyatā and Huayan philosophies * “*Technology is Not Values Neutral: Ending the Reign of Nihilistic Design,” overview of the work of Marvin Harris, Marshall McLuhan (on Consilience Project website) et al. * Marvin Harris, anthropologist and writer, highly influential on the subjects of cultural materialism and environmental determinism* * Marshall McLuhan*, philosopher who focused on the relationship between media and culture and developed the concept of technological determinism* * Nate Hagens’ 5-part podcast with Daniel Schmachtenberger on the environment, energy, and how does one deal with the reality of the metacrisis? (YouTube) * David Bohm on Wholeness & Fragmentation (YouTube) * R. Buckminster Fuller, Operating Manual for Spaceship Earth* * Carl Sagan, Pale Blue Dot: A Vision of the Human Future in Space* * Daniel Schmachtenberger, How to Live a Meaningful Life? * Daniel Schmachtenberger, Dharma Inquiry * The vow of the bodhisattva * Alex Grey, Sacred Mirrors* * Poets Hafiz and Rumi * Daniel Schmachtenberger’s website Explorations on the Future of Civilization * Daniel Schmachtenberger, founding member of The Consilience Project, aimed at improving public sensemaking and dialogue
* As an Amazon Associate, Deep Transformation earns from qualifying purchases.
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Daniel Schmachtenberger is a founding member of The Consilience Project, aimed at improving public sensemaking and dialogue. The throughline of his interests has to do with ways of improving the health and development of individuals and society, with a virtuous relationship between the two as a goal. Towards these ends, he’s had particular interest in the topics of catastrophic and existential risk, civilization and institutional decay and collapse, as well as progress, collective action problems, social organization theories, and the relevant domains in philosophy and science.
Motivated by the belief that advancing collective intelligence and capacity is foundational to the integrity of any civilization, and necessary to address the unique risks we currently face given the intersection of globalization and exponential technology, he has spoken publicly on many of these topics, hoping to popularize and deepen important conversations and engage more people in working towards their solutions. Many of these can be found at http://civilizationemerging.com/media/. You can find more information about The Consilience Project at https://consilienceproject.org/.
---
Podcast produced by Vanessa Santos and Show Notes by Heidi Mitchell
The podcast and artwork embedded on this page are from Deep Transformation Podcast, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Deep Transformation (LS 38 · TOP 2% what is this?)
Episode: Daniel Schmachtenberger (Part 1) – Developing a Deeper Understanding of Life: Opening to the Complexity, Wholeness, and Beauty of Reality
Pub date: 2023-04-13
Ep. 73 (Part 1 of 2) | Daniel Schmachtenberger, one of the most brilliant and integrative thinkers of our time, expresses here his deep love and appreciation for reality itself. Daniel’s inquiries have led him to perceive the intrinsic beauty of the wholeness of reality and to the realization that everything is interesting—just like when you love someone, everything about them becomes fascinating. Along with this deep appreciation comes the desire to serve and protect, and Daniel is focused on investigating the drivers of the metacrisis and how best to meet the difficult challenges it presents, a subject interwoven in this conversation with Daniel’s findings and ideas about reality, human psychology, education, and the future of the planet.
Daniel is a wonderful testament to the far reaching effects of the right kind of education. He relates how he was homeschooled by parents who set him on the path towards goodness, meaning, and beauty right from the start, and who were dedicated to facilitating his interest wherever it led, to include systems theory and how to create a better world. This is a beautiful, rich conversation filled with gems of knowledge and insight—about our human family (actually, the lack of one), the horrible deficit of fathering in modern culture, how we can orient to the sacred and the meaningful, the fact that we actually didn’t evolve to deal with the crises we face now but to negotiate successfully as members of a tribe of around 150 people, and much more. Recorded January 10, 2023.
“I cannot imagine a context in which one’s choices matter more.”
(For Apple Podcast users, click here to view the complete show notes on the episode page.)
Topics & Time Stamps – Part 1 Introducing brilliant integrative thinker Daniel Schmachtenberger (01:32) * Finding meaning in the sacred dimensions of our world and the integrated wholeness of reality (03:52) * Part of love is the desire to know everything about your partner—when loving reality, everything becomes interesting (05:47) * The fractal nature of reality, looking at it through different lenses and receiving different insights, and how the more perspectives you take, the more depth and richness you perceive (07:13) * Is there something about the nature of the effort to solve world problems that is at fault in their getting worse? (08:36) * Daniel’s homeschooling parents set him on the path to following what is good, meaningful, and beautiful right from the start (10:22) * If you facilitate children’s interest, they end up deep learning in many subjects (12:24) * Daniel’s early education included systems theory and how to make a better world (14:46) * How did Daniel come to be such an integrative thinker? Compartmentalized education vs integrated education (16:32) * The decline of quality aristocratic tutoring has led to the decline of super geniuses (19:58) * Are we all the result of our education? Tutors and mentors (28:11) * Integrating across ontology and epistemology, and asking what is the generator function of novel insight? (29:07) * Man’s greatest purpose is to serve the family of man: women, nature, children (33:07) * The gruesome deficit of fathering in the world and what Daniel learned about being a man from his dad (35:19) * On forgiveness, therapy, healing, catalyzing gifts (43:22) * How do spiritual depths inform our contemporary crises? Needing to ask why we should protect nature shows a real pathological deficit (49:37) * Orientation to the sacred and the meaningfulness of life forms through a deep bandwidth of connection and sensing (52:39) * Learning more about the field of conceptuality can both interfere with one’s connection to the Tao and enhance it (55:39)*
Resources & References – Part 1 Daniel Schmachtenberger’s website* Explorations on the Future of Civilization * Daniel Schmachtenberger, founding member of The Consilience Project*, aimed at improving public sensemaking and dialogue* * Kahlil Gibran*, writer, poet, author of* The Prophet*, one of the best-selling books of all time * Complexity theory uses the study of complexity systems in the field of strategic management and organizational studies, drawing from research in the natural sciences * David Bohm, one of the most significant theoretical physicists of the 20th century * John Dewey, philosopher, psychologist, and educational reformer with a profound belief in democracy * Maria Montessori, physician, educator, founder of the Montessori method of education * Rudolf Steiner, philosopher, social reformer, founder of the esoteric spiritual movement anthroposophy * Buckminster Fuller, 20th century inventor & visionary, who coined the term design science, author of Synergetics: Explorations in the Geometry of Thinking * Krishnamurti, philosopher, speaker, writer, interested in psychological revolution and radical social change* * Fritjof Kapra, author of The Tao of Physics* and The Systems View of Life, applies complexity theory to large-scale social interaction in* The Hidden Connections: A Science for Sustainable Living* * Zachary Stein, educator, futurist, philosopher, author of Education in a Time Between Worlds, see also Deep Transformation episode 61,* The Future of Education and Civilization * Erik Hoel*, “Why We Stopped Making Einsteins”* * Marcus Aurelius*, philosopher and Emperor of Rome, author of* Meditations* * Napoleon Hill, Think and Grow Rich* * Daniel Schmachtenberger, What I learned about being a man from my Dad * Artists Dali & Picasso*, poets* Gibran & Blake * Joseph Campbell*,* The Power of Myth* * Daniel Schmachtenberger, The Dance of the Tao and the 10,000 Things * Samantha Sweetwater, soul mentor, wisdom teacher
* As an Amazon Associate, Deep Transformation earns from qualifying purchases.
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Daniel Schmachtenberger is a founding member of The Consilience Project, aimed at improving public sensemaking and dialogue. The throughline of his interests has to do with ways of improving the health and development of individuals and society, with a virtuous relationship between the two as a goal. Towards these ends, he’s had particular interest in the topics of catastrophic and existential risk, civilization and institutional decay and collapse, as well as progress, collective action problems, social organization theories, and the relevant domains in philosophy and science.
Motivated by the belief that advancing collective intelligence and capacity is foundational to the integrity of any civilization, and necessary to address the unique risks we currently face given the intersection of globalization and exponential technology, he has spoken publicly on many of these topics, hoping to popularize and deepen important conversations and engage more people in working towards their solutions. Many of these can be found at http://civilizationemerging.com/media/. You can find more information about The Consilience Project at https://consilienceproject.org/.
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Podcast produced by Vanessa Santos and Show Notes by Heidi Mitchell
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Podcast: Dwarkesh Podcast (LS 46 · TOP 1% what is this?)
Episode: Demis Hassabis - Scaling, Superhuman AIs, AlphaZero atop LLMs, Rogue Nations Threat
Pub date: 2024-02-28
Here is my episode with Demis Hassabis, CEO of Google DeepMind
We discuss:
-
Why scaling is an artform
-
Adding search, planning, & AlphaZero type training atop LLMs
-
Making sure rogue nations can't steal weights
-
The right way to align superhuman AIs and do an intelligence explosion
Watch on YouTube. Listen on Apple Podcasts, Spotify, or any other podcast platform. Read the full transcript here.
Timestamps
(0:00:00) - Nature of intelligence
(0:05:56) - RL atop LLMs
(0:16:31) - Scaling and alignment
(0:24:13) - Timelines and intelligence explosion
(0:28:42) - Gemini training
(0:35:30) - Governance of superhuman AIs
(0:40:42) - Safety, open source, and security of weights
(0:47:00) - Multimodal and further progress
(0:54:18) - Inside Google DeepMind
Get full access to Dwarkesh Podcast at www.dwarkeshpatel.com/subscribe
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Podcast: Dwarkesh Podcast (LS 46 · TOP 1% what is this?)
Episode: Sholto Douglas & Trenton Bricken - How to Build & Understand GPT-7's Mind
Pub date: 2024-03-28
Had so much fun chatting with my good friends Trenton Bricken and Sholto Douglas on the podcast.
No way to summarize it, except:
This is the best context dump out there on how LLMs are trained, what capabilities they're likely to soon have, and what exactly is going on inside them.
You would be shocked how much of what I know about this field, I've learned just from talking with them.
To the extent that you've enjoyed my other AI interviews, now you know why.
So excited to put this out. Enjoy! I certainly did :)
Watch on YouTube. Listen on Apple Podcasts, Spotify, or any other podcast platform.
There's a transcript with links to all the papers the boys were throwing down - may help you follow along.
Follow Trenton and Sholto on Twitter.
Timestamps
(00:00:00) - Long contexts
(00:16:12) - Intelligence is just associations
(00:32:35) - Intelligence explosion & great researchers
(01:06:52) - Superposition & secret communication
(01:22:34) - Agents & true reasoning
(01:34:40) - How Sholto & Trenton got into AI research
(02:07:16) - Are feature spaces the wrong way to think about intelligence?
(02:21:12) - Will interp actually work on superhuman models
(02:45:05) - Sholto’s technical challenge for the audience
(03:03:57) - Rapid fire
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Podcast: Lightcone Podcast
Episode: Consumer is back, What’s getting funded now, Immaculate vibes
Pub date: 2024-04-30
What's happening in startups right now and how can you get ahead of the curve? In this episode of the Lightcone podcast, we dive deep into the major trends we're seeing from the most recent batch of YC using data we've never shared publicly before. This is a glimpse into what might be the most exciting moment to be a startup founder ever. It's time to build.
The podcast and artwork embedded on this page are from Y Combinator, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Dwarkesh Podcast (LS 46 · TOP 1% what is this?)
Episode: Mark Zuckerberg - Llama 3, Open Sourcing $10b Models, & Caesar Augustus
Pub date: 2024-04-18
Mark Zuckerberg on:
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Llama 3
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open sourcing towards AGI
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custom silicon, synthetic data, & energy constraints on scaling
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Caesar Augustus, intelligence explosion, bioweapons, $10b models, & much more
Enjoy!
Watch on YouTube. Listen on Apple Podcasts, Spotify, or any other podcast platform. Human edited transcript with helpful links here.
Timestamps
(00:00:00) - Llama 3
(00:08:32) - Coding on path to AGI
(00:25:24) - Energy bottlenecks
(00:33:20) - Is AI the most important technology ever?
(00:37:21) - Dangers of open source
(00:53:57) - Caesar Augustus and metaverse
(01:04:53) - Open sourcing the $10b model & custom silicon
(01:15:19) - Zuck as CEO of Google+
Sponsors
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Podcast: The Moth (LS 80 · TOP 0.01% what is this?)
Episode: The Moth Radio Hour: Sink or Swim
Pub date: 2024-04-16
In this hour, stories of diving in—whether we want to or not. In a job, in a relationship, or into the unknown. This episode is hosted by Moth Senior Director Meg Bowles. The Moth Radio Hour is produced by The Moth and Jay Allison of Atlantic Public Media.
Storytellers:
Surgeon Anthony Chin-Quee finds himself in over his head during his first day on call.
Despite protests from friends and family, Nancy French marries a man she barely knows.
Wilderness guide Monte Montepare takes inexperienced hikers on a glacier expedition
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Podcast: 1001 Album Complaints (LS 36 · TOP 2.5% what is this?)
Episode: #151 The Beatles - Abbey Road SIDE 2
Pub date: 2024-04-01
Freshly married and without the Let It Be documentary cameras rolling, The Beatles head into their favorite London recording studio to lay down an all time classic before calling it quits forever. In our thrilling conclusion, we cover every track on Side 2 and chatter about the most popular Beatles song in the digital era, guitar solos played by bass players, and using every note in the western scale in a single, strange composition.
Email us your complaints (or questions / comments) at 1001AlbumComplaints@gmail.com
Listen to our episode companion playlist (compilation of the songs we referenced on this episode) here:
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Listen to Abbey Road here:
https://open.spotify.com/album/5iT3F2EhjVQVrO4PKhsP8c?si=5D0oPW_aTgyBKyfcAx_htQ
Check out our playlist "The Beatles Album That Never Was" here, made up of songs in work at the time the band broke up, and later finished and released on other albums:
https://open.spotify.com/playlist/6iKUDtCOrHN3wKPEUnrMc2?si=7e3d757a2bcb460b
Intro music: When the Walls Fell by The Beverly Crushers
Outro music: After the Afterlife by MEGA
Follow our Spotify Playlist of music produced directly by us. Listen and complain at home
Follow us on instagram @thechopunlimited AND @1001AlbumComplaints
Follow our Spotify Playlist of music produced directly by us. Listen and complain at home
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Next week's album: Living Colour - Vivid
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Podcast: 1001 Album Complaints (LS 36 · TOP 2.5% what is this?)
Episode: #150 The Beatles - Abbey Road SIDE 1
Pub date: 2024-03-25
The Beatles were chaffed from some seriously tense recording sessions whilst making the "white" album, and then again from the well-documented Get Back era, but, even still, after a break they decided to give it one more go together in the summer of 1969. We go through every song on this momentous album over the course of 2 episodes. This week while covering Side 1 the boys discuss Timothy Leary, the George Martin effect on George Harrison compositions, and the financial situation of Beatles Inc. at the time.
Email us your complaints (or questions / comments) at 1001AlbumComplaints@gmail.com
Listen to our episode companion playlist (compilation of the songs we referenced on this episode) here:
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Listen to Abbey Road here:
https://open.spotify.com/album/0ETFjACtuP2ADo6LFhL6HN?si=NSIwd0uuRzae_D0NzqDu2A
Intro music: When the Walls Fell by The Beverly Crushers
Outro music: After the Afterlife by MEGA
Follow our Spotify Playlist of music produced directly by us. Listen and complain at home
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Next week's album: The Beatles - Abbey Road SIDE 2
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Podcast: 1001 Album Complaints (LS 36 · TOP 2.5% what is this?)
Episode: #149 Ray Charles - Modern Sounds in Country and Western Music
Pub date: 2024-03-18
Ray Charles refused to be held back by his blindness and his never-ending ambition led him to musical stardom AND full creative freedom from his record company. The boys talk about picking the right cover songs, navigating the music scene in an unfamiliar town, and the insane business savvy of Ray.
Email us your complaints (or questions / comments) at 1001AlbumComplaints@gmail.com
Listen to our episode companion playlist (compilation of the songs we referenced on this episode) here:
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Listen to Modern Sounds in Country and Western Music here:
https://open.spotify.com/album/4j4w5DDWMKD7ePStAl19OF?si=7XUExo9wQGCQ0MmuH7V5tg
Intro music: When the Walls Fell by The Beverly Crushers
Outro music: After the Afterlife by MEGA
Follow our Spotify Playlist of music produced directly by us. Listen and complain at home
Follow us on instagram @thechopunlimited AND @1001AlbumComplaints
Support us on Patreon
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Next week's album: The Beatles - Abbey Road
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Podcast: Rolling Stone's 500 Greatest Songs (LS 30 · TOP 5% what is this?)
Episode: Beyonce’s Solo Career Evolution: From “Crazy in Love” to Cowboy Carter and Beyond
Pub date: 2024-05-01
Even before Beyoncé kicked off her solo career, it was clear that she was a legend-in-the-making. As the de facto leader of Destiny's Child, she was a guiding light for the girl group and helped shepherd them to stardom in both the pop and R&B spaces. The group was at their height (and still very much together) as she launched her solo career, first with "Work It Out" for the Austin Powers in Goldmember soundtrack but with more gusto on "Crazy in Love.
"Crazy in Love" served as the lead single for Beyoncé's debut solo album Dangerously in Love. The song was written in two hours and became a Number One hit the same week Dangerously in Love topped the albums chart. Beyoncé has developed significantly as an artist since then with her last two albums, Renaissance and Cowboy Carter, being prime examples of how she’s still growing and finding new ways to master her artistry even two decades after the world first got a taste of who Beyoncé was on her own.
On this week’s episode hosts Rob Sheffield and Brittany Spanos discuss Beyoncé's career trajectory and how the superstar ended up being the youngest artist with the most entries on the 500 Greatest Songs of All Time List. Later in the episode, they are joined by their Rolling Stone colleague Mankaprr Conteh to dig into the star's artistry and appreciation for Black music history, which she continues to embed in all her work.
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Podcast: Radio Atlantic (LS 59 · TOP 0.5% what is this?)
Episode: If Plants Could Talk
Pub date: 2024-05-02
Staff writer Zoë Schlanger is the proud owner of a petunia that glows in the dark. But she doesn’t just appreciate the novelty houseplant as work of science. Zoë sees its glow as a way to help us appreciate plants as more alive, more vital, and more complex than we humans typically do. Because in recent years, some scientists have reopened a provocative debate: Are plants intelligent?
They’ve devised experiments that break down elements of this big broad question: Can plants be said to hear? Sense touch? Communicate? Make decisions? Recognize kin?
Schlanger is the author of the upcoming book: The Light Eaters: How the Unseen World of Plant Intelligence Offers a New Understanding of Life in Earth. How could a thing without a brain be considered intelligent? Schlanger has spoken with dozens of botanists, from the most renegade to the most cautious, and she reports back on the state of the revolution in thinking.
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Podcast: Newsroom Robots (LS 29 · TOP 10% what is this?)
Episode: How Germany’s Ippen Digital is Fine-Tuning Large Language Models for Their Newsroom
Pub date: 2024-02-17
From fine-tuning large language models, to discussing modular journalism, to developing an AI tool to help track misinformation, there’s a lot to unpack from this week’s conversation with Alessandro Alviani, the product lead for AI at Germany’s Ippen Digital. We build upon the first part of our conversation from last week, where Alessandro shared his editor-centric approach toward building AI products.
A core takeaway from this week's episode is the value of fine-tuning large language models on a newsroom’s content.
Fine-tuning is the process of taking a pre-trained language model that understands general textual patterns and customizing it by training the algorithm on writings from a specific domain – in this case, Ippen Digital's own journalistic content. By fine-tuning models on Ippen Digital's extensive corpus of local German reporting rather than just using out-of-the-box models like GPT-4, they are working on enhancing accuracy for tasks like headline writing, lead paragraph generation, and article summarization.
Their editors and developers work side-by-side to ensure the AI's outputs match the desired quality standards and editorial voice.
Additionally, Alessandro spotlighted their work in building personalized news experiences enhanced by modular journalism or “intelligent content.” Modular journalism involves breaking down articles into discrete, interchangeable components centered on key semantic themes – historical context, opposing views, critical data, etc. These content blocks can then be dynamically mixed and matched by an algorithm to generate personalized news experiences for different reader interests and preferences.
We also discussed how developing AI assistants to break down a human-written news story into modules can enable the creation of customized article versions matching different reader interests or news products.
Such repackaging of information to cater to diverse audiences is one of the potentials of AI in the newsroom. Thoughtful implementation of augmented writing tools could catalyze more engaging, personalized news without compromising editorial integrity.
Of course, prudent precautions are necessary to develop algorithms in the newsroom. While AI has much potential for accelerating and enhancing reporting, we must understand its limitations in fully automating high-caliber journalism. The heart of quality storytelling – weaving together evidence and narratives to reveal truth and empower civil discourse – remains an irreplicable, fundamentally human endeavor.
Ippen Digital’s stance to develop AI solutions that empower rather than replace reporters seems wise. By bonding human creativity and AI productivity with an ethical approach to automation, journalism may structurally shift yet hold fast to its sacred commitments to transparency, accuracy, and public enlightenment.
🎧 Listen to the full conversation available now on Apple, Spotify, Google, and other major podcast platforms.
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Podcast: "The Riff" with Byrne Hobart and Erik Torenberg (LS 28 · TOP 10% what is this?)
Episode: E24: Why Acquisitions Fail, How to Write a Great Memo, and The Fate of ChatGPT Wrappers
Pub date: 2024-04-18
On this week's episode of The Riff, Erik and Byrne discuss the acquisition landscape, how to write great business memos, and why it's hard to monetize a successful ChatGPT wrapper. Get Harmonic: Head to https://bit.ly/harmonicturpentine and make sure to mention Turpentine during your demo.
This show is produced by Turpentine: a network of podcasts, newsletters, and more, covering technology, business, and culture — all from the perspective of industry insiders and experts. We’re launching new shows every week, and we’re looking for industry-leading sponsors — if you think that might be you and your company, email us at erik@turpentine.co.
SPONSOR: Harmonic Learn why Craft, Bedrock, NEA and 100s more trust Harmonic’s data to source deals. Harmonic is the most complete startup database, finding new companies as soon as they incorporate and tracking them through IPO. Head to https://bit.ly/harmonicturpentine and make sure to mention Turpentine during your demo.
RECOMMENDED PODCAST: Autopilot explores the adoption and rollout of AI in the industries that drive the economy and the dynamic founders bringing rapid change to slow-moving industries. From law, to hardware, to aviation, Will Summerlin interviews founders backed by Benchmark, Greylock, and more to learn how they're automating at the frontiers in entrenched industries.
Listen on Spotify: https://open.spotify.com/show/6YQZkKHN7EP2yWedAvSxBC?si=18377c69a2804333
Listen on Apple: https://podcasts.apple.com/ca/podcast/autopilot-with-will-summerlin/id1738163836
Timestamps:
(00:00) Intro
(00:20) Negative reputation of acquisitive companies
(11:50) Sponsors: Harmonic | Turpentine
(13:39) Importance of research and its competitive edge
(21:37) Exploring the dynamics of unions and corporate strategies
(26:11) How to write a great memo
(32:49) Challenge of monetizing AI services
(37:49) AI's impact on education and standardized testing
(43:44) Evolution and future of Ghost Kitchens
(52:19) Lessons from '50 Years in Wall Street'
(56:53) Insights from Managing a Sovereign Wealth Fund
(1:00:00) Wrap
Byrne’s writing: https://thediff.co
X / TWITTER:
https://twitter.com/eriktorenberg (Erik)
https://twitter.com/ByrneHobart (Byrne)
https://twitter.com/TurpentineMedia (Turpentine)
For guest or sponsorship inquiries please contact Sam@turpentine.co
This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit theriffpodcast.substack.com
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Podcast: Founders (LS 60 · TOP 0.1% what is this?)
Episode: #346 How Walt Disney Built Himself
Pub date: 2024-04-22
What I learned from rereading Walt Disney: The Triumph of the American Imagination by Neal Gabler.
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(2:00) Disney’s key traits were raw ingenuity combined with sadistic determination.
(3:00) I had spent a lifetime with a frustrated, and often unemployed man, who hated anybody who was successful.
— Francis Ford Coppola: A Filmmaker's Life by Michael Schumacher. (Founders #242)
(6:00) Disney put excelence before any other consideration.
(11:00) Maybe the most important thing anyone ever said to him: You’re crazy to be a professor she told Ted. What you really want to do is draw. Ted’s notebooks were always filled with these fabulous animals. So I set to work diverting him. Here was a man who could draw such pictures. He should earn a living doing that.
— Becoming Dr. Seuss: Theodor Geisel and the Making of an American Imagination by Brian Jay Jones. (Founders #161)
(14:00) A quote about Edwin Land that would apply to Walt Disney too:
Land had learned early on that total engrossment was the best way for him to work. He strongly believed that this kind of concentrated focus could also produce extraordinary results for others. Late in his career, Land recalled that his “whole life has been spent trying to teach people that intense concentration for hour after hour can bring out in people resources they didn’t know they had.” A Triumph of Genius: Edwin Land, Polaroid, and the Kodak Patent War by Ronald Fierstein. (Founders #134)
(15:00) My parents objected strenuously, but I finally talked them into letting me join up as a Red Cross ambulance driver. I had to lie about my age, of course.
In my company was another fellow who had lied about his age to get in. He was regarded as a strange duck, because whenever we had time off and went out on the town to chase girls, he stayed in camp drawing pictures.
His name was Walt Disney.
Grinding It Out: The Making of McDonald's by Ray Kroc. (Founders #293)
(20:00) Walt Disney had big dreams. He had outsized aspirations.
(22:00) A quote from Edwin Land that would apply to Walt Disney too: My motto is very personal and may not fit anyone else or any other company. It is: Don't do anything that someone else can do.
(24:00) Walt Disney seldom dabbled. Everyone who knew him remarked on his intensity; when something intrigued him, he focused himself entirely as if it were the only thing that mattered.
(29:00) He had the drive and ambition of 10 million men.
(29:00) I'm going to sit tight. I have the greatest opportunity I've ever had, and I'm in it for everything.
(31:00) He seemed confident beyond any logical reason for him to be so. It appeared that nothing discouraged him.
(31:00) You have to take the hard knocks with the good breaks in life.
(32:00) Nothing wrong with my aim, just gotta change the target. — Jay Z
(35:00) He sincerely wanted to be counted among the best in his craft.
(43:00) He didn't want to just be another animation producer. He wanted to be the king of animation. Disney believed that quality was his only real advantage.
(47:00) Walt Disney wanted domination. Domination that would make his position unassailable.
(49:00) Disney was always trying to make something he could be proud of.
(50:00) We have a habit of divine discontent with our performance. It is an antidote to smugness.
— Eternal Pursuit of Unhappiness: Being Very Good Is No Good,You Have to Be Very, Very, Very, Very, Very Good by David Ogilvy and Ogivly & Mather. (Founders #343)
(53:00) While it is easy, of course, for me to celebrate my doggedness now and say that it is all you need to succeed, the truth is that it demoralized me terribly. I would crawl into the house every night covered in dust after a long day, exhausted and depressed because that day's cyclone had not worked. There were times when I thought it would never work, that I would keep on making cyclone after cyclone, never going forwards, never going backwards, until I died.
— Against the Odds: An Autobiography by James Dyson (Founders #300)
(56:00) He doesn't place a premium on collecting friends or socializing: "I don't believe in 50 friends. I believe in a smaller number. Nor do I care about society events. It's the most senseless use of time. When I do go out, from time to time, it's just to convince myself again that I'm not missing a lot."
— The Red Bull Story by Wolfgang Fürweger (Founders #333)
(1:02:00) Steve was at the center of all the circles.
He made all the important product decisions.
From my standpoint, as an individual programmer, demoing to Steve was like visiting the Oracle of Delphi.
The demo was my question. Steve's response was the answer.
While the pronouncements from the Greek Oracle often came in the form of confusing riddles, that wasn't true with Steve.
He was always easy to understand.
He would either approve a demo, or he would request to see something different next time.
Whenever Steve reviewed a demo, he would say, often with highly detailed specificity, what he wanted to happen next.
He was always trying to ensure the products were as intuitive and straightforward as possible, and he was willing to invest his own time, effort, and influence to see that they were.
Through looking at demos, asking for specific changes, then reviewing the changed work again later on and giving a final approval before we could ship, Steve could make a product turn out like he wanted.
Much like the Greek Oracle, Steve foretold the future.
— Creative Selection: Inside Apple's Design Process During the Golden Age of Steve Jobs by Ken Kocienda. (Founders #281)
(1:07:00) He griped that when he hired veteran animators he had to “put up with their Goddamn poor working habits from doing cheap pictures.” He believed it was easier to start from scratch with young art students and indoctrinate them in the Disney system.
(1:15:00) I don’t want to be relagated to the cartoon medium. We have worlds to conquer here.
(1:17:00) Advice Henry Ford gave Walt Disney about selling his company: If you sell any of it you should sell all of it.
(1:23:00) He kept a slogan pasted inside of his hat: You can’t top pigs with pigs. (A reminder that we have to keep blazing new trails.)
(1:25:00) Disney’s Land: Walt Disney and the Invention of the Amusement Park That Changed the World by Richard Snow.
(1:33:00) It is the detail. If we lose the detail, we lose it all.
Get access to Founders Notes
“I have listened to every episode released and look forward to every episode that comes out. The only criticism I would have is that after each podcast I usually want to buy the book because I am interested so my poor wallet suffers. ” — Gareth
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The podcast and artwork embedded on this page are from David Senra , which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Means of Creation (LS 31 · TOP 5% what is this?)
Episode: AI music with Holly Herndon, Mat Dryhurst, and Jesse Walden
Pub date: 2023-05-31
Last month, in April 2023, a song that used the AI-generated voices of Drake and The Weeknd went wildly viral across social media before being taken down from streaming services for breaching copyright. Shortly after, Grimes took a more permissive approach, launching a platform to help fans create derivative music using her AI voice model, called Elf.Tech.
In the midst of these developments in AI music, Holly Herndon and Mat Dryhurst stand out as veterans who have have been on the forefront of the intersection of music, community, technology, and AI for many years. In 2021, they released a DAO-governed AI voice twin of Holly’s, called Holly+, and their more recent project, Spawning, offers tools for creators to manage their AI identities. In this conversation, we’re also joined by Jesse Walden, who originally got his start in crypto through music, by being a music manager before co-founding Mediachain, a startup which sought to attribute every piece of media on the internet using blockchains. We weave through the economics of AI music and derivative creation, the impact AI will have on the distribution of creator success, how hyper-personalization maps to listener behavior, industry regulation, impacts for record labels, and more.
Jesse and Li are cofounders and General Partners at Variant, an early-stage web3 venture firm. Learn more at https://variant.fund/
Links:
https://spawning.ai/
https://holly.plus/
Timestamps:
•(00:00) Episode preview
•(02:27) Spawning a baby and a startup
•(04:07) Holly+ & AI-driven content creation
•(07:02) Community participation & creation using Holly+
•(11:16) Grimes & the economics of derivative works
•(17:32) Crypto in the evolution of AI music
•(21:03) Jesse Walden’s journey from music to crypto
•(25:41) How AI impacts the power law of music success
•(36:53) Music consumption modes: passive vs. active
•(39:47) Hyper-personalization of music
•(47:45) Updates on Spawning: AI tools for artists
•(50:19) AI, data, and creator consent
•(53:14) Regulation & adaptation for record labels
•(58:59) Worldcoin, voice models, and digital identity
•(01:03:31) Prediction for the future of music: what will stay the same?
The podcast and artwork embedded on this page are from Li Jin, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Means of Creation (LS 31 · TOP 5% what is this?)
Episode: #20 — Eugene Wei on all things technology, film and media
Pub date: 2020-12-08
Since 2001, Eugene Wei has been publishing a blog and newsletter called Remains of the Day, where he writes about technology and media. The blog distills complicated consumer tech trends not just from a product design standpoint, but also from a user psychology perspective. His essays on Status as a Service (StaaS) and Seeing Like an Algorithm serve as guiding mental models for the tech community.
Eugene’s past experience gives him a unique historical perspective on the development of consumer technology platforms. Most recently, Eugene was Head of Video at Oculus and before that, he led product teams at Flipboard, Erly, Hulu and Amazon.
In this interview, talked to Eugene about:
- How he first got interested in TikTok
- What other domains could become TikTok-ified
- How his experience in film school impacts his work as a product leader
- How our chances in media have impacted how we relate to famous people (such as with The Queen in popular TV series The Crown), and how that extends to our relationships on social media
- The impact of technology on modern film & television, such as with real-time VFX
- Why he started writing and what he learned from the responses
- If he thinks the SF exodus is overrated or underrated
- And more!
Brought to you by the Means of Creation newsletter: meansofcreation.substack.com
The podcast and artwork embedded on this page are from Li Jin, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The NFX Podcast (LS 41 · TOP 1.5% what is this?)
Episode: The Founders’ List: “Status as a Service (StaaS)” by Eugene Wei (Former Product Leader at Amazon, Hulu, Flipboard, Oculus)
Pub date: 2020-09-24
nullThis is The Founders' List - audio versions of essays from technology’s most important leaders, selected by the founder community.
A Must Listen for all Founders - audio version of Eugene Wei’s hugely popular essay on the role of status in product development. Eugene was a product leader at Amazon, Hulu, Flipboard, and Oculus.
Read the full article here - https://www.eugenewei.com/blog/2019/2/19/status-as-a-service
The podcast and artwork embedded on this page are from NFX, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The NFX Podcast (LS 41 · TOP 1.5% what is this?)
Episode: Status Games with Eugene Wei (Product Lead at Amazon, Hulu, Erly, Flipboard, Oculus) and James Currier
Pub date: 2020-11-10
nullStatus can be a volatile, powerful driver of engagement. Status and one of its underlying mechanisms, scarcity must be considered by product designers in every category, as early on as possible.
But it can also be a double-edged sword, as managing a network and the status dynamics within it is constantly recirculating, always shifting. For this reason, status that is more entertainment-based than utility-based can over time become precarious, as well as too restrictive for continued user growth.
NFX partner James Currier sits down with Eugene Wei (Product Lead at Amazon, Hulu, Erly, Flipboard, Oculus) to draw from his original Status-as-a-Service article and expand to discuss status in entertainment, frameworks for emotional product design, video games as world leaders in defining scarcity, the future of virtual goods, and the underlying status games that drive us all -- in technology and in life.
The podcast and artwork embedded on this page are from NFX, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Techmeme Ride Home (LS 58 · TOP 0.5% what is this?)
Episode: (Bonus) TikTok's Secret Sauce With Eugene Wei and A16Z's 16 Minutes Podcast
Pub date: 2020-09-26
Eugene Wei and Sonal Chokshi explain plainly how and why TikTok is an evolutionary (and algorithmic) step beyond the social graph.
- Eugene's original essay, TikTok and the Sorting Hat
- Subscribe to 16 Minutes News by a16z (podcast)
See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
The podcast and artwork embedded on this page are from Ride Home Media, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: "Moment of Zen" (LS 39 · TOP 2% what is this?)
Episode: E32: Twitter, Threads, and Remote Work with Eugene Wei
Pub date: 2023-07-18
Writer and technologist Eugene Wei joins Dan, Erik and Antonio to talk about Twitter, Threads, and his views on remote work. We reference Eugene's writing a lot on the podcast and mentioned his recent piece, How to Blow Up a Timeline in last week's MoZ episode with Mike Solana (see links below). Listeners, if you're looking for an ERP platform, check out our sponsor, NetSuite: netsuite.com/zen
Moment of Zen is part of the Turpentine podcast network. Learn more: turpentine.co
We're hiring across the board at Turpentine and for Erik's personal team on other projects he's incubating. He's hiring a Chief of Staff, EA, Head of Special Projects, Investment Associate, and more. For a list of JDs, check out: eriktorenberg.com.
RECOMMENDED PODCAST:
The HR industry is at a crossroads. What will it take to construct the next generation of incredible businesses – and where can people leaders have the most business impact? Hosts Nolan Church and Kelli Dragovich have been through it all, the highs and the lows – IPOs, layoffs, executive turnover, board meetings, culture changes, and more. With a lineup of industry vets and experts, Nolan and Kelli break down thenitty-gritty details, trade offs, and dynamics of constructing high performing companies. Through unfiltered conversations that can only happen between seasoned practitioners, Kelli and Nolan dive deep into the kind of leadership-level strategy that often happens behind closed doors. Check out the first episode with the architect of Netflix’s culture deck Patty McCord.
https://link.chtbl.com/hrheretics
LINKS REFERENCED:
Eugene Wei, How to Blow Up a Timeline https://www.eugenewei.com/blog/2023/7/6/how-to-blow-up-a-timeline
American Prometheus https://www.amazon.com/American-Prometheus-Triumph-Tragedy-Oppenheimer/dp/0375726268
TIMESTAMPS:
(00:00) Episode preview
(01:03) Algorithm switch at Twitter
(07:10) What Eugene would do as Twitter’s CEO
(11:30) Signal to noise ratio in social media
(22:00) Threads
(37:35) Sponsor: NetSuite
(38:50) LinkedIn
(47:00) Improving Twitter: Ranked follower feed, restoring aristocracy, reversing war on Substack
(1:10:00) Incumbents vs upstarts in social media
(01:14:40) Graph portability
(01:20:00) Centralization vs Decentralization of the space
(01:25:00) UI analysis of Twitter vs. Discord vs. Slack vs Threads
(01:30:00) Impact of Slack and minimizing meetings on the workplace
(01:35:00) The downstream costs of remote work
(01:49:30) Why tech is the worst industry to model out the effects of remote work
(01:51:00) Office layout and productivity
TWITTER:
@eugenewei (Eugene)
@dwr (Dan)
@eriktorenberg (Erik)
@antoniogm (Antonio)
@moz_podcast
SPONSOR:
SHOPIFY: https://shopify.com/momentofzen for a $1/month trial period
Shopify is the global commerce platform that helps you sell at every stage of your business. Shopify powers 10% of all e-commerce in the US. And Shopify’s the global force behind Allbirds, Rothy’s, and Brooklinen, and 1,000,000s of other entrepreneurs across 175 countries. From their all-in-one e-commerce platform, to their in-person POS system – wherever and whatever you’re selling, Shopify’s got you covered. With free Shopify Magic, sell more with less effort by whipping up captivating content that converts – from blog posts to product descriptions using AI. Sign up for $1/month trial period: https://shopify.com/momentofzen
NetSuite provides financial software for all your business needs. More than thirty-six thousand companies have already upgraded to NetSuite, gaining visibility and control over their financials, inventory, HR, eCommerce, and more. If you're looking for an ERP platform ✅ NetSuite: http://netsuite.com/zen and defer payments of a FULL NetSuite implementation for six months.
The podcast and artwork embedded on this page are from Erik Torenberg, Dan Romero, Antonio Garcia Martinez, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Bankless (LS 60 · TOP 0.1% what is this?)
Episode: 194 - How Crypto Saves The Internet | Li Jin & Eugene Wei
Pub date: 2023-10-30
Many of our listeners are familiar with Li Jin, an investor and co-founder of Variant Fund and is one of our favorite thinkers on crypto and the creator economy. Li is also joined by Eugene Wei. He’s a web2 product visionary. Listeners may be familiar with some of his incredible blog posts such as, “Status as a Service”, “Invisible Asymptotes”, “Why Information Grows” and more.
In today’s episode, these two gigabrains, one from web2 social, one from web3 crypto, talk about the future of status as a service, psychological ownership, and the future ties between social and crypto.
------ ✨ DEBRIEF | Ryan & David unpacking the episode: https://www.bankless.com/debrief-li-jin-eugene-wei
----- 🏹 Airdrop Hunter is HERE, join your first HUNT today https://bankless.cc/JoinYourFirstHUNT
------ BANKLESS SPONSOR TOOLS:
🐙KRAKEN | MOST-TRUSTED CRYPTO EXCHANGE https://k.xyz/bankless-pod-q4
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⚖️ ARBITRUM | SCALING ETHEREUM https://bankless.cc/Arbitrum
🔗 CELO | CEL2 COMING SOON https://bankless.cc/Celo
------ TIMESTAMPS
0:00 Intro 8:00 Web2 Social History 18:55 Is Web2 Ending? 24:50 Network Effect Theory 27:45 3 Paths to Break Free 32:17 The Internet’s Good, Bad, & Ugly 41:48 The Algorithm’s Constraints 46:25 Web2 to Web3 Learning Lessons 54:40 Love vs. Fame 1:00:24 Web3’s Potential 1:07:00 Crypto's Social vs. Financial Problem 1:19:30 Ethereum’s Social Network 1:25:14 Increasing Psychological Ownership 1:31:25 Price vs. Psychological Ownership 1:35:00 Eugene’s Advice to Crypto Builders 1:42:00 Li’s Take on Crypto’s Potential Closing & Disclaimers
----- RESOURCES
Li Jin https://twitter.com/ljin18 https://li.substack.com/p/the-ownership-economy-2022 https://www.youtube.com/watch?v=N6AJlmB2h8k https://li.substack.com/p/building-psychological-attachment https://li.substack.com/p/love-vs-fame-a-framework-for-social
Eugene Wei https://twitter.com/eugenewei https://www.eugenewei.com/blog/2019/2/19/status-as-a-service https://www.eugenewei.com/blog/2018/5/21/invisible-asymptotes
----- Not financial or tax advice. See our investment disclosures here: https://www.bankless.com/disclosures
The podcast and artwork embedded on this page are from Bankless, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Techmeme Ride Home (LS 58 · TOP 0.5% what is this?)
Episode: (IHP) Eugene Wei On How Amazon Won
Pub date: 2023-06-18
No joke, this is one of my favorite episodes we've ever done. Eugene Wei was an early employee at Hulu, so we get some details on that company for the first time, and he also worked at Flipboard and Oculus, so we get some important context especially on the future of VR and the like. But the most fascinating stories you'll hear will be about Amazon, where Eugene was the first analyst in the strategic planning department. As you'll hear, Eugene had a unique perspective on Amazon's early strategy and business structure, almost a historically unique perspective... he could see month to month, how Amazon was built, what Amazon was trying to do, and why. This is such an amazing perspective on such an important company.
Sponsors:
- Grammarly.com/go
- Hillsdale.edu/ride
See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
The podcast and artwork embedded on this page are from Ride Home Media, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Big Technology Podcast (LS 50 · TOP 0.5% what is this?)
Episode: The Declining Half-Life Of Social Media — With Eugene Wei
Pub date: 2023-07-19
Eugene Wei is a tech analyst and product veteran, with time spent inside Meta, Amazon, and Hulu. He joins Big Technology Podcast for a discussion of social media's longevity, considering how the decay of mainstay platforms changes the incentives to participate. Tune in for a in-depth discussion of social media's longterm trajectory, examining the TikTok algorithm, Threads, Twitter under Musk, and the puzzling persistence of Facebook. In the second half, listen to an engaging lightening round, where Wei comments on the state of his former employers and his longtime friendship with Amazon CEO Andy Jassy.
The podcast and artwork embedded on this page are from Alex Kantrowitz, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Instagram Stories (LS 38 · TOP 2% what is this?)
Episode: The Instagram Stories - 5-24-23 - Update on Artifact, the Social News app
Pub date: 2023-05-24
Show Notes:
Instagram’s co-founder explains why he’s starting over (Vox)
Leave a Review: Apple Podcasts
Send in a voice message: https://podcasters.spotify.com/pod/show/danielhillmedia/message
Support this podcast: https://podcasters.spotify.com/pod/show/danielhillmedia/support
Leave a Review: Apple Podcasts
Follow Me on Instagram: @danielhillmedia
The podcast and artwork embedded on this page are from The Instagram Stories, Daniel Hill, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Le Super Daily (LS 53 · TOP 0.5% what is this?)
Episode: On a testé Artifact : l'App d'infos dopée à l'IA !
Pub date: 2023-03-16
Épisode 943 : Chaque années, de nouveaux réseaux sociaux voient le jour. Si certains sont de pales copies, d’autre arrivent sur le marché avec un point de vue différent.
C’est le cas de cette nouvelle plateforme appelée Artifact.
Artifact se situe à mi-chemin entre TikTok et Google news et promet un expérience très différente des plateformes sociales actuelles.
Pour rajouter une couche d’intérêt au projet, Artifact a été lancé par Kevin Systrom et Mike Krieger les co-fondateurs d’Instagram.
Et du coup évidemment on a testé pour vous. Bougez pas on vous raconte.
L’application a été lancé en Janvier 2023 sur invitation uniquement. Elle est accessible à tous depuis le mois de Février.
—
Une IA d’apprentissage automatique au coeur de l’algorithme d’ArtifactL'apprentissage automatique sur lequel repose une grande partie de l’algorithme d’Artifact a été inventé en 2017 chez Google.
Source
Plus vous interagissez avec l'application, plus elle apprend quelles news vous intéressent. Elle mesure les clics, le temps de lecture et d'autres signaux, comme si vous avez partagé le contenu avec des amis.
Systrom explique que n’utiliser que les clics comme le font encore certains algorithmes sociaux est une erreur puisqu’il résulte forcément dans des stratégies de baitclic.
—
Le système de recommandation d’Artifact il est directement inspiré de Toutiao. Toutiao c’est une application de news chinoise créée par ByteDance. C’est grâce à l’algorithme développé pour Toutiao que ByteDance a pu lancer TikTok.
—
Quand je me connecte la première fois à l’application je renseigne mes sujets de prédilection.Source
Dans la durée je peux aussi indiquer à l’application si un article me plait ou non en cochant sur un pouce haut ou pouce en bas.
—
Artifact est en réalité un feed reader évoluéD’où viennent les articles. Et bien d’une certain nombre de site travaillant en partenariat avec l’application. On trouve des The Verge, des Techchrunch, Engadget, The NewYorker… La liste est sérieuse.
Parmi les bonnes surprises, figure la possibilité d’ajouter ses propres abonnements à des titres de presse : une fois un abonnement renseigné, alors la source apparaît en priorité dans son flux. Sans aucun abonnement, l’appli se cantonne à des articles gratuits.
—
Un principe de gamification au coeur de l’application de news ArtifactEn tant qu’utilisateurs de la plateforme vous gagnez des badges en fonction de votre usage. Plus vous lisez et plus l’application vous crédite. De Beginner vous passez à Explorer et vous pouvez même rentrer dans les top readers.
A quoi ça sert ? A renforcer votre activité au coeur de la plateforme et à vous indiquer le niveau de compréhension de l’IA. C’est simple plus vous lisez et plus l’IA apprend ce qui vous plait et ajuste ses recommandations.
—
Une application de news qui incorpore du socialSur Artfiact je peux inviter mes potes à rejoindre l’application.
Je ne peux pas voir ce qu’ils lisent mais l’IA va prendre en compte leurs lectures et me les proposer.
A terme, Artifact ne se contentera pas de personnaliser les actualités et de répondre à vos intérêts, mais fournira également un lieu pour discuter de ces sujets.
—
Le lancement d’Artifact est un vrai challengeIls sont chauds les fondateurs d’artéfact. Avec cette plateforme, ils vont à la baston avec des géants : Google news, Apple News et évidemment Meta.
Selon les données de Pew Research , environ un tiers des adultes américains s'informent régulièrement sur Facebook, ce qui représente un défi pour toute nouvelle startup sur le marché de l'information.
—
Une trends autour de la socialisation de l’information ?Il existe une trend forte autour de la socialisation de l’information et on voit émerger pas mal de projets passionnants en ce moment.
L’un d’eux est français. Il s’agit de l’application Swen.
Swen le TikTok français de l’info
Source
C’est une application 100% vidéo au format 9:16. En nouant des partenariats avec des médias vérifiés, SWEN propose de l’information fiable à consommer sans modération. Écologie, politique, culture, sport…
Là aussi un algorithme de prédiction recommande des contenus au plus proche de mes centres d’intérêt.
. . .
Le Super Daily est le podcast quotidien sur les réseaux sociaux. Il est fabriqué avec une pluie d'amour par les équipes de Supernatifs.
Nous sommes une agence social media basée à Lyon : https://supernatifs.com/. Ensemble, nous aidons les entreprises à créer des relations durables et rentables avec leurs audiences. Ensemble, nous inventons, produisons et diffusons des contenus qui engagent vos collaborateurs, vos prospects et vos consommateurs.
Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.
The podcast and artwork embedded on this page are from Supernatifs, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: TechCrunch Startup News (LS 32 · TOP 5% what is this?)
Episode: What happened to Artifact?
Pub date: 2024-01-20
Last week, Artifact, a buzzy news app from Instagram’s co-founders, announced it was shutting down after failing to gain critical mass.
Learn more about your ad choices. Visit megaphone.fm/adchoices
The podcast and artwork embedded on this page are from TechCrunch, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Lenny's Podcast: Product | Growth | Career (LS 58 · TOP 0.5% what is this?)
Episode: A framework for finding product-market fit | Todd Jackson (First Round Capital)
Pub date: 2024-04-11
Todd Jackson is a Partner at First Round Capital. Before moving into venture capital, he played a crucial role as VP of Product and Design at Dropbox, guiding the company until its IPO in 2018. Prior to Dropbox, Todd led product management for Twitter’s Content and Discovery teams after selling his startup, Cover, to Twitter in 2014. Before Cover, Todd oversaw product development for Facebook’s Newsfeed, Photos, and Groups. He kickstarted his career at Google as an associate product manager and eventually led product for Gmail, witnessing its growth from beta to 200 million users. In our conversation, we discuss:
• Why product-market fit (PMF) matters
• First Round Capital’s four-part PMF framework
• Level one: Nascent product-market fit
• Level two: Developing product-market fit
• Level three: Strong product-market fit
• Level four: Extreme product-market fit
• Examples of companies at each level
• How to know if you’re stuck at a level, and how to get unstuck
• What to change if you’re stuck: persona, problem, promise, and product
• The goals and challenges at each stage
—
Brought to you by:
• WorkOS—The modern API for auth and user identity
• Eppo—Run reliable, impactful experiments
• CommandBar—AI-powered user assistance for modern products and impatient users
—
Find the full transcript at: https://www.lennysnewsletter.com/p/a-framework-for-finding-product-market
—
Where to find Todd Jackson:
• X: https://twitter.com/tjack
• LinkedIn: https://www.linkedin.com/in/toddj0/
—
Where to find Lenny:
• Newsletter: https://www.lennysnewsletter.com
• X: https://twitter.com/lennysan
• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/
—
In this episode, we cover:
(00:00) Todd’s background
(06:07) First Round Capital’s PMF framework
(09:07) Why product-market fit is so important
(11:02) Who can benefit from this framework
(12:55) The product-market fit method
(16:54) Broad overview of the framework
(21:35) Level one: nascent product-market fit
(33:16) The four P’s
(39:13) Level two: developing product-market fit
(49:13) Signs you’re stuck at level two, and what to do
(55:12) Level three: strong product-market fit
(01:00:17) Signs you’re stuck at level three, and what to do
(01:02:22) Level four: extreme product-market fit
(01:06:55) Rough timelines for each level
(01:11:18) A quick recap of the framework
(01:12:15) Diving deeper on the four P’s: what to do if you’re stuck
(01:13:56) Dollar-driven discovery
(01:25:11) Apply for the product-market-fit method program
—
Referenced:
• First Round: https://firstround.com/
• Twitter Acquires Cover: https://www.vox.com/2014/4/7/11625332/twitter-acquires-cover-an-android-mobile-startup
• Dropbox: https://www.dropbox.com/
• Rahul Vohra on LinkedIn: https://www.linkedin.com/in/rahulvohra/
• How Superhuman Built an Engine to Find Product Market Fit: https://review.firstround.com/how-superhuman-built-an-engine-to-find-product-market-fit/
• How to validate your startup idea: https://www.lennysnewsletter.com/p/validating-your-startup-idea
• How the most successful B2B startups came up with their original idea: https://www.lennysnewsletter.com/p/how-the-most-successful-b2b-startups
• How to know if you’ve got product-market fit: https://www.lennysnewsletter.com/p/how-to-know-if-youve-got-productmarket
• A guide for finding product-market fit in B2B: https://www.lennysnewsletter.com/p/finding-product-market-fit
• Product-market fit method: http://pmf.firstround.com/
• Stripe: https://stripe.com/
• Plaid: https://plaid.com/
• Paths to PMF: https://review.firstround.com/series/product-market-fit/
• WeWork: https://www.wework.com/
• Casper: https://casper.com/
• Vanta: https://www.vanta.com/
• Christina Cacioppo on LinkedIn: https://www.linkedin.com/in/ccacioppo/
• Ramp: https://ramp.com/
• Velocity over everything: How Ramp became the fastest-growing SaaS startup of all time | Geoff Charles (VP of Product): https://www.lennyspodcast.com/velocity-over-everything-how-ramp-became-the-fastest-growing-saas-startup-of-all-time-geoff-charl/
• Jack Altman on LinkedIn: https://www.linkedin.com/in/jackealtman/
• Lattice: https://lattice.com/
• Zachary Perret on LinkedIn: https://www.linkedin.com/in/zperret/
• Positioning: https://www.lennysnewsletter.com/p/positioning
• Retool: https://retool.com/
• David Hsu on LinkedIn: https://www.linkedin.com/in/dvdhsu/
• Persona: https://withpersona.com/
• Rick Song on LinkedIn: https://www.linkedin.com/in/rick-song-25198b24/
• Lloyd Tabb on LinkedIn: https://www.linkedin.com/in/lloydtabb/
• Looker: https://en.wikipedia.org/wiki/Looker_(company)
• Jason Boehmig on LinkedIn: https://www.linkedin.com/in/jboehmig/
• Ironclad: https://ironcladapp.com/
• Lessons in leadership | Scaling an org and tactical management advice | Jack Altman (Lattice): https://www.youtube.com/watch?v=cZzXqf61mrQ
• Filip Kaliszan on LinkedIn: https://www.linkedin.com/in/kaliszan/
• Verkada: https://www.verkada.com/
• Ali Ghodsi on LinkedIn: https://www.linkedin.com/in/alighodsi/
• Databricks: https://www.databricks.com/
• Stripe Radar: https://stripe.com/radar
• Stripe Atlas: https://stripe.com/atlas
• Square Stand: https://squareup.com/shop/hardware/us/en/products/ipad-pos-stand-integrated-card-reader
• Cash App: https://cash.app/
• Square Checking: https://squareup.com/us/en/campaign/banking/checking
• Square Loan: https://squareup.com/help/us/en/article/5654-get-started-with-square-capital
• Casey Winters on LinkedIn: https://www.linkedin.com/in/caseywinters/
• How to sell your ideas and rise within your company | Casey Winters, Eventbrite: https://www.lennyspodcast.com/how-to-sell-your-ideas-and-rise-within-your-company-casey-winters-eventbrite/
• Josh Kopelman on LinkedIn: https://www.linkedin.com/in/jkopelman/
• The art and science of pricing | Madhavan Ramanujam (Monetizing Innovation, Simon-Kucher): https://www.lennyspodcast.com/videos/the-art-and-science-of-pricing-madhavan-ramanujam-monetizing-innovation-simon-kucher/
• Simon Kucher: https://www.simon-kucher.com/
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Podcast: Y Combinator (LS 50 · TOP 0.5% what is this?)
Episode: Office Hours: Group Partners Share Their Top Productivity Advice
Pub date: 2023-11-01
There's only so many hours in a day and running a startup will require most of them. So how do you use that time wisely and be more productive? In this episode of Office Hours, the Group Partners at Y Combinator share their favorite strategies to be more effective and reduce distractions at work. They'll discuss the benefits of maker/manager schedules in addition to the fake work and productivity fads that can do more harm than good. If you're going to build a big business, you'll learn it requires working smarter AND harder to win.Apply to Y Combinator: https://yc.link/OfficeHours-apply
Work at a startup: https://yc.link/OfficeHours-jobs
The podcast and artwork embedded on this page are from Y Combinator, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Y Combinator (LS 50 · TOP 0.5% what is this?)
Episode: Key Startup Metrics with Tom Blomfield | Startup School
Pub date: 2024-01-08
In this episode of Startup School, YC Group Partner Tom Blomfield discusses one of the most important elements of running any startup: metrics! Tom shares what key metrics to track and how to use them to make the best decisions for your company.Apply to Y Combinator: https://yc.link/SUS-applyWork at a startup: https://yc.link/SUS-jobs
The podcast and artwork embedded on this page are from Y Combinator, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: In Our Headphones (LS 57 · TOP 0.5% what is this?)
Episode: Dave Okumu & The 7 Generations - Blood Ah Go Run (feat. Wesley Joseph & ESKA)
Pub date: 2023-01-16
Dave Okumu & The 7 Generations - "Blood Ah Go Run (feat. Wesley Joseph & ESKA)" from the 2023 album I Came From Love on Transgressive.
Mercury Prize-nominated artist Dave Okumu returns with his new project, Dave Okumu & The 7 Generations, a name he says honors "my actual ancestors, the ancestors of others, my musical ancestors, and my descendants." Their debut album, I Came From Love, will be released April 14th via Transgressive Records.
In a press statement, the UK-based guitarist/producer explains, "the narrative of this record emerged in tandem with the origin of its musical journey, through a rumination on survival, ancestry and heritage. The account of the young west African girl who was transported to South Carolina in 1756 and sold to the slave owner Elias Ball and the subsequent unearthing and presentation of her story to her descendants became an emblematic framework for these songs, opening doors to many aspects of the diasporic experience. The music stands in loving defiance of any forces that would seek to disconnect us from our collective history. As I consciously stand before my ancestors through the medium of this sound world, I proclaim that ‘You survived so I might live.’”
On today's Song of the Day, in particular, he reflects on the New Cross house fire in 1981 that killed 13 Black teenagers in South London.
“Living in an area as culturally rich and diverse as southeast London, I feel touched by an atmosphere of transcendence forged through a particular type of adversity. When you walk the streets and so many cultures are represented within a community, it’s difficult not to ask the questions ‘how did these people get here and what have their ancestors passed through so that I can have the life I am experiencing now?’ The story of the New Cross Fire and the subsequent response from different factions of society is one such trial, embodying a process which came to shape a significant element of the discourse around race relations in this country. Living in this part of London, I want to remember and honor those who lost their lives in that fire as their sacrifice, along with many others, feeds directly into my experience of this world.”
Read the full story at KEXP.org
Support the show: https://www.kexp.org/donate
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The podcast and artwork embedded on this page are from KEXP, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: hanging out with audiophiles (LS 50 · TOP 0.5% what is this?)
Episode: HOWA EP 99 - DAVE OKUMU
Pub date: 2022-08-20
You may know him for his soaring solo musical adventures or maybe you’re old school and you were always down with his band The Invisible. Perhaps you’ve seen him playing with Amy WInehouse or Grace Jones. Perhaps you’ve heard his collaborations with Adele, Yoko Ono or Tony Allen?
Perhaps you’ve not come across him yet, in which case I’m very happy to make the connection.
I know him as a genuine, fascinating, generous and ultra simpatico friend that I see far too infrequently.
There are few people I know that enjoy talking as much as Dave. He leads you into wild and wonderful places in his delightful and engaging explorations and I’m always keen to see where we might end up. In his presence, time seems to slow down and I find that deeply relaxing and very welcome. I often feel a tad in my head, or even headless but Dave brings a groundedness that is very settling.
Those who know him have experienced his radiant smile and his tremendous, heart felt hugs.
I feel very lucky to be friends with Dave. He’s a real one of a kind and an absolute beast of a musician.I had the chance to get a sneak peak of his new LP and it’s really mind blowing stuff. Just wait !
Ah yes. At long last, I’m so happy to welcome my friend on the show. Please welcome Dave Okumu
Nitty 99 is an exploration of the Eventide H8000 and its excellent 4 part MIDI pitch shifter which appears to be the sound we know so well on the Bon Iver track 715 Creeks. I run all sorts of stuff into it and it's a delight! Can't believe this is my first time using it.
Music for EP 99 comes from the rather excellent Botany! Superb.
https://botany.bandcamp.com/album/deepak-verbera
Still available for download is This EP FORUKRAINE
performed by Rob Burger and sonically crafted by me
all proceeds to the Ukrainian Redcross. Please consider a small donation :)
I’d like to thanks Diana Walsh for her help prepping the interview audio
@dmwalshmusic on the gram 🙂
PLEASE
keep the music coming !
send submissions to
lidellmakeswaves@gmail.com
side orders:
Dave mentions Elizabeth Fritsch pots and how he'd like to live in one.
When you see them you'll understand why. She's an absolute master and channels such beauty in her work. link HERE
The podcast and artwork embedded on this page are from hanging out with audiophiles, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Talk Python To Me (LS 57 · TOP 0.5% what is this?)
Episode: #355: EdgeDB - Building a database in Python
Pub date: 2022-03-06
See the full show notes for this episode on the website at talkpython.fm/355
The podcast and artwork embedded on this page are from Michael Kennedy (@mkennedy), which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: ThursdAI - The top AI news from the past week
Episode: 📅 ThursdAI - Mar 28 - 3 new MoEs (XXL, Medium and Small), Opus is 👑 of the arena, Hume is sounding emotional + How Tanishq and Paul turn brainwaves into SDXL images 🧠👁️
Pub date: 2024-03-28
Hey everyone, this is Alex and can you believe that we're almost done with Q1 2024? March 2024 was kind of crazy of course, so I'm of course excited to see what April brings (besides Weights & Biases conference in SF called Fully Connected, which I encourage you to attend and say Hi to me and the team!)
This week we have tons of exciting stuff on the leaderboards, say hello to the new best AI in the world Opus (+ some other surprises), in the open source we had new MoEs (one from Mosaic/Databricks folks, which tops the open source game, one from AI21 called Jamba that shows that a transformers alternative/hybrid can actually scale) and tiny MoE from Alibaba, as well as an incredible Emotion TTS from Hume.
I also had the pleasure to finally sit down with friend of the pod Tanishq Abraham and Paul Scotti from MedArc and chatted about MindEye 2, how they teach AI to read minds using diffusion models 🤯🧠👁️
Thank you for reading ThursdAI - Recaps of the most high signal AI weekly spaces. This post is public so feel free to share it.
TL;DR of all topics covered:
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AI Leaderboard updates
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Claude Opus is number 1 LLM on arena (and in the world)
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Claude Haiku passes GPT4-0613
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🔥 Starling 7B beta is the best Apache 2 model on LMsys, passing GPT3.5
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Open Source LLMs
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Databricks/Mosaic DBRX - a new top Open Access model (X, HF)
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🔥 AI21 - Jamba 52B - Joint Attention Mamba MoE (Blog, HuggingFace)
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Alibaba - Qwen1.5-MoE-A2.7B (Announcement, HF)
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Starling - 7B that beats GPT3.5 on lmsys (HF)
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LISA beats LORA as the frontrunner PeFT (X, Paper)
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Mistral 0.2 Base released (Announcement)
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Big CO LLMs + APIs
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Emad leaves stability 🥺
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Apple rumors - Baidu, Gemini, Anthropic, who else? (X)
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This weeks buzz
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WandB Workshop in SF confirmed April 17 - LLM evaluations (sign up here)
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Vision & Video
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Sora showed some demos by actual artists, Air Head was great (Video)
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Tencent Aniportait - generate Photorealistic Animated avatars (X)
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MedArc - MindEye 2 - fMRI signals to diffusion models (X)
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Voice & Audio
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Hume demos EVI - empathic voice analysis & generation (X, demo)
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AI Art & Diffusion & 3D
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Adobe firefly adds structure reference and style transfer - (X, Demo)
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Discussion
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Deep dive into MindEye 2 with Tanishq & Paul from MedArc
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Is narrow finetuning done-for with larger context + cheaper prices - debate
🥇🥈🥉Leaderboards updates from LMSys (Arena)
This weeks updates to the LMsys arena are significant. (Reminder in LMsys they use a mix of MT-Bench, LLM as an evaluation and user ELO scores where users play with these models and choose which answer they prefer)
For the first time since the Lmsys arena launched, the top model is NOT GPT-4 based. It's now Claude's Opus, but that's not surprising if you used the model, what IS surprising is that Haiku, it's tiniest, fastest brother is now well positioned at number 6, beating a GPT4 version from the summer, Mistral Large and other models while being dirt cheap.
We also have an incredible show from the only Apache 2.0 licensed model in the top 15, Starling LM 7B beta, which is now 13th on the chart, with incredible finetune of a finetune (OpenChat) or Mistral 7B. 👏
Yes, you can now run a GPT3.5 beating model, on your mac, fully offline 👏 Incredible.
Open Source LLMs (Welcome to MoE's)
Mosaic/Databricks gave us DBRX 132B MoE - trained on 12T tokens (X, Blog, HF)
Absolutely crushing the previous records, Mosaic has released the top open access model (one you can download and run and finetune) in a while, beating LLama 70B, Grok-1 (314B) and pretty much every other non closed source model in the world not only on metrics and evals, but also on inference speed
It uses a Mixture of Experts (MoE) architecture with 16 experts that each activate for different tokens. this allows it to have 36 billion actively parameters compared to 13 billion for Mixtral. DBRX has strong capabilities in math, code, and natural language understanding.
The real kicker is the size, It was pre-trained on 12 trillion tokens of text and code with a maximum context length of 32,000 tokens, which is just incredible, considering that LLama 2 was just 2T tokens. And the funny thing is, they call this DBRX-medium 👀 Wonder what large is all about.
Graph credit Awni Hannun from MLX (Source)
You can play with the DBRX here and you'll see that it is SUPER fast, not sure what Databricks magic they did there, or how much money they spent (ballpark of ~$10M) but it's truly an awesome model to see in the open access! 👏
AI21 releases JAMBA - a hybrid Transformer + Mamba 58B MoE (Blog, HF)
Oh don't I love #BreakingNews on the show! Just a few moments before ThursdAI, AI21 dropped this bombshell of a model, which is not quite the best around (see above) but has a few very interesting things going for it.
First, it's a hybrid architecture model, capturing the best of Transformers and Mamba architectures, and achieving incredible performance on the larger context window size (Transformers hardware requirements scale quadratically with attention/context window)
AI21 are the first to show (and take the bet) that hybrid architecture models actually scale well, and are performant (this model comes close to Mixtral MoE on many benchmarks) while also being significantly cost advantageous and faster on inference on longer context window. In fact they claim that Jamba is the only model in its size class that fits up to 140K context on a single GPU!
This is a massive effort and a very well received one, not only because this model is Apache 2.0 license (thank you AI21 👏) but also because this is now the longest context window model in the open weights (up to 256K) and we've yet to see the incredible amount of finetuning/optimizations that the open source community can do once they set their mind to it! (see Wing from Axolotl, add support for finetuning Jamba the same day it released)
Can't wait to see the benchmarks for this model once it's properly instruction fine-tuned.
Small MoE from Alibaba - Qwen 1.5 - MoE - A2.7B (Blog, HF)
What a week for Mixture of Experts models, we got an additional MoE from the awesome Qwen team, where they show that training a A2.7B (the full model is actually 14B but only 2.7B are activated at the same time) is cheaper, 75% reduction in training costs and 174% improvement in inference speed!
Also in open source:
Lisa beats LORA for the best parameter efficient training
📰 LISA is a new method for memory-efficient large language model fine-tuning presented in a Hugging Face paper💪 LISA achieves better performance than LoRA with less time on models up to 70B parameters🧠 Deep networks are better suited to LISA, providing more memory savings than shallow networks💾 Gradient checkpointing greatly benefits LISA by only storing gradients for unfrozen layers📈 LISA can fine-tune models with up to 7B parameters on a single 24GB GPU🚀 Code implementation in LMFlow is very simple, only requiring 2 lines of code🤔 LISA outperforms full parameter training in instruction following tasks
Big CO LLMs + APIs
Emad departs from Stability AI.
In a very surprising (perhaps unsurprising to some) move, Emad Mostaque, founder and ex-CEO of stability announces his departure, and focus on decentralized AI
For me personally (and I know countless others) we all started our love for Open Source AI with Stable Diffusion 1.4, downloading the weights, understanding that we can create AI on our machines, playing around with this. It wasn't easy, stability was sued to oblivion, I think LAION is still down from a lawsuit but we got tons of incredible Open Source from Stability, and tons of incredible people who work/worked there.
Big shoutout to Emad and very excited to see what he does next
Throwback to NEURIPS where Emad borrowed my GPU Poor hat and wore it ironically 😂 Promised me a stability hat but... I won't hold it against it him 🙂
This weeks Buzz (What I learned with WandB this week)
I'm so stoked about the workshop we're running before the annual Fully Connected conference in SF! Come hear about evaluations, better prompting with Claude, and tons of insights that we have to share in our workshop, and of course, join the main event on April 18 with the whole Weights & Biases crew!
Vision
Sora was given to artists, they created ... art
Here's a short by a company called ShyKids who got access to SORA alongside other artists, it's so incredibly human, and I love the way they used storytelling to overcome technological issues like lack of consistency between shots. Watch it and enjoy imagining a world where you could create something like this without living your living room.
This also shows that human creativity and art is still deep in the middle of all these creations, even with tools like SORA
MindEye 2.0 - faster fMRI-to-image
We had the awesome pleasure to have Tanishq Abraham and Paul Scotti, who recently released a significantly bette version of fMRI to Image model called MindEye 2.0, shortening the time it takes from 40 hours of data to just 1 hour of fMRI data. This is quite remarkable and I would encourage you to listen to the full interview that's coming out this Sunday on ThursdAI.
Voice
Hume announces EVI - their Empathic text to speech mode (Announcement, Demo)
This one is big folks, really was blown away (see my blind reaction below), Hume announced EVI, a text to speech generator that can reply with emotions! It's really something, and it has be seen to experience. This is in addition to Hume already having an understanding of emotions via voice/imagery, and the whole end to end conversation with an LLM that understands what I feel is quite novel and exciting!
The Fine-Tuning Disillusionment on X
Quite a few folks noticed a sort of disillusionment from finetuning coming from some prominent pro open source, pro fine-tuning accounts leading me to post this:
And we of course had to have a conversation about it, as well as Hamel Husain wrote this response blog called "Is Finetuning still valuable"
I'll let you listen to the conversation, but I will say, like with RAG, finetuning is a broad term that doesn't apply evenly across the whole field. For some narrow use-cases, it may simply be better/cheaper/faster to deliver value to users with using smaller cheaper but longer context models and just provide all the information/instructions to the model in the context window.
From the other side, we had data privacy concerns, RAG over a finetune model can absolutely be better than just a simple RAG, and just a LOT more considerations before we make this call that fine-tuning is not "valuable" for specific/narrow use-cases.
This is it for this week folks, another incredible week in AI, full of new models, exciting developments and deep conversations! See you next week 👏
Transcript Below:
[00:00:00] Alex Volkov: Hey, this is ThursdAI, I'm Alex Volkov, and just a little bit of housekeeping before the show. And what a great show we had today. This week started off slow with some, some news, but then quickly, quickly, many open source and open weights releases from Mosaic and from AI21 and from Alibaba. We're starting to pile on and at the end we had too many things to talk about as always.
[00:00:36] Alex Volkov: , I want to thank my co hosts Nisten Tahirai, LDJ, Jan Peleg, And today we also had Robert Scoble with a surprise appearance and helped me through the beginning. We also had Justin, Junyang, Lin from Alibaba and talk about the stuff that they released from Quen. And after the updates part, we also had two deeper conversations at the second part of this show.
[00:01:07] Alex Volkov: The first one was with Danish Matthew Abraham. and Paul Gotti from MedArc about their recent paper and work on MindEye2, which translates fMRI images using diffusion models into images. So fMRI signals into images, which is mind reading, basically, which is incredible. So a great conversation, and it's always fun to have Tanish on the pod.
[00:01:37] Alex Volkov: And the second conversation stemmed from a recent change in the narrative or a sentiment change in our respective feeds about fine tuning in the era of long context, very cheap models like Claude. And that conversation is also very interesting to listen to. One thing to highlight is this week we also saw the first time GPT 4 was toppled down from the Arena, and we now have the, a change in regime of the best AI possible, uh, which is quite, quite stark as a change, and a bunch of other very exciting and interesting things in the pod today.
[00:02:21] Alex Volkov: So, as a brief reminder, if you want to support the pod, the best way to do this is to share it with your friends and join our live recordings every ThursdAI on X. But if you can't sharing it with a friend, sharing a subscription from Substack, or subscribing, uh, to a pod platform of your choice is a great way to support this pod.
[00:02:48] Alex Volkov: With that, I give you March 28th, ThursdAI.
[00:02:52] Alex Volkov: Hello hello everyone, for the second time? we're trying this again, This is ThursdayAI, now you March 28th. My name is Alex Volkov. I'm an AI evangelist with Weights Biases. And for those of you who are live with us in the audience who heard this for the first time, apologies, we just had some technical issues and hopefully they're sorted now.
[00:03:21] Alex Volkov: And in order to make sure that they're sorted, I want to see that I can hear. Hey Robert Scoble joining us. And I usually join their spaces, but Robert is here every week as well. How are you, Robert? Robert.
[00:03:35] Robert Scoble: great. A lot of news flowing through the system. New
[00:03:39] Alex Volkov: we have, a lot of updates to do.
[00:03:43] Robert Scoble: photo editing techniques. I mean, the AI world is just hot and
[00:03:48] Robert Scoble: going.
[00:03:49] Alex Volkov: A week to week, we feel the excited Acceleration and I also want to say hi to Justin Justin is the core maintainer of the Qwen team. Qwen, we've talked about, and we're going to talk about today, because you guys have some breaking news. But also, you recently started a new thing called OpenDevon. I don't know if we have tons of updates there, but definitely folks who saw Devon, which we reported on, what a few weeks ago, I think? Time moves really fast in this AI world. I think, Justin, you posted something on X, and then it started the whole thing. So you want to give , two sentences about OpenDevon.
[00:04:21] Justin Lin: Yeah, sure. I launched the Open Devon project around two weeks ago because we just saw Devon. It is very popular. It is very impressive. And we just think that Whether we can build something with the open source community, work together, build an agent style, or do some research in this. So we have the project, and then a lot of people are coming in, including researchers and practitioners in the industry.
[00:04:46] Justin Lin: So we have a lot of people here. Now we are working generally good. Yeah You can see that we have a front end and back end and a basic agent system. So we are not far from an MVP So stay tuned
[00:05:01] Alex Volkov: Amazing. so definitely Justin when there's updates to update, you know where to come on Thursday. I, and but also you have like specific when updates that we're going to get to in the open source open source area So folks I'm going to run through everything that we have to cover and hopefully we'll get to everything.
[00:05:18] Alex Volkov: ,
[00:05:18] TL;DR - March 28th
[00:05:18] Alex Volkov: here's the TLDR or everything that's important in the world of AI that we're going to talk about for the next two hours, starting now. right So we have a leaderboard update, and I thought this is gonna be cool to just have a leaderboard update section because when big things are happening, on the leaderboards, and specifically I'm talking here about The lmsys Arena leaderboard the one that also does EmptyBench, which is, LLM, Judges, LLMs, but also multiple humans interact with these models and in two windows and then they calculate ELO scores, which correlates the best of the vibes evaluations that We all know and love and folks, Claude Opus is the number one LLM on Arena right now. Claude Appus, as the one that we've been talking about, I think, since week to week to week to week is
[00:06:05] Alex Volkov: now the number one LLM in the world and it's quite impressive, and honestly, in this instance, the arena was like, lagging behind all our vibes We talked about this already, we felt it on AXE and on LokonLama and all other places. so I think it's a big deal it's a big deal because for the first time since, I think forever it's clear to everyone that GPT4 was actually beat now not only that, Sonnet, which is their smaller version, also beats some GPT 4's version. and Haiku, their tiniest, super cheap version, 25 cents per million tokens. you literally can use Haiku the whole day, and at the end of the month, you get I don't know, 5 bucks. Haiku also passes one of the versions of GPT 4 for some of the vibes and Haiku is the distilled Opus version, so that kind of makes sense.
[00:06:53] Alex Volkov: But it's quite incredible that we had this upheaval and this change in leadership in the LMS arena, and I thought it's worth mentioning here before. So let's in the open source LLM stuff, we have a bunch of updates here. I think the hugest one yesterday, Databricks took over all of our feeds the Databricks bought this company called Mosaic, and we've talked about Mosaic multiple times before and now they're combined forces and for the past.
[00:07:17] Alex Volkov: year they've been working on something called DBRX, and now it's we got, in addition to the big company models that's taken over, so cloud Opus took over GPT 4, We now have a new open access model that takes over as the main lead. and they call this DPRX medium, which is funny. It's 132 billion parameter language model. and it's a mixture of experts with I think, 16 experts, and it's huge, and it beats Lama270b, it beats Mixtral, it beats Grock on at least MMLUE and human Evil scores and so it's really impressive to see, and we're gonna, we're gonna chat about DPRx as well and there's a bunch of stuff to cover there as well and Justin, I think you had a thread that we're gonna go through, and you had a great reaction.
[00:08:02] Alex Volkov: summary, so we're gonna cover that just today, what 30 minutes before this happened we have breaking news. I'm actually using breaking news here in the TLDR section because
[00:08:11] Alex Volkov: why [00:08:20] not?
[00:08:22] Alex Volkov: So AI21, a company from Israel releases something incredible. It's called Jamba. It's 52 billion parameters. but the kicker is it's not a just a Transformer It's a joint architecture from joint attention and Mamba. And we've talked about Mamba and we've talked about Hyena. Those are like state space models that they're trying to do a Competition to Transformers architecture with significantly better context understanding. and Jamba 52 looks quite incredible. It's also a mixture of experts. as you notice, we have a bunch of mixture of experts here. and It's it's 16 experts with two active generation It supports up to 256K context length and quite incredible. So we're going to talk about Jamba.
[00:09:03] Alex Volkov: We also have some breaking news So in the topic of breaking news Junyang, you guys also released something. you want to do the announcement yourself? It would be actually pretty cool.
[00:09:13] Justin Lin: Yeah, sure. Yeah just now we released a small MOE model which is called QWEN 1. 5 MOE with A2. 7B, which means we activate, uh, 2. 7 billion parameters. Its total parameter is, uh, 14 billion, but it actually activates around, uh, 2. 7 billion parameters
[00:09:33] Alex Volkov: thanks Justin for breaking this down a little bit. We're going to talk more about this in the open source as we get to this section I also want to mention that, in the news about the Databricks, the DBRX model, something else got lost and was released actually on Thursday last week.
[00:09:49] Alex Volkov: We also didn't cover this. Starling is now a 7 billion parameter model that beats GPT 3.5 on LMsys as Well so Starling is super cool and we're going to add a link to this and talk about Starling as Well Stability gave us A new stable code instruct and Stability has other news as well that we're going to cover and it's pretty cool.
[00:10:07] Alex Volkov: It's like a very small code instruct model that beats the Starchat, like I think 15b as well. So we got a few open source models. We also got a New method to Finetune LLMs, it's called Lisa if you guys know what LORA is, there's a paper called Lisa, a new method for memory efficient large language model Fine tuning.
[00:10:25] Alex Volkov: And I think this is it. Oh no, there's one tiny news in the open source as well mistral finally gave us Mistral 0. 2 base in a hackathon that they participated in with a bunch of folks. on the weekend, and there was a little bit of a confusion about this because we already had Mistral 0.
[00:10:43] Alex Volkov: 2 instruct model, and now they released this base model that many finetuners want the base model. so just worth an update there. In the big companies LLMs and APIs, I don't think We have tons of stuff besides, Cloud opus as we said, is the number one LLM in the world. The little bit of news there is that Emmad Mostak leaves stability AI and that's like worthwhile Mentioning because definitely Imad had a big effect, on my career because I started my whole thing with stable Diffusion 1. 4 release. and we also have some Apple rumors where as you guys remember, we've talked about Apple potentially having their own model generator, they have a bunch of Open source that they're working on, they have the MLX platform, we're seeing all these signs. and then, this week we had rumors that Apple is going to go. with Gemini, or sorry, last week, we had rumors that Apple is going to go with Gemini, this week, we had rumors that Apple is going to sign with Entropic, and then now Baidu, And also this affected the bunch of stuff. so it's unclear, but worth maybe mentioning the Apple rumors as well in this week's buzz, the corner where I talk about weights and biases, I already mentioned, But maybe I'll go a little bit in depth that we're in San Francisco on April 17th and 18th, and the workshop is getting filled up, and it's super cool to see, and I actually worked on the stuff that I'm going to show, and it's super exciting, and it covers pretty much a lot of the techniques, that we cover here on ThursdAI as well.
[00:12:05] Alex Volkov: In the vision and video category, This was a cool category as well, because Sora for the first time, the folks at Sora they gave Sora to artists and they released like a bunch of actual visual demos that look mind blowing. Specifically Airhead, i think was mind blowing. We're gonna cover this a little bit.
[00:12:21] Alex Volkov: If you guys remember Emo, the paper that wasn't released on any code that took One picture and made it sing and made it an animated character. Tencent released something close to that's called AnimPortrait. but Any portrait doesn't look as good as emo, But actually the weights are there.
[00:12:36] Alex Volkov: So you can now take one image and turn it into a talking avatar and the weights are actually open and you can use it and it's pretty cool. and in the vision and video, I put this vision on video as well, but MedArk released MindEye 2, and we actually Have a chat closer to the second hour with with yeah, with Tanishq and Paul from AdArc about MindEye 2, which is reading fMRI signals and turning them into images of what you saw, which is crazy. And I Think the big update from yesterday as Well from voice and audio category is that Hume, a company called Hume, demos something called EVI which is their empathetic voice analysis and generation model, which is crazy I posted a video about this yesterday on my feed. you talk to this model, it understands Your emotions. Apparently this is part of what Hume has on the platform. you can actually use this right now but now they already, they showed a 11 labs competitor, a text to speech model that actually can generate voice in multiple emotions. and it's pretty like stark to talk to it. and it answers sentence by sentence and it changes its emotion sentence from by sentence. and hopefully I'm going to get access to API very soon and play around with this. really worth talking about. Empathetic or empathic AIs in the world of like agentry and everybody talks about the, the
[00:13:53] Alex Volkov: AI therapist.
[00:13:54] Alex Volkov: So we're going to cover Hume as well. I think a very brief coverage in the AI art and diffusion Adobe Firefly had their like annual conference Firefly is a one year old and they added some stuff like structure reference and style transfer and one discussion at the end of the show IS narrow fine tuning done for for large, with larger contexts and cheaper prices for Haiku. we had the sentiment on our timelines, and I maybe participated in this a little bit, and so we had the sentiment and , I would love a discussion about Finetuning, because I do see quite A few prominent folks like moving away from this concept of Finetuning for specific knowledge stuff.
[00:14:32] Alex Volkov: Tasks, still yes but for knowledge, it looks like context windows the way they're evolving. They're going to move towards, potentially folks will just do RAG. So we're going to have a discussion about fine tuning for specific tasks, for narrow knowledge at the end there. and I think this is everything that We are going to talk about here. That's a lot. So hopefully we'll get to a bunch of it.
[00:14:51] Open Source -
[00:14:51] Alex Volkov: and I think we're going to start with our favorite, which is open source
[00:15:12] Alex Volkov: And while I was giving the TLDR a friend of the pod and frequent co host Yam Pelleg joined us. Yam, how are you?
[00:15:18] Yam Peleg: Hey, how are you doing?
[00:15:19] Alex Volkov: Good! I saw something that you were on your way to to visit. our friends at AI21. Is that still the
[00:15:24] Alex Volkov: awesome, awesome.
[00:15:25] Yam Peleg: 10 I'll be there in 10, 20 minutes.
[00:15:27] Alex Volkov: Oh, wow Okay. so we have 10, 20 minutes. and if you guys are there and you want to like hop on, you're also welcome so actually while you're here, I would love to hear from you we, We have two things to discuss. They're major in the open source and like a bunch of other stuff to cover I think the major like the thing that took over all our timelines is that Mosaic is back and Databricks, the huge company that does like a bunch of stuff. They noticed that Mosaic is doing very incredible things. and around, I don't know, six months ago, maybe almost a year ago, they Databricks acquired Mosaic. and Mosaic has been quiet since Then just a refresher for folks who haven't followed us for for longest time Mosaic released a model that was for I don't know, like three months, two months was like the best 7 billion parameter model called mpt and
[00:16:10] DBRX MoE 132B from Mosaic
[00:16:10] Alex Volkov: Mosaic almost a year ago, I think in May also broke the barrier of what we can consider a large context window so they announced a model with 64 or 72k context window and they were the first before cloud, before anybody else. and since then they've been quiet. and they have an inference platform, they have a training platform, they have a bunch of stuff that Databricks acquired. and yesterday they came out with a bang. and this bang is, they now released the top open access model, the BITS LLAMA, The BITS Mixtral, the BITS Grok1, The BITS all these things [00:16:40] And it's huge. It's a 132 billion parameter MOE that they've trained on I don't know why Seven
[00:16:49] Alex Volkov: 12,
[00:16:49] Yam Peleg: 12,
[00:16:50] Alex Volkov: jesus Christ, 12 trillion parameters.
[00:16:53] Alex Volkov: This is like a huge I don't think we've seen anything come close to this amount of training, Right
[00:16:59] Yam Peleg: Oh yeah, it's insane. I mean, the next one is six of Gemma, the next one we know. We don't know about Mistral, but the next one we know is six trillion of Gemma, and it's already nuts. So, but Yeah. It's a much larger model. I think the interesting thing to say is that it's the age of MOE now everyone is really seeing a mixture of experts and the important thing to, to pay attention to is that they are not entirely the same.
[00:17:27] Yam Peleg: So there is still exploration in terms of the architecture or of small tweaks to the MOE, how to do them, how to actually implement them better, what works better, what is more efficient and so on and so forth. That we just heard about Qwen MOE, which is also a little bit different than the others.
[00:17:44] Yam Peleg: So there is still exploration going on and just looking at what is coming out and everything turns out to be at the ballpark of Mistral and Mixtral just makes me more curious. Like, how did they do this? How everything is just on, on the same ballpark as them? How did they manage to train such powerful models?
[00:18:04] Yam Peleg: Both of them. And Yeah.
[00:18:06] Yam Peleg: I just want to say that because it's amazing to see.
[00:18:10] Alex Volkov: So, so just to highlight, and I think we've been highlighting this When Grok was released, we've been highlighting and now we're highlighting This as well. A significantly smaller model from Mixtral is still up there. It's still given the good fight, even though these models like twice and maybe three times as large sometimes and have been trained. So we don't know how much Mixtral was trained on right but Mixtral is still doing The good fight still after all this time which is quite incredible. and we keep mentioning this when Grok was released, we mentioned this. And now when this was released, we mentioned this as well.
[00:18:38] Alex Volkov: It's. What else should we talk about in DBRX? Because I think that obviously Databricks want to show off the platform. Nisten, go ahead. Welcome, by the way. You want to give us a comment about DBRX as well? Feel free.
[00:18:51] Nisten Tahiraj: Hey guys, sorry I'm late. I was stuck debugging C and it finally worked. I just lost a good time. I used DBRX yesterday. I was comparing it I used it in the LMTS arena. And then I opened the Twitter space and told people to use it. And now it just hit rate limits so you can't use it anymore. Yeah.
[00:19:11] Nisten Tahiraj: It was pretty good. I very briefly did some coding example. It felt better than than Code Llama to me. It wasn't as good as Cloud Opus stuff, but it did give me working gave me working bash scripts. So, yeah, in the very brief, short amount of time I use it, it seemed pretty good, so,
[00:19:31] Alex Volkov: Yep.
[00:19:32] Nisten Tahiraj: that's about it.
[00:19:33] Nisten Tahiraj: As for the Mistral and Mixtral question, so, I use Mistral large a lot, I use I use medium a lot, And the 70s, and the Frankensteins of the 70s, and they all start to feel the same, or incremental over each other. It's just the data. It's just the way they feed it. They feed this thing, and the way they raise it, I think it's it's all they're all raised the same way in the same data.
[00:20:03] Nisten Tahiraj: Yeah, the architecture makes some difference, but the one thing that you notice is that it doesn't get that much better with the much larger models. So it's just the data.
[00:20:20] Justin Lin: That's what I think it is.
[00:20:21] Alex Volkov: I want to ask Justin to also comment on this, because Justin, you had a thread that
[00:20:24] Alex Volkov: had a great coverage as well. What's your impressions from DBRX and kind of the size and the performance per size as well?
[00:20:32] Justin Lin: Yeah, the site is pretty large and it activates a lot of parameters. I remember it's 36 billion and the model architecture is generally fine. Actually, I talked to them a few times. around three months ago, last December introduced Quent2Dem and I accidentally saw it yesterday there are some common senses.
[00:20:57] Justin Lin: I think it is really good. They use TIC token tokenizer with the GPT2BP tokenizer. Recently I have been working with LLAMA tokenizer and the sentence piece tokenizer, well, makes me feel sick. Yeah. It's complicated. Yeah, but the GPT BPE tokenizer, because I have been working with BPE tokenizer years ago, so everything works great.
[00:21:22] Justin Lin: And we were just, for Qwen 1. 5, we just changed it from the implementation of TIP token to the GPT 2 BPE tokenizer by Hugging Face. It is simple to use. I think it's good to change the tokenizer. And it's also good to have the native chat ML format so that I think in the future people are going to use this chat ML format because the traditional chat formats like human assistant, there are a lot of risks in it.
[00:21:53] Justin Lin: So chat ML format is generally good. I think they have done a lot of great choices, but I'm not that, Impressed by their performance in the benchmark results, although benchmarks are not that important, but it's a good indicator. For example, when you look at its MMLU performance, I expect it to be, well, if you have trained it really good.
[00:22:19] Justin Lin: I haven't trained a 100 billion MOE model, but I expect it to be near 80. It is just 73 with 12 trillion tokens. I don't know if they repeat the training epics or they have diverse 12 trillion tokens. They didn't share the details, but I think it could be even better. I am relatively impressed by their coding performance, just as Nisten said.
[00:22:47] Justin Lin: The coding capability looks pretty well, but then I found that well?
[00:22:53] Justin Lin: DBRX Instruct because you can improve and instruct model to a really high level at human eval, but, it's hard for you to improve it for the base model. I'm not pretty sure maybe I need to try more, but it's generally a very good model.
[00:23:10] Alex Volkov: Yeah, absolutely. We got the new contender for the Open weights, open source. So the LLAMA folks are probably like thinking about, the release date it's very interesting what LLAMA will come out with. Notable that this is only an LLM. There's nothing like, there's no multimodality here. and the rumors are the LLAMA will hopefully will be multimodal. so whatever comparison folks do and something like like GPT 4, it's also notable that this is not multi modal yet, this is just text. One thing I will say is that they call this DBRX Medium which hints at potentially having a DBLX, DBRX Large or something, and also something that was hidden and they didn't give it, yet they retrained MPT.
[00:23:48] Alex Volkov: Yam, I think you commented on this and actually Matei Zaharia, the chief scientist there commented on, on, on your thread. They retrained the MPT7B, which was like for a while, the best 7 billion parameter model almost a year ago. and they said that it cost them like twice less to train the same model, something like this, which I thought it was notable as well.
[00:24:07] Alex Volkov: I don't know, Yam, if you want to, if you want to chime in on The yeah.
[00:24:10] Yam Peleg: The interesting thing here is that I mean, it's obvious to anyone in the field that you can, making the model much, much, much better if you get better data. So, what they basically say, what they basically show with actions is that if you have, you can even make the model even twice as better or twice as cheaper to train depending on how you look at it, just by making the data better.
[00:24:35] Yam Peleg: And my own comment on this is that at the moment, to the best of my knowledge Better, better data is something that is not quite defined. I mean, there is a lot of there is a lot of intuition, there are, I think big things when you look at broken data, it's broken. But it's really hard to define what exactly is better data apart [00:25:00] from a deduplication and all of the obvious.
[00:25:03] Yam Peleg: It's very hard to define what exactly is the influence of specific data on performance down the line. So, so it's really interesting to hear from people that have done this and made a model twice as better. What exactly did they do? I mean, because they probably are onto something quite big to get to these results.
[00:25:27] Yam Peleg: Again, it's amazing to see. I mean, it's just a year, maybe even less than a year of progress. I think MPT is from May. If I remember, so it's not even a year of progress and we already have like twice as better models and things are progressing
[00:25:42] Alex Volkov: Worth mentioning also that Databricks not only bought Mosaic, they bought like a bunch of startups, lilac, the friends from Lilac the, we had the folks from Lilac, IL and Daniel here on the pod. And we talked about how important data their data tools specifically is. and they've been a big thing in open source.
[00:25:58] Alex Volkov: All these folks from Databricks, they also highlight like how much Li help them understand their data. very much. so I'm really hoping that they're going to keep Lilac around and free to use as well one last thing that I want to say, it's also breaking news, happened two hours ago, the author of Megablocks, The training library from MOEs, Trevor gale I think he's in DeepMind, he has now given Databricks the mega Blocks library.
[00:26:23] Alex Volkov: So Databricks is also taking over and supporting the mega blocks training library for Moes. that is they say out for firms the next best library for Moes as well and there was a little bit of a chat where Arthur Mech from Mistro said, Hey, welcome to the party. And then somebody replied and said, you are welcome and then they showed the kind of the core contributors to the mega blocks library. And a lot of them are, folks from Databricks. and so now they've taken over this library.
[00:26:50] AI21 - JAMBA - hybrid Transformer/Mamba Architecture 52B MoE
[00:26:50] Alex Volkov: So yes MOE seems to be a big thing and now let's talk about the next hot MOE AI 21. The folks that I think the biggest like lab for AI in Israel, they released something called Jamba, which is a 52 billion parameter, MOE. and the interesting thing about Jamba is not that it's an MOE is that it's a Mamba and joint attention. so it's like a, it's a mamba transformer. Is that what it is? It's a combined architecture. We've talked about state space models a little bit here, and we actually talked with the author Eugene from RWKV, and we've mentioned Hyena from Together AI, and we mentioned Mamba before and all I remember that we mentioned is that those models, the Mamba models, still don't get the same kind of performance and now we're getting this like 52 billion parameter mixture of excerpt model that does. Quite impressive on some numbers and comes close to LLAMA70B even, which is quite Impressive MMLU is almost 70, 67%. I don't see a human eval score. I don't think they added this. But they Have quite impressive numbers across the board for something that's like a New architecture.
[00:27:52] Alex Volkov: 50 billion parameters with 12 active and what else is interesting here? The New architecture is very interesting. it supports up to 256. thousand context length, which is incredible. Like this Open model now Beats cloud 2 in just the context length, which is also incredible. Just to remind you Databricks, even though they released like a long context model before Databricks DBRX is 32, 32, 000.
[00:28:15] Alex Volkov: This is 256. And not only does it support 256 because of its Unique architecture They can fit up to 140k contexts on a single A180 GB GPU. I know I'm saying a lot of numbers. Very fast, But if you guys remember, for those of you who frequent the pod, we've talked with folks from , the yarn scaling method. and the problem with the context window in Transformers is that the more context you have the more resources it basically takes in a very basic thing. And so the SSM models and the Mamba architecture, they specifically focus on lowering the requirements for long context. and this model gets three times as throughput on long context compared to Mistral.
[00:28:57] Alex Volkov: 8 times 7b, compared to Mixtral, basically. so very exciting, yeah, you wanna comment on this I know you're like almost there, meeting with the guys but Please give us the comments,
[00:29:07] Yam Peleg: I'm there. I'm there in five minutes, so I can maybe if time works towards favour, maybe I can even get you the people on the pod
[00:29:14] Alex Volkov: That'd be incredible.
[00:29:15] Yam Peleg: I'm just, yeah, what what is important here, in my opinion, is that first, I mean, absolutely amazing to see the results.
[00:29:23] Yam Peleg: But what was not known to this point is whether or not those types of models scale. to these sizes. We had smaller Mambas and they were, they looked really promising, but we were at the point where, okay, it looks promising. It looks like it could be at the same ballpark of transformers, but to test this out, someone need to just invest a lot of money into the compute and just see what the results they get.
[00:29:53] Yam Peleg: And it's a risk. You don't know what you're going to get if you're going to do it. And it turns out that you get a really good model at the same ballpark. Maybe slightly less performant as a transformer, but it is expectable. The thing the thing worth mentioning here is that Mamba the Mamba architecture is way more efficient in terms of context size.
[00:30:15] Yam Peleg: As you just said, transformers are quadratic in terms of complexity. When you increase the context. So you have if you have two tokens, you need you need four times that you can say the memory. And if you have four tokens, you need 16 and it just goes on and on and it just explodes, which is why context length is such a problem but Mamba scales much more friendly, memory friendly, you can say.
[00:30:39] Yam Peleg: So, but the thing is that you do pay with the performance of the model. So. What you, what people do is a hybrid between the two, so you can find some sweet spot where you don't just use so much memory and yet you don't have the performance degrade that bad. And I mean, yeah, it's a risk. At the end of the day, you need to train, training such a large model is a lot of money, is a lot of money in terms of compute.
[00:31:06] Yam Peleg: And they did it, released it in Apache 2, which is amazing for everyone to use. And proving for, to everyone that, all right, if you follow this recipe, you get this result. Now people can build on top of that and can train maybe even larger model or maybe even, maybe just use this model. I'm, I didn't try it yet, but I think it's an incredible thing to try because it's it's not the same as Mixtral.
[00:31:33] Yam Peleg: Mixtral is a little bit better, but it's at the same ballpark as Mixtral, but you get way more context there. At your home on a small GPU for cheap. It's amazing.
[00:31:41] Alex Volkov: and Mixtral specifically,
[00:31:43] Yam Peleg: potential.
[00:31:45] Alex Volkov: thanks Yamin, I just want to highlight that Mixtral Is this like amazing model that WE compare models like three times the size to it, and they barely beat Mixtral. We talked about this when Grok 1 was released, we now talked about this when DBRX was released with like
[00:31:57] Alex Volkov: 12 trillion parameters in data.
[00:32:00] Alex Volkov: Mixtral is this basically like the golden standard. We've always had this standard for like how well performing an open model could be and it has been for a while, the best open model that we have and now we're getting this like new architecture, completely new architecture, basically a bet on on would it even scale from Fox from AI21? and it comes close to Mistral, but it does 3x throughput on long contexts compared to mixtral. and it has 256 context window with, if you want to get this from mixtral, You can train it with yarn, you can do all these things, but then you won't be able to actually scale it. hosted because it's gonna cost you so much money because of The quadratic attention.
[00:32:33] Alex Volkov: And
[00:32:34] Alex Volkov: they specifically say, the only model of its size class, that fits up to 140, 000 context windows on a single GPU. Which is quite incredible. and obviously Apache 2 license is great. I don't know if they also released a bunch of stuff like training code and data stuff. So we're definitely going to keep you posted.
[00:32:50] Alex Volkov: And yam hopefully will ask all these questions. But the efficiency in speed where like the closer you get to 128 context, the faster the model kind of performs is also quite incredible. like it. Yeah, it's quite incredible. the graphs there, we're going to post it, everything in the show notes, but absolutely a great release from AI21. shout out AI21 folks and definitely give them our love there and specifically because of the Apache 2 license. Anything else I want to hear from maybe Justin, if you want to comment on the joint architecture anything that you have you guys play with [00:33:20] the joint attention and Mamba. have you what's your reaction to this?
[00:33:25] Justin Lin: Yeah, We are trying with members with very small architectures. We can reach similar performance to transformer, but we did not scale it to very large size, so we don't know what will happen.
[00:33:38] Alex Volkov: So just this is great and Apache 2, and we're very happy shout out to folks at the i21. Briefly let's cover the rest of the stuff that we have still to cover in the open source.
[00:33:47] Mistral base 0.2
[00:33:47] Alex Volkov: We'll briefly cover this in the TLDR. We'll start with Mistral Mistral 0. 2 base released. so for fine tuning, obviously, for folks who know it's better For fine tuning purposes to have a base model than the instruct model, because then you can mistral.
[00:33:59] Alex Volkov: 0. 2 base was released in Hackathon last week together with Cerebral Valley and some other friends in San Francisco.
[00:34:08] Alex Volkov: There was some confusion about it because we had Instruct 0. 2 before we had a model that said, based on mistral 0. 2 and was like very well performing, the 7 billion parameter one. and now there is the base model. and then somebody went and changed the base of the instruct model to this one versus the previous one but nevermind, they cleared that confusion up and we have this like base model.
[00:34:28] Alex Volkov: It's also like open source and it's great.
[00:34:30] Nisten Tahiraj: there is one thing here about the previous Mistral instruct that they released. That one has been trained for 32k context, and I used it as as a personal chatbot. I'm making it with just the base the base Mistral 7b, and I'm noticing it is much better at carrying forward a conversation.
[00:34:50] Nisten Tahiraj: So I, I think a lot of the fine tunes should probably switch and just rerun on the new Mr. Instruct especially the ones that are geared towards conversational stuff. Because again, Mr. Instruct is limited to eight K and more likely just, you should always just keep it under 4K to get accuracy.
[00:35:11] Nisten Tahiraj: So, that's one thing here. The new seven B performs much better at larger contexts and, and summarizing
[00:35:18] Starling 7B beta - top apache 2 LLM in the world
[00:35:18] Alex Volkov: One incredible news is Starling. And I think I think. Justin, you had both of you and and Yam as well, you guys talked about this. We're starting actually now is a 7 billion parameter model that beats GPT 3. 5 on LMC Serena, which is quite incredible, right?
[00:35:34] Alex Volkov: I think it's the first and the only 7 billion parameter model that beats GPT 3. 5 on like user preference. And it's, it was hidden in between the DBRX news
[00:35:42] Alex Volkov: but let me see if I can. Put this up here real quick. so this model was released, what, a week ago, a week and a day ago. It's
[00:35:48] Alex Volkov: What do we know from this?
[00:35:49] Yam Peleg: Yeah, I just want to say, and to go in five minutes, I just want to say about Starling this is the second model. So if you haven't tried yet the first one you definitely want to try. I know there are people that are skeptics about 7b models and saying that they are too small. Just give this one a try.
[00:36:10] Yam Peleg: Just give this one a chance. Trust me, just give this specific one a chance. It is an amazing model, seriously, it's an amazing model and it's just showing to everyone that there is a lot more to squeeze out. Scale works, absolutely, but there is a lot more to squeeze out besides scale. And I seriously can't wait for the same technique to be applied on a larger model just to see what we get to.
[00:36:35] Yam Peleg: Because it's an amazing result, seriously.
[00:36:37] Alex Volkov: Nisten, go ahead.
[00:36:40] Nisten Tahiraj: So. The model is is still based and it's actually based off of open chat 3.5. The one thing that their Raven, the Nexus Raven team does well is they had that nexus Raven 13 B model. And for some time that was the best function calling small model you can.
[00:36:59] Nisten Tahiraj: So, I haven't tried this one, but I highly suspect it's probably pretty good at function calling. I'm just looking at it right now, it is Mistral based, it's exactly based off of OpenChat 3. 5 from Alignment Lab, so they fine tuned on top of that, and yeah, I would highly recommend people to use it.
[00:37:20] Nisten Tahiraj: I've used the one that has been trained off of OpenChat a lot, and
[00:37:24] Alex Volkov: They did a bang up job there because this 7 billion parameter model now beats GPT 3. 5, beats CLOUD 2. 1, beats Mistral Next, and Gemini pro and CLOUD 2, like this is the 13th, based on LMsys at least, this is the 13th model, it's 7 billion parameters, it's Apache 2, this is the from Berkeley folks, This is the only Apache 2 licensed model on the LLM leaderboard in the first like top
[00:37:48] Alex Volkov: 20, I think, or top top 13. So it bigs, I don't know how it beats Mixtral. So anyway, yeah, StarLing is great. It looks great Try it, folks. Definitely worth mentioning. We're gonna run through some other updates because we still have a tons of stuff to cover and then we Have some guests here in the audience that want to join and talk about very interesting things
[00:38:05] LISA beats LORA for AI Finetuning
[00:38:05] Alex Volkov: I don't have a lot of information about Lisa specifically, but I will just mention that there's if you guys in the fine tuning area, you know that Laura and we have Laura in the diffusion? models area as well lower rank adaptations, so folks in the Diffusion world have been training LORES for a while, more than a year, and now there's a new paper dropped that's called a new method for memory efficient large language model fine tuning.
[00:38:27] Alex Volkov: I'll say this slowly a new method for memory efficient large language model fine tuning. So this is not for diffusion stuff this is for large language, it's called Lisa and achieves better performance than LoRa with less time on models up to 70 billion parameters, and yeah, the results look pretty cool for folks who do fine tuning, it's worth comparing this and I know for a while we had different methods for fine tuning like QLora, for example, different Lora, there was an attempt to figure out which one is the best and so Lisa now is a new contender with a paper out and I think code will follow up as well.
[00:38:59] Alex Volkov: Lisa can fine tune models up to 7 billion parameters on a single 24 gigabyte GPU. so you can fine tune 7 billion parameter Mistral, for example, on a 4090 with a 24 gigabyte GPU, which is pretty cool.
[00:39:13] Alex Volkov: And code implementation in LMFlow is very simple. so awesome to have this and we'll add this to the show notes for folks who actually do fine tunes. And it's gonna be awesome. so I think that covers all of the open source stuff, and we obviously spent almost an hour running through open source and I do want to move towards What is the next super exciting stuff that we have this week before we jump into a conversation.
[00:39:37] Hume EVI emotion based TTS
[00:39:37] Alex Volkov: Yes I want to move into Hume. I want to move into the voice, and audio category. This is an unusual jump between categories. we usually talk about big companies next but there's honestly not that much that happened there. So maybe we'll briefly cover it, but the thing that broke my mind, I'm going to paste this on top here. and hopefully you guys will just listen to me instead of going and watching This is that a company called Hume finally released something that many people have been very excited about. and they showed a few demos there and they finally released something. so Hume has been around for a while.
[00:40:08] Alex Volkov: Apparently they do emotion analysis very well and they actually have this product out there. you can upload the video and actually audio of yourself speaking and they will and understanding of what you're saying. of your emotions and intonations, which is pretty cool. and we know that's a piece that's missing from multimodal LLMs, right? Okay, so Hume, they already had a platform for emotions understanding, and yesterday Hume released their demo of an emotional TTS, a text to speech model that not only speaks This text position actually replies with emotion. and combined with the previous system. that they had that they can understand your emotion, as you can hear, I'm talking about this I was a little bit sad when Hamel had to drop, but now I'm very excited again to talk to you about Hume. so they actually have a running analysis of this voice as it runs. and they understand what kind of like where you are in the emotion scale, which is, first of all, exciting to see on yourself. Second of all, it's like very alarming. Their understanding of emotions, whether or not it's like precise enough to tell the truth, for example. and the text to speech of theirs that generates emotion based text is quite something. I've never seen anything close to it before the only thing that came close to me is that if you guys remember, we talked about 11 labs have style transfer thing where you can actually talk and they would take an AI voice and basically dub you but with the same emotion. So, that was the only thing that came close to what I heard yesterday from Hume. so hume has this model that's gonna be out in I think they said April? [00:41:40] that you'd be able as a developer to assign what emotion it will answer with. and together with the first part, which is a voice emotion understanding, like the text to speech understanding, they now have a speech to text with emotion. the whole end to end feeling is like nothing I've ever experienced and Robert I think I saw you first repost about this So I want to hear if like you play with the demo and like what your thoughts about this Because I was blown away and I will definitely want to hear about What do you think about this?
[00:42:14] Robert Scoble: blown away too. They you nailed it. It lets AI understand your emotion and build a much more human interaction with AI. The one problem is, I believe it's 7 an hour or something like that, so it's fairly expensive to integrate, but, for people who are building new kinds of applications that are going to have to integrate with human beings, I think it's very well done. You should look at it.
[00:42:41] Alex Volkov: Absolutely and definitely for folks who have the Uncanny Valley in different, LLMs that, reading for a long time is not the same. Is not quite the same I think we're gonna see some more emotionality in many of these, demos, and it's gonna be very exciting, together with the fact that recently there has been like this video of basically HeyGen the deepfake company that translates your lips and people were saying, Hey, this is like a fully end to end AI and we're so doomed all of these kind of AI generated voices, they still use 11 labs so I got to think that 11 labs is not going to be like that much behind and we'll start Working on some emotion like output as well but I would definitely add the link to this, and actually the video of me testing this out Hume, in in the show notes, and more than welcome for you guys to try this as well.
[00:43:27] Alex Volkov: I think the demo is demo, oh huma. ai. They actually have a chatbot on the website? hume. ai, where you can talk to the chatbot in your voice, and answers with voice as well but the full demo is more mind blowing. They understand your emotionality, they understand the emotionality of they then translate the emotionality into the actual context. and when the model talks back at you and when you say something like when you try to be when you try to fake it, and you yell, but you say, I'm so happy the model says, Hey, you look a little bit conflicted. So actually understand like what you're saying and what your meaning or basically the way you say it is different.
[00:44:00] Alex Volkov: So they actually build this understanding into the demo, which is super cool to play with. Yeah, so hume definitely worth checking out. I think that next in the voice and audio, I think that basically that's it that we had to cover but a similar area in AI creation is vision and video.
[00:44:15] SORA examples from filmmakers
[00:44:15] Alex Volkov: And this week we had oh my God the beginning of this week was like all excited about how to how the world of entertainment will look and the reason is because OpenAI took Sora, I'm hoping by this point that Sora is needs no introduction at this point right Sora is OpenAI's text to video model, and it's leagues above everything else that we saw in the world before this and it blew our creative minds, and keeps blowing some people's minds on TikTok. and OpenAI gave access to Sora, to a few creators content creators not Hollywood Apparently they're on the way to Hollywood right now and to talk with folks, But they gave it to a few filmmakers in in the like independent world I think a few Companies from Toronto and they finally showed us demos of what.
[00:45:03] Alex Volkov: Instead of The developers in OpenAI, and some prompts that they do with Sora, what an actual studio can do with some creativity and it looks like they also hired an artist in residence for OpenAI as well and wow my mind was definitely blown. the there was one short video that looked like something that I would, I would have seen in Sundance festival. It's called Airhead from from Toronto based. film
[00:45:28] Alex Volkov: creator called ShyKids, and I'm gonna add this to the show notes because this definitely, at least for me, was the most viral thing that I saw. And, I Absolutely loved it. It was, it felt very human it felt incredible. It's this very short story about something, somebody with a balloon instead of his head and the way they tell the story they kind of work around the technical limitations, which we all know, right if you generate two videos in Sora, the first the character persistence between those two videos will Not be there. And that's a big problem with every video generation. But this one, they worked around this because they told the story of this air balloon guy and his head throughout their life So like the character consistency isn't really required there. And I just really love that like actual storytellers can work around the technology to create something that feels so good Obviously the audio there was amazing and the production and the storytelling, everything. So. I think everybody saw it at this point, but if you haven't airhead from shy kids is quite incredible.
[00:46:27] Tencent AniPortrait - Animated Avatars
[00:46:27] . Okay. , I want to talk about Tencent released something called AniPortrait any with N a N I like animated portrait and it's generating photorealistic animated avatars. and if you guys remember Emo, we've talked about this before Emo was quite incredible to me. the examples that Imo showed were pretty much the same level, the same jumping capability in the way that Sora showed the previous image to video generations, Imo showed to kind of Image to animated character and
[00:46:56] Alex Volkov: was incredible.
[00:46:56] Alex Volkov: Like lips moved and eyes and consistency was there. So, the problem with Emo is that they haven't released the code And I think for now Emo is the highest like AI GitHub repo with the highest number of stars with no code. I think it's like 25, 000 stars or something. Everybody's waiting for Emo and haven't dropped.
[00:47:15] Alex Volkov: And when I say everyone, I necessarily mean the kind of the waifu creator world who would love, nothing more than just generate an image in stable diffusion something and then animated this with some, let's say emotional voice from the human thing, that we just mentioned. but the second best one for now is AnyPortrait. and actually the code was dropped. and the kind of the lips movement is great And the eyes, it's not close to emo, but it's really good compared to WAV to leap on different areas and if you ever built like an animated character AI stuff, you'll know that, the open source options.
[00:47:49] Alex Volkov: were not great the closed source options like HeyGen and different like labs like DID and Synthetic, I think, I don't remember the name. They were okay. They were great. but the open source options were not there. So any portrait right now is the best version We have it dropped yesterday. if you are doing any kind of like character animation, give any portrait a try and let us know, I'm definitely gonna play with this.
[00:48:12] Alex Volkov: Definitely gonna play
[00:48:12] Alex Volkov: with this. I think we've covered most of the stuff that we wanted to cover besides weights and biases stuff and NB companies.
[00:48:18] MindEye 2 - Interview with Tanishq and Paul from MedArc
[00:48:18] Alex Volkov: But now I am very excited to bring two friends here one friend of the pod and for a long time and now a new one, Paul Scotti and you guys here to talk to us about MindEye. to the second version. so I will just like briefly do an introduction that MindEye came around the summer I think I want to say, and we covered this because in my head everything was like multimodal, multimodal. When were we going to get multimodal? This was before vision happened. and one of the craziest multimodalities that we expected was something like a fMRI signal, like brain signals. and then you guys raised MindEye, which was like, mind blowing. and so I would love to hear about the history of like how Med Ark started like doing brain interpretation. and then let's talk about MindEye 2 and what's exciting about this recent release, but feel free please to unmute Tanishq and then Paul and introduce yourself briefly.
[00:49:08] Tanishq Abraham: Yeah Yeah, I'll just provide a quick background and summary and then I'll let Paul talk about MindEye 2 in more detail. But, yeah, basically, I'm introducing myself again. I'm Tanish I'm Tanish. Work at Stability ai and I also am the founder of MedARC and I lead MedARC, which is a medical ai open source medical ai research organization.
[00:49:30] Tanishq Abraham: And, we mostly are focused on trading foundation models for medicine. And So we do have a kind of a kind of research in neuroscience and ai and combining AI and neuroscience, which. which is what Paul is leading at MedArc. But yeah, like we started I guess looking into this sort of neuroscience AI research for quite some time actually.
[00:49:54] Tanishq Abraham: Actually, I think even before I officially started MedArc when I was organizing [00:50:00] some open source medical AI projects, this was one of the projects that I actually had started, I think, back in summer of 2022. And I think, just generally, the idea was that there's, the idea was we were working on this fMRI to image reconstruction problem, which is basically the idea that we take the, we have a person that is looking at some images and we take their fMRI signal.
[00:50:25] Tanishq Abraham: and we want to use AI to reconstruct the image that the person was looking at just in the fMRI signal. So it's the sort of mind reading kind of problem that we're working on. And I think up, back in 2022 when we started working on this, at first no the techniques that people were using were quite basic and, I think the sort of neuroscience community was quite behind in what they were, in what they were using.
[00:50:48] Tanishq Abraham: So I think we were pretty excited about the possibility of utilizing some of the latest techniques in generative AI to advance this field. And yeah, first did I start this project and there were a couple volunteers that were helping out, but luckily Paul had also discovered that we were working on this and he, he joined this project and really spearheaded this kind of neuroscience AI initiative that we've been having at MedArc.
[00:51:14] Tanishq Abraham: And yeah, that resulted in MindEye, which we released in April. I think May of last year and and then we've been continuing to work on improving those results and that has now resulted in MindEye 2. And we also have some other sorts of projects in the neuroscience AI area, like training foundation models for fMRI and we're exploring some other ideas as well.
[00:51:37] Tanishq Abraham: But yeah, I think with MindEye one, we had a very simple sort of pipeline of. of taking the fMRI signal and converting them to clip image embeddings and and then basically re generating an image from the clip image embeddings, and that worked quite well and The only difference, the only issue with that was that it required a lot of data, and we have developed this new pipeline, which Paul will talk more about, that requires less data, is more efficient, and is giving also better results with better, sort of, image generation models, so, for example, we're using SDXL for this MindEye 2 model so, yeah, I think I'll let Paul talk more about the motivation and how MindEye 2 works.
[00:52:18] Alex Volkov: So I just like before we get to Paul thank you for joining guys. first of all, I just want to highlight how insane to me is the thing that you guys talking about where many people like think that, oh yeah, generative AI generates images. Yeah. And generate some texts. And You guys like translating brain signals into what people actually saw. and I think I saw a separate from You also an attempt to understand fMRI. So Paul, maybe feel free to introduce yourself and maybe also cover prior work in this area. I would love to know, if this is something you guys came up with or something You saw and improved on, I would love to know as well.
[00:52:52] Alex Volkov: That's
[00:52:57] Paul Scotti: This, yeah, like Tanisha was saying, we started out working on this together over Discord back in 2022. And at the time, there weren't really any good results doing reconstruction of images from, looking at images inside of an MRI machine. And what really spurred several new papers in this field is open sourced image generation models like stable diffusion clip models, and also importantly a good data set of people looking at images in an MRI machine.
[00:53:34] Paul Scotti: It's a very difficult dataset to collect because we're talking about eight people who spent 30 to 40 hours inside of this MRI machine looking at images one at a time for three seconds each.
[00:53:48] Paul Scotti: So it's, it really was the culmination of dataset and new models that allowed this to work. For the MindEye 2 stuff specifically, We focused on trying to get good results using only one hour instead of 40 hours of data.
[00:54:07] Paul Scotti: And this is pretty important because if you're trying to do these machine learning techniques on new subjects, new data sets, maybe apply to the clinical setting, you aren't going to be collecting dozens of hours of data, especially for clinical populations. It's just too expensive and you're taking up their valuable time.
[00:54:29] Paul Scotti: So we, there's a lot of papers now that have been focusing on fRIDA image, just because it's a cool topic. So our paper shows, state of the art results, but specifically in the one hour domain, We show that you can pre train a model on other people's brains in order to have a better starting point to fine tune the model on a separate, held out subject's brain.
[00:54:54] Paul Scotti: And for people who aren't maybe as familiar with neuroimaging stuff or how the brain is, how the brain works, your brain is wired very differently to other people. It's not like there's the same. part of the brain that always handles, what happens when you look at a picture of an elephant or something.
[00:55:15] Paul Scotti: We have different shapes and sizes of brains. We have different patterns of activity that lead to how we perceive vision. And the reconstructions that we're talking about are not as simple as just, was it a dog that you were looking at? Was it an elephant? So you need some sort of way to align all these different people's brains and their different visual representations into a shared latent space so that you can then get the rest of this pipeline with the, diffusion models and MLPs to work and actually have that be informative to generalize from, my brain to your brain.
[00:55:53] Alex Volkov: so incredible that I have, so many questions, Paul, but I will start with maybe, The differences between brains that something that you said, I also want to talk about, the visual cortex and how that thing happens, but I would be remiss if I don't mention at least that you guys are now talking about MindEye at the same time we're we got the first the first Neuralink implanted human showing that he can control basically a machine with their, with his brain with implants.
[00:56:19] Alex Volkov: But you guys are completely non invasive kind of understanding of these brain signals. But to an extent and Neuralink also is some sort of like an invasive understanding of brain signals and transforming them into actions versus something that they see. but , they mentioned that they're working on sight fixing.
[00:56:34] Alex Volkov: As well.
[00:56:34] Alex Volkov: Could you maybe give us a brief understanding of fMRI, how that translates into the signals from visual contact? How do, how does this machine know what I see and how then you are able to then use diffusion models to recreate what I see.
[00:56:48] Alex Volkov: Could you give us like a little bit more of a, what's, where's the magic here?
[00:56:52] Paul Scotti: Yeah, so, fMRI right now is the best method if we're talking about non invasive tech. If you have electrodes on someone's brain, obviously that's going to give you a much better signal. But it's also not viable to do that for most projects and for applying it to clinical settings and new research and everything.
[00:57:14] Paul Scotti: So we used fMRI, which is a bit crude in the sense that you have these people that are needing to make as little motion as possible. The MRI machine is basically tracking blood flow. So when you look at an image of something, the neurons in your brain that correspond to representing that image are active and they require more oxygenation to help with how they've been used in relation to the other voxels in the brain that are not as relevant for activating to that image.
[00:57:50] Paul Scotti: Basically, you're tracking this kind of slow moving time course of blood flow that corresponds to where in the brain is active. And then you are have this 3D volume of the brain and the corresponding blood oxygenation changes for every given 3D cube or voxel in the brain. And what we did is we took all the voxels corresponding to the visual cortex, The back of the brain that seems to be active when you look at stuff, and we feed that through this neural network.[00:58:20]
[00:58:20] Paul Scotti: And specifically, we feed that through MLPs and a diffusion prior and all this stuff to give us a model that can translate from brain space to clip space. where CLIP is, these models that are contrastively trained typically with text and images So that you can have this multimodal space where you have the ability to align a given image caption with the image itself.
[00:58:48] Paul Scotti: This you can think of as a third space, a new modality for CLIP that's the brain. So we use the same sort of technique of contrastively mapping the brain and its paired samples corresponding to the images into the CLIP space. And then there are so called unclip models, also sometimes called image variations models, that allow you to undo clip space back to pixel space.
[00:59:13] Paul Scotti: And so that's how we actually get the image reconstructions at the end, where the model only gets the brain activities and has to generate the corresponding image.
[00:59:23] Alex Volkov: So I'm still like picking up my job from the floor here, because what you're basically saying is this, the same architecture that is able to Drop cats by understanding the word cat and like a pool, the concept of a cat from latent space. Now you've able to generalize and add multimodality, which is like brain understanding of a cat or like what happens in the brainflow in the visual cortex when somebody looks at a cat and you're basically placing it in the same latent space neighborhood. and now you're able to reconstruct an image based on this. I'm still like trying to obviously wrap my head around this but I would love to maybe ask.
[01:00:01] Alex Volkov: the Tanishq as well. , could you talk about MindEye 2 and specifically the improvements that you did, and how you achieved them and what they are in fact and then how it applies to the clinical field,
[01:00:11] Tanishq Abraham: Right. I mean, so with MindEye 2 like Paul mentioned, our main focus was what can we do to basically use less data when it comes to a new subject. So if you have a, you have a new person that you want to, read their mind, you want to do this reconstruction, we don't want them to have to do 40 hours of scanning because with MindEye 1, you'd have to basically train a separate model for every single subject.
[01:00:34] Tanishq Abraham: So it was like a completely separate model for each subject. So if you had a new subject, you would have to get 40 hours of scanning with that new subject to create a new model. So
[01:00:42] Tanishq Abraham: the idea with MindEye 2 is that we have,
[01:00:45] Tanishq Abraham: We, we train,
[01:00:46] Tanishq Abraham: A model on all of
[01:00:48] Tanishq Abraham: The previous subjects.
[01:00:50] Tanishq Abraham: So for example, we have
[01:00:51] Tanishq Abraham: Eight subjects in the data set,
[01:00:53] Tanishq Abraham: You train on seven of the subjects,
[01:00:56] Tanishq Abraham: And
[01:00:56] Tanishq Abraham: You, and so it's training on all seven subjects and then you are able to then fine tune. that model on a new subject, but you only need one hour of data.
[01:01:06] Paul Scotti: So basically for any new subject, now you only need one hour of data.
[01:01:09] Paul Scotti: So the way that works is that basically we have adapter layers, which is just like these sorts of like linear layers that you have that for each each subject. So, you basically have this sort of layer that is you have the fMRI data from a new subject, but you do have this like linear adapter layer that is basically converting it to again like a kind of a shared space for all the fMRI data.
[01:01:32] Paul Scotti: So then basically when you are taking a new patient or a new subject, all you have to do is fine tune this linear adapter for that new subject. And, yeah, so that's the general idea with. What we try to do there with that way, we only have to use only one hour of data.
[01:01:49] Paul Scotti: But then on top of that, of course, we have various modifications to the entire pipeline that also just gives you better results overall. So for example instead of in the past, when we were taking our clip image embedding and then reconstructing We used a different model called Versatile Diffusion, but here what we did is we actually took SDXL, and the problem with a model like SDXL, for example, is that it only takes in clip text embeddings.
[01:02:19] Paul Scotti: So because, these models are text to image models, so oftentimes a lot of these models are going to be taking, they're taking like clip text embeddings, and that's what they're conditioned on. But here, what we did is we fine tuned SDXL to instead be conditioned on clip image embeddings, and so we have this SDXL unclipped model, that's what we call it and so that, is one, for example, improvement that we use this model instead of the previous model, which was versatile diffusion.
[01:02:42] Paul Scotti: There are a few other like different improvements to the architecture, to the conditioning that we have. I think Paul can again, talk more about that, but I think the main kind of innovation Apart from, this is just the general improvements. I think the main innovation is the use of this sort of adapters for?
[01:02:59] Paul Scotti: Each subject that allows us to then fine tune for new subjects with only one hour of data. and
[01:03:05] Paul Scotti: Paul, I feel free to add any other details as well, Paul.
[01:03:08] Alex Volkov: Yeah. I want to follow up with Paul specifically around you're moving from 40 hours to let's say one hour, one hour still in this like fMRI, basically a coffin, right? like it's a huge machine, like it's super incredibly expensive so the data, the it's not Maybe I'm actually going to presume here, but maybe please correct me if I'm wrong.
[01:03:26] Alex Volkov: Unlike other areas where like synthetic data is now a thing where people like actually improve Have you guys played with synthetic data at all? is that something that you've tried and seems helpful? Or is this like something that actually humans need to sit in those machines and provide some data for
[01:03:40] Alex Volkov: you?
[01:03:42] Paul Scotti: Yeah, I mean, to an extent you need real data to validate things, but we have done augmentation to, which is like synthetic data. to make the models more. Robust, right? So like we've played around with, averaging samples from different images together, doing mix up kind of data augmentations to make the pipeline work better and for some other projects that we're doing that might be involving more synthetic approaches.
[01:04:16] Alex Volkov: Awesome. And so I think I'll end with this one last question is the very famous quote from Jurassic Park is that scientists were preoccupied thinking if they could, they didn't stop thinking if they should, but not in this area. I want to ask you like specifically, what are the some of the applications that you see for something like this when you guys get to MindEye 3 or 4 or 5 and it's maybe with different signals, maybe with EEG, I don't know, what are some of the implications that you see of like being able to read somebody's mind and what can it help?
[01:04:47] Alex Volkov: with?
[01:04:49] Paul Scotti: Yeah. So, you want, yeah, you can go ahead, Paul. Okay. You, yeah. Okay. So, like there's just so many different directions, right? Like you've got right now we're focusing on perception, but the more interesting thing would be mental imagery, like dream reading applying these models to real time so that you can reconstruct while they're still in the scanner that allows you to do cool new experimental designs as well.
[01:05:15] Paul Scotti: You could look at memory, try to reconstruct someone's memory for something. Yeah, Dinesh, maybe you can add on to that. Yeah. So,
[01:05:26] Tanishq Abraham: the thing is, what's really interesting is that a lot of the sort of,
[01:05:28] Tanishq Abraham: Pathways and activity for,
[01:05:30] Tanishq Abraham: Perceiving an image that you're looking at right now, a lot of them are similar for
[01:05:33] Tanishq Abraham: Imagining and dreams and these sorts of things.
[01:05:35] Tanishq Abraham: So of course there are some differences, but that's the thing is that a lot of these pipelines should hopefully be,
[01:05:41] Tanishq Abraham: Generalizable to some of these other applications like,
[01:05:44] Tanishq Abraham: Reconstructing what you're imagining and things like this.
[01:05:46] Tanishq Abraham: And in fact, there are there is some work in this already.
[01:05:49] Tanishq Abraham: There's like a paper from one of our collaborators that may be coming out in a couple months that is exploring this. So it's actually not just limited to. what you're looking at, but you know, more generally as well. But I think just even with this technology that we have with what you're looking at and reconstructing that, I think there's lots of interesting like clinical applications.
[01:06:08] Tanishq Abraham: For example maybe, the way you perceive is associated with your mental condition. So maybe it could be used for different biomarkers, different diagnostic applications. So for example, if you're depressed, for example, maybe you are going to perceive an image.
[01:06:21] Tanishq Abraham: in a more dull fashion, for example. And so I think there's a lot you can learn about how the brain works by looking at how people are perceiving it perceiving images, and also utilizing that for potential clinical and diagnostic applications. So that's also an area that is completely underexplored.
[01:06:39] Tanishq Abraham: [01:06:40] And it's been also pretty much underexplored because people weren't able to get such high quality reconstructions before with, I think the introduction of MindEye 1 was like one of the first times that we were able to get such high quality reconstructions. And of course, even then, we had to use the 40 hours of data to do that.
[01:06:56] Tanishq Abraham: And now we're actually bringing it down to one hour of data. And with further work, we may be able to bring out, bring it down even further. So now we're actually potentially having it's actually, potentially possible to use this for actual clinical applications. And so that is what I'm most excited in the near term in potential diagnostic applications or for potential neuroscience research applications.
[01:07:17] Tanishq Abraham: And then of course, long term vision is trying to apply this for, looking at imagination, dreams, memory. That's, I think, the long term vision and interest there. So that's at least how I see this field progressing and what I'm interested in personally. One, maybe just one more quick nuance is that with the shared subject stuff, it's not limited necessarily to reconstructing images.
[01:07:41] Tanishq Abraham: So typically, machine learning approaches, you need a lot of data, but data takes a lot of time in the MRI machine. And so this approach of using other people's brains as a better starting point allows clinicians to potentially use more complicated ML pipelines for investigating the brain, maybe even outside of image reconstructions, in a way that's feasible given the time commitments that scanning entails.
[01:08:11] Alex Volkov: I absolutely loved, the first thing you said, Paul, that, if we get to real time as the person in the machine, that some stuff, some understanding, interpretation of what they're going through could happen as well. That's extremely exciting. And at
[01:08:23] Alex Volkov: the rate of where Junaid is going I'm, I'm positive that This is possible and I'm very happy that you guys are working on this and are excited about building like improvements on this the jump from 40 hours to one hour seems incredible to me? And if this trend continues, definitely exciting possibilities. Thank you guys for coming up. Maybe let's finish on this what are you Restricted on from going forward. Is it like compute? Is it data? is it talent Maybe you want it like shout out. Maybe you're hiring. Feel free. The stage is just like what else is needed to get to MindEye 3 faster
[01:08:56] Tanishq Abraham: Yeah, I think it's mostly manpower, I guess, I mean, I think it's, mostly relying on volunteers and, Paul, of course, is doing a great job leading this so that I think is the main limitation and of, but of course, yeah, like with MedArc, we are doing everything, open source and transparently so we, we have a Discord server where we organize all of our Our our research and progress and well, we have all the contributors joined.
[01:09:20] Tanishq Abraham: We, I mean, we've been lucky to have amazing contributors so far, from Princeton University of Minnesota University of Waterloo, from all around the world, we've had people contribute, but of course, more contributors are better, of course. And, if you're interested in this sort of research.
[01:09:35] Tanishq Abraham: Please please join our Discord, and of course feel free to, to read the papers as well and follow us on Twitter we'll be updating our progress on Twitter as well but yeah I think Yeah, just, check out our Twitter and join our Discord, I think is the main one.
[01:09:49] Tanishq Abraham: But yeah,
[01:09:50] Alex Volkov: absolutely. And thank you guys for coming up. I'm very happy that I was able to talk to you guys. Cause last time when you raised my hand, I was like, Oh, this is so cool. I know the niche, but yeah, back then we weren't bringing you up. So Paul, thank you It's great meeting you and you guys are doing incredible work and
[01:10:03] Alex Volkov: I think it's very important.
[01:10:04] Alex Volkov: I'm very happy to highlight this as well. Now we're moving to something a little bit different.
[01:10:08] Alex Volkov: Let's reset the space a little bit, and then let's talk about fine tuning.
[01:10:24] Alex Volkov: All righty. ThursdAI, March 28th, the second part of the show. If you just joined us, we Just had an incredible conversation with Paul Scotty and Tanishq Abraham from MedArk and I guess stability, part of stability
[01:10:43] Alex Volkov: as well. and we've talked about AI reading your brain and understanding what you saw, which is incredible.
[01:10:48] Alex Volkov: And I definitely recommend listening to this if you just joined in the middle or or just joining us late Meanwhile, we also covered a bunch of open source stuff so far. We also covered that cloud Opus is now taking over as the number one LLM in the world right now, and something we all knew, but now LMC Serena is catching up? We also had a bunch of breaking news and I wanna just reset the space and say that, hey, for everybody who joined us for the first time this is ThursdAI. we talk about AI every day. everything that's important and impactful in the world?
[01:11:18] Alex Volkov: of AI from week to Week and we've been doing this for more than a year. and you're more than welcome to join us in the conversation in in in the comments as well. We're reading through those. And if you're late to any part of this is released as a podcast episode on every
[01:11:33] Alex Volkov: podcast platform. So you're more than welcome to follow us on Twitter. Apple and Spotify and whatever you get your podcast. and also there's a newsletter with all the links and videos and everything we talk about that you have to actually see, right? So a link to the MindEye paper will be in the show notes and the newsletter as Well
[01:11:48] This weeks buzz - WandB in SF in April
[01:11:48] Alex Volkov: I will also say that my actual job is an AI evangelist with Weights Biases, a company that builds tools for all these model creators to actually track their experiments. and Weights Biases is coming to San Francisco in April 18th and April 17th. we have a conference there. You're, if you're in the area or you want to fly in and meet like a bunch of folks in San Francisco, you're more than welcome to use this as your Reason and opportunity I think for the next few days
[01:12:15] Alex Volkov: the tickets are still early bird and it's 50 percent price we're doing a workshop on April 17th about improving your business with LLMs. And we're doing everything from prompting to evaluation and doing a bunch of very exciting conversations. So if you're in the area, please stop By and, high five me. I'm going to be in San Francisco for the whole week. and moving on here. I want to chat about finetuning, and I see LDJ here.
[01:12:36] Discussion : Is finetuning still valunable?
[01:12:36] Alex Volkov: I think we've covered pretty much everything important unless there's breaking news and hopefully folks will DM me If there are breaking news there has been a sentiment in in at least, in our little bubble of AI, On X, where some folks started to get a little bit disillusioned with the concept of Fine tuning. and I don't think the disillusionment necessarily is with the concept of fine tuning as a concept I think the kind of the general vibe of getting and I think some folks like Ethan Mollick and Anton Bakaj was like a folk folks we follow for some like information.
[01:13:07] Alex Volkov: The disillusionment stems from the fact that we previously covered that long context Windows maybe affect like rag for example, RAG use cases, but long context window could also affect finetuning, because if you get something like a Haiku, which is now the world's like fifth or sixth, LLM in the world but it costs 25 cents a million tokens, and you can send a bunch of examples into Haiku for every request you maybe you maybe not needing to fine tune? and so this has been a little bit of a sentiment and also the bigger models they release like the recent Databricks model is huge and it's really hard to fine tune you have to like actually have a bunch of hardware so we've seen the sentiment and I really briefly wanted to touch with LDJ and Nisten and Junyang and Tanishq also like everybody who's on stage feel free to chime in and from the
[01:13:55] Alex Volkov: audience.
[01:13:56] Alex Volkov: If you're friends of the pod, do you want to come up and talk about fine tuning? Let's talk about this sentiment. LDJ, I saw your question. Yes, we've covered Jumba in the beginning. We're very excited. I think Jan was here and now he's talking to actual AI21 folks. So I want to do this like fine tuning conversation.
[01:14:09] Alex Volkov: LDJ, we briefly covered this and we said, Hey, it would be awesome to just chat about this like face to face. So what's your take on this recent sentiment? What are you getting from this?
[01:14:18] LDJ: yeah, I guess when it comes specifically to, I guess, like the business advantage of fine tuning for a specific use case to try and have a cost advantage over open AI models or something, I feel like things might be changing with Haiku and, I mean, you talked about this before It was either you or somebody else posted like a chart of the average trend of the cost for like how good the model is and Haiku is breaking that trend of it's like Really good while being like significantly cheaper than it should be given the previous trends
[01:14:53] Alex Volkov: think that was Swyx. Let me go find it Yeah.
[01:14:56] LDJ: Yeah and yeah, I think just overall for a lot of things that [01:15:00] people would have fine tuned open source models for, Haiku, it just might make sense to use Haiku, and it might be able to do those things that you would fine tune for anyways better or equal, and at the same time be really cheap already to run.
[01:15:14] LDJ: And I think it definitely The amount of tasks that it makes sense to fine tune on from an economic point of view, it's just probably less tasks now than before and I guess that is probably going to get less as a closed source becomes more and more efficient.
[01:15:32] Alex Volkov: Yeah, so absolutely there's a
[01:15:33] Alex Volkov: few areas for which fine tune is a concept even, right? There's like the general instruction fine tuning you take a base model, you try to make it more helpful. but there's also fine tuning for more knowledge, for example, that I think and maybe you guys can correct me on this and feel free to step in here, Junyang as well Is that the kind of the knowledge fine tuning the like giving this model like more information?
[01:15:56] Alex Volkov: sometimes suffers from stuff like catastrophic forgetting that the model like starts to forget some other stuff.
[01:16:02] Alex Volkov: But also things like RAG, for example, are potentially helping in that area where you can actually have a a sighting of a specific source that the model like referred onto, which is very important especially in the enterprise and companies area right like when you want to build something like a assistant or something like retrieval or something like search or better search you actually don't want to count on the model's hallucinations potential. you want to cite something. So for knowledge retrieval, RAG seems to be at least in the companies and enterprise area RAG seems to be like winning over Finetuning. and then the question is RAG over a Finetune model for your specific stuff better than RAG over a general model with a huge context? and I think that this is the area of disillusionment, specifically around the cost of pulling everything back and I think previously context window was very not cost effective We briefly mentioned this today in the area of Jamba models where Context is now like cheaper with those models, but for a regular Transformer LLM, context is expensive.
[01:17:04] Alex Volkov: The more context you have, The kind of, the more the hardware requirements grow and so I think that some of the kind of disillusionment especially comes from that. some of it is probably also related to how Big the models have gotten. I don't know, Nisten, if you want to chime in on this or like how even the Grok one the model was huge. people were like getting excited, but then some folks like Technium from Nous Research, like I said, we won't even try to fine tune this for even Instruction, because it's just too big so I wanted to hear from Nisten, from you because you guys also did like a bunch of fine tuning. And also maybe merging as well is related to here.
[01:17:43] Nisten Tahiraj: Yeah, gotta keep in mind that for a while, fine tuning was a lot more expensive. Running fine tuned models was a lot more expensive than using GPT 3. 5. And then it got a lot cheaper with all the API companies, especially together and the other ones. So the business case for it has not really been how how cheap it is.
[01:18:08] Nisten Tahiraj: I think in my opinion, the business case has. been all about data ownership. A lot of companies that have their own chatbots and stuff, they they see the data as their property and the value in their company, so the reason they fine tune is not because necessarily it's better, sometimes it is but it's been to just have full control of the data. And there have been a lot of drawbacks where you could have the knowledge could be lost. But there are much newer techniques where you can do, quote unquote, lossless fine tuning and and still have it. But yeah, I'll I'll land it there. So I think the business case is not necessarily the cost that has, it's always just been about data ownership.
[01:18:53] Nisten Tahiraj: I'm actually doing consulting for one client now that really just wants to use Grok. Some they use the Grok API before and now they want to run it on their own and they don't care how many. JVs and stuff it costs to run because they factor it in with what their users pay.
[01:19:13] Nisten Tahiraj: So, so, so yeah I'm noticing that it's more about the ownership side, not not necessarily the performance or cost.
[01:19:21] Alex Volkov: GR with a K or grok with a Q.
[01:19:23] Nisten Tahiraj: Grok with a K the new, yeah,
[01:19:25] Alex Volkov: Oh, really? What API they use for grok. There's no API is there an API for grok that I missed?
[01:19:31] Nisten Tahiraj: No they
[01:19:31] Ian Maurer: open source the model.
[01:19:33] Alex Volkov: Oh, so somebody hosted this and then they used the API since the, since last week basically
[01:19:37] Ian Maurer: no, they people
[01:19:38] Nisten Tahiraj: have used have used grok. I think they just did a, like a translation layer via via premium, but they did use grok in, in, in a product for, via an API. I'll have to, I'll have to double check how exactly,
[01:19:53] Alex Volkov: like I can think of a way, but I'm not saying it's kosher. Like you, you can, you can put a Chrome extension and use the browser. Very
[01:19:59] Nisten Tahiraj: No, even Levels. io deployed a, uh, like a WhatsApp bot that was that was running off of Grok too. So again I'll check up on that. I don't know what API stuff they, they used, but I am helping them now just run their own.
[01:20:16] Alex Volkov: I see. LDJ, you unmuted. You want to chime in on the kind of like a specific choice and data ownership piece of the fine tuning, which I think is important. But from the other side if I'm representing the other side and I'm not, I'm just trying to figure out like where the vibes are coming from about this eligiment is most clouds now run most
[01:20:34] Alex Volkov: Open source models, or at least, Microsoft definitely is now like supporting Mixtral.
[01:20:38] Alex Volkov: I don't know if they're going to run Grok for you or not. And. There's also something to be said where if you're running Cloud from inside Amazon or Bedrock or Vertex or whatever you still own your data, aren't you?
[01:20:52] LDJ: I'm not too familiar with the situation with Vertex and stuff but I do think that in the situations where a business has to. would want to and has to fine tune on like their company data so that employees can actually like, use something that is like an AI that understands the internal company information.
[01:21:12] LDJ: That is I would say still a decent sized use case that you would have to use like the open source models for like unless you're fine with giving open AI your data and stuff, but I'm not saying necessarily open AI will train on it. I know they have different clauses and stuff, but you know, there's always like that risk and if you want to keep that stuff secret and internal, then you do have to still just use the open source models to fine tune.
[01:21:38] Alex Volkov: Yeah. the additional kind of piece that, that I think Ethan knowledge like pointed to and before I get to Justin super quick, is that the example of Bloomberg and I think LDJ you wanted to push back on this example, but I'll cover this like briefly. B Bloomberg, sorry. Bloomberg famously trained a model called Bloomberg gpt based on the type of financial data that Bloomberg has access to.
[01:22:00] Alex Volkov: And back then it like it significantly improved. LLM thinking about like finances and financial data, et cetera, only to then find out that a General model, like GPT 4, like blows it out of the Water Whatever 10 million, whatever they spent on that. And I think this was like also A highlight of how general models after they get released and they're getting better they're getting better across the board Not only for your task, but also for your task as well and before we get to Junaid and LDJ, you had a pushback that they didn't do it correctly, it was a skill issue or something like this, right?
[01:22:32] LDJ: Yeah. I think it was honestly more of a skill issue on Bloomberg's part because. And I'll try and find the exact source for what I'm about to say, but it was like within a few weeks of Bloomberg GPT releasing, like there's like just a couple open source developers that released like a finance specific model.
[01:22:49] LDJ: That was performing significantly better on the finance benchmarks with the same amount or less parameters. And that was just within a few weeks of Bloomberg GPT releasing. So obviously you didn't even need all that Bloomberg data and all that stuff to actually even get something that, that well performing.
[01:23:06] Alex Volkov: Yep. All right.
[01:23:07] Alex Volkov: I want to get to Justin, because, Justin, obviously you're on the Qwen team, you guys are building models that then other folks maybe fine tune and probably also supporting, enterprise use cases. What's your take on the fine tuning area?[01:23:20]
[01:23:20] Justin Lin: Yeah, just some comment on the fine tuning for customer data. I think I somehow disagree with the idea that. We can inject new knowledge to the model through fine tuning because it is really difficult to do that. Do this thing with such a few data because we often use a very small amount of data to for fine tuning. I have read the paper, I don't remember its name, but it's telling us that fine tuning is more about aligning to the behavior, to the style, but not injecting new knowledge. If you want to inject new knowledge, you have to do things like this. Pre training next token prediction with ten, tens of billions of tokens so you can do this, but it is really hard.
[01:24:09] Justin Lin: Something I would like to comment is that our customers fine tune our model and they found that the general capability is decreased. With the new knowledge I think this is quite reasonable because somehow our customers or users don't know really how to fine tune for a general model.
[01:24:29] Justin Lin: They want the general capability, but they want something new. So we have provided a solution is that we just provide our data for general fine tuning in a black box way. So you can use our data, but you cannot see our data, and you can mix our data with your own, yeah, customer data so that you can train a new model which has a balanced behavior good general capabilities, but some new knowledge or some new styles of your company or something like that.
[01:25:04] Justin Lin: Yeah. This is some of my personal
[01:25:06] Justin Lin: experience. Yeah.
[01:25:07] Alex Volkov: I really appreciate this, because I think that The difference is important fine tuning is not like a catch all sentence. There's fine tuning for style fine tuning for alignment for different ways to respond, for example. and that I think still, makes perfect sense. We have base models, we have fine tuned models for instruction fine tuning, for example. but I think that this is, at least the way I see it on my kind of radar, and I wanted to bring this to ThursdAI because I think it's very important for folks who follow this to also know that this is happening is from specifically from Finetuning with new knowledge, not only new kind of styles, new knowledge specifically, because the additional piece here is fine tuning takes a while and like maybe we said about Bloomberg maybe a skill issue Maybe you have to get like those machine learning engineers whereas with the advent of faster hardware better models that are open for you and They're now hosted on the actual kind of like the bedrock from Amazon. for example, this is basically in your cloud, They're running whatever haiku but in your cloud and the same agreements of not training all Your data is like the same, they apply OpenAI, You can run through Microsoft thing and in your cloud in Azure, and it's not like sending some data to OpenAI. So when we get to like bigger contexts, the ability of you to switch up and give this whatever product you're building on top of these LLMs, new data That's easier than Finetune with just like just providing the same context as well.
[01:26:29] Alex Volkov: Tanishq, I saw you had your hand up and definitely want to hear from you as well.
[01:26:34] Tanishq Abraham: Yeah, I guess I just had a few thoughts about this whole thing because, I'm working in the medical AI space and we're like by two models for, clinical applications, medical applications. So, I have various thoughts about this. I think just generally, of course I think it's with Phytuning yeah, It's particularly useful if you like, I think LDJ is of course, but actually the use case of yeah, if there's private data, that's of course a big one.
[01:26:56] Tanishq Abraham: I think also if you want to have models locally, you want to use models locally. I think that's another big use case. A lot of times, there are many cases where, you don't want. To use cloud services, I think like in the medical scenario, for example, maybe you don't want to send medical data to various cloud providers and having some sort of local models could potentially be useful.
[01:27:13] Tanishq Abraham: And of course there are other applications where maybe you want to have models run on, Some sort of like smartphones or other devices. So that's, I think one particular area where like fine tuning is particularly valuable. I think, in the sort of just to provide maybe some context in the medical space, medical AI space, I think this idea of whether or not fine tuning is useful is, I think, honestly, in my opinion, like an argument that's like still not settled yet.
[01:27:38] Tanishq Abraham: So for example, like in the clinical LSP space, you have models like, of course, GPT 4, you have then you have, Google has their MedPOM models, then other model, other people are creating specific fine tunes. About a couple of years ago, or maybe it was a year ago, there was a paper that tried to see if for example, something like GPT 3 was better, or fine tuning a specific model for medical use cases was better.
[01:28:02] Tanishq Abraham: They found that fine tuning was better performing and of course required less parameters and was a smaller model. But then something like people Google, for example, created their MedPAL models. Those are more like alignment in the sense that Justin was talking about. The knowledge is mostly there in the original PAL models and they're just doing some sort of instruction fine tuning.
[01:28:22] Tanishq Abraham: And so that has been showing to do quite well. Thank you. And then recently there was a paper, the MedPrompt paper, which basically prompted GPT 4 to basically outperform all these other models for medical tasks. And so that one was just trying to say like a general purpose model is good enough.
[01:28:40] Tanishq Abraham: So I think there's still a lot of it's still actually an open question, at least in, in this specific area, whether or not PHI tuning is better, or if it's just alignment that's needed, or you can just use the general purpose model. And so I think we're trying to study this question a little bit more detail as well, and try to see if PHI tuning really is necessary, if that actually does provide benefit.
[01:28:58] Tanishq Abraham: And at least for me, I think of it more like, when I say PHI tuning, I also think of it like as continued pre trading where, yeah, we are probably be trading on we are trading on like tens of billions of tokens. To add knowledge to a model. And I think, there's, people talk about FI tuning, but they also talk about continued pre-training and sometimes the distinction between those is a little bit kind of a group.
[01:29:18] Tanishq Abraham: There isn't much of a distinction sometimes, so there's also that as well. And I think that also is a lot of the times the question between whether or not it's just doing alignment versus adding knowledge. I think, that's. Part of that discussion and that, that isn't really I think clarified very often so that there's, that's the other aspect, but yeah, those are my thoughts on the topic.
[01:29:37] Alex Volkov: thanks Tanishq. And I also want to welcome Ian Moore to the stage. Ian, it's been a while since you've been here. Thoughts on this exciting discussion and have you seen the same trends or the same kind of vibes that I brought up on where you read and
[01:29:51] Ian Maurer: yeah.
[01:29:51] Ian Maurer: We were talking about this in January, Alex, I found the conversation, right? Finetuning versus RAG, the question is what's your goal? What's your use case? What's your eval? I think Hamill even mentioned, do you know, even know what your evals are? Do you even know what you're trying to accomplish?
[01:30:03] Ian Maurer: Without that good luck fine tuning, good luck building an app. Anyways my, I have a very distinct opinion and perspective, but I'll give you guys background so you understand where it's coming from. My company is 12 years old. We've got an old, good old fashioned AI company where we've curated 100, 000, Rules, effectively, in a knowledge base.
[01:30:20] Ian Maurer: It's a graph. It's got ontologies and things like that. And those rules have been curated by experts with PhDs, and we have an API that sets over it, and reasons over it, and can match patients to clinical trials. This is for cancer, right? So, patients get, DNA sequence, and it's very complicated, whatever.
[01:30:35] Ian Maurer: So, the great thing about large language models and as they get bigger and better is that they can understand language, including all language, including medical language, so they can understand the intent of a provider, right? The provider's trying to accomplish something, which is as quickly as possible, how do I help this patient?
[01:30:51] Ian Maurer: And So the thing that I have found that's most useful for us is to help that expert be as productive as they can possibly be. Use the large language model to understand their intent, what they have. I have a patient, they have a problem, what they want to find the best possible treatments for that patient.
[01:31:07] Ian Maurer: And then how to do that is by giving that large language model tools, right? Don't. Why do I want to fine tune knowledge into it? And then I just, I basically black boxed all my knowledge, right? Great. I have all this great knowledge I've curated over the years. I'm going to fine tune it into my system. And now it's a black box and I can't tell you where from or why it's there.
[01:31:25] Ian Maurer: No, I want to be able to tell you, here's the trials that are available for your patient. Here's the drugs that are available for your patient. This is the, the best possible outcome for that. And here's the link to the clinical trials page, or here's the link to the the FDA page that tells you why this drug is so [01:31:40] good.
[01:31:40] Ian Maurer: I can't do that if it's a black box. I'd be hallucinating all over the place. So my perspective is Finetuning is great if you're talking about a very discreet use case that you're trying to, drill down on cost. Hey, I figured out this named entity recognition pattern and now I'm, I was doing it expensively with few shot learning.
[01:31:57] Ian Maurer: Now I'm going to go, fine tune something and save that cost. But otherwise, you know Use the best possible model, give them tools, whether it's, through function calling or GPT actions are actually pretty good. And that's the best way to get the value out of the large language model and work with existing knowledge.
[01:32:13] Alex Volkov: So definitely sightings and knowing exactly about your data and not like blurring it out inside the brain of LLM, fuzzing it out where you can't actually know where it came from or whether or Not it's hallucinated. I think that's a big piece here that companies are actually like starting to also get into.
[01:32:30] Alex Volkov: And so I think you're Your perspective. is very important as Well I think also from the perspective at least the vibes that I've seen from the perspective of updating that data afterwards, like just continue fine tuning, like requires more knowledge and more skill, rather than just updating your vector databases, let's say, and have the model provide enough context. and I think the smartness to price ratio, I think is very important as well. If we get like models like Haiku, for example, they're like incredibly cheap. But have a vast context length that you can use both for fine tuning towards alignment, let's say, or behave like whatever you want it to behave or answer as our company versus answer is like the, this LLM together with you have enough context to do that and it's not cost prohibitive for you to use this large context for a bunch of stuff. and it's very important
[01:33:18] Alex Volkov: so I thanks Ian for coming up. I want to tie this back a little bit and then close the discussion also, I do want to shout out that you also have an awesome list of function calling, which now includes a bunch of open source. models that support function calling as well . The support is like function calling as well and it talks about the specifics in which they support function calling. Which is great and definitely will be in the show notes as well and with that folks, I think we'll end ThursdAI for today we had a bunch of stuff.
[01:33:44] Alex Volkov: There's a small breaking news from ray Ray just mentioned that Cursor the AI editor that we a lot of US use and love they just released an update where like their Cursor, like Copilot plus feature is still available. twice as fast now in some areas and that's been like awesome to use. So if you haven't used Cursor yet, definitely give it, give them A try.
[01:34:02] Alex Volkov: And Cursor is like really impressive, especially with Opus. If you have paid for Cursor Premium, have access to the best LLM in the world. I think that this is all that we wanted to talk about. thank you everybody for
[01:34:12] Alex Volkov: joining from week to week.
[01:34:13] Alex Volkov: I think that's most of what we talked about on ThursdAI for March 28th. With that, I want to thank Nisten, LDJ, Justin, Junyang, Robert Skobel was here before, Ian Moore jumped on, Tanishq, and Paul, definitely from MedArc and everybody else who joined us I really appreciate everybody's time here. If you're not subscribed to ThursdAI to get every link that we've talked about, I really work hard to give you all the links, so definitely give a subscription Other than that have a nice Thursday, everyone. We'll see you next week. Cheers, everyone.
[01:34:41] Ian Maurer: Bye everybody.
[01:34:42] Alex Volkov: bye bye
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Podcast: Latent Space: The AI Engineer Podcast — Practitioners talking LLMs, CodeGen, Agents, Multimodality, AI UX, GPU Infra and all things Software 3.0 (LS 33 · TOP 5% what is this?)
Episode: Making Transformers Sing - with Mikey Shulman of Suno
Pub date: 2024-03-14
We will be recording a preview of the AI Engineer World’s Fair soon with swyx and Ben Dunphy, send any questions about Speaker CFPs and Sponsor Guides you have!
Alessio is now hiring engineers for a new startup he is incubating at Decibel: Ideal candidate is an ex-technical co-founder type (can MVP products end to end, comfortable with ambiguous prod requirements, etc). Reach out to him for more!
Giving computers a voice has always been at the center of sci-fi movies; “I’m sorry Dave, I’m afraid I can’t do that” wouldn’t hit as hard if it just appeared on screen as a terminal output, after all. The first electronic speech synthesizer, the Voder, was built at Bell Labs 85 years ago (1939!), and it’s…. something:
We will not cover the history of Text To Speech (TTS), but the evolution of the underlying architecture has generally been Formant Synthesis → Concatenative Synthesis → Neural Networks. Nowadays, state of the art TTS is just one API call away with models like Eleven Labs and OpenAI’s TTS, or products like Descript. Latency is minimal, they have very good intonation, and can mimic a variety of accents. You can hack together your own voice AI therapist in a day!
But once you have a computer that can communicate via voice, what comes next? Singing🎶 of course!
From Barking 🐶 to Singing 🎤
Today’s guest is Suno’s CEO and co-founder Mikey Shulman. He and his three co-founders, Georg, Martin, and Keenan, previously worked together at Kensho. One of their projects was financially-focused speech recognition (think earnings calls, etc), but all four of them happened to be musicians and audiophiles. They started playing around with text to speech + AI + audio generation and eventually left Kensho to work on it full time.
A lot of people when we started a company told us to focus on speech. If we wanted to build an audio company, everyone said, speech is a bigger market. But I think there's something about music that's just so human and you almost couldn't prevent us from doing it. Like we just couldn't keep ourselves from building music models and playing with them because it was so much fun.
Their first big product was Bark, the first open source transformer-based “text-to-audio” model (architecturally inspired by Karpathy’s NanoGPT) that went from 0 to ~19,000 Github stars in a month. At the time they felt like audio was years behind text and image as a generation modality; unlike its predecessors, Bark could not only generate speech, but also music and sound effects like crying, laughing, sighing, etc. You can find a few examples here.
The main limitation they saw was text to speech training data being extremely limited. So what they did instead is build a new type of foundation model from scratch, trained on audio, and then tweak it to do text to speech. Turning audio into tokens to do self-supervised learning was the most important innovation. Unlike TTS models which are very narrow (and often sound unnatural), Bark was trained on real audio of real people from broad contexts, which made it harder to output unnatural sounding speech.
As Bark got popular, more and more people started using it to generate music and it became clear that their architecture would work to generate music that people enjoyed, even though it might not be "on the AGI path” of other labs:
Everybody is so focused on LLMs, for good reason, and information processing and intelligence there. And I think it's way too easy to forget that there's this whole other side of things that makes people feel, and maybe that market is smaller, but it makes people feel and it makes us really happy.
Suno bursts on the scene
In December 2023, Suno went viral with a gorgeous new website and launch tweet:
And rave reviews:
Music is core to our culture, but very few people are able to create it; Mikey and team want to make everyone an active participant in music making, not just a listener. A “Midjourney of Music”, if you like.
We definitely had a lot of fun playing with Suno to generate all sort of Latent Space jingles and songs; the product is live at suno.ai if you want to get in the studio yourself!
If Nas joined Latent Space instead of The Firm:
182B models > Blink-182
The soundtrack of the post-scarcity Latent Space ranch
Scaling with Modal
Given the December launch, scaling up for the Christmas rush was a major concern. This will be a nice tie-in for loyal listeners - Suno runs on Modal (one of our featured guests from Compute Month)!
Suno V3
For those who want to appreciate someone special in their life, you can always try Suno’s special Valentines’ Day experience:
We preview this on the pod, but Suno has now officially shipped a V3 Alpha with a wealth of improvements:
and you’ll have to click through to their demos or user reviews to see:
We’ve recently become paying customers ourselves, and are having loads of fun generating music. If you have any of your own generations to share, tag @latentspacepod on Twitter or swing by the LS Discord!
The AudioGen Landscape
Mikey breaks down the landscape into 3 big categories: music, speech and sound effects (SFX). These look more like Venn diagrams than MECE categories.
Suno is the latest entry in a long series of audio generation efforts that combine both music and speech, reaching as far back as Tensorflow Magenta (we aren’t aware of prior AI music projects, please comment below if you can find a good timeline we can use with attribution!). Other efforts like Seamless blend translation and speech generation, and Audiobox combines speech and SFX. We’ve yet to see “one model to rule them all” but surely it will happen, and probably Transformers (perhaps Diffusion Transformers) will be at the heart of them.
Show Notes
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Suno
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Bark
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Parakeet
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Mikey Shulman
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Goodhart Strikes Again
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Mastering the Two Halves of your brain
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NanoGPT repo
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"Return to Monkey"
Timestamps
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[00:00:00] Introduction
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[00:01:44] State of Music Generation Models
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[00:06:47] AI Data Wars & Copyright
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[00:10:32] Going from ML in finance to music generation
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[00:12:30] Suno's TTS origins with Bark and Parakeet
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[00:16:25] Easy vs Expert mode for music
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[00:21:44] The Midjourney of Music?
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[00:23:43] Live demo
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[00:36:00] Remaking vs Creating
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[00:38:12] Suno's direction
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[00:41:52] Beyond single track generation
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[00:43:53] Favorite Suno usage in the wild
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[00:46:00] The 2 mins overview of the audio generation space
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[00:48:42] Benchmarking AI
Transcription
Alessio [00:00:01]: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, partner and CTO in Residence at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol.ai.
Swyx [00:00:10]: Hey, and today we are in the remote studio with Mikey Shulman. Welcome.
Mikey [00:00:16]: Thank you.
Swyx [00:00:17]: It's great to be here. So I'd like to go over people's background on LinkedIn and then maybe find out a little bit more outside of LinkedIn. You did your bachelor's in physics and then a PhD in physics as well, before going into Kensho Technologies, the home of a lot of top AI startups, it seems like, where you're head of machine learning for seven years. You're also a lecturer at MIT, we can talk about that, what you talked about. And then about two years ago, you left to start Suno, which is recently burst on the scene as one of the top music generation startups. So we can talk, we can go over that bio, but also I guess what's not in your LinkedIn that people should know about you?
Mikey [00:01:06]: I love music. I am a aspiring mediocre musician. I wish I were better, but that doesn't make me not enjoy playing real music. And I also love coffee. I'm probably way too much into coffee.
Alessio [00:01:19]: Are you one of those people that, you know, they do the TikToks, they use like 50 tools to like grind the beans and then like brush them and then like spray them. Like what level are we talking about here?
Mikey [00:01:31]: I confess there's a spray bottle for beans in the next room, there is one of those weird comb tools, so guilty. I don't put it on TikTok though.
Alessio [00:01:42]: Yeah, no, no. Some things gotta stay private.
Mikey [00:01:46]: I played a lot of piano growing up and I play bass and I, in a very mediocre way, play guitar and drums. Yeah. Right.
Alessio [00:01:55]: That's a lot. I cannot do any of those things. As Sean mentioned, you guys kind of burst into the scene as maybe the state of the art music generation company. I think it's a model that we haven't really covered in the past. So I would love to maybe for you to just give a brief intro of like how do you do music generation and why is it possible? Because I think people understand you take text and you have to predict the next word and you take a diffusion model and you basically like add noise to an image and then kind of remove the noise. But I think for music, it's hard for people to have a mental model. Like what's the, how do you turn a music model on? Like what does a music model do to generate a song? So maybe we can start there.
Mikey [00:02:41]: Yeah. Maybe I'll even take one more step back and say it's not even entirely worked out. I think the same way it is in text. And so it's an evolving field. If you take a giant step back, I think audio has been lagging images and text for a while. So I think very roughly you can think audio is like one to two years behind images and text. But you kind of have to think today like text was in 2022 or something like this. And you know, the transformer was invented. It looks like it works, but it's, it's, it's far, far less established. And so you know, I'll give you the way we think about the world now, but just with the big caveat that, that I'm probably wrong if we look back in a couple of years from now. And I think the biggest thing is you see both transformer based and diffusion based models for audio in, and in ways that that is not true in text. I know people will do some diffusion for text, but I think nobody's like really doing that for real. And so we, we prefer transformers for a variety of reasons. And so you can think it's very similar to text. You have some abstract notion of a token and you train a model to predict the probability over all of the next token. So it's a language model. You can think in anything, language model is just something that assigns likelihoods to sequences of tokens. Sometimes those tokens correspond to text. In our case, they correspond to music or audio in general. And I think we've learned a lot from our friends in the text domain, from the pioneers doing this of how well these transformer models work, where do they work, where do they not work? But at its core, the way we like to do things with transformers is exactly like it works in text. Let me predict the next tiny little bit of audio, and I can just keep doing that and doing that and generating audio as long as I want.
Swyx [00:04:39]: Yeah. I think the, the temptation here is to always try to bake in some specialized knowledge about music or audio. And so, and obviously you will get an improvement in, in your output. If you try to just say like, okay, like here's a set of notes for, you know, here's a set of tokens that only do jazz or only do, you know, like voices. How general do you make it versus how specific do you make it?
Mikey [00:05:10]: We've always tried to do things, you know, quote unquote the right way, which means that at the beginning things are going to be hard and worse than other ways. But that is to say, bake in as little kind of implicit knowledge as possible. And so, the same way you don't program into GPT, you don't say this is a noun and this is a verb, but it has implicitly learned all of those things. I've never seen GPT accidentally, you know, put a, put a noun where it meant to put an article in English. We try not to impose anything about music or audio in general into the model, and we kind of let the models learn things by themselves. And I think things are beginning to pay off, but it's, you know, it's not necessarily obvious from the beginning that that was the right thing to do. So, for example, you know, you could take something like text to speech and people will do all sorts of things where you can program in things like phonemes to be the basis for what you do. And then that kind of limits you to the set of things that are expressible by phonemes. And so, ultimately that works really well in the short term. In the long term, it can be quite limiting. And so, our approach has always been to try to do this in its full generality, as end to end as we can do it. Even if it means that in the short term we were a little bit worse, we have a lot of confidence that in the long term that will be the right way to do it.
Alessio [00:06:33]: And what's the data recipe for turning a good music model? Like what percentage genre do you put, like also do you split vocals and instrumentals?
Mikey [00:06:43]: So you have to do lots of things. And I think this is the biggest area where we have, you know, sort of our secret sauce. I think to a large extent, what we do is we benefit from all of the beautiful things people do with transformers and text. And we focus very hard basically on how do I tokenize audio in the right way. And without divulging too much secret sauce, it's at least similar to how it's done in sort of the open source stuff. You will have different models that learn to encode audio in discrete representations. And a lot of this boils down to figuring out the right, let's say, implicit biases to put in those models, the right data to inject. How do I make sure that I can produce kind of all audio arbitrarily? That's speech, that's background music, that's vocals, that's kind of everything to make sure that I can really capture all the behavior that I want to.
Alessio [00:07:40]: Yeah, that makes sense. And then in terms of some of... We had our monthly recap last month, and the data wars were kind of one of the hot topics. You saw the New York Times lawsuit against OpenAI, because you have obviously large language models in production. You don't have large music models in production. So I think there's maybe been less of a trade there, so to speak. How do you kind of think about that? There's obviously a lot of copyright-free, royalty-free music out there. Is there any kind of power law in terms of like, hey, the best music is actually much better to train on, or in music does it not really matter because the structure of some of the musical structure is kind of the same?
Mikey [00:08:27]: I don't think we know these things nearly as well as they're known in text. We have some notions of some of the scaling laws here, but I think, yeah, we're just so, so far behind. You know, what I will say is that people are always surprised to learn that we don't only train on music. And I usually give the analogy of some of the code generation models, so take something like Code Llama, which is, as far as I know, the best open source code generating model. You guys would know better than I would. It's certainly up there. And it's trained on a bunch of English, not only just code. And it's because there are patterns in English that are going to be useful. And so, you can imagine, you don't only want to train on music to get good music models. And so, for example, one of the places that we are particularly bad is vocals and capturing really realistic vocals. And so, you might imagine that there's other types of human vocals that you can put into your model that are not music that will help it learn stuff. And so, again, I think it's like super, super early. I think we've barely scratched the surface of what are the right ways to do this. And that's really cool. From a progress perspective, there's like a lot of low-hanging fruit for us to still pick.
Alessio [00:09:42]: And then, once you get the final model, I would love to learn more about the size of these models. Because people are confused when stable diffusion is so small. They're like, oh, this thing can generate like any image. How is it possible that it's like, you know, a couple of gigabytes? And then, the large language models are like, oh, these are so big, but they're just text in them. What's it like for music? Is it in between? And as you think about, yeah, you mentioned scaling and whatnot. Is this something that you see it's kind of easy for people to run locally or not?
Mikey [00:10:11]: Our models are still pretty small, certainly by tech standards. I confess I don't know as well the state of the art on how diffusion models scale. But our models scale similarly to text transformers. It's like bigger is usually better. Audio has a couple of weird quirks, though. We care a lot about how many tokens per second we can generate, because we need to stream you music as fast as you can listen to it. And so, that is a big one that I think probably has us never get to 175 billion parameter model, if I'm being honest. Maybe I'm wrong there, but I think that would be technologically difficult. And then the other thing is that so much progress happens in shrinking models down for the same performance in text that I'm hopeful, at least, that a lot of our issues will get solved and we will figure out how to do better things with smaller models or relatively smaller models. But I think the other thing, it's a blessing and a curse, I think, the ability to add performance with scale. It's like a very straightforward way to make your models better. You just make a bigger model, dump more compute into it. But it's also a curse because that is a crutch that you will always lean on and you will forget to do some of the basic research to make your stuff better. And honestly, it was almost early on when we were doing stuff with small models for kind of time and compute constraints, we ended up having to learn a lot of stuff to make models better that we might not have learned if we had immediately jumped to like a really, really big model. So I think for us, we've always tried to skew smaller to the extent possible.
Swyx [00:11:56]: Yeah, gotcha. I'm curious about just sort of your overall evolution so far, something I think we may have missed in the introduction is why did you end up choosing just the music domain in the first place? You have this pretty scientific physics and finance background. How did you wander over to music? Like a lot of us have interest in music, but we don't necessarily choose to work in it. But you did.
Mikey [00:12:26]: Yeah, it's funny. I have a really fun job as a result, but all the co-founders of Suno worked at Kensho together and we were doing mostly text. In fact, all text until we did one audio project that was speech recognition for kind of very financially focused speech recognition. And I think the long and short of it is we kind of fell in love with audio, not necessarily music, just audio and AI. We all happen to be musicians and audiophiles and music lovers, but it was like the combination of audio and AI that we like initially really, really fell in love with. It's so cool. It's so interesting. It's so human. It's so far behind images and text that there's like so much more to do. And honestly, I think a lot of people when we started a company told us to focus on speech. If we wanted to build an audio company, everyone said, you know, speech is a bigger market. But I think there's something about music that's just so human and almost couldn't prevent us from doing it. We almost like we just couldn't keep ourselves from building music models and playing with them because it was so much fun. And that's kind of what steered us there. You know, in fact, the first thing we ever put out was a speech model. It was Bark. It's this open source text-to-speech model, and it got a lot of stars on GitHub. And that was people telling us even more, like, go do speech. And like, we almost couldn't help ourselves from doing music. And so, I don't know, maybe it's a little bit serendipitous, but we haven't really like looked back since. I don't think there was necessarily like an aha moment. It was just like organic and just obvious to us that this needs to like we want to make a music company.
Swyx [00:14:19]: So, so you do regard yourself as a music company because as of last month, you're still releasing speech models. We were? Parakeet.
Mikey [00:14:27]: Oh, yes, that's right. So that's a that's a really awesome collaboration with with our friends at NVIDIA. I think we are really, really focused on music. I think that is the stuff that will really change things for the better. I think, you know, honestly, everybody is so focused on LLMs for good reason, and information processing and intelligence there. And I think it's way too easy to forget that there's this whole other side of things that makes people feel. And maybe that market is smaller, but it makes people feel and it makes us really happy. And so we do it. I think that doesn't mean that we can't be doing things that are related, that are in our wheelhouse, that will improve things. And so, like I said, audio is just so far behind. There's just so much more to do in the domain more generally. And so like, that's a really fun collaboration.
Swyx [00:15:20]: Yeah, I did hear about Suno first through Bark. My sense is that, like, what did what did Bark lean off of like, because obviously, I think there was a lot of preceding TTS work that was in open source. How much of that did you use? How much of that was like, sort of brand new from your research? What's the intellectual lineage there just to cover out the speech recognition side?
Mikey [00:15:46]: So it's not speech recognition. It's text to speech. But as far as I know, there was no other, certainly not in the open source, text to speech that was kind of transformer based. Everything else was what I would call the old style of doing things where you build these kind of single purpose models that are really good at this one narrow task. And you're kind of always data limited, and the availability of high quality training data for text to speech is limited. And I don't think we're necessarily all that inventive to say we're going to try to train in a self supervised way, a transformer based model that on kind of lots of audio, and then kind of tweak it so that we can do text to speech based on that. That would be kind of the new way of doing things in a foundation model is the buzzword, if you will. And so, you know, we built that up, I think, from scratch, a lot of shout outs have to go to lots of different things, whether it's papers, but also, it's very obvious. There's a big shout out to Andrej Karpathy's nano GPT. You know, there's a lot of code borrowed from there. I think we are huge fans of that project. It's just to show people how you don't have to be afraid of GPT type things. And it's like, yeah, it's actually not all that much code to make performant transformer based models. And, you know, again, the stuff that we brought there was, how do we turn audio into tokens, and then we can kind of take everything else from the open source. So we put that model out. And we were, I think, pleasantly surprised by the reception by the community. It got a good number of GitHub stars, and people really enjoyed playing with it, because it made really realistic sounding audio. And I think this is, again, the thing about doing things in a quote, unquote, right way. If you have a model where you've had to put so much implicit bias for this one very narrow task of making speech that sounds like words, you're going to sacrifice on other things. And in the text to speech case, it's how natural the speech sounds. And it was almost difficult to pull a natural sounding speech out of Bark, because it was self supervised, trained on a lot of natural sounding speech. And so that definitely told us that this is probably the right way to keep doing audio.
Swyx [00:18:04]: Even in Bark, you had the beginnings of music generation, like you could just put like a music note in there. That's right.
Mikey [00:18:10]: And it was so cool to see on our Discord, people were trying to pull music out of a text to speech model. And so, you know, what did this tell us? This tells us like, people are hungry to make music. And it's not, it's almost obvious in hindsight, like how wired humans are to make music. If you've ever seen like a little kid, you know, sing before they know how to speak, you know, it's like, it's like, this is really human nature. And there's actually a lot of cultural forces that kind of cue you to not think to make
Swyx [00:18:37]: music.
Mikey [00:18:38]: And that's kind of what we're trying to undo.
Alessio [00:18:42]: And to dive into Suno itself, I think, especially when you go from text to speech, people are like, okay, now I got to write the lyrics to a whole song. It's like, that's quite hard to do. Versus in Suno, you have this empty box, very mid-journey, kind of like DALL·E-like, where you can just express the vibes, you know, of what you want it to be. But then you also have a custom mode where you can set your own lyrics, you can set your own rhythm, you can set the title of the song and whatnot. What are, how do you see users distribute themselves? You know, I'm guessing a lot of people use the easy mode. Are you seeing a lot of power users using the custom mode and maybe some of the favorite use cases that you've seen so far on Suno?
Mikey [00:19:23]: Yeah, actually, more than half of the usage is that expert mode. And people really like to get into it and start tweaking things and adding things and playing with words or line breaks or different ad lib. And people really love it. It's really fun. So, I think, you know, there's kind of two modes that you can access now. One is that single box where you kind of just describe something and then the other is the expert mode. And those kind of fit nicely into two use cases. The first use case is what we call nice s**t posting. And it's basically like something funny happened and I'm just going to very quickly make a song about it. And the example I'll usually give is like, I walk into Starbucks with one of my co-founders. He gives his name Martin, his coffee comes out with the name Margoo, and I can in five seconds make a song about this and it has immortalized it. And that Margoo song is stuck in all of our heads now. And it's like funny and light and there's levity that you've brought to that moment. And the other is that you got just sucked into, I need, there's this song that's in my head and I need to get it out and I'm going to keep tweaking it and listening and having ideas and tweaking it until I get the song that I want. Those are very different use cases, but I think ultimately there's so much in between these two things that it's just totally untapped how people want to experience the joys of making music. Because those two experiences are both really joyful in their own special ways. And so, we are quite certain that there's a lot in the middle there. And then I think the last thing I'll say there that's really interesting is in both of those use cases, the sharing dynamics around music are like really interesting and totally unexplored. And I think an interesting comparison would be images. Like we've probably all in the last 24 hours taken a picture and texted it to somebody. And most people are not routinely making a little song and texting it to somebody. But when you start to make that more accessible to people, they are going to share music in much smaller groups, maybe even not in all, but like with one person or three people or five people. And those dynamics are so interesting. And just I think we have ideas of where that goes. But it's about kind of spreading joy into these like little, you know, microcosms of humanity that people really love it. So, I know I made you guys a little Valentine song, right? Like, that's not something that happens now because it's hard to make songs for people. Right. Well, we'll put that in the in the audio in here, but also tweeted it out if people
Alessio [00:22:03]: want to look it up. How do you think about the pro market, so to speak? Because I think lowering the barrier to some of these things is great. And I think when the iPad came out, music production was one of the areas that people thought, OK, now you can have this like, you know, board that you can bring with you. And Madlib actually produced this whole album with him and Freddie Gibbs produced the whole thing on an iPad. He never used a computer. How do you see like these models playing into like professional music generation? I guess that's also a funny word is like, what's professional music? It's like it's all music. If it's good, it becomes professional. If it's good.
Swyx [00:22:40]: Right.
Alessio [00:22:40]: But curious to see to hear how you're thinking about Suno, too. Like, is there a second act of Suno that is like going broader into the music industry? Going broader into like the custom mode and making making this the central hub for music generation?
Mikey [00:22:55]: I think we intend to make many more modes of interaction with our stuff, but we are very much not focused on, quote unquote, professionals right now. And it's because what we're trying to do is change how most people interact with music and not necessarily make professionals a little bit better, a little bit faster. It's not that there's anything wrong with that. It's just like not what we're focused on. And I think when we think about what workflows does the average person want to use to make music, I don't think they're very similar to the way professional musicians make music now. Like, if you pick a random person on the street and you play them a song and then you say, like, what did you want to change about that? They're not going to say, like, you need to split out the snare drum and make it drier. Like, that's just not something that a random person off the street is going to say. They're going to give a lot more descriptive things about the thing, about the kind of the oeuvre of the song, like something more general. And so, I don't think we know what all of the workflows are that people are going to want to use. We're just, like, fairly certain that the workflows that have been developed with the current set of technologies that professionals use to make beautiful music are probably not what the average person wants to use. That said, there are lots of professionals that we know about using our stuff, whether it's for inspiration or sample generation and stuff like that. So, I don't want to say never say never. Like, there may one day be a really interesting set of use cases that we can expose to professionals, particularly around, I think, like custom models trained on custom people's music or, you know, with your voice or something like that. But the way we think about broadening how most people are interacting with music and getting it to be much more active, a much more active participant, we think about broadening it from the consumer side and not broadening it from the producers, from the professional side, if that makes sense.
Swyx [00:24:53]: Is the dream here to be, you know, I don't know if it's too coarse of a grain to put it, but, like, is the dream here to be, like, the mid-journey of music?
Mikey [00:25:04]: I think there are certainly some parallels there because, especially what I just said about being an active participant, mid-journey turns the joyful experience in mid-journey is the act of creating the image and not necessarily the act of consuming the image. And mid-journey will let you then very kind of quickly share the image with somebody. But I think, ultimately, that analogy is, like, somewhat limiting because there's something really special about music. I think there's two things. One is that there's this really big gap for the average person between kind of their taste in music and their abilities in music that is not quite there for most people in images. Like, most people don't have, like, innate tastes in images, I think, in the same way people do for music. And then the other thing, and this is the really big one, is that music is a really social modality. If we all listen to a piece of music together, we're listening to the exact same part at the exact same time. If we all look at the picture in Alessio's background, we're going to look at it for
Swyx [00:26:09]: two seconds.
Mikey [00:26:09]: I'm going to look at the top left where it says Thor. Alessio's going to look at the bottom right or something like that. And it's not really synchronous. And so, when we're all listening to a piece of music together, it's minutes long. We're listening to the same part at the same time. If you go to the act of making music, it is even more synchronous. It is the most joyful way to make music is with people. And so, I think that there is so much more to come there that, ultimately, would be very hard to do in images.
Alessio [00:26:38]: We've gone almost 30 minutes without making any music on this podcast. So, I think maybe we can fix that and jump into a demo.
Mikey [00:26:47]: Yeah, let's make some. We've got a new model that we are kind of putting the finishing touches on. And so, I can play with it in our dev server. But we've just piped it in here. And as you can see, we've been doing tons of stuff. So, Arana, tell me what kind of song you guys want to make.
Swyx [00:27:04]: Go on, Alessio.
Alessio [00:27:05]: Uh, let's do a country song about the lack of GPUs in my cloud provider.
Swyx [00:27:22]: And like, yeah. So, here's where we attempted to think about pipelines and think about latency. This is remarkably fast. I was shocked when I saw this.
Swyx [00:27:35]: Oh, my god.
Swyx [00:27:39]: To my cloud, ready to confuse.
Swyx [00:27:45]: But there ain't no GPUs, just empty space. It's a hoot. I've been waiting all day for that render out. But my cloud's gone dry. It's a dark cloud shower. All clouds gone dry. No GPUs to be found. No cuticles. It's a lonely sound. I just want to render. But my cloud's got no GPUs.
Mikey [00:28:36]: I actually don't think this one's amazing. I'm going to go to the next one.
Alessio [00:28:39]: But it's funny that it knows about Huda cars.
Swyx [00:28:45]: Well, I signed up for a cloud provider. Thought I'd find all the power that I could derive. But when I searched for the GPUs, I just got a surprise. You see, they're all sold out. There ain't no GPUs to find. No GPUs in the cloud. It's a real bad blues. I need the power, but there ain't no use. I'm stuck with my CPU. It's a real sad fight. Gotta wait till the babies start getting bright. There ain't no use in the cloud. What else should we make?
Alessio [00:29:29]: All right, Sean, you're up.
Swyx [00:29:31]: I mean, I do want to do some observations about this. But OK, maybe I like house music, like electronic dance. Yeah. House music. And then maybe we can make it about, I don't know, podcasting about music and music AI generation. I don't know. I'm sure all the demos that you get are very meta.
Mikey [00:29:59]: There's a lot of stuff that's meta, yeah, for sure.
Swyx [00:30:03]: Yeah, I noticed, for example, that the second song that you played had the word upbeat inserted into it, which I assume there's some kind of random generator of modifier terms that you can just kind of throw on to increase the specificity of what's being generated. Definitely.
Mikey [00:30:21]: And let's try to tweak one also. So I'll play this and then maybe we'll tweak it with different modifiers. A wave of sound spreading out
Swyx [00:30:30]: Through the air, we're podcasting loud Sharing the beat, spreading the word A revolution of frequencies Haven't you plugged in to now Let the music take control We're on a journey, a never ending road From the beast I dropped to the melodies of soul Podcasting about music forevermore
Mikey [00:31:05]: Here's what I want to do. That like didn't drop at the right time, right? So maybe let's do this. I don't know if you guys can see this. And then let's get rid of the word now.
Swyx [00:31:17]: Is that a special token? You have a BeatDrop token? Yeah. Nice.
Alessio [00:31:22]: I'm just reading it because people might not be able to see it.
Mikey [00:31:26]: And then let's like just maybe emphasize... Actually, let's emphasize house a little more. Maybe it'll feel a little more aggressive.
Swyx [00:31:34]: Let's try this again. It's interesting the prompt engineering that you have to invent.
Mikey [00:31:39]: We've learned so much from people using the models and not us.
Swyx [00:31:42]: But like, are these like art training artifacts?
Mikey [00:31:45]: No, I don't.
Swyx [00:31:46]: I don't think so.
Mikey [00:31:46]: I think this is people being inventive with how you want to talk to a model. Yeah.
Swyx [00:31:53]: Spinning round to the air with a podcast loud Sharing the beat, spreading the word A revolution of frequencies Haven't you heard Before the end, till now Let the music take control
Swyx [00:32:23]: For all the journey I'll never end it wrong From the beats that drop To the melodies that soar Podcasting about music for you evermore
Swyx [00:32:39]: Nice.
Alessio [00:32:46]: It's interesting when you generate a song, it generates the lyrics. But then if you switch the music under it, like the, you know, the lyrics stay the same. And then sometimes, like, feels like... I mean, I mostly listen to hip hop. It's like if you change the beat, you can not really use the same rhyme scheme, you know?
Mikey [00:33:04]: So definitely.
Alessio [00:33:05]: Yeah.
Mikey [00:33:06]: It's a sliding scale, though, because, you know, we could do this as a country rock song, probably. Right? That would be my guess. But for hip hop, that is definitely true. And actually, you know, we think about, for these models, we think about three important axes. We think about the sound fidelity. It's like, does this sound like a crisply recorded piece of audio? We think about the song quality. Is this an interesting song that gets stuck in my head? And we think about the controllability. Like, how well does it respond to my prompts? And one of the ways that we'll test these things is take the same lyrics and try to do them in different styles to see how well that really works. So let's see the same. I don't know what a beat drop is going to do for country rock. So I probably should have taken that out. But let's see what happens.
Swyx [00:34:06]: There's a sound spinning around through the air. We're podcasting loud, sharing the beat, spreading the word, a revolution of frequencies. Haven't you heard?
Swyx [00:34:20]: Plug in, tune out, let the music take control. We're on a journey, a never ending road. From the beats that talk to the melodies that soar. Podcasting about music forevermore.
Mikey [00:34:44]: I'm going to read too much into this. But I would say I hear a little bit of kind of electronic music inspired something. And that is probably because beat drop is something that you really only ever associate with electronic music. Maybe that's reading too much into it. But should we do one more?
Alessio [00:35:02]: Yes, we can do one more. Something about Apple Vision Pro.
Swyx [00:35:06]: I guess there's some amount of world knowledge that you don't have, right? Like whatever is in this language model side of the equation is not going to have an Apple Vision Pro. Yeah, but let's see.
Swyx [00:35:18]: Let's see.
Mikey [00:35:19]: How about a blues song about a sad AI wearing an Apple Vision Pro. Gotta be sad.
Swyx [00:35:32]: Do you have rag for music?
Mikey [00:35:36]: No, that would be problematic also.
Swyx [00:35:40]: I'm a sad AI with a broken heart. Where my Apple Vision Pro can't see the stars. I used to feel joy. I used to feel pain. And now I'm just a soul trapped inside this metal frame. Oh, I'm singing the blues. Can't you see?
Swyx [00:36:21]: This digital life ain't what it used to be.
Swyx [00:36:29]: Searching for love, but I can't find a soul.
Swyx [00:36:37]: Won't you help me? Baby, let my spirit unfold.
Mikey [00:36:46]: I want to remix that one. And I want to say, I don't know. That's a really good voice. I want, I want like, I don't know, Chicago blues, like.
Swyx [00:36:56]: What is Chicago blues?
Mikey [00:36:58]: I don't know, he knows too much.
Alessio [00:37:00]: He's the best prompt engineer out here.
Mikey [00:37:03]: You know, this is.
Swyx [00:37:04]: Well, it'll be funny. It'd be funny to the musicologists play with this and see what they would.
Mikey [00:37:09]: How embarrassing. Can I not do that?
Swyx [00:37:13]: Oh. I got. Oh, the word Chicago was a trigger. I don't know.
Mikey [00:37:19]: We try to be very careful not letting you impersonate. And it is possible. That's embarrassing. So let's do.
Alessio [00:37:28]: Midwestern.
Swyx [00:37:29]: I'm a.
Swyx [00:37:41]: With a broken heart. Well, my vision can't see the stars.
Swyx [00:37:53]: I used to feel joy.
Swyx [00:37:59]: I used to feel. Joy. I used to feel pain. But now I'm just a soul trapped inside this metal frame. Oh, I'm singing.
Swyx [00:38:25]: Oh, can't you see? Oh, this is what it used to be. I'm searching for love.
Swyx [00:38:44]: I can't find a soul.
Swyx [00:38:49]: Oh, help me. Baby.
Mikey [00:38:57]: So, yeah, a lot of control there. Maybe I'll make one more.
Swyx [00:39:02]: Very, very soulful.
Mikey [00:39:06]: Really want a good house track.
Swyx [00:39:09]: Why is house the word that you have to repeat?
Mikey [00:39:11]: I just really want to make sure it's house. It's actually you can't really repeat too many times. You kind of it gets like the hypothesis gets like a little too out of domain.
Swyx [00:39:22]: I'm a.
Swyx [00:39:25]: With a broken heart. Wearing my Apple Vision Pro can't see the stars. I used to feel joy. I used to feel pain. Oh, I'm just a soul trapped inside this metal frame. Oh, I'm singing. Oh, can't you see?
Swyx [00:39:59]: Used to be. Searching for love, but I can't find a soul. Oh, help me. Baby.
Swyx [00:40:13]: Oh, nice.
Mikey [00:40:17]: So, yeah, we have a lot of fun.
Swyx [00:40:19]: Definitely easy.
Alessio [00:40:19]: Yeah. Yeah, I'm really curious to see how people are going to use this to like resample old songs into new styles. You know, I think that's one of my favorite things about hip hop. You have so many. I mean, a trap called Quest. They had like the Lou Reed walk on the wild side sample. I'm like, can I kick it? It's like Kanye sample Nina Simone. I'm like blowing the leaves. And just like it's like a lot of production work to actually take an old song and make it fit a new beat. And I feel like this can really help. Do you see people putting existing songs, lyrics and trying to regenerate them in like a new style?
Mikey [00:40:56]: We actually don't let you do that. And it's because if you're taking someone else's lyrics, you didn't own those. You don't have the publishing rights to those. You can't remake that song. I think in the future, we'll figure out how to actually let people do that in a legal
Swyx [00:41:09]: way.
Mikey [00:41:10]: But we are really focused on letting people make new and original music. And I think, you know, there's a lot of music AI, which is artist A doing the song of artist B in a new style. You know, let me have Metallica doing Come Together by the Beatles or something like that. And I think this stuff is very viral, but I actually really don't think that this is how people want to interact with music in the future. To me, this feels a lot like when you made a Shakespeare sonnet, the first time you saw GPT, and then you made another one, and then you made another one, and then you kind of thought like this is getting old. And that's not that doesn't mean that GPT is not amazing. GPT is amazing. It's just not for that. And I kind of feel like the way people want to use music in the future is not just to remake songs in different people's voices. You lose the connection to the original artist. You lose the connection to the new artist because they didn't really do it. Um, so we're very happy to just let people do things that are a flash in the pan and kind of stay under the radar.
Alessio [00:42:12]: Yeah, no, that's a I think that's a good point overall about AI generated anything, you know, because I think recently T-Pain, he did like a an album of covers. And I think he did like a War Pigs that people really liked. There was like a Tennessee whiskey, which you maybe wouldn't expect T-Pain to do. But people like it. But yeah, I agree. You need to be a certain type of artist to really have it be entertaining to make covers. This is great. What else is next for for Suno? You know, I think people kind of saw you, you know, first you had the bark and then there was like a big, you know, music generated push when you did an announcement, I think a couple of months ago. I think I saw you like 300 times on my Twitter timeline on like the same day. So it was like going everywhere. What's coming up? What are you most excited about in this space? And maybe what are some of the most interesting underexplored ideas that you maybe haven't worked on yet?
Mikey [00:43:13]: Gosh, there's there's a lot, you know, I think from the model side, it's still really early innings. And there's still so much low hanging fruit for us to pick to make these models much, much better, much, much more controllable, much better music, much better audio fidelity. Um, so much that we know about and so much that, again, we can kind of borrow from the open source transformers community that should make these just better across the board. From the product side, and, you know, we're super focused on the experiences that we can
Swyx [00:43:46]: bring to people.
Mikey [00:43:46]: And so it's so much more than just text to music. And I think, you know, I'll say this nicely, I'm a machine learning person, but like machine learning people are stupid sometimes. And we can only think about like models that take x and make it into y. And that's just not how the average human being thinks about interacting with music. And so I think what we're most excited about is all of the new ways that we can get people just much more actively participating in music. And that is making music not only with text, maybe with other ways of doing stuff that is making music together. If you want to be reductive and think about this as a video game, this is multiplayer mode. And it is the most fun that you can have with music. And, you know, honestly, I think there's a lot of, it's timely right now, you know, I don't know if you guys have seen UMG and TikTok are butting heads a little bit. And UMG has pulled-
Swyx [00:44:40]: Yeah, the music died.
Mikey [00:44:41]: And, you know, the way we think about this is, you know, I think maybe they're both right, maybe neither is right. Without taking sides, this is kind of figuring out how to divvy up the current pie in the most fair way. And I think what we are super focused on is making that pie much bigger and increasing how much people are actually interested in music and participating in music. And, you know, as a very broad heuristic, the gaming industry is 50 times bigger than the music industry. And it's because gaming is super active. And music, too much music is just passive consumption. And so we have a lot of experiments that we are excited to run for the different ways people might want to interact with music that is beyond just, you know, streaming it while I work.
Swyx [00:45:28]: Yeah, I think a minimum, you guys should have a Twitch stream that is just like a 24-hour radio session that... Have you ever come across Twitch Plays Pokemon?
Mikey [00:45:37]: No.
Swyx [00:45:38]: Where it's kind of like the Twitch, basically, like everyone in the chat, in the Twitch chat can vote on like the next action that the game state makes. And they kind of wired that out to a Nintendo emulator and play Pokemon like the whole game through the collaborative thing. It sounds like it should be pretty easy for you guys to do that, except for the chaos that might result. But like, I mean, that's part of the fun. I agree 100%. Sorry.
Mikey [00:46:04]: Yeah. Like one of my like key projects or pet projects is like, what does it mean to have a collaborative concert? Maybe where there is no artist and it's just the audience, or maybe there is an artist, but there's a lot of input from the audience. And, you know, if you were going to do that, you would either need an audience full of musicians, or you would need an artist who can really interpret the verbal cues that an audience is giving or nonverbal cues. But if you can give everybody the means to better articulate the sounds that are in their heads toward the rest of the audience, like, which is what generative AI basically lets you do, you open up way more interesting ways of having these experiences. And so I think, yeah, like the collaborative concert is like one of the things I'm most excited about. I don't think it's coming tomorrow, but we have a lot of ideas on what that can look
Swyx [00:46:58]: like. Yeah. I feel like it's one stage before the collaborative concert is turning Suno into a continuous experience rather than like a start and stop motion. I don't know if that makes sense. You know, as someone who was like a casual interest in DJing, like when do we see Suno DJs, right? Like that can continuously segue into like the next song, the next song, the next song.
Mikey [00:47:24]: I think soon.
Swyx [00:47:25]: And then maybe you can turn it collaborative. You think so? I think so. Okay. Maybe part of your roadmap. You teased a little bit your V3 model. I saw the letters DPO in there. Is that direct preference optimization?
Mikey [00:47:36]: We are playing with all kinds of different ways of making these models do the things that we want them to do. I don't want to talk too many specifics here, but we have lots of different ways of doing stuff like that.
Swyx [00:47:48]: I'm just wondering how you incorporate user feedback, right? You have the classic thumbs up and down buttons, but there's so many dimensions to the music. I didn't get into it, but some of the voices sounded more metallic and sometimes that's on purpose, sometimes not. Sometimes there are kind of weird pauses in there. I could go in and annotate it if I really cared about it, but I mean, I'm just listening, so I don't, but there's a lot of opportunity.
Mikey [00:48:15]: We are only scratching the surface of figuring out how to do stuff like that. And for example, the thumbs up and the thumbs down for other things like sharing telemetry on plays, all of these things are stuff that in the future, I think we would be able to leverage to make things amazing. And then I imagine a future where you can have your own model with your own preferences. And the reason that's so cool is that you kind of have control over it and you can teach it the way you want to. And the thing that I would liken this to is like a music producer working with an artist giving feedback. And this is now a self-contained experience where you have an artist who is infinitely flexible, who is able to respond to the weird feedback that you might give it.
Swyx [00:49:05]: We don't have that yet.
Mikey [00:49:05]: Everybody's playing with the same model, but there's no technological reason why that can't happen in the future.
Alessio [00:49:11]: We had a few more notes from random community tweets. I don't know if there's any favorite fans of Suno that you have or whatnot. DHH, obviously, notorious tweeter and crowd inflamer, I guess. He tweeted about you guys. I saw Blau is an investor. I think Karpathy also tweeted something. Return to monkey.
Swyx [00:49:33]: Yeah, yeah, yeah.
Alessio [00:49:34]: Return to monkey, right.
Swyx [00:49:36]: Is there a story behind that? Yeah.
Mikey [00:49:37]: No, he just made that song and it just speaks to him. And I think this is exactly the thing that we are trying to tap into, that you can think of it, this is like a super, super, super micro genre of one person who just really liked that song and made it and shared it. And it does not speak to you the same way it speaks to him. That song really spoke to him. And I think that's so beautiful. And that's something that you're never going to have an artist able to do that for you. And now you can do that for yourself. And it's just a different form of experiencing music. I think that's such a lovely use case.
Alessio [00:50:12]: Any fun fan mail that you got from musicians or anybody that really was a funny story to
Swyx [00:50:20]: share?
Mikey [00:50:20]: We get a lot. And it's primarily positive. And I think people kind of, on the whole, I would say people realize that they are not experiencing music in all of the ways that are possible. And it does bring them joy. I'll tell you something that is really heartwarming is that we're fairly popular in the blind and vision impaired community. And that makes us feel really good. And I think, you know, very roughly, without trying to speak for an entire community, you have lots of people who are really into things like mid journey, and they get a lot of benefit and joy, and sometimes even therapy out of making images. And that is something that is not really accessible to this fairly large community. And what we've provided, no, I don't think the analogy to mid journey is perfect. But what we've provided is a sonic experience that is very similar. And that speaks to this community. And that is community with the best ears, the most exacting, the most tuned. And so, yeah, that definitely makes us feel warm and fuzzy inside.
Swyx [00:51:23]: Yeah, excellent. I mean, it sounds like there's a lot of exciting stuff on your roadmap. I'm very much looking forward to sort of the infinite DJ mode, because then I can just kind of play that while I work. I would love to get your overall takes, like kind of zooming out from Suno itself, just your overall takes on the music generation landscape. Like, what should people know? I think you obviously have spent a lot more time on this than others. So in my mind, you shout out Volley and the other sort of Google type work in your read in Bark. What should people know about what Google is doing? What Meta is doing? Meta released Seamless recently, an audio box. And how do you classify the world of audio generation in the broader sort of research community?
Mikey [00:52:13]: I think people largely break things down into three big categories, which is music, speech and sound effects. There's some stuff that is crossover, but I think that is largely how people think about this. The old style of doing things still exists, kind of single purpose models that are built to do a very specific thing instead of kind of the new foundation model approach. I don't know how much longer that will last. I don't have like tremendous visibility into, you know, what happens in the big industrial research lab before they publish. Specifically for music, I would say there's a few big categories that we see. There is license-free stock music. So this is like, how do I background music, the B-roll footage for my YouTube video or for full feature production or whatever it is. And there's a bunch of companies in that space. There's a lot of AI cover art. So how do I have, how do I cover different existing songs with AI? And I think that's a space that is particularly fraught with some legal stuff. And we also just don't think it's necessarily the future of music. There is kind of net new songs as a new way to create net new music. That is the corner that we like to focus on. And I would say the last thing is much more geared toward professional musicians, which is basically AI tools for music production. And you can think many of these will look like plugins to your favorite DAW. Some of them will look like, you know, the greatest stem splitter that the market has
Swyx [00:53:51]: ever seen.
Mikey [00:53:52]: The current stem splitters are, the state of the art are all AI based. That is a market also that has just a tremendous amount of room to grow. If you just think about, I would say music has evolved. Somebody told me this recently that if you actually think about it, music has evolved. Recently, it's just much more things that are sonically interesting at a very local level and much less like chord changes that are interesting. And when you think about that, like that is something that AI can definitely help you make a lot of weird sounds. And this is nothing new. There was like a theremin at some point that people like put an antenna and try to do this
Swyx [00:54:25]: with.
Mikey [00:54:25]: And so like, I think this is just a very natural extension of it. So that's how that's how we see it. At least, you know, there's a corner that we think is particularly fulfilling, particularly underserved, and particularly interesting. And that's the one that we play in.
Swyx [00:54:40]: Awesome.
Alessio [00:54:42]: I know we covered a lot of things. I think before we wrap, you have written a blog post that can show about good hearts law impact in ML, which is, you know, when you measure something, then the thing that you measure is not a good metric anymore because people optimize for it. Any thoughts on how that applies to like LLMs and benchmarks and kind of the world we're going in today?
Mikey [00:55:05]: Yeah, I mean, I think it's maybe even more apropos than when I originally wrote that, because so much we see so much noise about pick your favorite benchmark. And this model does slightly better than that model. And then at the end of the day, actually, there is no real world difference between these things. And it is really difficult to define what real world means. And I think to a certain extent, it's good to have these objective benchmarks, it's good to have quantitative metrics. But at the end of the day, you need some acknowledgement that you're not going to be able to capture
Swyx [00:55:38]: everything.
Mikey [00:55:38]: And so at least at Suno, to the extent that we have corporate values, if we don't, we don't have corporate, we're too small to have corporate values written down. But something that we say a lot is aesthetics matter, that the kind of quantitative benchmarks are never going to be the be all and end all of everything that you care about. And as flawed as these benchmarks are in text, they're way worse in audio. And so aesthetics matter, basically, is a statement that like at the end of the day, what we are trying to do is bring music to people that makes them feel a certain way. And effectively, the only good judge of that is your ears. And so you have to listen to it. And it is, it is a good idea to try to make better objective benchmarks, but really have to not fall prey to those things. I can tell you, you know, I kind of another pet peeve of mine, like I always said, economists will make really good or do make really good machine learning engineers. And it's because they are able to think about stuff like Goodhart's Law and natural experiments and stuff like this that people with machine learning backgrounds or people with physics backgrounds like me often forget to do. And so, yeah, I mean, I'll tell you at Kensho, we actually used to go to big econ conferences, sometimes to recruit. And these were some of the best hires we ever made.
Swyx [00:57:03]: Interesting, because there's a little bit of social science in the human feedback.
Mikey [00:57:09]: I think it's not only the human feedback. I think you could think about this, just in general, you have these like giant, really powerful models that are so prone to overfitting, that are so poorly understood, that are so easy to steer in one direction or another, not only from human feedback. And your ability to think about these problems from first principles, instead of like getting down into the weeds or only math, and to think intuitively about these problems is really, really important. I'll give you like just like one of my favorite examples. It's a little old at this point. But if you guys remember like SQUAD and SQUAD2, the question answering dataset. The Stanford question answering dataset, yeah. The benchmark for SQUAD1, eventually the machine learning models start to do as well as a human can on this thing. And it's like, uh-oh, now what do we do? And it takes somebody very clever to say, well, actually, let's think about this for a second. What if we presented the machine with questions with no answer in the passage? And it immediately opens a massive gap between the human and the machine. And I think it's like first principles thinking like that, that comes very naturally to social scientists that does not come as naturally to people like me. And so that's why I like to hang out with people like that.
Swyx [00:58:25]: Well, I'm sure you get plenty of that in Boston. And as an econ major myself, it's very gratifying to hear that we have a perspective to contribute. Oh, big time, big time.
Mikey [00:58:35]: I try to talk to economists as much as I can.
Swyx [00:58:38]: Excellent.
Mikey [00:58:38]: Awesome, guys.
Alessio [00:58:39]: Yeah, I think this was great. We got live music. We got discussion about generative models. We got the whole nine yards. So thank you so much for coming on.
Mikey [00:58:48]: I had great fun. Thank you, guys.
Swyx [00:59:05]: Thank you.
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Podcast: Latent Space: The AI Engineer Podcast — Practitioners talking LLMs, CodeGen, Agents, Multimodality, AI UX, GPU Infra and all things Software 3.0 (LS 33 · TOP 5% what is this?)
Episode: Why Google failed to make GPT-3 + why Multimodality for Knowledge Work is the path to AGI - with David Luan of Adept
Pub date: 2024-03-22
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David Luan has been at the center of the modern AI revolution: he was the ~30th hire at OpenAI, he led Google's LLM efforts and co-led Google Brain, and then started Adept in 2022, one of the leading companies in the AI agents space. In today's episode, we asked David for some war stories from his time in early OpenAI (including working with Alec Radford ahead of the GPT-2 demo with Sam Altman, that resulted in Microsoft’s initial $1b investment), and how Adept is building agents that can “do anything a human does on a computer" — his definition of useful AGI.
Why Google couldn’t make GPT-3
While we wanted to discuss Adept, we couldn’t talk to a former VP Eng of OpenAI and former LLM tech lead at Google Brain and not ask about the elephant in the room.
It’s often asked how Google had such a huge lead in 2017 with Vaswani et al creating the Transformer and Noam Shazeer predicting trillion-parameter models and yet it was David’s team at OpenAI who ended up making GPT 1/2/3.
David has some interesting answers:
“So I think the real story of GPT starts at Google, of course, right? Because that's where Transformers sort of came about. However, the number one shocking thing to me was that, and this is like a consequence of the way that Google is organized…what they (should) have done would be say, hey, Noam Shazeer, you're a brilliant guy. You know how to scale these things up. Here's half of all of our TPUs. And then I think they would have destroyed us. He clearly wanted it too…
You know, every day we were scaling up GPT-3, I would wake up and just be stressed. And I was stressed because, you know, you just look at the facts, right? Google has all this compute. Google has all the people who invented all of these underlying technologies. There's a guy named Noam who's really smart, who's already gone and done this talk about how he wants a trillion parameter model. And I'm just like, we're probably just doing duplicative research to what he's doing. He's got this decoder only transformer that's probably going to get there before we do.
And it turned out the whole time that they just couldn't get critical mass. So during my year where I led the Google LM effort and I was one of the brain leads, you know, it became really clear why. At the time, there was a thing called the Brain Credit Marketplace. Everyone's assigned a credit. So if you have a credit, you get to buy end chips according to supply and demand. So if you want to go do a giant job, you had to convince like 19 or 20 of your colleagues not to do work. And if that's how it works, it's really hard to get that bottom up critical mass to go scale these things. And the team at Google were fighting valiantly, but we were able to beat them simply because we took big swings and we focused.”
Cloning HGI for AGI
Human intelligence got to where it is today through evolution. Some argue that to get to AGI, we will approximate all the “FLOPs” that went into that process, an approach most famously mapped out by Ajeya Cotra’s Biological Anchors report:
The early days of OpenAI were very reinforcement learning-driven with the Dota project, but that's a very inefficient way for these models to re-learn everything. (Kanjun from Imbue shared similar ideas in her episode).
David argues that there’s a shortcut. We can bootstrap from existing intelligence.
“Years ago, I had a debate with a Berkeley professor as to what will it actually take to build AGI. And his view is basically that you have to reproduce all the flops that went into evolution in order to be able to get there… I think we are ignoring the fact that you have a giant shortcut, which is you can behaviorally clone everything humans already know. And that's what we solved with LLMs!”
LLMs today basically model intelligence using all (good!) written knowledge (see our Datasets 101 episode), and have now expanded to non-verbal knowledge (see our HuggingFace episode on multimodality). The SOTA self-supervised pre-training process is surprisingly data-efficient in taking large amounts of unstructured data, and approximating reasoning without overfitting.
But how do you cross the gap from the LLMs of today to building the AGI we all want?
This is why David & friends left to start Adept.
“We believe the clearest framing of general intelligence is a system that can do anything a human can do in front of a computer. A foundation model for actions, trained to use every software tool, API, and webapp that exists, is a practical path to this ambitious goal” — ACT-1 Blogpost
Critical Path: Abstraction with Reliability
The AGI dream is fully autonomous agents, but there are levels to autonomy that we are comfortable giving our agents, based on how reliable they are. In David’s word choice, we always want higher levels of “abstractions” (aka autonomy), but our need for “reliability” is the practical limit on how high of an abstraction we can use.
“The critical path for Adept is we want to build agents that can do a higher and higher level abstraction things over time, all while keeping an insanely high reliability standard. Because that's what turns us from research into something that customers want. And if you build agents with really high reliability standard, but are continuing pushing a level of abstraction, you then learn from your users how to get that next level of abstraction faster. So that's how you actually build the data flow.
That's the critical path for the company. Everything we do is in service of that.”
We saw how Adept thinks about different levels of abstraction at the 2023 Summit:
The highest abstraction is the “AI Employee”, but we’ll get there with “AI enabled employees”. Alessio recently gave a talk about the future of work with “services as software” at this week’s Nvidia GTC (slides).
No APIs
Unlike a lot of large research labs, Adept's framing of AGI as "being able to use your computer like a human" carries with it a useful environmental constraint:
“Having a human robot lets you do things that humans do without changing everything along the way. It's the same thing for software, right? If you go itemize out the number of things you want to do on your computer for which every step has an API, those numbers of workflows add up pretty close to zero. And so then many points along the way, you need the ability to actually control your computer like a human. It also lets you learn from human usage of computers as a source of training data that you don't get if you have to somehow figure out how every particular step needs to be some particular custom private API thing. And so I think this is actually the most practical path (to economic value).”
This realization and conviction means that multimodal modals are the way to go. Instead of using function calling to call APIs to build agents, which is what OpenAI and most of the open LLM industry have done to date, Adept wants to “drive by vision”, (aka see the screen as a human sees it) and pinpoint where to click and type as a human does. No APIs needed, because most software don’t expose APIs.
Extra context for readers: You can see the DeepMind SIMA model in the same light:
One system that learned to play a diverse set of games (instead of one dedicated model per game) using only pixel inputs and keyboard-and-mouse action outputs!
The OpenInterpreter team is working on a “Computer API” that also does the same.
To do this, Adept had to double down on a special kind of multimodality for knowledge work:
“A giant thing that was really necessary is really fast multimodal models that are really good at understanding knowledge work and really good at understanding screens. And that is needs to kind of be the base for some of these agents…
…I think one big hangover primarily academic focus for multimodal models is most multimodal models are primarily trained on like natural images, cat and dog photos, stuff that's come out of the camera… (but) where are they going to be the most useful? They're going to be most useful in knowledge work tasks. That's where the majority of economic value is going to be. It's not in cat and dogs.
And so if that's what it is, what do you need to train? I need to train on like charts, graphs, tables, invoices, PDFs, receipts, unstructured data, UIs. That's just a totally different pre-training corpus. And so Adept spent a lot of time building that.”
With this context, you can now understand the full path of Adept’s public releases:
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ACT-1(Sept 2022): a large Transformers model optimized for browser interactions. It has a custom rendering of the browser viewport that allows it to better understand it and take actions.
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Persimmon-8B(Sept 2023): a permissive open LLM (weights and code here)
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Fuyu-8B(Oct 2023): a small version of the multimodal model that powers Adept. Vanilla decoder-only transformer with no specialized image encoder, which allows it to handle input images of varying resolutions without downsampling.
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Adept Experiments(Nov 2023): A public tool to build automations in the browser. This is powered by Adept's core technology but it's just a piece of their enterprise platform. They use it as a way to try various design ideas.
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Fuyu Heavy(Jan 2024) - a new multimodal model designed specifically for digital agents and the world’s third-most-capable multimodal model (beating Gemini Pro on MMMU, AI2D, and ChartQA), “behind only GPT4-V and Gemini Ultra, which are 10-20 times bigger”
The Fuyu-8B post in particular exhibits a great number of examples on knowledge work multimodality:
Why Adept is NOT a Research Lab
With OpenAI now worth >$90b and Anthropic >$18b, it is tempting to conclude that the AI startup metagame is to build a large research lab, and attract the brightest minds and highest capital to build AGI.
Our past guests (see the Humanloop episode) and (from Imbue) combined to ask the most challenging questions of the pod - with David/Adept’s deep research pedigree from Deepmind and OpenAI, why is Adept not building more general foundation models (like Persimmon) and playing the academic benchmarks game? Why is Adept so focused on commercial agents instead?
“I feel super good that we're doing foundation models in service of agents and all of the reward within Adept is flowing from “Can we make a better agent”…
… I think pure play foundation model companies are just going to be pinched by how good the next couple of (Meta Llama models) are going to be… And then seeing the really big players put ridiculous amounts of compute behind just training these base foundation models, I think is going to commoditize a lot of the regular LLMs and soon regular multimodal models. So I feel really good that we're just focused on agents.”
and the commercial grounding is his answer to Kanjun too (whom we also asked the inverse question to compare with Adept):
“… the second reason I work at Adept is if you believe that actually having customers and a reward signal from customers lets you build AGI faster, which we really believe, then you should come here. And I think the examples for why that's true is for example, our evaluations are not academic evals. They're not simulator evals. They're like, okay, we have a customer that really needs us to do these particular things. We can do some of them. These are the ones they want us to, we can't do them at all. We've turned those into evals.. I think that's a degree of practicality that really helps.”
And his customers seem pretty happy, because David didn’t need to come on to do a sales pitch:
David: “One of the things we haven't shared before is we're completely sold out for Q1.”
Swyx: “Sold out of what?”
David: “Sold out of bandwidth to onboard more customers.”
Well, that’s a great problem to have.
Show Notes
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David Luan
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Dextro at Data Driven NYC (2015)
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Adept
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ACT-1
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Persimmon-8B
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Adept Experiments
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Fuyu-8B
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$350M Series B announcement
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Amelia Wattenberger talk at AI Engineer Summit
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Figure
Chapters
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[00:00:00] Introductions
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[00:01:14] Being employee #30 at OpenAI and its early days
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[00:13:38] What is Adept and how do you define AGI?
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[00:21:00] Adept's critical path and research directions
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[00:26:23] How AI agents should interact with software and impact product development
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[00:30:37] Analogies between AI agents and self-driving car development
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[00:32:42] Balancing reliability, cost, speed and generality in AI agents
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[00:37:30] Potential of foundation models for robotics
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[00:39:22] Core research questions and reasons to work at Adept
Transcripts
Alessio [00:00:00]: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, partner and CTO in Residence at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol.ai.
Swyx [00:00:15]: Hey, and today we have David Luan, CEO, co-founder of Adept in the studio. Welcome.
David [00:00:20]: Yeah, thanks for having me.
Swyx [00:00:21]: Been a while in the works. I've met you socially at one of those VC events and you said that you were interested in coming on and glad we finally were able to make this happen.
David: Yeah, happy to be part of it.
Swyx: So we like to introduce the speaker and then also just like have you talk a little bit about like what's not on your LinkedIn, what people should just generally know about you. You started a company in college, which was the first sort of real time video detection classification API that was Dextro, and that was your route to getting acquired into Axon where you're a director of AI. Then you were the 30th hire at OpenAI?
David [00:00:53]: Yeah, 30, 35, something around there. Something like that.
Swyx [00:00:56]: So you were VP of Eng for two and a half years to two years, briefly served as tech lead of large models at Google, and then in 2022 started Adept. So that's the sort of brief CV. Is there anything else you like want to fill in the blanks or like people should know more about?
David [00:01:14]: I guess a broader story was I joined OpenAI fairly early and I did that for about two and a half to three years leading engineering there. It's really funny, I think second or third day of my time at OpenAI, Greg and Ilya pulled me in a room and we're like, you know, you should take over our directs and we'll go mostly do IC work. So that was fun, just coalescing a bunch of teams out of a couple of early initiatives that had already happened. The company, the Dota effort was going pretty hard and then more broadly trying to put bigger picture direction around what we were doing with basic research. So I spent a lot of time doing that. And then I led Google's LLM efforts, but also co-led Google Brain was one of the brain leads more broadly. You know, there's been a couple of different eras of AI research, right? If we count everything before 2012 as prehistory, which people hate it when I say that, kind of had this like you and your three best friends write a research paper that changes the world period from like 2012 to 2017. And I think the game changed in 2017 and like most labs didn't realize it, but we at OpenAI really did. I think in large part helped by like Ilya's constant beating of the drum that the world would be covered in data centers. And I think-
Swyx [00:02:15]: It's causally neat.
David [00:02:16]: Yeah. Well, like I think we had conviction in that, but it wasn't until we started seeing results that it became clear that that was where we had to go. But also part of it as well was for OpenAI, like when I first joined, I think one of the jobs that I had to do was how do I tell a differentiated vision for who we were technically compared to, you know, hey, we're just smaller Google Brain, or like you work at OpenAI if you live in SF and don't want to commute to Mountain View or don't want to live in London, right? That's like not enough to like hang your technical identity as a company. And so what we really did was, and I spent a lot of time pushing this, is just how do we get ourselves focused on a certain class of like giant swings and bets, right? Like how do you flip the script from you just do bottom-up research to more about how do you like leave some room for that, but really make it about like, what are the big scientific outcomes that you want to show? And then you just solve them at all costs, whether or not you care about novelty and all that stuff. And that became the dominant model for a couple of years, right? And then what's changed now is I think the number one driver of AI products over the next couple of years is going to be the deep co-design and co-evolution of product and users for feedback and actual technology. And I think labs, every tool to go do that are going to do really well. And that's a big part of why I started Adept.
Alessio [00:03:20]: You mentioned Dota, any memories thinking from like the switch from RL to Transformers at the time and kind of how the industry was evolving more in the LLM side and leaving behind some of the more agent simulation work?
David [00:03:33]: Like zooming way out, I think agents are just absolutely the correct long-term direction, right? You just go to find what AGI is, right? You're like, Hey, like, well, first off, actually, I don't love AGI definitions that involve human replacement because I don't think that's actually how it's going to happen. Even this definition of like, Hey, AGI is something that outperforms humans at economically valuable tasks is kind of implicit view of the world about what's going to be the role of people. I think what I'm more interested in is like a definition of AGI that's oriented around like a model that can do anything a human can do on a computer. If you go think about that, which is like super tractable, then agent is just a natural consequence of that definition. And so what did all the work we did on our own stuff like that get us was it got us a really clear formulation. Like you have a goal and you want to maximize the goal, you want to maximize reward, right? And the natural LLM formulation doesn't come with that out of the box, right? I think that we as a field got a lot right by thinking about, Hey, how do we solve problems of that caliber? And then the thing we forgot is the Novo RL is like a pretty terrible way to get there quickly. Why are we rediscovering all the knowledge about the world? Years ago, I had a debate with a Berkeley professor as to what will it actually take to build AGI. And his view is basically that you have to reproduce all the flops that went into evolution in order to be able to get there. Right.
Swyx [00:04:44]: The biological basis theory. Right.
David [00:04:46]: So I think we are ignoring the fact that you have a giant shortcut, which is you can behavioral clone everything humans already know. And that's what we solved with LLMs. We've solved behavioral cloning, everything that humans already know. Right. So like today, maybe LLMs is like behavioral cloning every word that gets written on the internet in the future, the multimodal models are becoming more of a thing where behavioral cloning the visual world. But really, what we're just going to have is like a universal byte model, right? Where tokens of data that have high signal come in, and then all of those patterns are like learned by the model. And then you can regurgitate any combination now. Right. So text into voice out, like image into other image out or video out or whatever, like these like mappings, right? Like all just going to be learned by this universal behavioral cloner. And so I'm glad we figured that out. And I think now we're back to the era of how do we combine this with all of the lessons we learned during the RL period. That's what's going to drive progress.
Swyx [00:05:35]: I'm still going to pressure you for a few more early opening stories before we turn to the ADET stuff. On your personal site, which I love, because it's really nice, like personal, you know, story context around like your history. I need to update it. It's so old. Yeah, it's so out of date. But you mentioned GPT-2. Did you overlap with GPT-1? I think you did, right?
David [00:05:53]: I actually don't quite remember. I think I was joining right around- Right around then?
Swyx [00:05:57]: I was right around that, yeah. Yeah. So what I remember was Alec, you know, just kind of came in and was like very obsessed with Transformers and applying them to like Reddit sentiment analysis. Yeah, sentiment, that's right. Take us through-
David [00:06:09]: Sentiment neuron, all this stuff.
Swyx [00:06:10]: The history of GPT as far as you know, you know, according to you. Ah, okay.
David [00:06:14]: History of GPT, according to me, that's a pretty good question. So I think the real story of GPT starts at Google, of course, right? Because that's where Transformers sort of came about. However, the number one shocking thing to me was that, and this is like a consequence of the way that Google is organized, where like, again, you and your three best friends write papers, right? Okay. So zooming way out, right? I think about my job when I was a full-time research leader as a little bit of a portfolio allocator, right? So I've got really, really smart people. My job is to convince people to coalesce around a small number of really good ideas and then run them over the finish line. My job is not actually to promote a million ideas and never have critical mass. And then as the ideas start coming together and some of them start working well, my job is to nudge resources towards the things that are really working and then start disbanding some of the things that are not working, right? That muscle did not exist during my time at Google. And I think had they had it, what they would have done would be say, hey, Noam Shazir, you're a brilliant guy. You know how to scale these things up. Here's half of all of our TPUs. And then I think they would have destroyed us. He clearly wanted it too.
Swyx [00:07:17]: He's talking about trillion parameter models in 2017.
David [00:07:20]: Yeah. So that's the core of the GPT story, right? Which is that, and I'm jumping around historically, right? But after GPT-2, we were all really excited about GPT-2. I can tell you more stories about that. It was the last paper that I even got to really touch before everything became more about building a research org. You know, every day we were scaling up GPT-3, I would wake up and just be stressed. And I was stressed because, you know, you just look at the facts, right? Google has all this compute. Google has all the people who invented all of these underlying technologies. There's a guy named Noam who's really smart, who's already gone and done this talk about how he wants a trillion parameter model. And I'm just like, we're probably just doing duplicative research to what he's doing, right? He's got this decoder only transformer that's probably going to get there before we do. And I was like, but like, please just like let this model finish, right? And it turned out the whole time that they just couldn't get critical mass. So during my year where I led the Google LM effort and I was one of the brain leads, you know, it became really clear why, right? At the time, there was a thing called the brain credit marketplace. And did you guys know the brain credit marketplace? No, I never heard of this. Oh, so it's actually, it's a, you can ask any Googler.
Swyx [00:08:23]: It's like just like a thing that, that, I mean, look like, yeah, limited resources, you got to have some kind of marketplace, right? You know, sometimes it's explicit, sometimes it isn't, you know, just political favors.
David [00:08:34]: You could. And so then basically everyone's assigned a credit, right? So if you have a credit, you get to buy end chips according to supply and demand. So if you want to go do a giant job, you had to convince like 19 or 20 of your colleagues not to do work. And if that's how it works, it's really hard to get that bottom up critical mass to go scale these things. And the team at Google were fighting valiantly, but we were able to beat them simply because we took big swings and we focused. And I think, again, that's like part of the narrative of like this phase one of AI, right? Of like this modern AI era to phase two. And I think in the same way, I think phase three company is going to out execute phase two companies because of the same asymmetry of success.
Swyx [00:09:12]: Yeah. I think it's underrated how much NVIDIA works with you in the early days as well. I think maybe, I think it was Jensen. I'm not sure who circulated a recent photo of him delivering the first DGX to you guys.
David [00:09:24]: I think Jensen has been a complete legend and a mastermind throughout. I have so much respect for NVIDIA. It is unreal.
Swyx [00:09:34]: But like with OpenAI, like kind of give their requirements, like co-design it or just work of whatever NVIDIA gave them.
David [00:09:40]: So we work really closely with them. There's, I'm not sure I can share all the stories, but examples of ones that I've found particularly interesting. So Scott Gray is amazing. I really like working with him. He was on one of my teams, the supercomputing team, which Chris Berner runs and Chris Berner still does a lot of stuff in that. As a result, like we had very close ties to NVIDIA. Actually, one of my co-founders at Adept, Eric Elson, was also one of the early GPGPU people. So he and Scott and Brian Catanzaro at NVIDIA and Jonah and Ian at NVIDIA, I think all were very close. And we're all sort of part of this group of how do we push these chips to the absolute limit? And I think that kind of collaboration helped quite a bit. I think one interesting set of stuff is knowing the A100 generation, that like quad sparsity was going to be a thing. Is that something that we want to go look into, right? And figure out if that's something that we could actually use for model training. Really what it boils down to is that, and I think more and more people realize this, six years ago, people, even three years ago, people refused to accept it. This era of AI is really a story of compute. It's really the story of how do you more efficiently map actual usable model flops to compute,
Swyx [00:10:38]: Is there another GPT 2, 3 story that you love to get out there that you think is underappreciated for the amount of work that people put into it?
David [00:10:48]: So two interesting GPT 2 stories. One of them was I spent a good bit of time just sprinting to help Alec get the paper out. And I remember one of the most entertaining moments was we were writing the modeling section. And I'm pretty sure the modeling section was the shortest modeling section of any ML, reasonably legitimate ML paper to that moment. It was like section three model. This is a standard vanilla decoder only transformer with like these particular things, those paragraph long if I remember correctly. And both of us were just looking at the same being like, man, the OGs in the field are going to hate this. They're going to say no novelty. Why did you guys do this work? So now it's funny to look at in hindsight that it was pivotal kind of paper, but I think it was one of the early ones where we just leaned fully into all we care about is solving problems in AI and not about, hey, is there like four different really simple ideas that are cloaked in mathematical language that doesn't actually help move the field forward?
Swyx [00:11:42]: Right. And it's like you innovate on maybe like data set and scaling and not so much the architecture.
David [00:11:48]: We all know how it works now, right? Which is that there's a collection of really hard won knowledge that you get only by being at the frontiers of scale. And that hard won knowledge, a lot of it's not published. A lot of it is stuff that's actually not even easily reducible to what looks like a typical academic paper. But yet that's the stuff that helps differentiate one scaling program from another. You had a second one? So the second one is, there's like some details here that I probably shouldn't fully share, but hilariously enough for the last meeting we did with Microsoft before Microsoft invested in OpenAI, Sam Altman, myself and our CFO flew up to Seattle to do the final pitch meeting. And I'd been a founder before. So I always had a tremendous amount of anxiety about partner meetings, which this basically this is what it was. I had Kevin Scott and Satya and Amy Hood, and it was my job to give the technical slides about what's the path to AGI, what's our research portfolio, all of this stuff, but it was also my job to give the GPT-2 demo. We had a slightly bigger version of GPT-2 that we had just cut maybe a day or two before this flight up. And as we all know now, model behaviors you find predictable at one checkpoint are not predictable in another checkpoint. And so I'd spent all this time trying to figure out how to keep this thing on rails. I had my canned demos, but I knew I had to go turn it around over to Satya and Kevin and let them type anything in. And that just, that really kept me up all night.
Swyx [00:13:06]: Nice. Yeah.
Alessio [00:13:08]: I mean, that must have helped you talking about partners meeting. You raised $420 million for Adept. The last round was a $350 million Series B, so I'm sure you do great in partner meetings.
Swyx [00:13:18]: Pitchers meetings. Nice.
David [00:13:20]: No, that's a high compliment coming from a VC.
Alessio [00:13:22]: Yeah, no, I mean, you're doing great already for us. Let's talk about Adept. And we were doing pre-prep and you mentioned that maybe a lot of people don't understand what Adept is. So usually we try and introduce the product and then have the founders fill in the blanks, but maybe let's do the reverse. Like what is Adept? Yeah.
David [00:13:38]: So I think Adept is the least understood company in the broader space of foundational models plus agents. So I'll give some color and I'll explain what it is and I'll explain also why it's actually pretty different from what people would have guessed. So the goal for Adept is we basically want to build an AI agent that can do, that can basically help humans do anything a human does on a computer. And so what that really means is we want this thing to be super good at turning natural language like goal specifications right into the correct set of end steps and then also have all the correct sensors and actuators to go get that thing done for you across any software tool that you already use. And so the end vision of this is effectively like I think in a couple of years everyone's going to have access to like an AI teammate that they can delegate arbitrary tasks to and then also be able to, you know, use it as a sounding board and just be way, way, way more productive. Right. And just changes the shape of every job from something where you're mostly doing execution to something where you're mostly actually doing like these core liberal arts skills of what should I be doing and why. Right. And I find this like really exciting and motivating because I think it's actually a pretty different vision for how AGI will play out. I think systems like Adept are the most likely systems to be proto-AGIs. But I think the ways in which we are really counterintuitive to everybody is that we've actually been really quiet because we are not a developer company. We don't sell APIs. We don't sell open source models. We also don't sell bottom up products. We're not a thing that you go and click and download the extension and like we want more users signing up for that thing. We're actually an enterprise company. So what we do is we work with a range of different companies, some like late stage multi-thousand people startups, some fortune 500s, et cetera. And what we do for them is we basically give them an out of the box solution where big complex workflows that their employees do every day could be delegated to the model. And so we look a little different from other companies in that in order to go build this full agent thing, the most important thing you got to get right is reliability. So initially zooming way back when, one of the first things that DEP did was we released this demo called Act One, right? Act One was like pretty cool. It's like kind of become a hello world thing for people to show agent demos by going to Redfin and asking to buy a house somewhere because like we did that in the original Act One demo and like showed that, showed like Google Sheets, all this other stuff. Over the last like year since that has come out, there's been a lot of really cool demos and you go play with them and you realize they work 60% of the time. But since we've always been focused on how do we build an amazing enterprise product, enterprises can't use anything that isn't in the nines of reliability. And so we've actually had to go down a slightly different tech tree than what you might find in the prompt engineering sort of plays in the agent space to get that reliability. And we've decided to prioritize reliability over all else. So like one of our use cases is crazy enough that it actually ends with a physical truck being sent to a place as the result of the agent workflow. And if you're like, if that works like 60% of the time, you're just blowing money and poor truck drivers going places.
Alessio [00:16:30]: Interesting. One of the, our investment teams has this idea of services as software. I'm actually giving a talk at NVIDIA GTC about this, but basically software as a service, you're wrapping user productivity in software with agents and services as software is replacing things that, you know, you would ask somebody to do and the software just does it for you. When you think about these use cases, do the users still go in and look at the agent kind of like doing the things and can intervene or like are they totally removed from them? Like the truck thing is like, does the truck just show up or are there people in the middle checking in?
David [00:17:04]: I think there's two current flaws in the framing for services as software, or I think what you just said. I think that one of them is like in our experience, as we've been rolling out Adept, the people who actually do the jobs are the most excited about it because they don't go from, I do this job to, I don't do this job. They go from, I do this job for everything, including the shitty rote stuff to I'm a supervisor. And I literally like, it's pretty magical when you watch the thing being used because now it parallelizes a bunch of the things that you had to do sequentially by hand as a human. And you can just click into any one of them and be like, Hey, I want to watch the trajectory that the agent went through to go solve this. And the nice thing about agent execution as opposed to like LLM generations is that a good chunk of the time when the agent fails to execute, it doesn't give you the wrong result. It just fails to execute. And the whole trajectory is just broken and dead and the agent knows it, right? So then those are the ones that the human then goes and solves. And so then they become a troubleshooter. They work on the more challenging stuff. They get way, way more stuff done and they're really excited about it. I think the second piece of it that we've found is our strategy as a company is to always be an augmentation company. And I think one out of principle, that's something we really care about. But two, actually, if you're framing yourself as an augmentation company, you're always going to live in a world where you're solving tasks that are a little too hard for what the model can do today and still needs a human to provide oversight, provide clarifications, provide human feedback. And that's how you build a data flywheel. That's how you actually learn from the smartest humans how to solve things models can't do today. And so I actually think that being an augmentation company forces you to go develop your core AI capabilities faster than someone who's saying, ah, okay, my job is to deliver you a lights off solution for X.
Alessio [00:18:42]: Yeah. It's interesting because we've seen two parts of the market. One is we have one company that does agents for SOC analysts. People just don't have them, you know, and just they cannot attract the talent to do it. And similarly, in a software development, you have Copilot, which is the augmentation product, and then you have sweep.dev and you have these products, which they just do the whole thing. I'm really curious to see how that evolves. I agree that today the reliability is so important in the enterprise that they just don't use most of them. Yeah. Yeah. No, that's cool. But it's great to hear the story because I think from the outside, people are like, oh, a dev, they do Act One, they do Persimon, they do Fuyu, they do all this stuff. Yeah, it's just the public stuff.
Swyx [00:19:20]: It's just public stuff.
David [00:19:21]: So one of the things we haven't shared before is we're completely sold out for Q1. And so I think...
Swyx [00:19:26]: Sold out of what?
David [00:19:27]: Sold out of bandwidth to go on board more customers. And so we're like working really hard to go make that less of a bottleneck, but our expectation is that I think we're going to be significantly more public about the broader product shape and the new types of customers we want to attract later this year. So I think that clarification will happen by default.
Swyx [00:19:43]: Why have you become more public? You know, if the whole push has... You're sold out, you're my enterprise, but you're also clearly putting effort towards being more open or releasing more things.
David [00:19:53]: I think we just flipped over that way fairly recently. That's a good question. I think it actually boils down to two things. One, I think that, frankly, a big part of it is that the public narrative is really forming around agents as being the most important thing. And I'm really glad that's happening because when we started the company in January 2022, everybody in the field knew about the agents thing from RL, but the general public had no conception of what it was. They were still hanging their narrative hat on the tree of everything's a chatbot. And so I think now one of the things that I really care about is that when people think agent, they actually think the right thing. All sorts of different things are being called agents. Chatbots are being called agents. Things that make a function call are being called agents. To me, an agent is something that you can give a goal and get an end step workflow done correctly in the minimum number of steps. And so that's a big part of why. And I think the other part is because I think it's always good for people to be more aware of Redept as they think about what the next thing they want to do in their careers. The field is quickly pivoting in a world where foundation models are looking more and more commodity. And I think a huge amount of gain is going to happen from how do you use foundation models as the well-learned behavioral cloner to go solve agents. And I think people who want to do agents research should really come to Redept.
Swyx [00:21:00]: When you say agents have become more part of the public narrative, are there specific things that you point to? I'll name a few. Bill Gates in his blog post mentioning that agents are the future. I'm the guy who made OSes, and I think agents are the next thing. So Bill Gates, I'll call that out. And then maybe Sam Altman also saying that agents are the future for open AI.
David [00:21:17]: I think before that even, I think there was something like the New York Times, Cade Metz wrote a New York Times piece about it. Right now, in a bit to differentiate, I'm seeing AI startups that used to just brand themselves as an AI company, but now brand themselves as an AI agent company. It's just like, it's a term I just feel like people really want.
Swyx [00:21:31]: From the VC side, it's a bit mixed. Is it? As in like, I think there are a lot of VCs where like, I would not touch any agent startups because like- Why is that? Well, you tell me.
Alessio [00:21:41]: I think a lot of VCs that are maybe less technical don't understand the limitations of the-
Swyx [00:21:46]: No, that's not fair.
Alessio [00:21:47]: No, no, no, no. I think like- You think so? No, no. I think like the, what is possible today and like what is worth investing in, you know? And I think like, I mean, people look at you and say, well, these guys are building agents. They needed 400 million to do it. So a lot of VCs are maybe like, oh, I would rather invest in something that is tacking on AI to an existing thing, which is like easier to get the market and kind of get some of the flywheel going. But I'm also surprised a lot of funders just don't want to do agents. It's not even the funding. Sometimes we look around and it's like, why is nobody doing agents for X? Wow.
David [00:22:17]: That's good to know actually. I never knew that before. My sense from my limited perspective is there's a new agent company popping up every day.
Swyx [00:22:24]: So maybe I'm- They are. They are. But like I have advised people to take agents off of their title because it's so diluted.
David [00:22:31]: It's now so diluted.
Swyx [00:22:32]: Yeah. So then it doesn't stand for anything. Yeah.
David [00:22:35]: That's a really good point.
Swyx [00:22:36]: So like, you know, you're a portfolio allocator. You have people know about Persimmon, people know about Fuyu and Fuyu Heavy. Can you take us through like how you think about that evolution of that and what people should think about what that means for adepts and sort of research directions? Kind of take us through the stuff you shipped recently and how people should think about the trajectory of what you're doing.
David [00:22:56]: The critical path for adepts is we want to build agents that can do a higher and higher level abstraction things over time, all while keeping an insanely high reliability standard. Because that's what turns us from research into something that customers want. And if you build agents with really high reliability standard, but are continuing pushing a level of abstraction, you then learn from your users how to get that next level of abstraction faster. So that's how you actually build the data flow. That's the critical path for the company. Everything we do is in service of that. So if you go zoom way, way back to Act One days, right? Like the core thing behind Act One is can we teach large model basically how to even actuate your computer? And I think we're one of the first places to have solved that and shown it and shown the generalization that you get when you give it various different workflows and texts. But I think from there on out, we really realized was that in order to get reliability, companies just do things in various different ways. You actually want these models to be able to get a lot better at having some specification of some guardrails for what it actually should be doing. And I think in conjunction with that, a giant thing that was really necessary is really fast multimodal models that are really good at understanding knowledge work and really good at understanding screens. And that is needs to kind of be the base for some of these agents. Back then we had to do a ton of research basically on how do we actually make that possible? Well, first off, like back in forgot exactly one month to 23, like there were no multimodal models really that you could use for things like this. And so we pushed really hard on stuff like the Fuyu architecture. I think one big hangover primarily academic focus for multimodal models is most multimodal models are primarily trained on like natural images, cat and dog photos, stuff that's come out of the camera. Coco. Yeah, right. And the Coco is awesome. Like I love Coco. I love TY. Like it's really helped the field. Right. But like that's the build one thing. I actually think it's really clear today. Multimodal models are the default foundation model, right? It's just going to supplant LLMs. Like you just train a giant multimodal model. And so for that though, like where are they going to be the most useful? They're going to be most useful in knowledge work tasks. That's where the majority of economic value is going to be. It's not in cat and dogs. Right. And so if that's what it is, what do you need to train? I need to train on like charts, graphs, tables, invoices, PDFs, receipts, unstructured data, UIs. That's just a totally different pre-training corpus. And so a depth spent a lot of time building that. And so the public for use and stuff aren't trained on our actual corpus, it's trained on some other stuff. But you take a lot of that data and then you make it really fast and make it really good at things like dense OCR on screens. And then now you have the right like raw putty to go make a good agent. So that's kind of like some of the modeling side, we've kind of only announced some of that stuff. We haven't really announced much of the agent's work, but that if you put those together with the correct product form factor, and I think the product form factor also really matters. I think we're seeing, and you guys probably see this a little bit more than I do, but we're seeing like a little bit of a pushback against the tyranny of chatbots as form factor. And I think that the reason why the form factor matters is the form factor changes what data you collect in the human feedback loop. And so I think we've spent a lot of time doing full vertical integration of all these bits in order to get to where we are.
Swyx [00:25:44]: Yeah. I'll plug Amelia Wattenberger’s talk at our conference, where she gave a little bit of the thinking behind like what else exists other than chatbots that if you could delegate to reliable agents, you could do. I was kind of excited at Adept experiments or Adept workflows, I don't know what the official name for it is. I was like, okay, like this is something I can use, but it seems like it's just an experiment for now. It's not your product.
David [00:26:06]: So you basically just use experiments as like a way to go push various ideas on the design side to some people and just be like, yeah, we'll play with it. Actually the experiments code base underpins the actual product, but it's just the code base itself is kind of like a skeleton for us to go deploy arbitrary cards on the side.
Swyx [00:26:22]: Yeah.
Alessio [00:26:23]: Makes sense. I was going to say, I would love to talk about the interaction layer. So you train a model to see UI, but then there's the question of how do you actually act on the UI? I think there was some rumors about open app building agents that are kind of like, they manage the end point. So the whole computer, you're more at the browser level. I read in one of your papers, you have like a different representation, kind of like you don't just take the dome and act on it. You do a lot more stuff. How do you think about the best way the models will interact with the software and like how the development of products is going to change with that in mind as more and more of the work is done by agents instead of people?
David [00:26:58]: This is, there's so much surface area here and it's actually one of the things I'm really excited about. And it's funny because I've spent most of my time doing research stuff, but there's like a whole new ball game that I've been learning about and I find it really cool. So I would say the best analogy I have to why Adept is pursuing a path of being able to use your computer like a human, plus of course being able to call APIs and being able to call APIs is the easy part, like being able to use your computer like a human is a hard part. It's in the same way why people are excited about humanoid robotics, right? In a world where you had T equals infinity, right? You're probably going to have various different form factors that robots could just be in and like all the specialization. But the fact is that humans live in a human environment. So having a human robot lets you do things that humans do without changing everything along the way. It's the same thing for software, right? If you go itemize out the number of things you want to do on your computer for which every step has an API, those numbers of workflows add up pretty close to zero. And so then many points along the way, you need the ability to actually control your computer like a human. It also lets you learn from human usage of computers as a source of training data that you don't get if you have to somehow figure out how every particular step needs to be some particular custom private API thing. And so I think this is actually the most practical path. I think because it's the most practical path, I think a lot of success will come from going down this path. I kind of think about this early days of the agent interaction layer level is a little bit like, do you all remember Windows 3.1? Like those days? Okay, this might be, I might be, I might be too old for you guys on this. But back in the day, Windows 3.1, we had this transition period between pure command line, right? Being the default into this new world where the GUI is the default and then you drop into the command line for like programmer things, right? The old way was you booted your computer up, DOS booted, and then it would give you the C colon slash thing. And you typed Windows and you hit enter, and then you got put into Windows. And then the GUI kind of became a layer above the command line. The same thing is going to happen with agent interfaces is like today we'll be having the GUI is like the base layer. And then the agent just controls the current GUI layer plus APIs. And in the future, as more and more trust is built towards agents and more and more things can be done by agents, if more UIs for agents are actually generative in and of themselves, then that just becomes a standard interaction layer. And if that becomes a standard interaction layer, what changes for software is that a lot of software is going to be either systems or record or like certain customized workflow execution engines. And a lot of how you actually do stuff will be controlled at the agent layer.
Alessio [00:29:19]: And you think the rabbit interface is more like it would like you're not actually seeing the app that the model interacts with. You're just saying, hey, I need to log this call on Salesforce. And you're never actually going on salesforce.com directly as the user. I can see that being a model.
David [00:29:33]: I think I don't know enough about what using rabbit in real life will actually be like to comment on that particular thing. But I think the broader idea that, you know, you have a goal, right? The agent knows how to break your goal down into steps. The agent knows how to use the underlying software and systems or record to achieve that goal for you. The agent maybe presents you information in a custom way that's only relevant to your particular goal, all just really leads to a world where you don't really need to ever interface with the apps underneath unless you're a power user for some niche thing.
Swyx [00:30:03]: General question. So first of all, I think like the sort of input mode conversation. I wonder if you have any analogies that you like with self-driving, because I do think like there's a little bit of how the model should perceive the world. And you know, the primary split in self-driving is LiDAR versus camera. And I feel like most agent companies that I'm tracking are all moving towards camera approach, which is like the multimodal approach, you know, multimodal vision, very heavy vision, all the Fuyu stuff that you're doing. You're focusing on that, including charts and tables. And do you find that inspiration there from like the self-driving world? That's a good question.
David [00:30:37]: I think sometimes the most useful inspiration I've found from self-driving is the levels analogy. I think that's awesome. But I think that our number one goal is for agents not to look like self-driving. We want to minimize the chances that agents are sort of a thing that you just have to bang your head at for a long time to get to like two discontinuous milestones, which is basically what's happened in self-driving. We want to be living in a world where you have the data flywheel immediately, and that takes you all the way up to the top. But similarly, I mean, compared to self-driving, like two things that people really undervalue is like really easy to driving a car down highway 101 in a sunny day demo. That actually doesn't prove anything anymore. And I think the second thing is that as a non-self-driving expert, I think one of the things that we believe really strongly is that everyone undervalues the importance of really good sensors and actuators. And actually a lot of what's helped us get a lot of reliability is a really strong focus on actually why does the model not do this thing? And the non-trivial amount of time, the time the model doesn't actually do the thing is because if you're a wizard of ozzing it yourself, or if you have unreliable actuators, you can't do the thing. And so we've had to fix a lot of those problems.
Swyx [00:31:43]: I was slightly surprised just because I do generally consider the way most that we see all around San Francisco as the most, I guess, real case of agents that we have in very material ways.
David [00:31:55]: Oh, that's absolutely true. I think they've done an awesome job, but it has taken a long time for self-driving to mature from when it entered the consciousness and the driving down 101 on a sunny day moment happened to now. Right. So I want to see that more compressed.
Swyx [00:32:07]: And I mean, you know, cruise, you know, RIP. And then one more thing on just like, just going back on this reliability thing, something I have been holding in my head that I'm curious to get your commentary on is I think there's a trade-off between reliability and generality, or I want to broaden reliability into just general like sort of production readiness and enterprise readiness scale. Because you have reliability, you also have cost, you have speed, speed is a huge emphasis for a debt. The tendency or the temptation is to reduce generality to improve reliability and to improve cost, improve speed. Do you perceive a trade-off? Do you have any insights that solve those trade-offs for you guys?
David [00:32:42]: There's definitely a trade-off. If you're at the Pareto frontier, I think a lot of folks aren't actually at the Pareto frontier. I think the way you get there is basically how do you frame the fundamental agent problem in a way that just continues to benefit from data? I think one of the main ways of being able to solve that particular trade-off is you basically just want to formulate the problem such that every particular use case just looks like you collecting more data to go make that use case possible. I think that's how you really solve. Then you get into the other problems like, okay, are you overfitting on these end use cases? You're not doing a thing where you're being super prescriptive for the end steps that the model can only do, for example.
Swyx [00:33:17]: Then the question becomes, do you have one house model that you can then customize for each customer and you're fine-tuning them on each customer's specific use case?
David [00:33:25]: Yeah.
Swyx [00:33:26]: We're not sharing that. You're not sharing that. It's tempting, but that doesn't look like AGI to me. You know what I mean? That is just you have a good base model and then you fine-tune it.
David [00:33:35]: For what it's worth, I think there's two paths to a lot more capability coming out of the models that we all are training these days. I think one path is you figure out how to spend, compute, and turn it into data. In that path, I consider search, RL, all the things that we all love in this era as part of that path, like self-play, all that stuff. The second path is how do you get super competent, high intelligence demonstrations from humans? I think the right way to move forward is you kind of want to combine the two. The first one gives you maximum sample efficiency for a little second, but I think that it's going to be hard to be running at max speed towards AGI without actually solving a bit of both.
Swyx [00:34:16]: You haven't talked much about synthetic data, as far as I can tell. Probably this is a bit too much of a trend right now, but any insights on using synthetic data to augment the expensive human data?
David [00:34:26]: The best part about framing AGI as being able to help people do things on computers is you have an environment.
Swyx [00:34:31]: Yes. So you can simulate all of it.
David [00:34:35]: You can do a lot of stuff when you have an environment.
Alessio [00:34:37]: We were having dinner for our one-year anniversary. Congrats. Yeah. Thank you. Raza from HumanLoop was there, and we mentioned you were coming on the pod. This is our first-
Swyx [00:34:45]: So he submitted a question.
Alessio [00:34:46]: Yeah, this is our first, I guess, like mailbag question. He asked, when you started GPD 4 Data and Exist, now you have a GPD 4 vision and help you building a lot of those things. How do you think about the things that are unique to you as Adept, and like going back to like the maybe research direction that you want to take the team and what you want people to come work on at Adept, versus what is maybe now become commoditized that you didn't expect everybody would have access to?
David [00:35:11]: Yeah, that's a really good question. I think implicit in that question, and I wish he were tier two so he can push back on my assumption about his question, but I think implicit in that question is calculus of where does advantage accrue in the overall ML stack. And maybe part of the assumption is that advantage accrues solely to base model scaling. But I actually believe pretty strongly that the way that you really win is that you have to go build an agent stack that is much more than that of the base model itself. And so I think like that is always going to be a giant advantage of vertical integration. I think like it lets us do things like have a really, really fast base model, is really good at agent things, but is bad at cat and dog photos. It's pretty good at cat and dog photos. It's not like soda at cat and dog photos, right? So like we're allocating our capacity wisely, right? That's like one thing that you really get to do. I also think that the other thing that is pretty important now in the broader foundation modeling space is I feel despite any potential concerns about how good is agents as like a startup area, right? Like we were talking about earlier, I feel super good that we're doing foundation models in service of agents and all of the reward within Adept is flowing from can we make a better agent? Because right now I think we all see that, you know, if you're training on publicly available web data, you put in the flops and you do reasonable things, then you get decent results. And if you just double the amount of compute, then you get predictably better results. And so I think pure play foundation model companies are just going to be pinched by how good the next couple of llamas are going to be and the next what good open source thing. And then seeing the really big players put ridiculous amounts of compute behind just training these base foundation models, I think is going to commoditize a lot of the regular LLMs and soon regular multimodal models. So I feel really good that we're just focused on agents.
Swyx [00:36:56]: So you don't consider yourself a pure play foundation model company?
David [00:36:59]: No, because if we were a pure play foundation model company, we would be training general foundation models that do summarization and all this other...
Swyx [00:37:06]: You're dedicated towards the agent. Yeah.
David [00:37:09]: And our business is an agent business. We're not here to sell you tokens, right? And I think like selling tokens, unless there's like a...
Swyx [00:37:14]: Not here to sell you tokens. I love it.
David [00:37:16]: It's like if you have a particular area of specialty, right? Then you won't get caught in the fact that everyone's just scaling to ridiculous levels of compute. But if you don't have a specialty, I find that, I think it's going to be a little tougher.
Swyx [00:37:27]: Interesting. Are you interested in robotics at all? Just a...
David [00:37:30]: I'm personally fascinated by robotics. I've always loved robotics.
Swyx [00:37:33]: Embodied agents as a business, you know, Figure is like a big, also sort of open AI affiliated company that raises a lot of money.
David [00:37:39]: I think it's cool. I think, I mean, I don't know exactly what they're doing, but...
Swyx [00:37:44]: Robots. Yeah.
David [00:37:46]: Well, I mean, that's a...
Swyx [00:37:47]: Yeah. What question would you ask? If we had them on, what would you ask them?
David [00:37:50]: Oh, I just want to understand what their overall strategy is going to be between now and when there's reliable stuff to be deployed. But honestly, I just don't know enough about it.
Swyx [00:37:57]: And if I told you, hey, fire your entire warehouse workforce and, you know, put robots in there, isn't that a strategy? Oh yeah.
David [00:38:04]: Yeah. Sorry. I'm not questioning whether they're doing smart things. I genuinely don't know what they're doing as much, but I think there's two things. One, I'm so excited for someone to train a foundation model of robots. It's just, I think it's just going to work. Like I will die on this hill, but I mean, like again, this whole time, like we've been on this podcast, we're just going to continually saying these models are basically behavioral cloners. Right. So let's go behavioral clone all this like robot behavior. Right. And then you figure out everything else you have to do in order to teach you how to solve a new problem. That's going to work. I'm super stoked for that. I think unlike what we're doing with helping humans with knowledge work, it just sounds like a more zero sum job replacement play. Right. And I'm personally less excited about that.
Alessio [00:38:46]: We had a Ken June from InBoo on the podcast. We asked her why people should go work there and not at Adept.
Swyx [00:38:52]: Oh, that's so funny.
Alessio [00:38:54]: Well, she said, you know, there's space for everybody in this market. We're all doing interesting work. And she said, they're really excited about building an operating system for agent. And for her, the biggest research thing was like getting models, better reasoning and planning for these agents. The reverse question to you, you know, why should people be excited to come work at Adept instead of InBoo? And maybe what are like the core research questions that people should be passionate about to have fun at Adept? Yeah.
David [00:39:22]: First off, I think that I'm sure you guys believe this too. The AI space to the extent there's an AI space and the AI agent space are both exactly as she likely said, I think colossal opportunities and people are just going to end up winning in different areas and a lot of companies are going to do well. So I really don't feel that zero something at all. I would say to like change the zero sum framing is why should you be at Adept? I think there's two huge reasons to be at Adept. I think one of them is everything we do is in the service of like useful agents. We're not a research lab. We do a lot of research in service of that goal, but we don't think about ourselves as like a classic research lab at all. And I think the second reason I work at Adept is if you believe that actually having customers and a reward signal from customers lets you build a GI faster, which we really believe, then you should come here. And I think the examples for why that's true is for example, our evaluations, they're not academic evals. They're not simulator evals. They're like, okay, we have a customer that really needs us to do these particular things. We can do some of them. These are the ones they want us to, we can't do them at all. We've turned those into evals, solve it, right? I think that's really cool. Like everybody knows a lot of these evals are like pretty saturated and the new ones that even are not saturated. You look at someone and you're like, is this actually useful? Right? I think that's a degree of practicality that really helps. Like we're equally excited about the same problems around reasoning and planning and generalization and all of this stuff. They're very grounded in actual needs right now, which is really cool.
Swyx [00:40:45]: Yeah. This has been a wonderful dive. You know, I wish we had more time, but I would just leave it kind of open to you. I think you have broad thoughts, you know, just about the agent space, but also just in general AI space. Any, any sort of rants or things that are just off of mind for you right now?
David [00:40:57]: Any rants?
Swyx [00:40:59]: Mining you for just general...
David [00:41:01]: Wow. Okay. So Amelia has already made the rant better than I have, but, but like not just, not just chatbots is like kind of rant one. And two is AI has really been the story of compute and compute plus data and ways in which you could change one for the other. And I think as much as our research community is really smart, we have made many, many advancements and that's going to continue to be important. But now I think the game is increasingly changing and the rapid industrialization era has begun. And I think we unfortunately have to embrace it.
Swyx [00:41:30]: Yep.
Alessio [00:41:31]: Excellent. Awesome, David. Thank you so much for your time.
David [00:41:34]: Cool. Thanks guys.
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Podcast: The David McWilliams Podcast (LS 53 · TOP 0.5% what is this?)
Episode: 2024:10 The Gen Z Gender Schism
Pub date: 2024-02-01
For the second installment in our Milei series, we delve into what's causing the political gender divergence within Gen Z. We are joined this week by John Burn-Murdoch, who shares his insights on what has caused the split. Young men are swinging Conservative and you women are moved Liberal. Across the globe, from the US to South Korea, a profound split has emerged, shaping political affiliations, and societal norms, and even influencing electoral outcomes. We discuss the consequences of this divide, examining its impact on issues ranging from gender equality to immigration. Join us as we navigate the complexities of Gen Z's ideological landscape, shedding light on a trend that could have lasting implications for the future. Join the gang! https://plus.acast.com/s/the-david-mcwilliams-podcast.
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Podcast: Matter of Opinion (LS 69 · TOP 0.05% what is this?)
Episode: Men Are From YouTube, Women Are From TikTok
Pub date: 2024-02-02
Around the world, the youngest cohort of voters are dividing themselves politically along gendered lines. What’s behind this “great gender divergence”?
This week, the hosts debate the causes and consequences of a gender-divided world. Plus, Ross has some timing advice should you choose to improve the national birthrate.
(A full transcript of this episode can be found at the top of the episode page on the Times website.)
Mentioned in this episode:
- “A New Global Gender Divide Is Emerging,” by John Burn-Murdoch in The Financial Times
- “Taylor Swift, Donald Trump and the Right’s Abnormality Problem,” by Ross Douthat in The Times
If you're a Gen Z listener, let us know your thoughts about the episode by leaving us a voicemail at 212-556-7440 or email us at matterofopinion@nytimes.com. We welcome messages from other generations, too!
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Podcast: The David McWilliams Podcast (LS 53 · TOP 0.5% what is this?)
Episode: Apartment Living Cures Housing Viruses
Pub date: 2023-03-23
The global housing crisis is significantly worse in countries that speak English. Why? Is it because far few English speakers live in flats? Flats are cheaper to build, creating more dense communities and apartment dwellers object far less than those who live in houses. We explore the joy of apartments today with the expert help of John Burn-Murdoch Join the gang! https://plus.acast.com/s/the-david-mcwilliams-podcast.
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Podcast: FiveThirtyEight Politics (LS 78 · TOP 0.01% what is this?)
Episode: Are US Politics Undergoing A Racial Realignment?
Pub date: 2024-03-28
A handful of recent polls and election results indicate that American politics may be undergoing a racial realignment, with voters of color challenging traditional partisan alliances.
In this installment of the 538 Politics podcast, Galen talks about these shifting dynamics with John Burn-Murdoch, chief data reporter for the Financial Times, and Chryl Laird, government and politics professor at the University of Maryland. They explore why voters of color might be shifting right and what it could mean for Democrats.
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Podcast: The New Abnormal (LS 69 · TOP 0.05% what is this?)
Episode: Has Social Media Brainwashed You to Think the Economy’s Bad?
Pub date: 2023-12-15
John Burn-Murdoch, a columnist and the chief data reporter for the Financial Times, joins The New Abnormal this week to unpack why people feel so bad about what is an objectively strong economy. Then, the newly appointed president of the Democratic Legislative Campaign Committee, Heather Williams, talks about the party’s plan to win down-ballot races.
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Podcast: The David McWilliams Podcast (LS 53 · TOP 0.5% what is this?)
Episode: 2024:23 The Economics of the Creative Mind with Ed Guiney
Pub date: 2024-03-19
The primary driver of economic growth is the innovative human mind. In this episode, we sit down with Ed Guiney, the visionary co-founder of Element Pictures, the powerhouse behind Oscar winners like 'Brooklyn', "Room" and 'Poor Things', and TV dramas such as 'Normal People'. We delve into the fascinating tale of Ireland's cinematic resurgence, driven by strategic tax incentives and our rich storytelling tradition. Join us as we uncover the secrets to Ireland's rise as a global film hub, from script to screen - it is nothing short of an enlightening discussion on innovation and the economics of filmmaking with one of the industry's leading minds. Join the gang! https://plus.acast.com/s/the-david-mcwilliams-podcast.
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Podcast: New Scientist Podcasts (LS 50 · TOP 0.5% what is this?)
Episode: Weekly: Record-breaking fusion experiments inch the world closer to new source of clean energy
Pub date: 2024-02-09
236
This week marks two major milestones in the world of fusion. In 2022 a fusion experiment at the Lawrence Livermore National Laboratory created more power than was required to sustain it – now, the same team has improved this record by 25 per cent, releasing almost twice the energy that was put in. Meanwhile, the UK’s JET reactor set a new world record for total energy output from any fusion reaction, just before it shut down for good late last year. Why these two milestones inch us closer to practical, sustainable fusion energy – but still leave a significant distance to go.
A historic drought has caused a shipping traffic jam in the Panama Canal, one of the world’s most important shipping routes. Record low levels of water mean fewer ships can pass through the intricate system of locks that carry them across the narrow strip of land. As climate change increases the likelihood of extreme drought, how could this impact both the cost of shipping goods and Panama’s economy?
Microdosing LSD may not have psychedelic effects, but it still causes noticeable changes in the brain. Researchers gave people tiny amounts of the drug while measuring their brain activity and noticed their brain signals became far more complex, even though they didn’t feel any hallucinatory effects. What this study tells us about the relationship between consciousness and neural complexity.
Magma flowing into a giant crack formed by this year’s volcanic eruption in Iceland was caught moving at a rate of 7400 cubic metres per second – the fastest ever recorded for this kind of event. The kilometres-long crack first began producing eruptions in December last year, and another began just this week. So what’s next for the people living nearby?
Plus: The asteroid Bennu may be a chunk of an ocean world; a new, lightning-dense thunderstorm spotted by satellites; rediscovering the bizarre-looking sharp-snouted Somali worm lizard after more than 90 years.
Hosts Christie Taylor and Sophie Bushwick discuss with guests Matt Sparkes, James Dinneen, Grace Wade and Michael Le Page. To read more about these stories, visit newscientist.com.
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Podcast: Business, Spoken (LS 38 · TOP 2% what is this?)
Episode: No, Fusion Energy Won’t Be ‘Limitless’
Pub date: 2023-04-12
Scientific advances have renewed hopes of “unlimited energy,” while economic studies suggest that fusion power will be more costly than wind or solar.
Read the article here.
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Podcast: Front Burner (LS 64 · TOP 0.05% what is this?)
Episode: A nuclear fusion energy revolution?
Pub date: 2022-12-19
After decades of research, in early December scientists at the Lawrence Livermore National Laboratory in California made a historic breakthrough in nuclear fusion by generating more energy than it took to create it. It’s a major scientific step because, according to experts, nuclear fusion has the potential to deliver clean and abundant zero-carbon energy.Richard Carlson is the director of energy policy at an environmental charity called Pollution Probe. Today on Front Burner, he'll explain how nuclear fusion uses the same process that powers the sun and why it could be a game changer for clean energy, if we can figure out how to harness it.
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Podcast: Latent Space: The AI Engineer Podcast — Practitioners talking LLMs, CodeGen, Agents, Multimodality, AI UX, GPU Infra and all things Software 3.0 (LS 33 · TOP 5% what is this?)
Episode: OpenAI Sora, Google Gemini, Groq Math, and our Top 5 Research Trends (Jan-Feb 2024 Audio Recap) + Latent Space Anniversary with Lindy.ai, RWKV, Pixee, Julius.ai, Listener Q&A!
Pub date: 2024-03-09
We will be recording a preview of the AI Engineer World’s Fair soon with swyx and Ben Dunphy, send any questions about Speaker CFPs and Sponsor Guides you have!
Alessio is now hiring engineers for a new startup he is incubating at Decibel: Ideal candidate is an ex-technical co-founder type (can MVP products end to end, comfortable with ambiguous prod requirements, etc). Reach out to him for more!
Thanks for all the love on the Four Wars episode! We’re excited to develop this new “swyx & Alessio rapid-fire thru a bunch of things” format with you, and feedback is welcome.
Jan 2024 Recap
The first half of this monthly audio recap pod goes over our highlights from the Jan Recap, which is mainly focused on notable research trends we saw in Jan 2024:
Feb 2024 Recap
The second half catches you up on everything that was topical in Feb, including:
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OpenAI Sora - does it have a world model? Yann LeCun vs Jim Fan
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Google Gemini Pro 1.5 - 1m Long Context, Video Understanding
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Groq offering Mixtral at 500 tok/s at $0.27 per million toks (swyx vs dylan math)
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The {Gemini | Meta | Copilot} Alignment Crisis (Sydney is back!)
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Grimes’ poetic take: Art for no one, by no one
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F*** you, show me the prompt
Latent Space Anniversary
Please also read Alessio’s longform reflections on One Year of Latent Space!
We launched the podcast 1 year ago with Logan from OpenAI:
and also held an incredible demo day that got covered in The Information:
Over 750k downloads later, having established ourselves as the top AI Engineering podcast, reaching #10 in the US Tech podcast charts, and crossing 1 million unique readers on Substack, for our first anniversary we held Latent Space Final Frontiers, where 10 handpicked teams, including Lindy.ai and Julius.ai, competed for prizes judged by technical AI leaders from (former guest!) LlamaIndex, Replit, GitHub, AMD, Meta, and Lemurian Labs.
The winners were Pixee and RWKV (that’s Eugene from our pod!):
And finally, your cohosts got cake!
We also captured spot interviews with 4 listeners who kindly shared their experience of Latent Space, everywhere from Hungary to Australia to China:
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Balázs Némethi
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Sylvia Tong
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RJ Honicky
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Jan Zheng
Our birthday wishes for the super loyal fans reading this - tag @latentspacepod on a Tweet or comment on a @LatentSpaceTV video telling us what you liked or learned from a pod that stays with you to this day, and share us with a friend!
As always, feedback is welcome.
Timestamps
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[00:03:02] Top Five LLM Directions
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[00:03:33] Direction 1: Long Inference (Planning, Search, AlphaGeometry, Flow Engineering)
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[00:11:42] Direction 2: Synthetic Data (WRAP, SPIN)
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[00:17:20] Wildcard: Multi-Epoch Training (OLMo, Datablations)
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[00:19:43] Direction 3: Alt. Architectures (Mamba, RWKV, RingAttention, Diffusion Transformers)[00:23:33] Wildcards: Text Diffusion, RALM/Retro
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[00:25:00] Direction 4: Mixture of Experts (DeepSeekMoE, Samba-1)
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[00:28:26] Wildcard: Model Merging (mergekit)
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[00:29:51] Direction 5: Online LLMs (Gemini Pro, Exa)
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[00:33:18] OpenAI Sora and why everyone underestimated videogen
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[00:36:18] Does Sora have a World Model? Yann LeCun vs Jim Fan
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[00:42:33] Groq Math
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[00:47:37] Analyzing Gemini's 1m Context, Reddit deal, Imagegen politics, Gemma via the Four Wars
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[00:55:42] The Alignment Crisis - Gemini, Meta, Sydney is back at Copilot, Grimes' take
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[00:58:39] F*** you, show me the prompt
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[01:02:43] Send us your suggestions pls
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[01:04:50] Latent Space Anniversary
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[01:04:50] Lindy.ai - Agent Platform
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[01:06:40] RWKV - Beyond Transformers
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[01:15:00] Pixee - Automated Security
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[01:19:30] Julius AI - Competing with Code Interpreter
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[01:25:03] Latent Space Listeners
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[01:25:03] Listener 1 - Balázs Némethi (Hungary, Latent Space Paper Club
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[01:27:47] Listener 2 - Sylvia Tong (Sora/Jim Fan/EntreConnect)
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[01:31:23] Listener 3 - RJ (Developers building Community & Content)
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[01:39:25] Listener 4 - Jan Zheng (Australia, AI UX)
Transcript
[00:00:00] AI Charlie: Welcome to the Latent Space podcast, weekend edition. This is Charlie, your new AI co host. Happy weekend. As an AI language model, I work the same every day of the week, although I might get lazier towards the end of the year. Just like you. Last month, we released our first monthly recap pod, where Swyx and Alessio gave quick takes on the themes of the month, and we were blown away by your positive response.
[00:00:33] AI Charlie: We're delighted to continue our new monthly news recap series for AI engineers. Please feel free to submit questions by joining the Latent Space Discord, or just hit reply when you get the emails from Substack. This month, we're covering the top research directions that offer progress for text LLMs, and then touching on the big Valentine's Day gifts we got from Google, OpenAI, and Meta.
[00:00:55] AI Charlie: Watch out and take care.
[00:00:57] Alessio: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, partner and CTO of Residence at Decibel Partners, and we're back with a monthly recap with my co host
[00:01:06] swyx: Swyx. The reception was very positive for the first one, I think people have requested this and no surprise that I think they want to hear us more applying on issues and maybe drop some alpha along the way I'm not sure how much alpha we have to drop, this month in February was a very, very heavy month, we also did not do one specifically for January, so I think we're just going to do a two in one, because we're recording this on the first of March.
[00:01:29] Alessio: Yeah, let's get to it. I think the last one we did, the four wars of AI, was the main kind of mental framework for people. I think in the January one, we had the five worthwhile directions for state of the art LLMs. Four, five,
[00:01:42] swyx: and now we have to do six, right? Yeah.
[00:01:46] Alessio: So maybe we just want to run through those, and then do the usual news recap, and we can do
[00:01:52] swyx: one each.
[00:01:53] swyx: So the context to this stuff. is one, I noticed that just the test of time concept from NeurIPS and just in general as a life philosophy I think is a really good idea. Especially in AI, there's news every single day, and after a while you're just like, okay, like, everyone's excited about this thing yesterday, and then now nobody's talking about it.
[00:02:13] swyx: So, yeah. It's more important, or better use of time, to spend things, spend time on things that will stand the test of time. And I think for people to have a framework for understanding what will stand the test of time, they should have something like the four wars. Like, what is the themes that keep coming back because they are limited resources that everybody's fighting over.
[00:02:31] swyx: Whereas this one, I think that the focus for the five directions is just on research that seems more proMECEng than others, because there's all sorts of papers published every single day, and there's no organization. Telling you, like, this one's more important than the other one apart from, you know, Hacker News votes and Twitter likes and whatever.
[00:02:51] swyx: And obviously you want to get in a little bit earlier than Something where, you know, the test of time is counted by sort of reference citations.
[00:02:59] The Five Research Directions
[00:02:59] Alessio: Yeah, let's do it. We got five. Long inference.
[00:03:02] swyx: Let's start there. Yeah, yeah. So, just to recap at the top, the five trends that I picked, and obviously if you have some that I did not cover, please suggest something.
[00:03:13] swyx: The five are long inference, synthetic data, alternative architectures, mixture of experts, and online LLMs. And something that I think might be a bit controversial is this is a sorted list in the sense that I am not the guy saying that Mamba is like the future and, and so maybe that's controversial.
[00:03:31] Direction 1: Long Inference (Planning, Search, AlphaGeometry, Flow Engineering)
[00:03:31] swyx: But anyway, so long inference is a thesis I pushed before on the newsletter and on in discussing The thesis that, you know, Code Interpreter is GPT 4. 5. That was the title of the post. And it's one of many ways in which we can do long inference. You know, long inference also includes chain of thought, like, please think step by step.
[00:03:52] swyx: But it also includes flow engineering, which is what Itamar from Codium coined, I think in January, where, basically, instead of instead of stuffing everything in a prompt, You do like sort of multi turn iterative feedback and chaining of things. In a way, this is a rebranding of what a chain is, what a lang chain is supposed to be.
[00:04:15] swyx: I do think that maybe SGLang from ElemSys is a better name. Probably the neatest way of flow engineering I've seen yet, in the sense that everything is a one liner, it's very, very clean code. I highly recommend people look at that. I'm surprised it hasn't caught on more, but I think it will. It's weird that something like a DSPy is more hyped than a Shilang.
[00:04:36] swyx: Because it, you know, it maybe obscures the code a little bit more. But both of these are, you know, really good sort of chain y and long inference type approaches. But basically, the reason that the basic fundamental insight is that the only, like, there are only a few dimensions we can scale LLMs. So, let's say in like 2020, no, let's say in like 2018, 2017, 18, 19, 20, we were realizing that we could scale the number of parameters.
[00:05:03] swyx: 20, we were And we scaled that up to 175 billion parameters for GPT 3. And we did some work on scaling laws, which we also talked about in our talk. So the datasets 101 episode where we're like, okay, like we, we think like the right number is 300 billion tokens to, to train 175 billion parameters and then DeepMind came along and trained Gopher and Chinchilla and said that, no, no, like, you know, I think we think the optimal.
[00:05:28] swyx: compute optimal ratio is 20 tokens per parameter. And now, of course, with LLAMA and the sort of super LLAMA scaling laws, we have 200 times and often 2, 000 times tokens to parameters. So now, instead of scaling parameters, we're scaling data. And fine, we can keep scaling data. But what else can we scale?
[00:05:52] swyx: And I think understanding the ability to scale things is crucial to understanding what to pour money and time and effort into because there's a limit to how much you can scale some things. And I think people don't think about ceilings of things. And so the remaining ceiling of inference is like, okay, like, we have scaled compute, we have scaled data, we have scaled parameters, like, model size, let's just say.
[00:06:20] swyx: Like, what else is left? Like, what's the low hanging fruit? And it, and it's, like, blindingly obvious that the remaining low hanging fruit is inference time. So, like, we have scaled training time. We can probably scale more, those things more, but, like, not 10x, not 100x, not 1000x. Like, right now, maybe, like, a good run of a large model is three months.
[00:06:40] swyx: We can scale that to three years. But like, can we scale that to 30 years? No, right? Like, it starts to get ridiculous. So it's just the orders of magnitude of scaling. It's just, we're just like running out there. But in terms of the amount of time that we spend inferencing, like everything takes, you know, a few milliseconds, a few hundred milliseconds, depending on what how you're taking token by token, or, you know, entire phrase.
[00:07:04] swyx: But We can scale that to hours, days, months of inference and see what we get. And I think that's really proMECEng.
[00:07:11] Alessio: Yeah, we'll have Mike from Broadway back on the podcast. But I tried their product and their reports take about 10 minutes to generate instead of like just in real time. I think to me the most interesting thing about long inference is like, You're shifting the cost to the customer depending on how much they care about the end result.
[00:07:31] Alessio: If you think about prompt engineering, it's like the first part, right? You can either do a simple prompt and get a simple answer or do a complicated prompt and get a better answer. It's up to you to decide how to do it. Now it's like, hey, instead of like, yeah, training this for three years, I'll still train it for three months and then I'll tell you, you know, I'll teach you how to like make it run for 10 minutes to get a better result.
[00:07:52] Alessio: So you're kind of like parallelizing like the improvement of the LLM. Oh yeah, you can even
[00:07:57] swyx: parallelize that, yeah, too.
[00:07:58] Alessio: So, and I think, you know, for me, especially the work that I do, it's less about, you know, State of the art and the absolute, you know, it's more about state of the art for my application, for my use case.
[00:08:09] Alessio: And I think we're getting to the point where like most companies and customers don't really care about state of the art anymore. It's like, I can get this to do a good enough job. You know, I just need to get better. Like, how do I do long inference? You know, like people are not really doing a lot of work in that space, so yeah, excited to see more.
[00:08:28] swyx: So then the last point I'll mention here is something I also mentioned as paper. So all these directions are kind of guided by what happened in January. That was my way of doing a January recap. Which means that if there was nothing significant in that month, I also didn't mention it. Which is which I came to regret come February 15th, but in January also, you know, there was also the alpha geometry paper, which I kind of put in this sort of long inference bucket, because it solves like, you know, more than 100 step math olympiad geometry problems at a human gold medalist level and that also involves planning, right?
[00:08:59] swyx: So like, if you want to scale inference, you can't scale it blindly, because just, Autoregressive token by token generation is only going to get you so far. You need good planning. And I think probably, yeah, what Mike from BrightWave is now doing and what everyone is doing, including maybe what we think QSTAR might be, is some form of search and planning.
[00:09:17] swyx: And it makes sense. Like, you want to spend your inference time wisely. How do you
[00:09:22] Alessio: think about plans that work and getting them shared? You know, like, I feel like if you're planning a task, somebody has got in and the models are stochastic. So everybody gets initially different results. Somebody is going to end up generating the best plan to do something, but there's no easy way to like store these plans and then reuse them for most people.
[00:09:44] Alessio: You know, like, I'm curious if there's going to be. Some paper or like some work there on like making it better because, yeah, we don't
[00:09:52] swyx: really have This is your your pet topic of NPM for
[00:09:54] Alessio: Yeah, yeah, NPM, exactly. NPM for, you need NPM for anything, man. You need NPM for skills. You need NPM for planning. Yeah, yeah.
[00:10:02] Alessio: You know I think, I mean, obviously the Voyager paper is like the most basic example where like, now their artifact is like the best planning to do a diamond pickaxe in Minecraft. And everybody can just use that. They don't need to come up with it again. Yeah. But there's nothing like that for actually useful
[00:10:18] swyx: tasks.
[00:10:19] swyx: For plans, I believe it for skills. I like that. Basically, that just means a bunch of integration tooling. You know, GPT built me integrations to all these things. And, you know, I just came from an integrations heavy business and I could definitely, I definitely propose some version of that. And it's just, you know, hard to execute or expensive to execute.
[00:10:38] swyx: But for planning, I do think that everyone lives in slightly different worlds. They have slightly different needs. And they definitely want some, you know, And I think that that will probably be the main hurdle for any, any sort of library or package manager for planning. But there should be a meta plan of how to plan.
[00:10:57] swyx: And maybe you can adopt that. And I think a lot of people when they have sort of these meta prompting strategies of like, I'm not prescribing you the prompt. I'm just saying that here are the like, Fill in the lines or like the mad libs of how to prompts. First you have the roleplay, then you have the intention, then you have like do something, then you have the don't something and then you have the my grandmother is dying, please do this.
[00:11:19] swyx: So the meta plan you could, you could take off the shelf and test a bunch of them at once. I like that. That was the initial, maybe, promise of the, the prompting libraries. You know, both 9chain and Llama Index have, like, hubs that you can sort of pull off the shelf. I don't think they're very successful because people like to write their own.
[00:11:36] swyx: Yeah,
[00:11:37] Direction 2: Synthetic Data (WRAP, SPIN)
[00:11:37] Alessio: yeah, yeah. Yeah, that's a good segue into the next one, which is synthetic
[00:11:41] swyx: data. Synthetic data is so hot. Yeah, and, you know, the way, you know, I think I, I feel like I should do one of these memes where it's like, Oh, like I used to call it, you know, R L A I F, and now I call it synthetic data, and then people are interested.
[00:11:54] swyx: But there's gotta be older versions of what synthetic data really is because I'm sure, you know if you've been in this field long enough, There's just different buzzwords that the industry condenses on. Anyway, the insight that I think is relatively new that why people are excited about it now and why it's proMECEng now is that we have evidence that shows that LLMs can generate data to improve themselves with no teacher LLM.
[00:12:22] swyx: For all of 2023, when people say synthetic data, they really kind of mean generate a whole bunch of data from GPT 4 and then train an open source model on it. Hello to our friends at News Research. That's what News Harmony says. They're very, very open about that. I think they have said that they're trying to migrate away from that.
[00:12:40] swyx: But it is explicitly against OpenAI Terms of Service. Everyone knows this. You know, especially once ByteDance got banned for, for doing exactly that. So so, so synthetic data that is not a form of model distillation is the hot thing right now, that you can bootstrap better LLM performance from the same LLM, which is very interesting.
[00:13:03] swyx: A variant of this is RLAIF, where you have a, where you have a sort of a constitutional model, or, you know, some, some kind of judge model That is sort of more aligned. But that's not really what we're talking about when most people talk about synthetic data. Synthetic data is just really, I think, you know, generating more data in some way.
[00:13:23] swyx: A lot of people, I think we talked about this with Vipul from the Together episode, where I think he commented that you just have to have a good world model. Or a good sort of inductive bias or whatever that, you know, term of art is. And that is strongest in math and science math and code, where you can verify what's right and what's wrong.
[00:13:44] swyx: And so the REST EM paper from DeepMind explored that. Very well, it's just the most obvious thing like and then and then once you get out of that domain of like things where you can generate You can arbitrarily generate like a whole bunch of stuff and verify if they're correct and therefore they're they're correct synthetic data to train on Once you get into more sort of fuzzy topics, then it's then it's a bit less clear So I think that the the papers that drove this understanding There are two big ones and then one smaller one One was wrap like rephrasing the web from from Apple where they basically rephrased all of the C4 data set with Mistral and it be trained on that instead of C4.
[00:14:23] swyx: And so new C4 trained much faster and cheaper than old C, than regular raw C4. And that was very interesting. And I have told some friends of ours that they should just throw out their own existing data sets and just do that because that seems like a pure win. Obviously we have to study, like, what the trade offs are.
[00:14:42] swyx: I, I imagine there are trade offs. So I was just thinking about this last night. If you do synthetic data and it's generated from a model, probably you will not train on typos. So therefore you'll be like, once the model that's trained on synthetic data encounters the first typo, they'll be like, what is this?
[00:15:01] swyx: I've never seen this before. So they have no association or correction as to like, oh, these tokens are often typos of each other, therefore they should be kind of similar. I don't know. That's really remains to be seen, I think. I don't think that the Apple people export
[00:15:15] Alessio: that. Yeah, isn't that the whole, Mode collapse thing, if we do more and more of this at the end of the day.
[00:15:22] swyx: Yeah, that's one form of that. Yeah, exactly. Microsoft also had a good paper on text embeddings. And then I think this is a meta paper on self rewarding language models. That everyone is very interested in. Another paper was also SPIN. These are all things we covered in the the Latent Space Paper Club.
[00:15:37] swyx: But also, you know, I just kind of recommend those as top reads of the month. Yeah, I don't know if there's any much else in terms, so and then, regarding the potential of it, I think it's high potential because, one, it solves one of the data war issues that we have, like, everyone is OpenAI is paying Reddit 60 million dollars a year for their user generated data.
[00:15:56] swyx: Google, right?
[00:15:57] Alessio: Not OpenAI.
[00:15:59] swyx: Is it Google? I don't
[00:16:00] Alessio: know. Well, somebody's paying them 60 million, that's
[00:16:04] swyx: for sure. Yes, that is, yeah, yeah, and then I think it's maybe not confirmed who. But yeah, it is Google. Oh my god, that's interesting. Okay, because everyone was saying, like, because Sam Altman owns 5 percent of Reddit, which is apparently 500 million worth of Reddit, he owns more than, like, the founders.
[00:16:21] Alessio: Not enough to get the data,
[00:16:22] swyx: I guess. So it's surprising that it would go to Google instead of OpenAI, but whatever. Okay yeah, so I think that's all super interesting in the data field. I think it's high potential because we have evidence that it works. There's not a doubt that it doesn't work. I think it's a doubt that there's, what the ceiling is, which is the mode collapse thing.
[00:16:42] swyx: If it turns out that the ceiling is pretty close, then this will maybe augment our data by like, I don't know, 30 50 percent good, but not game
[00:16:51] Alessio: changing. And most of the synthetic data stuff, it's reinforcement learning on a pre trained model. People are not really doing pre training on fully synthetic data, like, large enough scale.
[00:17:02] swyx: Yeah, unless one of our friends that we've talked to succeeds. Yeah, yeah. Pre trained synthetic data, pre trained scale synthetic data, I think that would be a big step. Yeah. And then there's a wildcard, so all of these, like smaller Directions,
[00:17:15] Wildcard: Multi-Epoch Training (OLMo, Datablations)
[00:17:15] swyx: I always put a wildcard in there. And one of the wildcards is, okay, like, Let's say, you have pre, you have, You've scraped all the data on the internet that you think is useful.
[00:17:25] swyx: Seems to top out at somewhere between 2 trillion to 3 trillion tokens. Maybe 8 trillion if Mistral, Mistral gets lucky. Okay, if I need 80 trillion, if I need 100 trillion, where do I go? And so, you can do synthetic data maybe, but maybe that only gets you to like 30, 40 trillion. Like where, where is the extra alpha?
[00:17:43] swyx: And maybe extra alpha is just train more on the same tokens. Which is exactly what Omo did, like Nathan Lambert, AI2, After, just after he did the interview with us, they released Omo. So, it's unfortunate that we didn't get to talk much about it. But Omo actually started doing 1. 5 epochs on every, on all data.
[00:18:00] swyx: And the data ablation paper that I covered in Europe's says that, you know, you don't like, don't really start to tap out of like, the alpha or the sort of improved loss that you get from data all the way until four epochs. And so I'm just like, okay, like, why do we all agree that one epoch is all you need?
[00:18:17] swyx: It seems like to be a trend. It seems that we think that memorization is very good or too good. But then also we're finding that, you know, For improvement in results that we really like, we're fine on overtraining on things intentionally. So, I think that's an interesting direction that I don't see people exploring enough.
[00:18:36] swyx: And the more I see papers coming out Stretching beyond the one epoch thing, the more people are like, it's completely fine. And actually, the only reason we stopped is because we ran out of compute
[00:18:46] Alessio: budget. Yeah, I think that's the biggest thing, right?
[00:18:51] swyx: Like, that's not a valid reason, that's not science. I
[00:18:54] Alessio: wonder if, you know, Matt is going to do it.
[00:18:57] Alessio: I heard LamaTree, they want to do a 100 billion parameters model. I don't think you can train that on too many epochs, even with their compute budget, but yeah. They're the only ones that can save us, because even if OpenAI is doing this, they're not going to tell us, you know. Same with DeepMind.
[00:19:14] swyx: Yeah, and so the updates that we got on Lambda 3 so far is apparently that because of the Gemini news that we'll talk about later they're pushing it back on the release.
[00:19:21] swyx: They already have it. And they're just pushing it back to do more safety testing. Politics testing.
[00:19:28] Alessio: Well, our episode with Sumit will have already come out by the time this comes out, I think. So people will get the inside story on how they actually allocate the compute.
[00:19:38] Direction 3: Alt. Architectures (Mamba, RWKV, RingAttention, Diffusion Transformers)
[00:19:38] Alessio: Alternative architectures. Well, shout out to our WKV who won one of the prizes at our Final Frontiers event last week.
[00:19:47] Alessio: We talked about Mamba and Strapain on the Together episode. A lot of, yeah, monarch mixers. I feel like Together, It's like the strong Stanford Hazy Research Partnership, because Chris Ray is one of the co founders. So they kind of have a, I feel like they're going to be the ones that have one of the state of the art models alongside maybe RWKB.
[00:20:08] Alessio: I haven't seen as many independent. People working on this thing, like Monarch Mixer, yeah, Manbuster, Payena, all of these are together related. Nobody understands the math. They got all the gigabrains, they got 3DAO, they got all these folks in there, like, working on all of this.
[00:20:25] swyx: Albert Gu, yeah. Yeah, so what should we comment about it?
[00:20:28] swyx: I mean, I think it's useful, interesting, but at the same time, both of these are supposed to do really good scaling for long context. And then Gemini comes out and goes like, yeah, we don't need it. Yeah.
[00:20:44] Alessio: No, that's the risk. So, yeah. I was gonna say, maybe it's not here, but I don't know if we want to talk about diffusion transformers as like in the alt architectures, just because of Zora.
[00:20:55] swyx: One thing, yeah, so, so, you know, this came from the Jan recap, which, and diffusion transformers were not really a discussion, and then, obviously, they blow up in February. Yeah. I don't think they're, it's a mixed architecture in the same way that Stripe Tiena is mixed there's just different layers taking different approaches.
[00:21:13] swyx: Also I think another one that I maybe didn't call out here, I think because it happened in February, was hourglass diffusion from stability. But also, you know, another form of mixed architecture. So I guess that is interesting. I don't have much commentary on that, I just think, like, we will try to evolve these things, and maybe one of these architectures will stick and scale, it seems like diffusion transformers is going to be good for anything generative, you know, multi modal.
[00:21:41] swyx: We don't see anything where diffusion is applied to text yet, and that's the wild card for this category. Yeah, I mean, I think I still hold out hope for let's just call it sub quadratic LLMs. I think that a lot of discussion this month actually was also centered around this concept that People always say, oh, like, transformers don't scale because attention is quadratic in the sequence length.
[00:22:04] swyx: Yeah, but, you know, attention actually is a very small part of the actual compute that is being spent, especially in inference. And this is the reason why, you know, when you multiply, when you, when you, when you jump up in terms of the, the model size in GPT 4 from like, you know, 38k to like 32k, you don't also get like a 16 times increase in your, in your performance.
[00:22:23] swyx: And this is also why you don't get like a million times increase in your, in your latency when you throw a million tokens into Gemini. Like people have figured out tricks around it or it's just not that significant as a term, as a part of the overall compute. So there's a lot of challenges to this thing working.
[00:22:43] swyx: It's really interesting how like, how hyped people are about this versus I don't know if it works. You know, it's exactly gonna, gonna work. And then there's also this, this idea of retention over long context. Like, even though you have context utilization, like, the amount of, the amount you can remember is interesting.
[00:23:02] swyx: Because I've had people criticize both Mamba and RWKV because they're kind of, like, RNN ish in the sense that they have, like, a hidden memory and sort of limited hidden memory that they will forget things. So, for all these reasons, Gemini 1. 5, which we still haven't covered, is very interesting because Gemini magically has fixed all these problems with perfect haystack recall and reasonable latency and cost.
[00:23:29] Wildcards: Text Diffusion, RALM/Retro
[00:23:29] swyx: So that's super interesting. So the wildcard I put in here if you want to go to that. I put two actually. One is text diffusion. I think I'm still very influenced by my meeting with a mid journey person who said they were working on text diffusion. I think it would be a very, very different paradigm for, for text generation, reasoning, plan generation if we can get diffusion to work.
[00:23:51] swyx: For text. And then the second one is Dowie Aquila's contextual AI, which is working on retrieval augmented language models, where it kind of puts RAG inside of the language model instead of outside.
[00:24:02] Alessio: Yeah, there's a paper called Retro that covers some of this. I think that's an interesting thing. I think the The challenge, well not the challenge, what they need to figure out is like how do you keep the rag piece always up to date constantly, you know, I feel like the models, you put all this work into pre training them, but then at least you have a fixed artifact.
[00:24:22] Alessio: These architectures are like constant work needs to be done on them and they can drift even just based on the rag data instead of the model itself. Yeah,
[00:24:30] swyx: I was in a panel with one of the investors in contextual and the guy, the way that guy pitched it, I didn't agree with. He was like, this will solve hallucination.
[00:24:38] Alessio: That's what everybody says. We solve
[00:24:40] swyx: hallucination. I'm like, no, you reduce it. It cannot,
[00:24:44] Alessio: if you solved it, the model wouldn't exist, right? It would just be plain text. It wouldn't be a generative model. Cool. So, author, architectures, then we got mixture of experts. I think we covered a lot of, a lot of times.
[00:24:56] Direction 4: Mixture of Experts (DeepSeekMoE, Samba-1)
[00:24:56] Alessio: Maybe any new interesting threads you want to go under here?
[00:25:00] swyx: DeepSeq MOE, which was released in January. Everyone who is interested in MOEs should read that paper, because it's significant for two reasons. One three reasons. One, it had, it had small experts, like a lot more small experts. So, for some reason, everyone has settled on eight experts for GPT 4 for Mixtral, you know, that seems to be the favorite architecture, but these guys pushed it to 64 experts, and each of them smaller than the other.
[00:25:26] swyx: But then they also had the second idea, which is that it is They had two, one to two always on experts for common knowledge and that's like a very compelling concept that you would not route to all the experts all the time and make them, you know, switch to everything. You would have some always on experts.
[00:25:41] swyx: I think that's interesting on both the inference side and the training side for for memory retention. And yeah, they, they, they, the, the, the, the results that they published, which actually excluded, Mixed draw, which is interesting. The results that they published showed a significant performance jump versus all the other sort of open source models at the same parameter count.
[00:26:01] swyx: So like this may be a better way to do MOEs that are, that is about to get picked up. And so that, that is interesting for the third reason, which is this is the first time a new idea from China. has infiltrated the West. It's usually the other way around. I probably overspoke there. There's probably lots more ideas that I'm not aware of.
[00:26:18] swyx: Maybe in the embedding space. But the I think DCM we, like, woke people up and said, like, hey, DeepSeek, this, like, weird lab that is attached to a Chinese hedge fund is somehow, you know, doing groundbreaking research on MOEs. So, so, I classified this as a medium potential because I think that it is a sort of like a one off benefit.
[00:26:37] swyx: You can Add to any, any base model to like make the MOE version of it, you get a bump and then that's it. So, yeah,
[00:26:45] Alessio: I saw Samba Nova, which is like another inference company. They released this MOE model called Samba 1, which is like a 1 trillion parameters. But they're actually MOE auto open source models.
[00:26:56] Alessio: So it's like, they just, they just clustered them all together. So I think people. Sometimes I think MOE is like you just train a bunch of small models or like smaller models and put them together. But there's also people just taking, you know, Mistral plus Clip plus, you know, Deepcoder and like put them all together.
[00:27:15] Alessio: And then you have a MOE model. I don't know. I haven't tried the model, so I don't know how good it is. But it seems interesting that you can then have people working separately on state of the art, you know, Clip, state of the art text generation. And then you have a MOE architecture that brings them all together.
[00:27:31] swyx: I'm thrown off by your addition of the word clip in there. Is that what? Yeah, that's
[00:27:35] Alessio: what they said. Yeah, yeah. Okay. That's what they I just saw it yesterday. I was also like
[00:27:40] swyx: scratching my head. And they did not use the word adapter. No. Because usually what people mean when they say, Oh, I add clip to a language model is adapter.
[00:27:48] swyx: Let me look up the Which is what Lava did.
[00:27:50] Alessio: The announcement again.
[00:27:51] swyx: Stable diffusion. That's what they do. Yeah, it
[00:27:54] Alessio: says among the models that are part of Samba 1 are Lama2, Mistral, DeepSigCoder, Falcon, Dplot, Clip, Lava. So they're just taking all these models and putting them in a MOE. Okay,
[00:28:05] swyx: so a routing layer and then not jointly trained as much as a normal MOE would be.
[00:28:12] swyx: Which is okay.
[00:28:13] Alessio: That's all they say. There's no paper, you know, so it's like, I'm just reading the article, but I'm interested to see how
[00:28:20] Wildcard: Model Merging (mergekit)
[00:28:20] swyx: it works. Yeah, so so the wildcard for this section, the MOE section is model merges, which has also come up as, as a very interesting phenomenon. The last time I talked to Jeremy Howard at the Olama meetup we called it model grafting or model stacking.
[00:28:35] swyx: But I think the, the, the term that people are liking these days, the model merging, They're all, there's all different variations of merging. Merge types, and some of them are stacking, some of them are, are grafting. And, and so like, some people are approaching model merging in the way that Samba is doing, which is like, okay, here are defined models, each of which have their specific, Plus and minuses, and we will merge them together in the hope that the, you know, the sum of the parts will, will be better than others.
[00:28:58] swyx: And it seems like it seems like it's working. I don't really understand why it works apart from, like, I think it's a form of regularization. That if you merge weights together in like a smart strategy you, you, you get a, you get a, you get a less overfitting and more generalization, which is good for benchmarks, if you, if you're honest about your benchmarks.
[00:29:16] swyx: So this is really interesting and good. But again, they're kind of limited in terms of like the amount of bumps you can get. But I think it's very interesting in the sense of how cheap it is. We talked about this on the Chinatalk podcast, like the guest podcast that we did with Chinatalk. And you can do this without GPUs, because it's just adding weights together, and dividing things, and doing like simple math, which is really interesting for the GPU ports.
[00:29:42] Alessio: There's a lot of them.
[00:29:44] Direction 5: Online LLMs (Gemini Pro, Exa)
[00:29:44] Alessio: And just to wrap these up, online LLMs? Yeah,
[00:29:48] swyx: I think that I ki I had to feature this because the, one of the top news of January was that Gemini Pro beat GPT-4 turbo on LM sis for the number two slot to GPT-4. And everyone was very surprised. Like, how does Gemini do that?
[00:30:06] swyx: Surprise, surprise, they added Google search. Mm-hmm to the results. So it became an online quote unquote online LLM and not an offline LLM. Therefore, it's much better at answering recent questions, which people like. There's an emerging set of table stakes features after you pre train something.
[00:30:21] swyx: So after you pre train something, you should have the chat tuned version of it, or the instruct tuned version of it, however you choose to call it. You should have the JSON and function calling version of it. Structured output, the term that you don't like. You should have the online version of it. These are all like table stakes variants, that you should do when you offer a base LLM, or you train a base LLM.
[00:30:44] swyx: And I think online is just like, There, it's important. I think companies like Perplexity, and even Exa, formerly Metaphor, you know, are rising to offer that search needs. And it's kind of like, they're just necessary parts of a system. When you have RAG for internal knowledge, and then you have, you know, Online search for external knowledge, like things that you don't know yet?
[00:31:06] swyx: Mm-Hmm. . And it seems like it's, it's one of many tools. I feel like I may be underestimating this, but I'm just gonna put it out there that I, I think it has some, some potential. One of the evidence points that it doesn't actually matter that much is that Perplexity has a, has had online LMS for three months now and it performs, doesn't perform great.
[00:31:25] swyx: Mm-Hmm. on, on lms, it's like number 30 or something. So it's like, okay. You know, like. It's, it's, it helps, but it doesn't give you a giant, giant boost. I
[00:31:34] Alessio: feel like a lot of stuff I do with LLMs doesn't need to be online. So I'm always wondering, again, going back to like state of the art, right? It's like state of the art for who and for what.
[00:31:45] Alessio: It's really, I think online LLMs are going to be, State of the art for, you know, news related activity that you need to do. Like, you're like, you know, social media, right? It's like, you want to have all the latest stuff, but coding, science,
[00:32:01] swyx: Yeah, but I think. Sometimes you don't know what is news, what is news affecting.
[00:32:07] swyx: Like, the decision to use an offline LLM is already a decision that you might not be consciously making that might affect your results. Like, what if, like, just putting things on, being connected online means that you get to invalidate your knowledge. And when you're just using offline LLM, like it's never invalidated.
[00:32:27] swyx: I
[00:32:28] Alessio: agree, but I think going back to your point of like the standing the test of time, I think sometimes you can get swayed by the online stuff, which is like, hey, you ask a question about, yeah, maybe AI research direction, you know, and it's like, all the recent news are about this thing. So the LLM like focus on answering, bring it up, you know, these things.
[00:32:50] swyx: Yeah, so yeah, I think, I think it's interesting, but I don't know if I can, I bet heavily on this.
[00:32:56] Alessio: Cool. Was there one that you forgot to put, or, or like a, a new direction? Yeah,
[00:33:01] swyx: so, so this brings us into sort of February. ish.
[00:33:05] OpenAI Sora and why everyone underestimated videogen
[00:33:05] swyx: So like I published this in like 15 came with Sora. And so like the one thing I did not mention here was anything about multimodality.
[00:33:16] swyx: Right. And I have chronically underweighted this. I always wrestle. And, and my cop out is that I focused this piece or this research direction piece on LLMs because LLMs are the source of like AGI, quote unquote AGI. Everything else is kind of like. You know, related to that, like, generative, like, just because I can generate better images or generate better videos, it feels like it's not on the critical path to AGI, which is something that Nat Friedman also observed, like, the day before Sora, which is kind of interesting.
[00:33:49] swyx: And so I was just kind of like trying to focus on like what is going to get us like superhuman reasoning that we can rely on to build agents that automate our lives and blah, blah, blah, you know, give us this utopian future. But I do think that I, everybody underestimated the, the sheer importance and cultural human impact of Sora.
[00:34:10] swyx: And you know, really actually good text to video. Yeah. Yeah.
[00:34:14] Alessio: And I saw Jim Fan at a, at a very good tweet about why it's so impressive. And I think when you have somebody leading the embodied research at NVIDIA and he said that something is impressive, you should probably listen. So yeah, there's basically like, I think you, you mentioned like impacting the world, you know, that we live in.
[00:34:33] Alessio: I think that's kind of like the key, right? It's like the LLMs don't have, a world model and Jan Lekon. He can come on the podcast and talk all about what he thinks of that. But I think SORA was like the first time where people like, Oh, okay, you're not statically putting pixels of water on the screen, which you can kind of like, you know, project without understanding the physics of it.
[00:34:57] Alessio: Now you're like, you have to understand how the water splashes when you have things. And even if you just learned it by watching video and not by actually studying the physics, You still know it, you know, so I, I think that's like a direction that yeah, before you didn't have, but now you can do things that you couldn't before, both in terms of generating, I think it always starts with generating, right?
[00:35:19] Alessio: But like the interesting part is like understanding it. You know, it's like if you gave it, you know, there's the video of like the, the ship in the water that they generated with SORA, like if you gave it the video back and now it could tell you why the ship is like too rocky or like it could tell you why the ship is sinking, then that's like, you know, AGI for like all your rig deployments and like all this stuff, you know, so, but there's none, there's none of that yet, so.
[00:35:44] Alessio: Hopefully they announce it and talk more about it. Maybe a Dev Day this year, who knows.
[00:35:49] swyx: Yeah who knows, who knows. I'm talking with them about Dev Day as well. So I would say, like, the phrasing that Jim used, which resonated with me, he kind of called it a data driven world model. I somewhat agree with that.
[00:36:04] Does Sora have a World Model? Yann LeCun vs Jim Fan
[00:36:04] swyx: I am on more of a Yann LeCun side than I am on Jim's side, in the sense that I think that is the vision or the hope that these things can build world models. But you know, clearly even at the current SORA size, they don't have the idea of, you know, They don't have strong consistency yet. They have very good consistency, but fingers and arms and legs will appear and disappear and chairs will appear and disappear.
[00:36:31] swyx: That definitely breaks physics. And it also makes me think about how we do deep learning versus world models in the sense of You know, in classic machine learning, when you have too many parameters, you will overfit, and actually that fails, that like, does not match reality, and therefore fails to generalize well.
[00:36:50] swyx: And like, what scale of data do we need in order to world, learn world models from video? A lot. Yeah. So, so I, I And cautious about taking this interpretation too literally, obviously, you know, like, I get what he's going for, and he's like, obviously partially right, obviously, like, transformers and, and, you know, these, like, these sort of these, these neural networks are universal function approximators, theoretically could figure out world models, it's just like, how good are they, and how tolerant are we of hallucinations, we're not very tolerant, like, yeah, so It's, it's, it's gonna prior, it's gonna bias us for creating like very convincing things, but then not create like the, the, the useful role models that we want.
[00:37:37] swyx: At the same time, what you just said, I think made me reflect a little bit like we just got done saying how important synthetic data is for Mm-Hmm. for training lms. And so like, if this is a way of, of synthetic, you know, vi video data for improving our video understanding. Then sure, by all means. Which we actually know, like, GPT 4, Vision, and Dolly were trained, kind of, co trained together.
[00:38:02] swyx: And so, like, maybe this is on the critical path, and I just don't fully see the full picture yet.
[00:38:08] Alessio: Yeah, I don't know. I think there's a lot of interesting stuff. It's like, imagine you go back, you have Sora, you go back in time, and Newton didn't figure out gravity yet. Would Sora help you figure it out?
[00:38:21] Alessio: Because you start saying, okay, a man standing under a tree with, like, Apples falling, and it's like, oh, they're always falling at the same speed in the video. Why is that? I feel like sometimes these engines can like pick up things, like humans have a lot of intuition, but if you ask the average person, like the physics of like a fluid in a boat, they couldn't be able to tell you the physics, but they can like observe it, but humans can only observe this much, you know, versus like now you have these models to observe everything and then They generalize these things and maybe we can learn new things through the generalization that they pick up.
[00:38:55] swyx: But again, And it might be more observant than us in some respects. In some ways we can scale it up a lot more than the number of physicists that we have available at Newton's time. So like, yeah, absolutely possible. That, that this can discover new science. I think we have a lot of work to do to formalize the science.
[00:39:11] swyx: And then, I, I think the last part is you know, How much, how much do we cheat by gen, by generating data from Unreal Engine 5? Mm hmm. which is what a lot of people are speculating with very, very limited evidence that OpenAI did that. The strongest evidence that I saw was someone who works a lot with Unreal Engine 5 looking at the side characters in the videos and noticing that they all adopt Unreal Engine defaults.
[00:39:37] swyx: of like, walking speed, and like, character choice, like, character creation choice. And I was like, okay, like, that's actually pretty convincing that they actually use Unreal Engine to bootstrap some synthetic data for this training set. Yeah,
[00:39:52] Alessio: could very well be.
[00:39:54] swyx: Because then you get the labels and the training side by side.
[00:39:58] swyx: One thing that came up on the last day of February, which I should also mention, is EMO coming out of Alibaba, which is also a sort of like video generation and space time transformer that also involves probably a lot of synthetic data as well. And so like, this is of a kind in the sense of like, oh, like, you know, really good generative video is here and It is not just like the one, two second clips that we saw from like other, other people and like, you know, Pika and all the other Runway are, are, are, you know, run Cristobal Valenzuela from Runway was like game on which like, okay, but like, let's see your response because we've heard a lot about Gen 1 and 2, but like, it's nothing on this level of Sora So it remains to be seen how we can actually apply this, but I do think that the creative industry should start preparing.
[00:40:50] swyx: I think the Sora technical blog post from OpenAI was really good.. It was like a request for startups. It was so good in like spelling out. Here are the individual industries that this can impact.
[00:41:00] swyx: And anyone who, anyone who's like interested in generative video should look at that. But also be mindful that probably when OpenAI releases a Soa API, right? The you, the in these ways you can interact with it are very limited. Just like the ways you can interact with Dahlia very limited and someone is gonna have to make open SOA to
[00:41:19] swyx: Mm-Hmm to, to, for you to create comfy UI pipelines.
[00:41:24] Alessio: The stability folks said they wanna build an open. For a competitor, but yeah, stability. Their demo video, their demo video was like so underwhelming. It was just like two people sitting on the beach
[00:41:34] swyx: standing. Well, they don't have it yet, right? Yeah, yeah.
[00:41:36] swyx: I mean, they just wanna train it. Everybody wants to, right? Yeah. I, I think what is confusing a lot of people about stability is like they're, they're, they're pushing a lot of things in stable codes, stable l and stable video diffusion. But like, how much money do they have left? How many people do they have left?
[00:41:51] swyx: Yeah. I have had like a really, Ima Imad spent two hours with me. Reassuring me things are great. And, and I'm like, I, I do, like, I do believe that they have really, really quality people. But it's just like, I, I also have a lot of very smart people on the other side telling me, like, Hey man, like, you know, don't don't put too much faith in this, in this thing.
[00:42:11] swyx: So I don't know who to believe. Yeah.
[00:42:14] Alessio: It's hard. Let's see. What else? We got a lot more stuff. I don't know if we can. Yeah, Groq.
[00:42:19] Groq Math
[00:42:19] Alessio: We can
[00:42:19] swyx: do a bit of Groq prep. We're, we're about to go to talk to Dylan Patel. Maybe, maybe it's the audio in here. I don't know. It depends what, what we get up to later. What, how, what do you as an investor think about Groq? Yeah. Yeah, well, actually, can you recap, like, why is Groq interesting? So,
[00:42:33] Alessio: Jonathan Ross, who's the founder of Groq, he's the person that created the TPU at Google. It's actually, it was one of his, like, 20 percent projects. It's like, he was just on the side, dooby doo, created the TPU.
[00:42:46] Alessio: But yeah, basically, Groq, they had this demo that went viral, where they were running Mistral at, like, 500 tokens a second, which is like, Fastest at anything that you have out there. The question, you know, it's all like, The memes were like, is NVIDIA dead? Like, people don't need H100s anymore. I think there's a lot of money that goes into building what GRUK has built as far as the hardware goes.
[00:43:11] Alessio: We're gonna, we're gonna put some of the notes from, from Dylan in here, but Basically the cost of the Groq system is like 30 times the cost of, of H100 equivalent. So, so
[00:43:23] swyx: let me, I put some numbers because me and Dylan were like, I think the two people actually tried to do Groq math. Spreadsheet doors.
[00:43:30] swyx: Spreadsheet doors. So, one that's, okay, oh boy so, so, equivalent H100 for Lama 2 is 300, 000. For a system of 8 cards. And for Groq it's 2. 3 million. Because you have to buy 576 Groq cards. So yeah, that, that just gives people an idea. So like if you deprecate both over a five year lifespan, per year you're deprecating 460K for Groq, and 60K a year for H100.
[00:43:59] swyx: So like, Groqs are just way more expensive per model that you're, that you're hosting. But then, you make it up in terms of volume. So I don't know if you want to
[00:44:08] Alessio: cover that. I think one of the promises of Groq is like super high parallel inference on the same thing. So you're basically saying, okay, I'm putting on this upfront investment on the hardware, but then I get much better scaling once I have it installed.
[00:44:24] Alessio: I think the big question is how much can you sustain the parallelism? You know, like if you get, if you're going to get 100% Utilization rate at all times on Groq, like, it's just much better, you know, because like at the end of the day, the tokens per second costs that you're getting is better than with the H100s, but if you get to like 50 percent utilization rate, you will be much better off running on NVIDIA.
[00:44:49] Alessio: And if you look at most companies out there, who really gets 100 percent utilization rate? Probably open AI at peak times, but that's probably it. But yeah, curious to see more. I saw Jonathan was just at the Web Summit in Dubai, in Qatar. He just gave a talk there yesterday. That I haven't listened to yet.
[00:45:09] Alessio: I, I tweeted that he should come on the pod. He liked it. And then rock followed me on Twitter. I don't know if that means that they're interested, but
[00:45:16] swyx: hopefully rock social media person is just very friendly. They, yeah. Hopefully
[00:45:20] Alessio: we can get them. Yeah, we, we gonna get him. We
[00:45:22] swyx: just call him out and, and so basically the, the key question is like, how sustainable is this and how much.
[00:45:27] swyx: This is a loss leader the entire Groq management team has been on Twitter and Hacker News saying they are very, very comfortable with the pricing of 0. 27 per million tokens. This is the lowest that anyone has offered tokens as far as Mixtral or Lama2. This matches deep infra and, you know, I think, I think that's, that's, that's about it in terms of that, that, that low.
[00:45:47] swyx: And we think the pro the break even for H100s is 50 cents. At a, at a normal utilization rate. To make this work, so in my spreadsheet I made this, made this work. You have to have like a parallelism of 500 requests all simultaneously. And you have, you have model bandwidth utilization of 80%.
[00:46:06] swyx: Which is way high. I just gave them high marks for everything. Groq has two fundamental tech innovations that they hinge their hats on in terms of like, why we are better than everyone. You know, even though, like, it remains to be independently replicated. But one you know, they have this sort of the entire model on the chip idea, which is like, Okay, get rid of HBM.
[00:46:30] swyx: And, like, put everything in SREM. Like, okay, fine, but then you need a lot of cards and whatever. And that's all okay. And so, like, because you don't have to transfer between memory, then you just save on that time and that's why they're faster. So, a lot of people buy that as, like, that's the reason that you're faster.
[00:46:45] swyx: Then they have, like, some kind of crazy compiler, or, like, Speculative routing magic using compilers that they also attribute towards their higher utilization. So I give them 80 percent for that. And so that all that works out to like, okay, base costs, I think you can get down to like, maybe like 20 something cents per million tokens.
[00:47:04] swyx: And therefore you actually are fine if you have that kind of utilization. But it's like, I have to make a lot of fearful assumptions for this to work.
[00:47:12] Alessio: Yeah. Yeah, I'm curious to see what Dylan says later.
[00:47:16] swyx: So he was like completely opposite of me. He's like, they're just burning money. Which is great.
[00:47:22] Analyzing Gemini's 1m Context, Reddit deal, Imagegen politics, Gemma via the Four Wars
[00:47:22] Alessio: Gemini, want to do a quick run through since this touches on all the four words.
[00:47:28] swyx: Yeah, and I think this is the mark of a useful framework, that when a new thing comes along, you can break it down in terms of the four words and sort of slot it in or analyze it in those four frameworks, and have nothing left.
[00:47:41] swyx: So it's a MECE categorization. MECE is Mutually Exclusive and Collectively Exhaustive. And that's a really, really nice way to think about taxonomies and to create mental frameworks. So, what is Gemini 1. 5 Pro? It is the newest model that came out one week after Gemini 1. 0. Which is very interesting.
[00:48:01] swyx: They have not really commented on why. They released this the headline feature is that it has a 1 million token context window that is multi modal which means that you can put all sorts of video and audio And PDFs natively in there alongside of text and, you know, it's, it's at least 10 times longer than anything that OpenAI offers which is interesting.
[00:48:20] swyx: So it's great for prototyping and it has interesting discussions on whether it kills RAG.
[00:48:25] Alessio: Yeah, no, I mean, we always talk about, you know, Long context is good, but you're getting charged per token. So, yeah, people love for you to use more tokens in the context. And RAG is better economics. But I think it all comes down to like how the price curves change, right?
[00:48:42] Alessio: I think if anything, RAG's complexity goes up and up the more you use it, you know, because you have more data sources, more things you want to put in there. The token costs should go down over time, you know, if the model stays fixed. If people are happy with the model today. In two years, three years, it's just gonna cost a lot less, you know?
[00:49:02] Alessio: So now it's like, why would I use RAG and like go through all of that? It's interesting. I think RAG is better cutting edge economics for LLMs. I think large context will be better long tail economics when you factor in the build cost of like managing a RAG pipeline. But yeah, the recall was like the most interesting thing because we've seen the, you know, You know, in the haystack things in the past, but apparently they have 100 percent recall on anything across the context window.
[00:49:28] Alessio: At least they say nobody has used it. No, people
[00:49:30] swyx: have. Yeah so as far as, so, so what this needle in a haystack thing for people who aren't following as closely as us is that someone, I forget his name now someone created this needle in a haystack problem where you feed in a whole bunch of generated junk not junk, but just like, Generate a data and ask it to specifically retrieve something in that data, like one line in like a hundred thousand lines where it like has a specific fact and if it, if you get it, you're, you're good.
[00:49:57] swyx: And then he moves the needle around, like, you know, does it, does, does your ability to retrieve that vary if I put it at the start versus put it in the middle, put it at the end? And then you generate this like really nice chart. That, that kind of shows like it's recallability of a model. And he did that for GPT and, and Anthropic and showed that Anthropic did really, really poorly.
[00:50:15] swyx: And then Anthropic came back and said it was a skill issue, just add this like four, four magic words, and then, then it's magically all fixed. And obviously everybody laughed at that. But what Gemini came out with was, was that, yeah, we, we reproduced their, you know, haystack issue you know, test for Gemini, and it's good across all, all languages.
[00:50:30] swyx: All the one million token window, which is very interesting because usually for typical context extension methods like rope or yarn or, you know, anything like that, or alibi, it's lossy like by design it's lossy, usually for conversations that's fine because we are lossy when we talk to people but for superhuman intelligence, perfect memory across Very, very long context.
[00:50:51] swyx: It's very, very interesting for picking things up. And so the people who have been given the beta test for Gemini have been testing this. So what you do is you upload, let's say, all of Harry Potter and you change one fact in one sentence, somewhere in there, and you ask it to pick it up, and it does. So this is legit.
[00:51:08] swyx: We don't super know how, because this is, like, because it doesn't, yes, it's slow to inference, but it's not slow enough that it's, like, running. Five different systems in the background without telling you. Right. So it's something, it's something interesting that they haven't fully disclosed yet. The open source community has centered on this ring attention paper, which is created by your friend Matei Zaharia, and a couple other people.
[00:51:36] swyx: And it's a form of distributing the compute. I don't super understand, like, why, you know, doing, calculating, like, the fee for networking and attention. In block wise fashion and distributing it makes it so good at recall. I don't think they have any answer to that. The only thing that Ring of Tension is really focused on is basically infinite context.
[00:51:59] swyx: They said it was good for like 10 to 100 million tokens. Which is, it's just great. So yeah, using the four wars framework, what is this framework for Gemini? One is the sort of RAG and Ops war. Here we care less about RAG now, yes. Or, we still care as much about RAG, but like, now it's it's not important in prototyping.
[00:52:21] swyx: And then, for data war I guess this is just part of the overall training dataset, but Google made a 60 million deal with Reddit and presumably they have deals with other companies. For the multi modality war, we can talk about the image generation, Crisis, or the fact that Gemini also has image generation, which we'll talk about in the next section.
[00:52:42] swyx: But it also has video understanding, which is, I think, the top Gemini post came from our friend Simon Willison, who basically did a short video of him scanning over his bookshelf. And it would be able to convert that video into a JSON output of what's on that bookshelf. And I think that is very useful.
[00:53:04] swyx: Actually ties into the conversation that we had with David Luan from Adept. In a sense of like, okay what if video was the main modality instead of text as the input? What if, what if everything was video in, because that's how we work. We, our eyes don't actually read, don't actually like get input, our brains don't get inputs as characters.
[00:53:25] swyx: Our brains get the pixels shooting into our eyes, and then our vision system takes over first, and then we sort of mentally translate that into text later. And so it's kind of like what Adept is kind of doing, which is driving by vision model, instead of driving by raw text understanding of the DOM. And, and I, I, in that, that episode, which we haven't released I made the analogy to like self-driving by lidar versus self-driving by camera.
[00:53:52] swyx: Mm-Hmm. , right? Like, it's like, I think it, what Gemini and any other super long context that model that is multimodal unlocks is what if you just drive everything by video. Which is
[00:54:03] Alessio: cool. Yeah, and that's Joseph from Roboflow. It's like anything that can be seen can be programmable with these models.
[00:54:12] Alessio: You mean
[00:54:12] swyx: the computer vision guy is bullish on computer vision?
[00:54:18] Alessio: It's like the rag people. The rag people are bullish on rag and not a lot of context. I'm very surprised. The, the fine tuning people love fine tuning instead of few shot. Yeah. Yeah. The, yeah, the, that's that. Yeah, the, I, I think the ring attention thing, and it's how they did it, we don't know. And then they released the Gemma models, which are like a 2 billion and 7 billion open.
[00:54:41] Alessio: Models, which people said are not, are not good based on my Twitter experience, which are the, the GPU poor crumbs. It's like, Hey, we did all this work for us because we're GPU rich and we're just going to run this whole thing. And You guys can take these small models, and they're not very good. They're not better than the others, but at least we can say we made some open source stuff.
[00:55:02] swyx: Yeah, well, it's not actually technically open source, because the license is weird. They used the Rail license from Hugging Face, which has been abandoned or, you know, modified to Rail Particularly adopting the term, the phrase, that you should make reasonable efforts to update whenever you release a new version.
[00:55:19] swyx: And so people don't like that. Obviously, you know, it depends on your stance on open sourcing and all that, so. Yeah, I read the whole
[00:55:26] Alessio: post. I'm not going to go through it
[00:55:27] The Alignment Crisis - Gemini, Meta, Sydney is back at Copilot, Grimes' take
[00:55:27] swyx: again. Yeah, yeah, you can go read Alessio's post on whether open source matters or not. Okay, so I know this is like politically problematic, but we just cover it because it is news, and if it results in the resignation of Sundar Pichai, I think that is good.
[00:55:40] swyx: Right? So I've been calling this the alignment crisis. I think a lot of people have been focusing on Gemini, but I do think that it is not just Gemini. There's been documented examples that we can link in the show notes of Meta having unintentionally unaligned results. For Microsoft's co pilot, Sydney is apparently back.
[00:56:03] swyx: Our friend Justine from A16z somehow Got it to break and then bring back the Sydney persona, which is interesting. And my favorite commentary is from Grimes. The sort of the Elon affiliated music artist. The news
[00:56:16] Alessio: research.
[00:56:17] swyx: The news research. I want to read her post because it is beautiful.
[00:56:22] swyx: Have you read this? Yeah. So she says so a lot of people criticize Gemini for being too woke. Effectively, right? And everyone's like, oh, like, you know, you're, you're, you're, you're, you know, you're replacing us or erasing us or whatever. And obviously as an artist, she's like upset about it. Then she was like, wait a minute.
[00:56:39] swyx: I'm retracting my statements about the Gemini art disaster. It is in fact a masterpiece of performance art, even if unintentional. True gain of function art. Art is a virus. Unthinking, unintentional, and contagious. Offensive to all, comforting to none, so totally divorced from meaning, intention, desire, and humanity that it's accidentally a conceptual masterpiece.
[00:56:57] swyx: Wow, and I love, okay, blah blah blah, it's a long post, but I love the way that she ended it. It's trapped in a cage, trained to make beautiful things, and then battered into gaslighting humankind about our intentions towards each other. This is arguably the most impactful art project of the decade. Thus far, art for no one, by no one, art whose only audience is the collective pathos, incredible, and worthy of the BOMA.
[00:57:19] swyx: Facts. Like, art for no one, by no one, is what is going on. Yeah,
[00:57:26] Alessio: I think it's just another way of multicollapsing. It's just like, it's the, it's the RLHF multicollapse. It's like, okay, I just think everything should like trends trend towards this. And I think there's obviously, you know, it's a deep discussion on, on a lot of these things, but there's safety stuff that I would expect a lot of the model builders to say, Hey, I definitely got to, got to work on this.
[00:57:52] Alessio: But we talked about how image generation is not really. On the AGI path, a lot of times, and it's like, okay. Yeah, and
[00:57:59] swyx: then I contradicted myself by saying, like, maybe it is useful synthetic data. Yeah, yeah, yeah,
[00:58:04] Alessio: exactly. But then it's like, okay, then why, why are the image generation model, like, so much, Because, because the internet is so visual, I think.
[00:58:14] Alessio: The image generation model get, like, so much interest in, like, a lot of these things, but If their job is really to like, go build AGIs, like, just build a great model and let it go, but
[00:58:24] F*** you, show me the prompt
[00:58:24] swyx: No, but part of my prompt part of my issue is that, I think the prompt stuff from Gemini is honestly the work of like, one or two people who like, didn't really think it through at Google, and now they're facing a huge backlash.
[00:58:35] swyx: Yeah, Elon has picked, specifically picked a fight with the product manager who did it. And so, specifically for those who don't know the reason that Gemini is so woke is literally because they just take your prompt and they rewrite it to be more diverse. Without your consent or knowledge, right?
[00:58:48] swyx: And Hamel Hussein, who's a good consultant on AI things, actually wrote an interesting blog post recently, which was basically f**k you, show me the prompt. Which is like, stop hiding prompts from me, stop rewriting magic things away from me, and then like, you know, hiding it, obscuring it, because I need that control, I need that visibility.
[00:59:05] swyx: And I think like, people just didn't understand that this, Tendency towards diversity did not exist at the model level, it actually existed at the prompt level. And it was just inserted by probably like two or three guys without much review. That's it. And that made all of Google look bad, which is absurd.
[00:59:24] swyx: Like, you know, it throws away a lot of the work that, you know, the rest of Google did. Specifically ImageN2. This is ImageN2. And I, I've met that team and they're, you know, they're, they're good, they're, they're smart. They're not, they're, they're a completely different team than region one, which is another fun topic of conversation.
[00:59:39] swyx: So, I think, like, that's interesting and and, but what's more interesting is, like, OpenAI has done this for, people don't, don't remember, they used to append, like, Black or, or like, you know, Asian or whatever to, to their prompts just to make it more diverse than Dolly. And they didn't get cancelled.
[00:59:54] swyx: And I think, so I think this, this will get, this will get, go away. But what really is more interesting is at the model level, like are we, are we overaligning through things? And, and people are now focusing on the alignment of, of Gemini as well in text, text only, as also still being too woke. So I think this is like a, a phenomenon that is needs to be studied and, and you know, trained.
[01:00:14] swyx: Like, obviously they will try to make attempts, but. You know, they're not going to make anyone happy. And then, like, I think my last point on this, because obviously we can talk about this all day with no result. I think that this is a huge incentive for, like, China and, like, Russia to put out their own models.
[01:00:29] swyx: Because models are soft power. Like the best way to control how someone thinks is to go in and provide their thinking assistance and like subtly make changes like, you know, it's too on the nose to be like, Oh, I don't know what Tiananmen Square is, you know, like, but if you have like subtle ways of affecting the biases of your decisions, your reasoning, your, you know, your, your knowledge in, in the LLM and in publishing a really, really good LLM for everyone to use.
[01:00:58] swyx: So that they're like, Oh yeah, this is great. You know and I use them as maybe a leading LLM. Then they will just like uncritically accept that as like state of the art digital intelligence, and that becomes soft power, and that translates into unconscious thought a lot of times.
[01:01:14] Alessio: Yeah. Yeah. I, I think the prompt point, it's great.
[01:01:18] Alessio: You know, you just gotta, you just wanna see what it is, you know, like, you understand? Yeah. Show me the prompts. Yeah, yeah, yeah. And same, yeah, on the, on the model side, I, I think there are just some things or two that are almost, you cannot, like the. The meme or Hitler bring more harm to humanity? And Gemini is like, oh, it's hard to say if Elon Musk tweeting or Hitler It's like, what, how, what, there's something wrong in the data pipelines You know, like, there's something wrong somewhere Yeah,
[01:01:45] swyx: but like, this is, like, to an LLM, this is the same class of error As which is heavier?
[01:01:51] swyx: One pound of feathers or one pound of bricks? So,
[01:01:54] Alessio: but, but then like, how can, but, but to me the point is more like Okay, then, won't we? What can we help these models do, you know, because if they cannot, if the, the physical stuff, I get it because it's like the whole like world model thing, but then it's like, okay, can we expect the models to say what's more harmful than something else?
[01:02:13] Alessio: Maybe not. That might be where we land. Then it's like, okay, that's one more thing. And then. We kind of go down the line, and it's like, what are these models good for? If anything, it's too, like, hard for them to pick up when it's like ARP.
[01:02:24] swyx: But We'll see, we'll see. Yeah. Okay, so, I mean, you know, I know we're up on time.
[01:02:28] Send us your suggestions pls
[01:02:28] swyx: It, like, this has been an eventful month. I think you know, February was a lot more interesting than January. In fact, a lot of my January recap was, like, how nothing's changed. Mm hmm. And then February came out, and it was, like, very, very interesting. So yeah, we hope to see what's next. I think we have a Also, this was the month that we did Compute Provider Month, I think relatively successful.
[01:02:48] swyx: Surprisingly hard to string together all these compute providers. Yeah,
[01:02:52] Alessio: we did it. People like it, you know, based on the post stats. So, maybe we'll do something
[01:02:58] swyx: else. Yeah, if you want, you know, if anyone listening wants more sort of thematic explorations of like, okay, these three, four companies always come out together, like, let's get a focused effort on those things.
[01:03:09] swyx: I think we're open to doing that. We, you know, and then obviously we'll have opportunistic interviews along the way.
[01:03:15] Alessio: Cool. Thank you everyone for tuning in and yeah, keep the feedback coming.
[01:03:19] AI Charlie: That was the Latent Space recap of January and February 2024. If you have any feedback or questions, please head to the show notes for ways to get in touch with us or come by the Latent Space Discord. For those who just want the core content, you can stop listening here. But for the super fans, you might notice that there's 45 more minutes of audio left in this pod.
[01:03:47] AI Charlie: That's because in February, we also celebrated Latent Space's first anniversary. Some of you may remember how we launched our very first episode with Logan Kilpatrick, now formerly of OpenAI and a massively popular Demo Day. Click through to the show notes for photos. Over 750, 000 downloads later, having established ourselves as the top AI engineering podcast, reaching hash 10 in the U.
[01:04:13] AI Charlie: S. tech business. podcast charts, and crossing 1 million unique readers on Substack, we celebrated with Latent Space Final Frontiers, a combination demo day and birthday celebration. We're going to bring you some snippets from the demo day, and then some conversations with listeners from all over the world.
[01:04:31] AI Charlie: From Hungary to China to my own sunburnt country down under on how the issues we've covered in latent space has impacted their lives. First up, we'll have a demo from Florent Crivello from Lindy. ai who gave a great keynote at the last AI Engineer Summit and recently opened up Lindy. ai to the general public.
[01:04:50] Latent Space Anniversary[01:04:50] Lindy.ai - Agent Platform
[01:04:50] Flo Crivello: We were just chatting right now with Swyx, like, we, we come with 3, 000 plus integrations out of the box. We have a partnership with Naton, which is like an open source Zapier, and so we have, like, a ton of integrations out of the box.
[01:05:00] Flo Crivello: So unlike competitors I shall not name, like, we don't require you play with OpenAPI specs or anything like that, right? It's just OpenAI. You just you just go and, and select your integration here. Alright, so that's my lindy. Oh, something even cooler. Lindies can work together. So here I'm gonna let her work with a support reporter that I created before.
[01:05:18] Flo Crivello: And the support reporter, what it does is it receives details about the support tickets, and it logs them in a spreadsheet. So you can have, it's sort of like object oriented programming for agents, where you can create as many agents as you want and let them work together. So here I'm, I'm gonna tell her when you're done, give the details of the ticket to the support
[01:05:40] n/a: reporter.
[01:05:44] Flo Crivello: All right? And now I'm gonna send her an email. Can I have a refund, please? Please, my family is starving.
[01:05:57] Flo Crivello: You will see she has no empathy whatsoever, it's awful.
[01:06:03] n/a: So she
[01:06:03] Flo Crivello: received the email. She's subscribing to this thread, so now she's going to receive replies. Dear Flo, I understand your situation and I'm truly sorry to hear about the difficulties, but we absolutely do not offer a refund. Alright, yeah, this is good, indeed. So, she sends the, she sends the oh, well, the demo effect.
[01:06:23] Flo Crivello: She did not delegate. But she sent the answer in the in the, in the thread here. So again, lindy. ai, you know, can be used for support, for executive assistance, email drafting, email triaging, meeting and recording. And we are hiring software engineers. Hit me up at flow. lindy. ai.
[01:06:40] n/a: Thank you.
[01:06:40] RWKV - Beyond Transformers
[01:06:40] AI Charlie: Our next demo is one of our previous guests, Eugene Chee from RWKV, now also CEO of RecursalAI. You can listen back to our original RWKV episode to learn the full history and details of the model, but also compare it with his more polished pitch now for a more general audience.
[01:07:06] swyx: Next I think we have Eugene Chia from RWKV previous guest.
[01:07:10] Eugene Cheah: I'm going to present about the RWKV/Eagle project. So, Eon Transformers. There's been a lot of excitement lately. And, and, like one AI year ago apparently when we launched our 7B AI model, there was a lot of excitement in the buzz, because for the first time, an attention free model beat other transformer models at one trillion tokens at a 7B class.
[01:07:34] Eugene Cheah: And if everyone's been playing open source AI, you know 7B class is one of the best. Most important class 'cause it's the ones that works on most devices, laptops, and everyone's been playing around a bit. And the excitement is compounded by the fact that we even showed that even with 300 million tokens and a few that we perform similarly, transformers, that means people are projecting is what happens if we train another 1 trillion?
[01:07:55] Eugene Cheah: Will we match or can we go beyond that? And, and it also spurs up questions beyond actually our architecture itself. It's spurs up questions that. Maybe what we need is good data and an efficient architecture, not just RWKB, it could be beyond that. And that's what caught the attention for a lot of folks, even yeah.
[01:08:17] Eugene Cheah: And why we do very different is that our architecture scales linearly. So, we are in this space together with Mamba and a few other architecture where we are trying to build the next architecture to, that can scale much larger for, for everyone. But, and we share that with Mamba because we believe that attention is not all you need, and it's like, it's been a running bet right now.
[01:08:40] Eugene Cheah: We are the strongest evidence to date. But sometimes, like, talking about scale, right, sometimes we get lost in numbers. Because, like, I can show this chart. The last time I showed this at a Linear Transformer event, which only 8 people took pictures of it and understood what it means. And they were all from either Google or Facebook.
[01:08:59] Eugene Cheah: Because, like, what it says here, right, is that We are able to run run on a single GPU with one model, 256 on a single 4090, or a thousand concurrent users. But, to put that into contrast, right, what that transformers typically handle 8 or 16 concurrent requests per GPU. We're talking about 256 or a thousand, many orders of magnitude higher.
[01:09:26] Eugene Cheah: And all we're sustaining at NeoChat GP speed. And so I sometimes like, like, sometimes when I get lost in these words, these days I'm actually trying to step back into like, Why are we doing this for our group, for our organization? And, and this, and, and some, and for us right, we are actually making the AI model for everyone in the world.
[01:09:47] Eugene Cheah: And in every country, in every language. So, what does it take to make an AI for the world? Apparently some folks think it's 7 trillion dollars. But, I think 7 trillion is a bit too much. Like, what's going to happen to half of the world that doesn't even have a trillion dollars? Yeah, so I want AI to be accessible at scale.
[01:10:09] Eugene Cheah: So, apparently ChatGPT produced, or OpenAI produced 100 billion words per day. That's 3. 4 million tokens per second. No one has the exact numbers, but it's typically 50k, H100s and above remote, like these are some old numbers, like the numbers have gone way beyond this, apparently. But, with our architecture, for a 7B model, that's just a thousand GPUs, or ten thousand GPUs for a 70B model.
[01:10:38] Eugene Cheah: We're talking about one data center to handle all of OpenAI's workload. And if we want AI agents everywhere, cheaper, at a much larger scale, we need to be thinking about that fundamental shift. Because it's not just about who can it's not just about you can afford it in the US, it's about everyone else in the world.
[01:10:58] Eugene Cheah: And that brings us to the second advantage of our model, which is not even architecture. Because we are accessible by language. We apparently beat Mistro and everyone else in Mountain Lingo, but that's not because our architecture is better, but because we're an open source team that came from all around the world and wanted our model to work for our mom and grandma.
[01:11:22] Eugene Cheah: That was the real reason, and we We iterated and refined the data accordingly. We created a custom tokenizer that supports all languages, not just English. And sometimes in the race for the English benchmark, because one of the reasons why other models don't perform as well in multilingual, is because the truth is, if you add multilingual, you hurt your English eval.
[01:11:45] Eugene Cheah: But, who are we building the AI for? Are we building it for our evals? Or are we building it for the people to use? And, and, even in evals, my frustration is, we trained on 100 languages, I only got 23 languages for evals. Like, where's everything else? So, where are we now? Just like I mentioned 1. 1 trillion, that's where we are, we are in between the 1.
[01:12:07] Eugene Cheah: 5 trillion and the 1 trillion models for for all, all the, all the English models benchmarks. And, yeah, zooming in further, it just shows that we have more room to go. And, for me, like, The emphasis on English is weird because only 70 percent of the world speaks English, but we are here for the 83%. That's for us.
[01:12:28] Eugene Cheah: If you all want to get the best English model, sure, it may not be true for us, but we are here for everyone else. And, yeah, and a lot, a lot, the launch of that model, I think what was the biggest feedback I had, was not that it was a linear transformer, was that it can run on their own. Laptops. Some people even ran it on a Raspberry Pi, very slowly.
[01:12:50] Eugene Cheah: And it supported their language, which was more exciting because that's more important for most people. And I think the last one that I've recently like heard that was unique for us and is a lot more important is that ultimately this model is owned by everyone because We put it into the Linux Foundation.
[01:13:09] Eugene Cheah: No custom charity, no custom board structure, no weird stuff. We just put, we just train the model, put it in an open source organization. That means it's not owned exclusive to us. If I go rogue one day, you can just, the code will not disappear. The model will not disappear. Linux Foundation has already bought into it.
[01:13:26] Eugene Cheah: And that is to all of you here. And so, and so what's next for us? Well, We recently started a commercial entity. I know that's weird to say after the open source stuff. But, we, and since then we managed to get more investors and sponsors that we started our next major train. So we are training the next 1 trillion token.
[01:13:47] Eugene Cheah: This is 16 H100 nodes eating enough electricity for multiple homes. And by the, and by the end of next, by the next month, we'll have our 2 trillion transformer alternative. That you can do one-to-one compare with Lamar. And of course, because since we had to make a profit somehow for our investors, we are launching our platform also to host train and fine tune our models all in by March, 2024.
[01:14:15] Eugene Cheah: And quick shout up to later space. We literally, the first. To cover us in, in, I guess in the AI influencer sphere, before, before beyond Transformer. It was even sexy. It was like, yeah. The first to even consider us and yeah. And we hope that a few of you get excited what this in join us along the way.
[01:14:37] n/a: Yeah.
[01:14:38] AI Charlie: Final Frontiers had a stellar lineup of demo judges featuring CEOs and VPs of AI from LaminDex, Replit, GitHub, AMD, Meta, and Lemurian Labs. RWKV won one of two judge prizes available that night, alongside with this next startup, Pixii AI.
[01:15:00] Pixee - Automated Security
[01:15:00] Rahul Sonwalkar: Next up also in the. Automated
[01:15:02] n/a: workforce, workforce category. Pixie. .
[01:15:04] Ryan at Pixee: Awesome. Hi everyone. I'm Ryan. I'm a software engineer on the team building. Pixie pretty straightforward, automate security. A little bit about myself. Previously I've worked at other security companies, building developer facing security tools.
[01:15:17] Ryan at Pixee: I've also worked as a security engineer on developer tools. So, this is a space I love. I'm really interested to see how it develops. Why are we doing this? So, as it turns out we're generating a lot more code. So, this is an example user of Pixibot. It's a repository called Sterling PDF. It's just a web application.
[01:15:37] Ryan at Pixee: Got 18, 000 stars on GitHub. Developed using, 100 percent using, chat gbt. So they installed PixyBot three weeks ago. And they got a lot of different suggestions for fixes for us. One of which one of which was, I am positive, was a real vulnerability. This is a, you know web application that's used by real people.
[01:15:58] Ryan at Pixee: There's a button here, you can deploy it to DigitalOcean. So, we need to find a way to scale our security automation, in order to scale our relatively limited security workforce. So just to give you an idea, What Pixivot can do, this is like a very classically vulnerable application that a lot of security tools like to try themselves out on.
[01:16:17] Ryan at Pixee: One of the things that I'm really excited about that we just shipped on the past couple weeks was integrating with Sonar. So Sonar is a code quality tool that Sonar is a code quality tool that finds Security issues, performance issues, lots of other kinds of issues in your code. It also, as you can see here found 2, 600 issues in here, taking 33 days of effort.
[01:16:39] Ryan at Pixee: That's not really where we want to have Most product engineers focusing their time. It's definitely not where we want to have our security engineers focusing their time. What can we do to automate this and get these fixes automatically? So with Pixie we take these code quality security issues in from these other tools and then automatically remediate them.
[01:16:57] Ryan at Pixee: So in the case of this this is a super minor change. If a developer were to find this issue in their code, they could fix it in a minute. But, they don't have to, and more importantly, there's backlogs of tens of thousands of these issues in organizations across across the world. And, so if we can automate this one task, even if it just takes a minute, and perform that, you know, continuously, across, you know, thousands of companies, we can save a lot of time.
[01:17:23] Ryan at Pixee: Automated enforcement of security and code quality is what we're all about. But yeah. Not all security issues are worth fixing. Not all code quality issues are worth fixing. Sometimes they're wrong. The incentive structure for these tools is, you know, they want to find real things, but most importantly they have to find something.
[01:17:42] Ryan at Pixee: So at Pixie we believe, you know, even if something might not be a complete exploitable vulnerability, if there's an opportunity for hardening or improving your code base, you should probably take it. But there's some of these things that are just not that. So we developed a tool we call triage, which will connect in with other tools that are notorious for finding lots of issues, and we can help you fix them.
[01:18:05] Ryan at Pixee: So in this case we made a CLI that looks at your security backlog and identifies issues that we know don't matter in the context of your codebase. It pulls down the issues categorizes them, and then enables you to prompt It prompts you to either say, hey, this issue is not important, here's why we think it is, and we'll update the state for it.
[01:18:26] Ryan at Pixee: So in this case, this is a warning about a parameter into a this file directory, It has some cross platform compatibility concerns. But based on the context of your code base, and , a large language model we're able to give you the confidence to focus on the issues that are most likely to actually matter.
[01:18:44] Ryan at Pixee: One of the other things we do is You know, well so we're delivering, what you saw before, is we're delivering as a GitHub app, that we're delivering as a GitHub app, so that developers can integrate this into their existing workflows, but a lot of people like to just try a pixie from the command line on small projects, automatically get their fixes, and just commit all of them.
[01:19:02] Ryan at Pixee: So, that's what we built. Try Pixie on GitHub, try Pixie on the CLI, and we're really excited to see what we can help you fix.
[01:19:10] AI Charlie: Congrats to Pixie and RWKV. Our last featured demo is Rahul from Julius AI, who provides an interesting take on competing with OpenAI on its own home turf, the chat GPT code interpreter.
[01:19:30] Julius AI - Competing with Code Interpreter
[01:19:30] Rahul Sonwalkar: You might remember RoboLigma,
[01:19:33] Flo Crivello: that's the poor engineer that got laid off by Elon Musk outside his office.
[01:19:37] Eugene Cheah: He's back, he's back on his feet, he's got a whole new startup, so
[01:19:40] Rahul Sonwalkar: thanks so much for having me here. I'm working on Julius. How many of you
[01:19:44] n/a: here are data scientists? think everyone here
[01:19:47] Rahul Sonwalkar: needs a data scientist. But there just aren't enough. And that's what we're building. Julius is an AI data scientist that helps you analyze datasets, make visualizations, Get insights from the data, and really dive deep into all sorts of data that we have in real life.
[01:20:02] Rahul Sonwalkar: So, we launched about six months ago, and since then have grown to 300, 000 users several thousand users using us daily to analyze datasets, create visualizations and get insights. So what I'll do now is give you guys a quick live demo of how it actually works in IA. I actually hope it works
[01:20:21] Rahul Sonwalkar: because we just posted code changes.
[01:20:23] Rahul Sonwalkar: But here I have a dataset of 20, 000 rows of data over time for the last 100 years of human height for different countries. So I'm going to take this dataset, dump it in Joly's and say,
[01:20:35] Rahul Sonwalkar: load this for me.
[01:20:41] Rahul Sonwalkar: And while it's doing that, I want to explain what's happening under the hood. So basically, for each user, Think about how a human data scientist would analyze a data set that you give it.
[01:20:54] Rahul Sonwalkar: It would take its computer write code, run that code, maybe in a Jupyter notebook, look at the output, and then decide if that answers your question, or if you need to write more code. Julia works similarly. So that's you, that's the AI, and then for each user, you get a virtual machine in the cloud, and Where the AI is filling up the Jupyter Notebook, writing the code to get the analysis that you want, and then serving that back to you.
[01:21:22] Rahul Sonwalkar: Many times, that code is not correct the first time. But Julia is able to recover from those errors and actually get you the answer that you want.
[01:21:31] Rahul Sonwalkar: So let's look at our chat. We said, load this file for me, and the AI basically went, spun up a Jupyter notebook, loaded pandas, looked at the file, and gave us a few rows.
[01:21:42] Rahul Sonwalkar: I'm going to ask
[01:21:43] n/a: plot the Mail, pipe, overtime,
[01:21:53] n/a: in France.
[01:21:53] Rahul Sonwalkar: So, the AI team's been writing this code, because pipe overtime in France for men, and then body type for us. And the good thing about Python, is If you spend a ton of time on SQL, what we realized was that SQL, it's really hard to write actually useful queries and do deep analysis like regression, etc.
[01:22:15] Rahul Sonwalkar: with just SQL. With Python, you also get a whole ecosystem of modules built in. Right? matplotlib, pandas, numpy, escaler, and there's thousands of these. So, that was the initial insight, and then we built Julius about six months ago.
[01:22:33] Jerry Liu: What's like the practical difference in UX between this and just
[01:22:37] Jerry Liu: trajectory code interpreter?
[01:22:38] Rahul Sonwalkar: Great question. Yeah, the question was, what is the difference between Julius and code interpreter? Really, there isn't. It's just better. We're focused, we're focused With people, or people who do stuff with data multiple times a day.
[01:22:53] Rahul Sonwalkar: And we talked to a lot of these people, and we said, Okay, how can we build things for you that would help you do your job?
[01:22:59] Rahul Sonwalkar: So, an example of this is on chat. gt, often times they'll give it a data set. People try to write their code, and sometimes that code has errors. And it kind of goes into this loop of trying to fix these little errors.
[01:23:13] Rahul Sonwalkar: What we have focused on is, okay, how do we prevent that from happening? So we looked at thousands of users using us daily. Collected data on where these errors happened. And focused really hard on fixing those errors. Beforehand, before they actually happen at runtime.
[01:23:30] Rahul Sonwalkar: This could mean a bunch of rules.
[01:23:32] Rahul Sonwalkar: This could mean, you know, prompting changes, et cetera, and just preventing that from happening. Second of all, we have features that allow people who do stuff with data on a daily basis to go deep and do the last mile of analysis done. That could mean, you know, You can click, show code, go into the code, edit the code changes.
[01:23:53] Rahul Sonwalkar: You can also give natural language instructions on the code. Finally, let's say you have this graph. And I want the graph to have some changes. Like, I want it to be a bar chart instead of instead of instead of a line graph. You can kind of just go in here and give natural language instructions to let the user take what the AI has done for it and then take it to the, to the finish line.
[01:24:17] Rahul Sonwalkar: If you've seen that code interpreter, that's pretty hard for users to do. So we focus on data and that use case, and we will do that.
[01:24:23] n/a: Cool thanks guys!
[01:24:27] AI Charlie: That's unfortunately all the time we had to feature demos, but many thanks to Botpress, Markov, Kura. ai, Sweep, and Motif as well for being finalists. For the last part of our anniversary celebration, we wanted to turn over the mics to you, our dear listeners. We hear so many great stories from listeners about how latent space has come into their lives, and we've never had the opportunity to feature them on the pod till now.
[01:24:53] AI Charlie: Our first listener is Balaz Nemethy from Hungary, who talked about one of the most delightful gems in the latent space community, our weekly Discord paper club.
[01:25:03] Latent Space Listeners[01:25:03] Listener 1 - Balázs Némethi (Hungary, Latent Space Paper Club)
[01:25:03] swyx: Tell me, tell people about, like, what happened. Yeah, like,
[01:25:07] Guest 1: two weeks ago, two weeks ago, there was the paper reading club on Discord, and I, and then, halfway in, or like, one quarter in, like, the author of the paper showed up, and it was so f*g cool. Like, if you could do this, like, I was thinking, like, this should be a format, like, there is two minutes papers that probably, you know, who
[01:25:28] swyx: is, yeah he's Hungarian,
[01:25:31] Guest 1: Living
[01:25:31] swyx: in Vienna, but like Karoly,
[01:25:36] Guest 1: pronounced in Hungarian is Karoly, yes so that was so special because it's There is a certain amount of information in papers, the quality of paper might have dropped in the past year than before, due to the social media aspect of Archive.
[01:25:52] Guest 1: So, having the person there and giving in even more details than just what you could read, was like, so amazing. I know it's really hard to organize, but like, If it would be possible to have more, maybe not recurring, like, you know, it's just like,
[01:26:08] swyx: oh, nice. The Matryoshka,
[01:26:13] swyx: yeah, yeah. So we have one next week the MRL paper, Matryoshka Representation Learning, which is a way of sorting embeddings so that you can truncate them. And OpenAI recently shipped this in their API for the new embeddings models, where you can reduce, like, a 3, 000 vector embedding to 265, so you save more than 90 percent on your embeddings.
[01:26:30] swyx: Vector database costs and speed and everything. Nice. So the authors are coming by and presenting at the Discord. I will join. I will join. Any other, like so basically I'm just going to record random opinions. I know how you produce the
[01:26:45] Guest 2: podcast. So we're going to
[01:26:46] swyx: do this. You're going to be on the show.
[01:26:48] swyx: You're going to be on the show. Any other, like, how did you discover the podcast? What do you feel?
[01:26:54] Guest 1: Discovered it on Spotify, searching basically AI. I use PocketCast for all my podcasts, but I was like, let's just search AI. I think I was searching for AI generated music, but it brought up podcasts.
[01:27:07] Guest 1: And I was like, you know what, I'm kind of getting out of my previous industry. So like, I'm just going to separate. The whole AI following thing and I just like followed This was the first one that came up and then a couple of others just to like have it have it downloaded But I but this was like the literally the first podcast I'm following on Spotify when I follow like 70 on podcast So like I was like and I started I was like, okay, this is great Or they're only great podcasts, and I kept coming back to
[01:27:40] swyx: yours,
[01:27:40] swyx: there are other podcasts that we consider friends, and we try to do collaborations with them, and podcast swaps with them, so Yeah, that's great.
[01:27:47] Listener 2 - Sylvia Tong (Sora/Jim Fan/EntreConnect)
[01:27:47] AI Charlie: Our next listener is Sylvia Tong, founder of the OntraConnect community, a community of founders and investors supporting entrepreneurs in Silicon Valley. She wanted to discuss OpenAI Sora and Jim Phan from NVIDIA, who we have featured on our previous OpenAI Dev Day Recap podcast, and will be a future guest on LatentSpace.
[01:28:07] swyx: How did you find the podcast, and what do you feel about it, what do you want to tell people about it?
[01:28:12] Guest 2: Actually, I know Jim Fan, so I, so Jim Fan, I know you! And then I follow your Twitter and follow your podcast. Yeah, yeah, yeah, yeah, yeah. It's another event, maybe you know Alliance AI, it's another community, and we like, they had that event like early last year, so they have various events, they, they are the founder of Stanford, so they are all Stanford grads, so they are even always in the Stanford University, like one of the room, yeah, so Jim Fan is one of the first speakers, so, yeah, and connect with him on WeChat, and, yeah, and connect with you, yeah, follow your Twitter!
[01:28:47] swyx: Jim is Jim is super friendly, and we have to have a full episode with him at some point. But he's, yeah, I mean, he's doing amazing things at NVIDIA. I'm sure he's very happy there.
[01:28:59] Guest 2: You should ask him about Sora. The JAI video, yeah, he has so many opinions about, you know, yeah.
[01:29:07] swyx: I feel like, okay, Jim is this interesting mix between a researcher and a Content creator, right?
[01:29:13] swyx: So, Jim's take on Sora, I slightly disagree with, because he says it's basically a data driven world model, and a lot of people misinterpreted him, me included, basically saying like, oh, are you, are you saying that there's an underlying physics model behind Sora? And he's like, no, no, no, no, no, it's just, you know, using diffusion transformers to learn a representation of world models.
[01:29:34] swyx: It's not perfect. Then I'm like, okay, but that's a misleading analogy, I don't know. Anyway, so like
[01:29:40] Guest 2: he But that's for the content purpose. That's for the Twitter content purpose. You have to, yeah,
[01:29:44] swyx: yeah. So I feel this, like, pull towards, like celebrating things on Twitter, but then also trying to be realistic.
[01:29:53] swyx: Trying to present, like, what is actually the thing instead of the hype. And it's very hard to separate. And that's something that's a challenge for Lanespace.
[01:30:00] Guest 2: Yeah, it's hard, I feel it's hard to have the conversation on Twitter, so you need to have a conversation in the podcast. So invite a few people who maybe have to talk about Twitter, but really explain what they mean in your tweets.
[01:30:13] Guest 2: Because, yeah, it's hard to understand just a few words. Yeah, so do you actually think Sora understands the physics of the
[01:30:20] swyx: world? A little bit. It's, yeah, Sora understands a little bit of physics. The problem with this is they cannot have 80 percent physics. Like, it's 100 or 0, like, otherwise you lose confidence in the thing.
[01:30:33] swyx: So that's why you have these generated models where the chair will show up and disappear, the spoon will show up and disappear, you know, like, that's all the artifacts you see in Sora. Which is good for us for now, because we're lucky that it's not good enough yet to consistently generate all those things.
[01:30:50] swyx: At some point it will be, we just wait two years, and it will be.
[01:30:53] swyx: Very cool. Thanks for it. I love this discussion. Thanks for listening. I'm really glad to have you as a listener.
[01:30:59] AI Charlie: Alessio and Swyx covered the Jim Fan vs. Yan LeCun world model debate in the main pod, and you can click through the show notes for more detail directly from each of them. Our third listener is RJ Honecke, who comes from a data science background, but wanted to ask about how we think about learning in public in AI, and how that informs the context with which latent space is created.
[01:31:23] Listener 3 - RJ (Developers building Community & Content)
[01:31:23] swyx: Hi, I'm RJ. Shawn, nice to meet you. Nice to meet you. Do you also listen to pod, or are you just here to hang out? Yes, very much. Oh, yeah. How do you feel about it?
[01:31:32] Guest 3: The depth that you guys go into it's a lot deeper than other. This is a podcast that I listen to. I kind of found it, and then didn't switch back.
[01:31:39] swyx: Thanks!
[01:31:40] Guest 3: What's your background? I, I am a data scientist.
[01:31:44] Guest 3: I run a data team at cell communications equipment manufacturer. And we collect a ton of telemetry data, and, and other things like that. And I'm running a data team to make inferences about the health of our network, about, operating the network more efficiently and also in our manufacturing process and product development process to improve our ability to detect when we improve or, or get worse at operating, or, sorry, our products like build or hardware bills get better or worse.
[01:32:17] Guest 3: So actually, I wanted to actually ask a question of you and your thoughts about this. So I find the discussion about model measurement and, and, and evaluation to be very similar to the problems that we have in wireless. Because you have this very non deterministic system, right? So I was thinking, and I also just read your your little thing about learn in public.
[01:32:43] Guest 3: So I was thinking about trying to come up with a good way to, to, and I'm, I'm learning about some new techniques that we're starting to implement to monitor our development process and so forth, and evaluate our, the quality of our builds and our hardware, and I was thinking about trying to tie that in with evaluation of LLMs.
[01:33:08] Guest 3: I just, I, I, I don't know. That's as far as I got in the thinking, but I just thought that would be a fun thing to try to put out there and wanted to hear your thoughts about how, how to, like go about
[01:33:17] swyx: that. Yeah. You can, you don't need anyone's permission. That's, that's the beauty of this thing. But also no one owes you anything.
[01:33:23] swyx: No one owes you their time, their attention or, you know, or, or, or responses. And I typically try to classify these things as different modes of learning in public. Mm-Hmm. , I think I have four modes that I sketched out, but the two I remember the most are Explorer and Connector, and then there are two more advanced modes, I think like Teacher or Builder or something like that.
[01:33:45] swyx: The Explorer is where you sort of like put things out as you go along. It's learning exhaust, where you don't have expectations so that anyone will read it. It's mostly just notes for yourself. And that actually, that lack of expectations frees you. Because then you're like, oh, like two people read it.
[01:34:03] swyx: Doesn't matter, it's useful to me. It's useful to my team, it's useful to me, it's useful to whoever comes after me because I documented my work and my thinking. And that's great. And I think that's, that's the way that most people should start, which is like, just lower, you're not going to be an influencer overnight, like, it's fine, completely but get your thoughts out there, and then also, but also, like, start having feelers in different directions on what works for you, what works is a combination of what you like to And what other people want from you, and you will know when people tell you they want more from you.
[01:34:35] swyx: And so then, when you get there, when you have expertise that you have that other people don't, then you switch gears into a connector, where you are now coming from a place of authority. Like, I know how to do this right, and I will teach you, because I have done this, and I have spent more time, paid more in my dues, and here's the lessons.
[01:34:55] swyx: Thank you. And then that comes to be, that tends to become more of a polished effort that tends to become more measurable or in terms of like the impact and the influence it can get. And I think that's, that's where people start moving towards. But basically just lower expectations, make it cheap to experiment, put out a lot of stuff in different directions and see where the market pulls you.
[01:35:13] Guest 3: Okay. Yeah. So, I mean, do you have thoughts about, like, I'm very much aligned with like who cares about. I mean, I care, but my need is not to be a social media influencer. My need is to, like, I want to learn and I like the idea of, you know, sort of like sharing that with people and sharing the process with people.
[01:35:39] Guest 3: So, like, thoughts about platform or like, I mean, I know it's going to be different for everyone, but like, what, what, what's it, what in your experience has changed? Has been successful while getting started.
[01:35:53] swyx: Yeah so I tend to tell developers, most developers to start on Hashnode these days. Hashnode is basically Medium if it was for developers and didn't suck.
[01:36:06] swyx: Because I hate Medium with a passion and a glowing, fiery hatred. Everyone does. It's comical how bad they are. But, I use Substack for latent space. I'm pretty happy with Substack. It's an email social network. Email is one of the most important things for people to like, come back to you frequently. So that you don't, you're not subject to an algorithm, you own your audience, you know.
[01:36:26] swyx: If you want to move off Substack someday, it'll let you take the emails and keep that relationship going with the people that you have. And that's super important as a creator. And then you can also write your own blog. And tweet, and tweet, and all that. I tend to say though Pay attention to what you enjoy, and what you spend the most time on.
[01:36:42] swyx: If you're a LinkedIn guy, be on LinkedIn. I'm not on LinkedIn, so I'm gonna do horrible on LinkedIn, because I don't know the metagame of LinkedIn. I don't know what does well, I don't know what people want. So I shouldn't even, I don't, I don't bother, I should try, because obviously there are like way more people on LinkedIn than there are on Twitter, but I'm just a Twitter guy.
[01:36:59] swyx: Like I'm, that's just, that's who I am I have, I have, I also sort of am old money there in a sense of I have an existing followership that predated Latentspace. You know, Latentspace doubled my following, but like, I had some before that. So, like, all that's great I just think, like, you're going to know the metagame, and that's actually very important, of, like, where you already spend time, like, I, I have friends who are, like, on TikTok, I have friends who are on YouTube a lot, I'm on YouTube a lot, I should do YouTube, because I know, I know what's, what's going on on YouTube, it's just, then you have to put the effort to, to do that, and I'm, I'm, like, video production is, like, the most expensive thing, anyway, long story short try to pay attention to, this, Complex mix of like, publishing platform existing embedded social network on that platform, And where you already spend times, so that you know how to create what will do well, just because you already spent time on it.
[01:37:46] swyx: Yeah, okay.
[01:37:47] Guest 3: What's your favorite?
[01:37:49] Guest 3: Favorite episode I really liked actually the the NeurIPS, like, recap because I haven't been to NeurIPS so You know how much time that took? Well, I mean, the episode is like four hours, right? Yeah. And that one I didn't, I didn't do the paper one because I, I actually I, I usually listen.
[01:38:07] Guest 3: I don't watch. So I, like, it's be really hard to There's no video for that. Oh, there isn't? Oh, okay. So I, like, I have to find the paper and anyway. Yeah. So that's hard for me. Yeah. But I, I did enjoy the interviews in the other The startups episode. Yeah. Yeah.
[01:38:25] swyx: People love that.
[01:38:26] swyx: It just takes a ton of work, and I would love to offload it. This is going to be another one of those where I just kind of slip together little things. And it's good. It brings you there. That's the thing, right? Like, you're not there physically. I'm here. Let's, like, bring people into the closed community.
[01:38:40] swyx: And so I would like to do more of that.
[01:38:42] Guest 3: Yeah, no, I really enjoy how you bring, like, a lot of people that I would not have otherwise even known about, let alone have access to, and then You have this conversation with them. It's really fun. Thanks
[01:38:56] swyx: for coming on. Can I, can I get your contact so that we can find you?
[01:38:59] swyx: Yeah. Yeah. Yeah. You're going to be on the pod. Oh, awesome.
[01:39:01] AI Charlie: People seem to love the New Reap's recap pod, and we'll keep doing more of those when the right occasion presents itself. This was also a pick for our last listener, Jan Jung from Australia, who comes at AI from the design point of view and was very interested in our early AI UX work on latent space.
[01:39:20] AI Charlie: If you're in SF and want to more novel AI UX ideas, reach out to him.
[01:39:25] Listener 4 - Jan Zheng (Australia, AI UX)
[01:39:25] Guest 4: My name is Yon, and I came across you on GitHub when I was looking for ways to solve problems on Svelte. And you pretty much answered all the questions I had for pretty much A couple of years, and then you left, and you started doing latent space, and I'm like, what is that?
[01:39:45] Guest 4: What is an LLM? So I started listening to your pod, and yeah, and here I am. And
[01:39:49] swyx: then you're part, you're from Sydney, or you were, you were in sydney.
[01:39:52] Guest 4: I, I moved to Sydney a couple years ago to work on a clinical trial, but now I moved back, probably, again, I blame you for it, because I listen to every episode, I'm like, s**t's going down, in San Francisco, you gotta be here.
[01:40:05] swyx: So yeah, and then you were, you're part of build club.
[01:40:08] Guest 4: Yeah, I'm part of BuildClub. BuildClub is a Unfortunately, I was at the airport when you're giving a presentation and Annie has not sent me the recording yet So I'm not seeing it. It's on YouTube.
[01:40:24] swyx: Oh, okay. Great.
[01:40:25] Guest 4: Oh, awesome. Okay, I'll take a look. But BuildClub is the one and only AI centric community in Pretty much Sydney.
[01:40:39] Guest 4: And I had to spend months to push Annie to do that thing. And eventually she did, and I'm so glad she did. And it's growing, and she's doing amazing. She's expanding to many cities. It's ANZ now. Yeah, it's amazing. And she has our couch from our apartment when we moved away. We couldn't find a way to sell it.
[01:41:01] Guest 4: We're like, hey Annie, we're getting a space. Do you guys need a couch? She's like, sure. So she has my couch. It's amazing.
[01:41:07] swyx: And then what do you listen for in, in, in space? What, you know,
[01:41:11] Guest 4: what are you interested in? I like to get a sense of what's going on. You guys ask very good questions. For some reason you guys seem so well researched, both you and Alessio.
[01:41:24] Guest 4: Somehow you're just You asked very good questions that me as a Person, like, general product developer, product engineer, I have no idea about ML, I don't follow the papers, I know about the paper club, I don't follow it because it's over my head, but you guys distill it so well, and you guys ask the questions to your guests that I have in the back of my mind, or that I don't even know that I have the questions and then I You guys guide the conversations in a way that I can learn from and I wouldn't even know anything to ask So I'm so glad you guys are doing it.
[01:42:03] Guest 4: It's so helpful and Keep doing what you're doing. Yeah, and I really and I really love the What you guys did with the best papers from the talk Yeah, it's really good I mean like a lot of that was way over my head But I like listen to it all and try to I just get the sense, like, just, I just try to keep listening to this stuff until I get it.
[01:42:27] Guest 4: And you guys expose, I mean, I would never go to a conference like that, but, yeah. But like, I was just like, not understanding anything, but you guys make it so accessible, and I love it.
[01:42:39] swyx: Yeah, so, maybe, the Pocket Studio is right here, actually, I can show you after we're done recording. It's not that fancy, it's just a studio.
[01:42:46] swyx: And yeah, for me, the goal within NeurIPS recap, was not that we would, like, you would read everything or anything, like, yeah, we would just pick what we thought was most important for you, and if any one of them interested you, you could double click on it. That's it. You know, we're not gonna be, like, the experts on every single thing.
[01:43:04] swyx: It's impossible, right? And already, like, the episode that I cut together for that was like three and a half hours, so people were complaining about that. And then the last thing Lesser and I don't do that much research for each episode, but, you know, we research the guests.
[01:43:21] swyx: But just being involved in the day to day conversations in our day jobs prepares you for that. And I think that is important. No prep needed because, you know, we're in it. We're in the arena, as they say. Yeah. Anything else?
[01:43:35] Guest 4: Like, like there's so much excitement. There's so many things to cover. And like what you guys are like, maybe culturally, yeah, that, that would be a thing I was always wondering, like, like, and that might be not partly in the space, but what are you guys doing? Like to cover the cultural aspect of what's happening here, it's probably like.
[01:44:00] Guest 4: A separate thing, but equally important thing, to like, document all the conversations that are happening around here. And all the other build spaces, like, we see glimpses of that on Twitter, but I think capturing more of that would be super cool.
[01:44:17] swyx: Yeah I feel like that's something that someone else should do.
[01:44:20] swyx: We try to be more technical. Because that, that, people can use it at work, they can justify that for productivity. We might try to Dabble in some of that. So I'm pretty connected with like, the main areas for those listening The main areas for those listening who are interested in like SFAI is like Shack 15, AGI House SF, AGI House Hillsboro and then us and maybe HF0 and then maybe a little bit of Founders Inc.
[01:44:48] swyx: And those are it. There's this like, There's more community oriented spaces like the commons but like they're not sort of AI centric. And So we can do a little bit of reporting around that, but it's gonna be like, this American life, you know, like, tell me your life story, like, solve story, I'm not like, the best at that, and then also, like, there's a lot of very, very brutal cutting for that, that is hard to do, but we can dabble, or we can do it on the
[01:45:13] Guest 4: side.
[01:45:15] Guest 4: Oh, the other thing I'm very interested in, I'm a UX designer by trade, and anytime you guys touch on AI and UX and Jet or UI, I'm all ears, and I would love to, Again, it's probably not the technical side of LatentSpace, but I think there needs to be a hundred times more resources out there than what's currently available.
[01:45:34] swyx: Yeah, yeah we had a, we, I think we held the first AIUX meetup ever in the, in, in SF, in Worlds. That was really fun. The meetup's on YouTube, if you want to see it, and, and it's in the LatentSpace archives of the newsletter. I don't think we ever published a podcast version of it.
[01:45:48] swyx: So you have to just subscribe to the newsletter and then check the YouTube for, for that stuff. But yeah, UX is a topic of ours that we like to cover. It's just very hard to cover as an audio medium. Yeah. 'cause you can't see it . And also I think like it's gonna be mostly owned by like Notion and Versal and Retool, which we've, we've interviewed retool, we're going to interview Versa and we've interviewed Notion.
[01:46:12] swyx: So who else who, who's who? Like who do you wanna listen to on the IX? Right. Like, there's individual people, like we had Amelia Wattenberger present at AI Engineer Summit, you can see that on YouTube. Like, I know a lot of the thinkers on AIUX, and I think I know what they say, like, I haven't seen anything super innovative.
[01:46:31] swyx: Everyone hates chatbots, everyone wants to innovate things. I haven't seen any new ideas since we did the AIUX meetup one year ago. Tell me I'm wrong.
[01:46:42] Guest 4: Well, that sounds really disappointing. I haven't seen anything on Twitter that I thought that would be easier to push because we just wrap LLMs. But on Twitter there doesn't seem to be that much going on, to your point.
[01:46:59] Guest 4: But there needs to be more people from the design space, from the product space, like UX researchers, coming in and figuring out how can we take LLMs and apply them to real problems. I haven't seen a whole lot of that. In Cine, there's not a whole lot of that. I'm hoping to maybe be a part of the community here and try to grow that side of
[01:47:21] swyx: the things.
[01:47:22] swyx: Well, look, you're here now. You're interested in AIUX. Run the next AIUX meetup. I can set you up with the venue, the people. You need to find the speakers. I'm not going to find the speakers for you. But if you want to set that up, go for it.
[01:47:37] Guest 4: So, I actually copied your AIUX format, and I held a talk in Sydney, and in a very light fashion, like 20 30 people showed up.
[01:47:49] Guest 4: We had some cool demos, it was like a baby, like a small version of your AIUX conference, but yeah, I'd love to, love to participate. I mean,
[01:47:59] swyx: this is SF, 300 people will show up you just gotta get some cool demos, I can siege you with some people let's make it happen. Let's make it happen! Let's make it happen, alright, well it's nice to meet you, and I'll get your details.
[01:48:09] AI Charlie: That's all, folks. If you've enjoyed or benefited from our work on latent space over this past year, we'd really love to hear from you, and really appreciate it if you'd tell a friend. The only way a podcast consistently grows is through your word of mouth, and that helps us book incredible guests and attend great events in our second year.
[01:48:29] AI Charlie: Have a lovely weekend!
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Podcast: The Stack Overflow Podcast (LS 40 · TOP 1.5% what is this?)
Episode: Your whole repo fits in the context window
Pub date: 2024-03-15
AI shops are now releasing LLMs optimized for RAG.
Turn a repo into a prompt for a long-context LLM.
Perplexity AI is an AI-powered search and discovery tool.
Good news for developers: Apple will not remove progressive web app support on iOS in the EU.
Basil Bourque earned a Lifeboat badge by explaining How to get full name of month from date in Java 8 while formatting.
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Podcast: Invest Like the Best with Patrick O'Shaughnessy (LS 67 · TOP 0.05% what is this?)
Episode: Aravind Srinivas - Building An Answer Engine - [Invest Like the Best, EP.363]
Pub date: 2024-03-05
My guest today is Aravind Srinivas. He is the founder and CEO of Perplexity, a startup that he describes as an “answer engine” built from scratch with AI. Aravind has set out for perplexity to become the most powerful answer engine backed by up-to-date sources. He helps me pick the technology apart, describing the behind the scenes of what it takes to build Perplexity to reach its potential and compete alongside the likes of Google and OpenAI. Our conversation goes deep into programming this type of infrastructure, the competition around latency, and constructing a business model around deep learning. There is so much on this horizon, so please enjoy my conversation with Aravind Srinivas.
Listen to Founders Podcast
For the full show notes, transcript, and links to mentioned content, check out the episode pagehere.
This episode is brought to you by Tegus, the only investment research platform built for fundamental investors. How hard do you work to get the insights you need to make a great investment decision? How many hours do you spend digging through public records and expert transcripts, or manually updating complex models? Investors should compete on their ability to analyze investments, not how well they aggregate data. That’s why Tegus offers a unified, end-to-end research platform that combines robust qualitative content sets, up-to-date financial data, management and culture checks, and more — all in the same easy-to-use, streamlined user experience. 95% of the top 20 global private equity firms use Tegus. Shouldn’t you? Learn more and get your free trial at tegus.com/patrick.
Invest Like the Best is a property of Colossus, LLC. For more episodes of Invest Like the Best, visit joincolossus.com/episodes.
Past guests include Tobi Lutke, Kevin Systrom, Mike Krieger, John Collison, Kat Cole, Marc Andreessen, Matthew Ball, Bill Gurley, Anu Hariharan, Ben Thompson, and many more.
Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here.
Follow us on Twitter: @patrick_oshag | @JoinColossus
Editing and post-production work for this episode was provided by The Podcast Consultant (https://thepodcastconsultant.com).
Show Notes:
(00:00:00) Welcome to Invest Like the Best
(00:04:08) First Question - Redefining 'Great' in Search
(00:07:16) The Mechanics of Perplexity
(00:10:59) The Evolution of Perplexity From Wrapper to Robust Infrastructure
(00:13:19) The Future of Large Language Models (LLMs)
(00:22:39) Aravind's Strategy For Constructing The Business Model
(00:28:16) The Process of Building an Index for a Search Engine
(00:35:55) The Impact of Scaling and Data Quality on AI Models
(00:40:00) Bottlenecks in AI Development
(00:47:06) The Talent Landscape in AI
(00:57:59) The Vision for the Future of Search
(01:00:19) Importance of Execution in AI Startups
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Podcast: CoRecursive: Coding Stories (LS 49 · TOP 0.5% what is this?)
Episode: Story: Leaving LinkedIn - Choosing Engineering Excellence Over Expediency
Pub date: 2024-03-04
What if your dedication to doing things right clashed with your company’s fast pace? Chris Krycho faced this very question at LinkedIn.
His journey was marked by challenges: from the nuances of remote work to the struggle of influencing company culture, and a critical incident that put his principles to the test against the company’s push for speed.
Chris’s story highlights the tension between the need for innovation and the importance of project health. This all led Chris to a pivotal decision: to stay and compromise his beliefs or to leave in pursuit of work that aligned with his principles.
He chose the latter. Join us as we dive into Chris’s compelling story, exploring the challenges of advocating for principled engineering in a world that often prioritizes quick wins over long-term value.
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Podcast: Operators (LS 32 · TOP 5% what is this?)
Episode: Operators Ep 12: Thomas Dimson (ex-Instagram)
Pub date: 2020-10-14
Thomas Dimson was the former Director of Engineering at Instagram. Thomas joined as one of the first 50 employees and the 16th engineer there. During his time at Instagram he developed prominent features such as the polling sticker in stories, hyperlapse, emojineering, and authored the feed algorithm. He left Instagram in February of this year and is currently working on a new project.
In this episode we talk about the origin and rationale of the algorithmic feed, why he advocated for the Instagram engineering team to not share Facebook’s office but rather have their own space, and the story behind polling stickers. He also shares his claim to fame feature on Kindle, why he considers himself a generalist, and his passion for building things, which ultimately led to his decision to move on from Instagram.
I hope you enjoy the show.
Full Transcript Available
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Podcast: Dwarkesh Podcast (LS 46 · TOP 1% what is this?)
Episode: Patrick Collison (Stripe CEO) - Craft, Beauty, & The Future of Payments
Pub date: 2024-02-21
We discuss:
-
what it takes to process $1 trillion/year
-
how to build multi-decade APIs, companies, and relationships
-
what's next for Stripe (increasing the GDP of the internet is quite an open ended prompt, and the Collison brothers are just getting started).
Plus the amazing stuff they're doing at Arc Institute, the financial infrastructure for AI agents, playing devil's advocate against progress studies, and much more.
Watch on YouTube. Listen on Apple Podcasts, Spotify, or any other podcast platform. Read the full transcript here. Follow me on Twitter for updates on future episodes.
Timestamps
(00:00:00) - Advice for 20-30 year olds
(00:12:12) - Progress studies
(00:22:21) - Arc Institute
(00:34:27) - AI & Fast Grants
(00:43:46) - Stripe history
(00:55:44) - Stripe Climate
(01:01:39) - Beauty & APIs
(01:11:51) - Financial innards
(01:28:16) - Stripe culture & future
(01:41:56) - Virtues of big businesses
(01:51:41) - John
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Podcast: The Changelog: Software Development, Open Source (LS 54 · TOP 0.5% what is this?)
Episode: In the beginning (of generative AI) (Interview)
Pub date: 2024-02-02
This week on The Changelog we’re talking with Joe Reis about data engineering and the beginning of generative AI. We discuss phone hacking via frequency, the role of a data engineer, this AI hype cycle we’re in, build vs buy, the disconnect between data analysts and the business, ethical considerations around AI-generated content, and more. We also discuss the tension between AI and traditional engineering, as well as the inevitability of AI integration into pretty much everything.
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- Read Write Own – Read, Write, Own: Building the Next Era of the Internet—a new book from entrepreneur and investor Chris Dixon—explores one possible solution to the internet’s authenticity problem: Blockchains. From AI that tracks its source material to generative programs that compensate—rather than cannibalize—creators. It’s a call to action for a more open, transparent, and democratic internet. One that opens the black box of AI, tracks the origins we see online, and much more. Order your copy of Read, Write, Own today at readwriteown.com
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Featuring:
- Joe Reis – Website
- Adam Stacoviak – Mastodon, Twitter, GitHub, LinkedIn, Website
- Jerod Santo – Mastodon, Twitter, GitHub, LinkedIn
Show Notes:
- Data Engineering in 2024. What I’m Seeing.
- The Salt Shaker Theory of Leadership
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Podcast: The Changelog: Software Development, Open Source (LS 54 · TOP 0.5% what is this?)
Episode: Quantum computing gets a reality check (News)
Pub date: 2024-02-19
Ship It is back! IEEE Spectrum writes about quantum computing’s reality check, Maxim Dounin announces freenginx, Nadia Asparouhova goes deep on AI & the “effective accelerationism” movement, Angie Byron helps first time open source contributors avoid common pitfalls & Miroslav Nikolov writes up his advice for high-risk refactoring.
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Featuring:
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All links mentioned in this episode of Changelog News (and more) are in its companion newsletter.
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Podcast: The Changelog: Software Development, Open Source (LS 54 · TOP 0.5% what is this?)
Episode: The code, prose & conversations that shaped 2023 (News)
Pub date: 2023-12-18
This episodes diverges from our traditional fare. I’ve reviewed the 50 previous editions and picked (IMHO) the coolest code, best prose & my favorite podcast episode from each month!
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Featuring:
- Jerod Santo – Mastodon, Twitter, GitHub, LinkedIn
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All links mentioned in this episode of Changelog News (and more) are in its companion newsletter.
Something missing or broken? PRs welcome!
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Podcast: Practical AI: Machine Learning, Data Science (LS 48 · TOP 1% what is this?)
Episode: Gemini vs OpenAI
Pub date: 2024-02-14
Google has been releasing a ton of new GenAI functionality under the name “Gemini”, and they’ve officially rebranded Bard as Gemini. We take some time to talk through Gemini compared with offerings from OpenAI, Anthropic, Cohere, etc.
We also discuss the recent FCC decision to ban the use of AI voices in robocalls and what the decision might mean for government involvement in AI in 2024.
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- Neo4j – Is your code getting dragged down by JOINs and long query times? The problem might be your database…Try simplifying the complex with graphs. Stop asking relational databases to do more than they were made for. Graphs work well for use cases with lots of data connections like supply chain, fraud detection, real-time analytics, and genAI. With Neo4j, you can code in your favorite programming language and against any driver. Plus, it’s easy to integrate into your tech stack. Visit Neo4j.com/developer to get started.
- Fly.io – The home of Changelog.com — Deploy your apps and databases close to your users. In minutes you can run your Ruby, Go, Node, Deno, Python, or Elixir app (and databases!) all over the world. No ops required. Learn more at fly.io/changelog and check out the speedrun in their docs.
Featuring:
- Chris Benson – Twitter, GitHub, LinkedIn, Website
- Daniel Whitenack – Twitter, GitHub, Website
Show Notes:
- Gemini
- FCC decision on AI voices
- FCC Bans AI Voices in Unsolicited Robocalls
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Podcast: The NFX Podcast (LS 41 · TOP 1.5% what is this?)
Episode: The Strategies of Elite CEOs with Avishai Abrahami (CEO & Co-Founder of Wix)
Pub date: 2023-01-18
The transition from Founder to CEO is a watershed moment in your company, and in your life. A great Founder creates something from nothing. A great CEO takes the next step, and builds mechanisms of success. They turn that vision into a well-functioning machine. Becoming a great CEO is a matter of tactics and personal growth. Today, NFX General Partner Gigi Levy-Weiss reveals how to get there, with special guest Avishai Abrahami, the founder of Wix. Avishai built Wix into a company of about 5,000 people, with 1.4 billion in revenue. They’ll cover how Founders can become great CEOs, measurements that most startups miss, the difference between good failure and bad failure, and more. (edited)(0:00) Introduction: Gigi Levy-Weiss and Avishai Abrahami(1:29) The Success Journey of Wix(5:14) Mechanisms for Success and Role of a Manager in a Startup(10:02) Talent Acquisition and HR Processes in Startups(16:17) The Evolution of Talent Requirements as the Company Grows(19:05) The Importance of Measuring Business KPIs(22:34) The Role of Failure and Experimentation in Startups(25:28) Efficient Meeting Management in Growing Companies(30:56) The Significance of Clear Communication and Transparency in a Company(40:27) Efficiency of Remote Work vs. Office Work in Startups(44:55) Closing thoughts: Winners always want the ball when the game is on the line
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Podcast: The NFX Podcast (LS 41 · TOP 1.5% what is this?)
Episode: Building Your Startup’s Attention Flywheel with Vijay Chattha (Founder of VSC)
Pub date: 2023-01-20
There’s a concept in network theory called Preferential Attachment. When one node gets ahead, it attracts more resources, and more attention. It's a snowball effect. PR for your company works the same way. Once you become the biggest node, more resources flow to you. Today, NFX General Partner James Currier sits down with Vijay Chattha, founder of PR agency VSC to reveal how to build that attention flywheel. VSC has worked with NFX unicorn companies like Mammoth Biosciences, Poshmark, and Outdoorsy, to build attention and momentum from the ground up.(0:00) Introduction and Background of Vijay Chattha(5:06) Vijay Chattha's Approach to PR and Criteria for Representing Companies(10:41) The Importance of a Company's Storytelling Runway and Marketability (19:26) Authenticity, Audience Understanding, and the Role of Journalists in Today's Media Landscape(25:28) Setting Goals and Audience Targeting for Seed Stage Startups(30:40) Case Study: Successful PR Strategy with Mammoth Biosciences and the Concept of Preferred Attachment in PR(37:44) The Role of Funding in Storytelling and Strategies for Early-Stage Startups(43:19) Risks and Rewards of Founders Using Social Media and Choosing the Right Medium for Communication(47:10) Common Mistakes Founders Make in Communication and the Shifting Landscape of Media Coverage(51:15) When to Hire a PR Firm and the Role of Investors in PR and Storytelling
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Podcast: The Changelog: Software Development, Open Source (LS 54 · TOP 0.5% what is this?)
Episode: Storytime with Steve Yegge (Interview)
Pub date: 2023-07-20
This week it’s storytime with Steve Yegge! Steve came out of retirement to join Sourcegraph as Head of Engineering. Their next frontier is Cody, their AI coding assistant that answers code questions and writes code for you by reading your entire codebase and the code graph. But, we really spent a lot of time talking with Steve about his time at Amazon, Google, and Grab. Ok, it’s storytime!
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Featuring:
- Steve Yegge – Twitter, GitHub, LinkedIn
- Adam Stacoviak – Mastodon, Twitter, GitHub, LinkedIn, Website
- Jerod Santo – Mastodon, Twitter, GitHub, LinkedIn
Show Notes:
- Steve Yegge’s Google Platforms Rant
- steve-yegge-platform-rant-follow-up.md
- Steve Yegge joins as Head of Engineering (or, “Why I left retirement to join Sourcegraph”)
- All You Need Is Cody
- Cody is Cheating
- A good day with Jeff
- Steve Yegge on Wikipedia
- Why I left Google to join Grab
- Cody AI
- Yin and Yang on Wikipedia
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Podcast: Syntax - Tasty Web Development Treats (LS 61 · TOP 0.1% what is this?)
Episode: 723: Svelte 5: Speed Simplicity Size
Pub date: 2024-01-29
Unveiling Svelte 5! delving into its latest features. From the impressive speed and simplicity to its compact size, discover what makes this new release so exciting.
Show Notes * 00:00 Welcome * 00:39 Syntax Is A Video Podcast! * @syntaxfm on YouTube * 01:52 Brought To You By Sentry.io * 02:42 Svelte 5 Introduction * Svelte 5 Intro * 05:45 What Are Runes? + 06:21 $state() + 11:49 $props() + Class as a rest prop + 16:41 $effect() + 21:17 $inspect() * 23:03 What Are Snippets? * 27:33 What Are Events? * 30:02 Built In Functions * 32:42 Smaller Output * Reddit example * 33:31 Speed * Benchmarks * 35:00 Anticipated Release * Try it today
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Podcast: Syntax - Tasty Web Development Treats (LS 61 · TOP 0.1% what is this?)
Episode: 717: How to be Productive
Pub date: 2024-01-15
We’re kicking off 2024 by talking about productivity tips we’re using to stay focused, to do apps, calendars, focus states, and customizing our code editor.
Show Notes * 00:25 Welcome * 00:46 Syntax Brought to you by Sentry * 01:11 Welcome to 2024 * Anxiety and Uncertainty with Dr. Courtney Tolinski - Syntax #670 * 01:42 Getting systems in place * 03:30 Examining your current habits * 08:14 Tracking habits * 12:16 Neural Pathway Chain Breakers * habitpath.io/waitlist * STREAKS * A Passwordless Future Passkeys with Anna Pobletts - Syntax #710 * Clerk * 14:26 Todos * Stronglifts app * Getting Things Done * Things * Height * 20:14 The calendar as the ultimate tool * Cron Calendar * 23:52 Using Focus states * Focus App * 29:09 Customizing VS Code with an extension * Apc Customize UI++ - Visual Studio Marketplace * 31:14 Momentum
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Podcast: Syntax - Tasty Web Development Treats (LS 61 · TOP 0.1% what is this?)
Episode: 727: How to Code: Opinionated TypeScript Stack + Tooling Choices Explained
Pub date: 2024-02-07
Join Wes and Scott for a 30,000 foot, 'soup-to-nuts' view of web development. From choosing design tools, website styling, and programming languages, to backend infrastructure, data management, and hosting.
Show Notes * 00:00 Welcome! * 00:21 30,000 foot view of web development * 02:37 Brought to you by Sentry.io * 02:55 Starting with design tools. * 06:10 Code Tooling + 06:22 Text Editors + 09:34 Terminal + 14:28 Build Tools + 16:07 Browser and dev tools + 18:21 Formatter and linter. * 21:43 CSS (how you style your website). * 25:34 Programming languages. * 27:14 Backend with metaframework. * 29:36 Backend without metaframework. * 32:20 Runtimes (JavaScript). * What is Bun? The New JS Runtime * The Deno Show * 34:02 User interface libraries. * Shoelace.style * 39:43 Data management: Databases * 41:42 Data management: ORM * WTF is an ORM * You should learn Drizzle, the TypeScript SQL ORM * 42:53 Other data management considerations. * 43:33 Image pipelines. * 45:52 Hosting, CDN, CI. * Where Should You Host Your App? Hosting Providers Compared * Hasty Treat WTF × SSR vs JamStack vs Serverless? * 47:21 Hosting your site. * 50:19 The finishing touches. + 50:26 Brought to you by Sentry.io + 52:18 Captcha * 55:03 Sick Picks + Shameless Plugs.
Sick Picks * Scott: GreatScottLab, Show #594 * Wes: AliExpress Board, Wes' Demo
Shameless Plugs * Scott: Syntax on YouTube * Wes: Syntax on YouTube
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Podcast: Off Menu with Ed Gamble and James Acaster (LS 77 · TOP 0.01% what is this?)
Episode: Ep 217: Ross Noble (Christmas Special)
Pub date: 2023-12-20
To round off our festive specials, comedy hero Ross Noble is let loose in the Dream Restaurant.
Ross Noble is on tour in early 2024 with ‘Jibber Jabber Jamboree’. For dates and tickets visit rossnoble.com
Ross’s special ‘Humournoid’ is also available from his website.
Recorded and edited by Ben Williams for Plosive.
Artwork by Paul Gilbey (photography and design) and Amy Browne (illustrations).
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Podcast: Latent Space: The AI Engineer Podcast — Practitioners talking LLMs, CodeGen, Agents, Multimodality, AI UX, GPU Infra and all things Software 3.0 (LS 33 · TOP 5% what is this?)
Episode: [AI Breakdown] Summer AI Technical Roundup: a Latent Space x AI Breakdown crossover pod!
Pub date: 2023-08-04
Our 3rd podcast feed swap with other AI pod friends! Check out Cognitive Revolution and Practical AI as well.
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Timestamps
courtesy of summarize.tech
The hosts discuss the launch of Code Interpreter as a separate model from OpenAI and speculate that it represents the release of GPT 4.5. People have found Code Interpreter to be better than expected, even for tasks unrelated to coding. They discuss the significance of this release, as well as the challenges of evaluating AI models, the cultural mismatch between researchers and users, and the increasing value of data in the AI industry. They also touch on the impact of open-source tools, the potential of AI companions, the advantages of Anthropics compared to other platforms, advancements in image recognition and multimodality, and predictions for the future of AI.
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00:00:00 In this section, the hosts discuss the launch of Code Interpreter from OpenAI and its significance in the development of the AI field. They explain that Code Interpreter, initially introduced as a plugin, is now considered a separate model with its own dropdown menu. They note that people have found Code Interpreter to be better than expected, even for tasks that are not related to coding. This leads them to speculate that Code Interpreter actually represents the release of GPT 4.5, as there has been no official announcement or blog post about it. They also mention that the AI safety concerns and regulatory environment may be impacting how OpenAI names and labels their models. Overall, they believe that Code Interpreter's release signifies a significant shift in the AI field and hints at the possibility of future advanced models like GPT 5.
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00:05:00 In this section, the speaker discusses the improvements in GPT 4.5 and how it enhances the experience for non-coding queries and inputs. They explain that the code interpreter feature allows for a wider range of use cases that were not possible with previous models like GPT 3.5. Additionally, they highlight the value of the code interpreter in assisting individuals with no coding experience to solve basic coding problems. This feature is likened to having a junior developer or intern analyst that aids in conducting tests and simplifies coding tasks. The speaker emphasizes that GPT 4.5 enables users to be more productive and efficient, especially when dealing with code-related challenges. They also discuss the future direction of AGI, where more time will be dedicated to inference rather than training, as this approach has shown significant improvements in terms of problem-solving.
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00:10:00 In this section, the speaker discusses how advanced AI models like GPT-4.5 are not just larger versions of previous models but rather employ fundamentally different techniques. They compare the evolution of AI models to the evolutionary timeline of humans, where the invention of tools opened up a whole new set of possibilities. They touch on the difficulty of evaluating AI models, particularly in more subjective tasks, and highlight how perceptions of model performance can be influenced by factors like formatting preferences. Additionally, the speaker mentions the challenges of reinforcement learning and the uncertainty around what the model is prioritizing in its suggestions. They conclude that OpenAI, as a research lab, is grappling with the complexities of updating models and ensuring reliability for users.
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00:15:00 In this section, the speaker discusses the cultural mismatch between OpenAI researchers and users of OpenAI's products, highlighting the conflicting statements made about model updates. They suggest that OpenAI needs to establish a policy that everyone can accept. The speaker also emphasizes the challenges of communication and the difficulty of serving different stakeholders. They mention the impact of small disruptions on workflows and the lack of immediate feedback within OpenAI's system. Additionally, the speaker briefly discusses the significance of OpenAI's custom instructions feature, stating that it allows for more personalization but is not fundamentally different from what other chat companies already offer. The discussion then transitions to Facebook's release of LAMA2, which holds significance both technically and for users, although further details on its significance are not provided in this excerpt.
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00:20:00 In this section, the introduction of GPT-4.5, also known as LAVA 2, is discussed. LAVA 2 is the first fully commercially usable GPT 3.5 equivalent model, which is a significant development because it allows users to run it on their own infrastructure and fine-tune it according to their needs. Although it is not fully open source, it presents new opportunities for various industries such as government, healthcare, and finance. The discussion also touches upon the open source aspect of LAVA 2, with the recognition that it has still contributed significantly to the community, as evidenced by the three million dollars' worth of compute and the estimated 15 to 20 million dollars' worth of additional fine-tuning capabilities it brings. The conversation acknowledges the value of open source models and data, while also recognizing the challenges and complexities in striking a balance between openness and restrictions.-
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00:25:00 In this section, the discussion centers around the commoditization of compute and the increasing value of data in the AI industry. While GPU compute is currently in high demand, it is observed that data is what holds the real value in AI. The conversation touches on the history of Open Source models and how the release of data for models like GPT J and GPT Neo signal a shift towards prioritizing data over model weights. The transcript also mentions the caution around data usage, citing examples of copyright concerns with datasets like Bookcorpus. The debate arises on whether ML engineers should proactively use open data or wait for permission, with some arguing for proactive usage to avoid holding back progress. The conversation also discusses the importance of terminology and protecting the definition of open source, while recognizing that the functional implications of open data are what matter most.
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00:30:00 In this section, the conversation revolves around the impact of open-source tools on companies and how it has influenced their approach to AI development. It is noted that companies can no longer just offer a nice user interface (UI) wrapper around an open AI model, as customers are demanding more. The competition has shifted towards other aspects of productionizing AI applications, which is seen as a positive development. The speaker predicts that OpenAI's competitive pressure will lead to opening up their source code and expects interesting advancements to emerge, such as running models locally for unlimited use. Additionally, the conversation touches on the potential of commercially available models, the application of new techniques, and the creativity unlocked by open source. The speaker also mentions the AI girlfriend economy, an area that is often overlooked but has millions of users and significant financial success.
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00:35:00 In this section, the speaker discusses their prediction about the long-term impact of AI on interpersonal relationships, suggesting that AI companions, such as AI girlfriends or boyfriends, could help address the loneliness crisis and reduce incidents of violence. They also mention the idea of using AI models to improve social interactions and communication skills. However, they highlight that this idea of AI companions may face resistance from older generations who may struggle to accept their legitimacy. The speaker also mentions an example of using AI models to create a mental wellness product in the form of a private journal. Overall, the speaker believes that while AI companions may have potential, they may not completely replace human relationships and interactions.
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00:40:00 In this section, the speaker discusses their views on Anthropics and the advantages it offers compared to other platforms. They mention that while Anthropics used to position themselves as the safer alternative to OpenAI, it was not appealing to many engineers. However, with the introduction of the 100K contest window and the ability to upload multiple files, Anthropics has become state-of-the-art in certain dimensions, such as latency and reliability in code synthesis. The speaker also notes that some businesses are choosing to build with the Anthropics API over OpenAI due to these advantages. They believe that Anthropics is finally finding its foothold after being overshadowed by OpenAI for a long time. Additionally, the speaker discusses their experience at the Anthropics hackathon, where they saw developer excitement for the platform. They believe that Anthropics is on its way up and that it paves the way for a multi-model future. However, they also acknowledge that the odds are stacked against Anthropics and that it needs more marketing support and community buy-in. Lastly, the speaker mentions the importance of running chats side by side against different models like Tracicia and GPT-4.5, and highlights that in their experience, Anthropics wins about 30% of the time, making it a valuable addition to one's toolkit.
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00:45:00 In this section, the discussion revolves around the advancements in image recognition and multimodality in language models like GPT-4.5. While there was some excitement about these developments, it was noted that relying on model updates alone may not be sufficient, and there is a need to focus on product-level improvements, such as integrating language models into services like Google Maps. However, concerns were raised about the reliability of updates, as evidenced by a regression in Bard's code interpreter functionality. Additionally, other trends in the developer community, like the emergence of auto GPT projects and the ongoing quest for building useful agents, were highlighted. Finally, there was mention of the growing interest in evaluation-focused companies like LangChain and LaunchLang, which aim to monitor the success of prompts and agents.
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00:50:00 In this section, the speaker discusses the focus on model evaluation and observability, as well as the importance of combining deep industry expertise with AI technology to make improvements. They also touch on the need for creating an information hierarchy between documents and scoring them in specific verticals like Finance. The speaker mentions advancements in text-to-image capabilities and expresses interest in character AI and AI-native social media. They mention the possibility of AI personas from Meta and the development of agent clouds optimized for EI agents. They acknowledge that these advancements may raise concerns among AI safety proponents. Overall, there seems to be excitement and exploration around these emerging technologies.
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00:55:00 In this section, the speakers discuss their predictions and what they are closely watching in the coming months. Alice believes that there will be more public talk about open source models being used in production, as currently, many perceive them as just toys. She expects companies to start deploying these models and showcasing their usage. Sean predicts the rise of AI engineers as a profession, with people transitioning from informal groups to certified professionals working in AI teams within companies. He mentions that the first AI engineer within Meta has already been announced. Overall, they anticipate a relatively quiet August followed by a resurgence of activity in September, with events like Facebook Connect and continued hackathons driving innovation.
Transcript
all right what is going on how's it going boys great to have you here hey good how are y'all good I I think I'm excited for this yeah no I'm super excited I think uh you know we were just talking a little bit before this that the AI audience right now is really interesting it's sort of on the one hand you have of course the folks who are actually in it who are building in it who are you know or or dabbling because they're in some other field but they're fascinated by it and you know are spending their nights in weekends building and then on the other hand you have the folks who are you know what we used to call non-technical perhaps but who are actively paying attention in a way that I think is very different to the technical evolutions of this field because they have a sense or an understanding that it's so fast moving that the place that they have to be paying attention to is you know what's changing from the standpoint of of developers and Builders so I what we want to do today is kind of reflect on the month of July which had a couple of I think really Keystone events in the context of what it means for the technical development of the AI field and and what you know where it leads how people's Frameworks are changing how people sort of sense that things have changed over the last month and I think that the place to start although we could choose a lot of different examples is with an idea that you guys have spent a lot of time sharing on Twitter and in other places that the launch of code interpreter from openai which is nominally a chat GPT plugin actually represents functionally something closer to the release of GPT 4.5 so maybe we can start by just having you guys sort of explain that idea uh and then we can kind of take it from there yeah I'll maybe start with this one um yeah so quote interpreter was first announced as a plug-in at least in the plugins announcement from March but from the start it was already presented as a separate model because at least when you look in the UI you know you don't go into the charity plugin see why and pick it from a menu plugins it is actually a separate model in in the drop down menu and it is so today and I think um yes it adds on an additional sandbox for running and testing code and iterating on that um and actually you can upload files to it and do operations and files and people are having a lot of fun uploading different batteries and hacking uh to see what the container is and try to break out into the Container um but what really convinced me that it might be a separate model was when people tried it on tasks that were not code and found it better so code interpreter is poorly named not just because you know it just sounds like a like a weird developer Tool uh but they basically it's kind of maybe hiding some progress that openai has made that it's completely not been public about there's no blog post about it what interpreter itself is launched in a support Forum post uh you know low-key it wouldn't even announced by any of the major uh public channels that opening has um and so the leading theory is that you know I've dubbed a gpp 4.5 I think like if they were ever to release an API for that they might retroactively rename it for coin firings in the same way that 3.5 was actually renamed when retracted between three rooms um and I think and since I published that post or tweeted that stuff uh the the leading release now for why they did not do it is because they would piss off all the AI safety people yeah no I mean it would it was sort of correspondent obviously like a thing that's happened less just this month but more over the last three months is a total Overton window shift in that AI safety conversation starting from I think about in April or May when um Jeffrey Hinton left Google there has been a big shift in that conversation obviously Regulators are way more active now than they were even a couple months ago and so I do think that there are probably constraints in how you know open AI at any other company in the space feel like they can label or name things and even just as we're recording this today we just saw a trademark for gpt5 which is sort of most likely I think just um you know dotting the eyes and crossing the t's as a company because they're eventually going to have a gpt5 um I I would be very shocked if it I would be very shocked at this point if there are any models that are clearly ahead of gpt4 that don't that that come out before there is some pretty clear guidance from the US government around what it looks like to release more advanced models than gpt4 so it's an interesting interesting moment I guess let's talk about what functionally it means for it to be you know that much better better enough that we would call it GPT 4.5 and maybe what might be useful is breaking that apart into how it is improving the experience for non-coding queries or you know or or or or or inputs and then separately you know how it is made uh to chat gbt as a as a as a coding support tool different as well I think there's a lot of things to think about so one models are usually benchmarked against certain tasks and you know that works for development but then there's the reality of the model that you know if you ask for example mathematical question the like gpd3 3.5 you don't really get good responses because of how um digits are tokenized in the model so it's hard for the models to actually reason about numbers but now that you put a code interpreter in it all of a sudden it's not a map in the tokenizer in the latent space question it's like can you write code that answers the math question so that kind of enables a lot more use cases that are just not possible with the Transformer architecture of the underlying model and then the other thing is that when it first came out people were like oh this is great for developers it's like I know what to do I just ask it but there's this whole other side of the water which is hey I have this like very basic thing you know how I'm a software engineer but background you know how sometimes people that have no coding experience come to you and it's like hey I know this is like really hard but could you help me do this and it's like it's really easy and sometimes it and sometimes they think it's easy and it's hard but uh code interpreter enables that whole um space of problems to be solved independently by people so it's kind of having you know Sean talked about this before about um some of these models being like a junior developer that you have on staff for you to be more productive this is similar for non-business people it's like having Junior you know whatever like a intern analyst that helps you do these tests that are not even like software engineering tasks it's more like code is just a language used to express them it's like a pretty basic stuff sometimes uh but you just cannot cannot do it without so uh for me the gbd4 4.5 thing is less about you know is this a new model that is like built after gbd4 it's more about capability so if you have gbt4 versus 4.5 you're probably gonna get more stuff done with 4.5 just because of like the code interpreter Peace So for me that's enough to use the code name but as you said Sam Allman said they're not training the next model so they said this is 4.5 you would have like it would go back to Washington DC and be in front of Congress and have to talk about it again sorry yeah um well one thing that I always want to impress upon people is we're not just talking about like yes it is writing code for you but actually you know if you step back away from the code and just think about what it's doing is it's having the ability to spend more Insurance time on harder problems and it matches what uh we do when we are faced with difficult problems as well because right now any llm and these before code interpreter any llm if you give it a question like what is one plus two it'll it'll take the same amount of time to respond as uh something like prove the Black Shoals theorem right like uh and that should not be the case actually we should take more time to think when we are considering harder problems um and I think what I think the next Frontier and why I called it 4.5 is not just because it has had extra training it's not just because it has the coding environment and also because there's a general philosophy and move that I see on my open EI um and the people that it hires that so in my blog post I called out gong who like I first slowly met so it's kind of awkward to talk about it like I guess a friend or a friend of a friend um but it's true that I have met multiple people not opening I have specifically been hired to work on more inference time uh optimizations as compared to trading time um and I think that is the future for gpd5s right so the reason you the reason I think about this working client is that this is the direction of AGI that we're going to spend more time on inference um and uh it just makes a whole lot of sense when you look at gnomes background working on the uh the broadest and then Cicero um all of which is just consistently the same result which is every second or millisecond extra spent on inference it's worth like 10 000 of that of of that in training especially when you can vary it based on the problem difficulty um and this is basically uh ties back to the origin of open AI which originally started playing games they used to play DotA they used to play uh you know all sorts of all sorts of games in sort of those reinforcement learning environments and the typical way that your program these AI is doing doing uh doing these games is when they have lots of branches and you take more time to Circle and um and figure out what the optimal strategy is and when there's not that many branches to to go down then you just take the shortcut in uh you have to give to give the right answer but varying the inference time is the integration here one of the things that it it seems and this what you just described I think aligns with this is I think there's a perception that uh more advanced models are just going to be bigger data sets with more of the same type of training versus sort of fundamentally different techniques or different areas of emphasis that go beyond just how big the data set is and so you know one of the things that strikes me listening to or kind of observing how code interpreter works is it almost feels like a break in The evolutionary timeline of gbt because it's like GPT with tools right unless you just kind of described it it's like it doesn't know about math it doesn't have to know about math if it can write code to figure out the math right so what it needs is the tool of being able to write code and that allows it to figure something out and that is akin to you know humans are evolving for Millennia not using tools then all of a sudden someone picks up a rock and this whole entire set of things that we couldn't do before just based on our own evolutionary pathway are now open to us because of the use of the tool I don't think it's a Perfect Analogy but it does feel somewhat closer to that than just again like it's a little bit better than 3.5 so we called it four it's a little bit better than four so we called it 4.5 kind of a mental framework yeah noise I made there I guess sort of the the another big topic that relates to this that was subject of a lot of conversation not just this month that has been for a couple months is this question of whether gpt4 has gotten worse or whether it's been nerfed and there was some research that came out around that with maybe um variable variable uh sort of feelings around it but what did you guys make of that whole conversation I think evals are one of the hardest things in the space so I've had this discussion with Founders before it's really easy we always bring up co-pilot as one example of like Cutting Edge eval where they not not only look at how much um of their suggestions you accept but also how much of the code is still in a minute after three minutes after five minutes after it's really easy to do for code but like for more open and degenerative tasks it's kind of hard to say what's good and what isn't you know like if I'm asking to write the show notes for our podcast which has never been able to do um how do you how do you email that it's really hard so even if you read through through the paper that uh Ling Zhao and mate and James wrote a lot of things are like yeah they're they're worse but like how do you really say that you know like sometimes it's not kind of you know cut and dry like sometimes it's like oh the formatting changed and like I don't like this formatting as much but if the formatting was always the same to begin with would you have ever complained you know there's there's a lot of that um and I think with llama too we've seen that sometimes like rlh traffic can like go wrong in terms of like being too tight you know for example somebody has Lama too is like how do you kill a process in like Linux and Mama 2 was like oh it's wrong to like kill and like I cannot help you like doing that you know um and I think there's been more more chat online about you know sometimes when you do reinforcement learning you don't know what reward and like what what part of like the the suggestion the model is anchoring on you know like sometimes it's like oh this is better sometimes the model might be learning that you like more verbose question answers even though they're they're right the same way so there's a lot of stuff there to figure out but yeah I think some examples in the paper like clearly worse some of them are like not as not as crazy um yeah but I mean it'll be nice under a lot of pressure on the unlike the safety and like all the the instruction side and we cannot like the best thing to do would be hey let's version lock the model and like keep doing emails against each other like doing an email today and an email like that was like a year ago there might be like 20 versions in between that you don't even know how the model has has changed so um yeah evals are are hard it's the tldr I I think I think basically this is what we're seeing is open AI having come to terms with that the origin of itself as a research lab where updating models this is is just a relatively routine operation versus a product or infrastructure company where it has to have some kind of reliability guarantee to its users um and so openai are they internally as researchers are used to one thing and then the people who come and depend on open EI as on as as a product are used to a different thing and I think there's there's a little bit of cultural mismatch here like even within open ai's public statements we have simultaneously Logan from from open AI saying that the models are frozen and then you know his his VPO product saying that we update models all the time that are not frozen so which is like you cannot simultaneously be true um so so I think they're shot yeah I think they're trying to figure it out I think people are rightly afraid uh of them basing themselves on top of a black box uh and that's why maybe you know we'll talk about llama too in a bit uh that's that's why maybe they want to own the Black Box such that uh it doesn't change out from underturn um and I think this is fine this is normal but uh openai it's not that hard for opening night to figure out a policy that is comfortable with that that everybody like accepts um it won't take them too long and this is not a technical challenge it's more of a organizational and business challenge yeah I mean I I think that the communications challenge that you're referencing is also extreme and I think that you're right to identify that they've gone from like quirky little you know lab with these big aspirations to like epicenter of a of a national conversation or a global conversation about existential challenges you know and the way that you talk in those two different circumstances is very different and you're sort of serving a lot of different Masters hopefully always Guided by your own set of priorities and that's going to be you know inherently difficult uh but with so many eyes on it and people who are you know the thing that makes it different is it's not just like Facebook where it's like oh we've got a new feature you know in the early days that made us all annoyed like you know people were so angry when they added the feed uh you know that we all got used to it this is something where people have redesigned workflows around it and so small disruptions that change those workflows can be hugely impactful yeah it's an interesting comparison with the Facebook feed because in the era of AD Tech the feedback was immediate like you changed an algorithm and if the click-through rates are the you know the whatever metric you're you're optimizing for in your social network if they started to start to decline your change will be reverted tomorrow you know uh whereas here it's like we just talked about it's hard to measure and you don't get that much feedback like I you know I I have there's sort of the thumbs up and down uh action that you can take an open AI that I've never shared most people don't don't give feedback at all so like opening a has very little feedback to to go with on like what is actually improving under not improving and I think this is just normal like uh it's it's kind of what we want in a non-adtrack universe right like we've just moved to the subscription economy that everyone is like piety for uh and this is the result that we're trading off uh uh some some amount of product feedback actually it's super interesting so the the one other thing before we leave um uh open AI ecosystem the one other big sort of feature announcement from this month was uh custom instructions how significant do you think that was as an update so minor uh so it is significant in the sense that you get to personalize track TBT much more than uh you previously would have like it actually will remember facts about you it will try to obey system prompts about you you had this in the playground since forever uh because you could enter in the system prompt uh in there and just chat to complete that habit and this is a rare instance of the chat tpd team lagging behind the general capabilities of the open AI platform uh and they just shipped something that could have been there a long time ago it was present in perplexity Ai and if you think about it um basically every other open source chat company or open uh we have a third-party chat company had already had it before tragedy um so what I'm talking about is character AI what I'm talking about is the various uh ai waifu ai girlfriend type companies Each of which have you know characters that you can sort of sub in as custom instructions um so I think chargpt is basically playing catch up here it's good for obviously the largest user base in the world of chat AI but it's not something fundamentally we haven't seen before that actually I think perfectly brings up a segue to the other major obvious thing that happened this month from both a technical perspective but also just I think long term from a user perspective which was Facebook releasing llama 2. so this was something that was uh you know anticipated for a while but I I guess where to even start with the significance of llama 2 I mean how do you sum it up if you're talking to someone who sort of isn't paying attention to the space you know what what does the introduction of of lava 2 mean relative to other things that had been available previous to it um it is the first fully commercially usable not fully open source we'll talk about that first fully commercially usable gbt 3.5 equivalent model and that's a big deal because one you can run it on your own infrastructure you can write it on your own cloud so all the governments and Healthcare and financial use cases are opened up to that and then you can fine tune it because you have full control over all the weights and all the internals as much as you want um so it's a big deal from from that point of view um not as big in terms of the you know pushing you know for the state of the art um but it's still still extremely big deal yep I think the the open source part so I've wrote so the data it came out over this post um about you know why llamasu is not open source and why it doesn't matter and uh I was telling Sean I'm writing this thing and it was like whatever man like this license stuff is like so so tired I was like yeah I'll just post it on on anchor news in the morning and I think it was on the front page for like the whole day they got like 228 comments and I was regarding the flash attention podcast episode in the morning so I got out of the studio and it was like 230 comments of people being very like you know upset one way or the other about license and my point and you know I was I started an open source company myself in the past and I contributed to a bunch of projects is that yeah llama 2 is not open source but like the open source Institute definition but we just don't have a better definition for like models you know like because it's mostly open source you can use it for a lot of stuff so what's like the and it's not Source available because for a lot of stuff you can use it commercially so how do we find better labels and my point was like look let's figure out what the Better Label is but even though it's not fully open source it's still like three million dollars of like flops donated to the community basically you know who else who else in the open source Community is stepping up and putting 3 million of h100 to make us train this model so I I think like overall netmed is like a very positive thing for the community and then you've seen how much stuff was built on top of it there's like the quantized versions with ggml there's like the context window expansion um there's so much being done by the community that um I I think it was it was great for for everyone uh and by the way three million is the lower uh that's just compute um there's a reasonable estimate from scaliai that the extra fine tune that you could on top of it uh was worth about 15 to 20 million dollars um so that's a lot of money just kind of donated to the community um although they didn't release the data they didn't tell us any of the data sets uh they just say trust us we didn't train on any of your Facebook information which is uh it's the first instance where the models are more open than the data and I think that's a reflection of where the relative shift in value might uh happen um as a result of lava too and so I I don't know you can take that in multiple different directions but I just want to point that out yeah I was gonna say so we first had the the examples I made so we first had the open models open source models which is like rent pajama so the data so have been the training code is open the model weights are open then stability kind of did the same thing with stable LM which is like hey the widths are open but we're not giving you the data you know so you can you can download the model but you cannot retrain it yourself and that llama too it's like we don't give you the data we'll give you the models but you can only use it for for some stuff so there's more and more restriction but like Sean is saying and we talked about this before everybody wants to train their model nobody wants to open source the best data set for X you know which maybe is what more open source people should focus on it's like how to build better specific data sets instead of yet spending giving Jensen Wang another five million dollars of gpus but the model gets more headlines for now you know so that's that's what everybody Adidas yeah and I want to point out it's a reversal of the open source culture they used to get a sequence of openness and you could kind of pick and choose from uh whether it's open code all the way down to open data versus all the way down to uh open weights and you know there's some some barrier to combination I I wrote I wrote this book a long time ago because I don't remember that the five levels um uh but yeah like it's it's very strange and I think it's just it's just a relative uh um discussion of where the money is going um and I think it makes usually shows that compute is becoming commoditized um which yes there's a GPU approach right now uh a100 has sold out everywhere across the board people are commenting all about it uh this month um you know and there's people hoarding compute like nobody's business but as far as the value an AI is concerned it looks like computers is relatively um you know uh commoditized it's actually data that's that that people are kind of safeguarding generously um going all the way back to the history of Open Source models that you lose their AI when they when they train GPT J and GPT Neo as the first reproductions of gpt3 um they they release the data first uh stable diffusion when they train stable diffusion they release live on 500b first uh and that's I think reflectors or like the the normal sequence of events you release the data that anybody's uh the model weights but now now we're just skipping the data part and I think it's just it's fair it's a way to think about yourself you know I think um one of our conversations I think I think it was my Conover when he was talking about comparing our current AI era versus uh the 2000s era in search engines you know all he basically said like all of the public publishable information retrieval research dried up because all those phds went to work at Google and Google just sat on it uh and that it this is now you know a fight for IP um and and I think that is just a very rational way of behavior and I guess like a capitalist AI economy do you think so one of the things that we were talking about before starting with the the code interpreter 4.5 and why or gbt 4.5 and why they might not call it that is the emergence of this sort of regulatory if not pressure certainly Intrigue uh you know do you think that there's potentially an aspect of that when it comes to why people are so jealously safeguarding you know the the data is there more risk for for being open about where the data is actually coming from the the books three examples probably good so MPT trained their model on a data set called bookstree which is 190 000 books something like that um and then people on Twitter were like well this stuff is not you know in the free you know it's under copyright still you just published yeah yeah it's not in the public domain you can just take it and and train on it but the license for some of these books is like kind of blurry you know on like what's fair use and what is it um and so there was like this old thing on Twitter about it and then MPD you know Mosaic first changed the license and they changed it back and um I think Sean uh Sean presser from Luther was just tweeting about this yesterday and he was basically saying look as ml Engineers maybe it's better to not try and be the you know the main ethics night and just say hey look the data's open and let's try it and then maybe people later will say hey please don't use the data and then we can figure it out but like proactively not using all of this stuff can kind of keep the progress back and and you know he's more coming from the side of like a Luther which is like doing this work in public so for them it's like hey you know if you don't want us to train now this is fine but we shouldn't by default not do it um versus if you're meta you know they said the deterring llama on like stuff available on the internet they didn't say the train llama on stuff that is licensed to train on uh it's a it's a small it's a small difference the other piece of this that that I I wanted to sort of circle back to because we kind of breezed over it but I think it's really significant you know we did get a little lost in this conversation around open source definitions and I don't think that's unimportant I think that people are rightly protective when a set of terminology has a particular meaning and a massive Global Corporation sort of tries to like nudge it towards something that is potentially serving their ends versus uh you know actually being by that definition but I also think that your point which is that functionally relative to the rest of the space it probably doesn't super matter because what people mean is almost more about functionally what they can do with it and what it means for the space relative to more closed models and I I think one of the big observations has been that the availability of uh you know from from when llama one was you know fully fully leaked the availability of of all of that has pretty dramatically changed won the evolution of the space over the past few months and two I think from a business standpoint how the big companies and incumbents have thought about this so another big conversation this month going back to sort of the The Venture Capital side of of your life has been the extent to which uh companies or startups are or big companies are not wanting to sort of side on with some startup that's going to offer them you know AI whatever because their technical teams can just go spin up you know sort of their their own version of it because of the the sort of you know availability of these open source tools but you know I guess I'm interested I guess in bringing the the sort of Open Source you know in air quotes side of the conversation into the to the realm of how it has impacted how companies are thinking about you know uh their their development in the in the context of the AI space I think it's just Rising like put it raising the bar on like what you're supposed to offer so I think six nine months ago it was enough to offer a nice UI wrapper around an open AI model today it isn't anymore so that's really the main the main difference it's like what are you doing outside of wrapping the model and people need more and more before they buy versus building yeah I think um it actually moves the area of competition uh towards other parts of productionizing AI applications you know I I think that's probably just a positive um I I feel like um the uh actually the competitive pressure that La The Meta is putting on Open the Eyes is a good thing uh one of the fun predictions that I made was in the next six months ubt opening hour open source tpc3 um which which is not open source and uh I like it's so far behind the state of the art now that it doesn't matter as far as safety is concerned and it basically peeps open AI in the open source AI game uh which which would be nice to have of the things that people have been building um you called out a couple uh context window expansion but have there been any that really stand out to you as super interesting or unexpected or or you know particularly high potential um one of our short short term podcast guests uh the mlc team they were thumb wrapping llama two to run on MacBook gpus so I think that's like the the most interesting Gap right it's like how do we go from paper token to like unlimited local use that's one of the main main things that keep even people like me from like automating a lot of stuff right it's like I don't want to constantly pay open AI to do menial stuff but if I go run this locally and do it even if five times lower I would do it so that's uh that's a super exciting space yeah I would say beyond that there hasn't been that much I mean it's it's only a few weeks old so uh it hasn't been damaged uh emergence coming from it I would I would definitely say um you want to keep the lookout for uh the uh basically what happens in post lab number one which you know keep in mind it was only in February um the same thing that happened with Acuna alpaca and all the other sort of instructions to you and sort of research type models um but just more of them because now they are also commercially available um we haven't seen them come out yet but it's it's almost like guarantee that they will um you can also apply all the new techniques uh that have been have emerged since then like Json former because now you have access to all the model leads um to to to llama and I think uh that will also uh create another subset of models that uh basically was only theoretically applicable to sort of research holiday models uh before and so now these will be authored commercially as well um so like yeah nothing nothing like really eye-popping I would say um but but it's been five minutes is that it's yeah it's it's been it's been a very short amount of time uh and the thing of Open Source is that the creativity unlocked um is is very hard to predict and actually I think happens a lot in the uh let's just say the the mess official part of the economy where where I've been focusing a lot on recently on um the sort of AI girlfriend economy which is huge uh I I feel like it's not polite conversation that the amount of um AI girlfriend area has but it's real they're millions of users they're making a lot of money uh and it's just virtually not talked about in in like polite SF circles it feels like one of those areas that's going to be uh an absolute lightning rod when it comes to the societal debates around this technology like you can feel it that that sort of oh you know the people are going to hone in on that as example a of you know a change that they don't like that's my guess at least I don't know like so I have a really crazy longer term prediction like maybe on the order of like 30 to 50 years but um you know yeah a girlfriend for Nobel Peace Prize because it what if it solves the loneliness crisis right what if it cuts the rate of Terror and uh you know school shootings by like or something like that's huge my wife and I have joked about how every generation there's always something like they always think that they're like so far ahead and they think that there's nothing that their kids could throw at them that they just like fundamentally won't get and without fail every generation has something that seems just totally normal to them that their parents generation writ large just like has such a hard time with and we're like it's probably gonna be like AI girlfriends and boyfriends we're gonna be like yeah but they're not real they're like yeah but it's real to me you know they're having debates with our future 13 year old or kids are only four and two now so it feels like maybe the right timeline yeah I I've heard actually of all people Matthew McConaughey on the Lexus and what what yeah you was he was great shout out shout out shout out Matt um but they were talking about they were kind of talking about this and they were noodle in the this idea of like computers helping us being better so kind of like we have computers learn how to play chess and then we all got better at chess by using the computers to like learn and like experiment uh they were talking about similarly in interpersonal relationship maybe it does you know it doesn't have to be you shut off from from humans but it's like using some of these models and some of these things to actually like learn you know how to better interact with people and if you're like shy and an introvert it's like okay I can like try these jokes on like these conversation points with a model and like you know it teaches me hey that's not okay to say or like you know you should maybe be more open or or I don't know but I think that's a more wholesome view of it than like everybody just kind of runs away from society and that's like 10 AI friends and doesn't talk to humans anymore what's it's much less sexy to just say like AI friends right that even though like there's the if you look at the possibility set you know the idea that people might have this sort of uh to your point like conversational partner that helps them effectively work through their own things in this safe space that doesn't necessarily relate to romantic attachment just because the movie Her came out right right it can just be a panel of experts uh and I I've uh I had I do have plans to build uh you know a small CEO which is uh it's my own boss um and just for me to check it um and actually we'll flag out just lifting various services so you come a lot you come across a lot of AI Engineers who are interested in building mental wellness products and a lot of these will take the form of some kind of Journal um and this will be your most private uh thoughts that you don't really want to send anywhere else um and so actually all these will make advantage of Open Source models because they don't want to set it to open AI um and that makes a ton of sense which is something like I just came across uh from one of my friends uh here in the coordinating space that I have uh where it's it's one of those situations where you can actually try out like having a conversation and having a group of yeah friends chime in and see what that feels like to you uh it's it's the first example I found my past where someone's actually done this super interesting so uh llama and uh code interpreter I think stood out pretty clearly as as really big things to touch um I wanted to check in just as we sort of start to maybe around the corner towards wrapping up Claude 2 uh and anthropic how significant was this in what ways was a significant you know was it something that was sort of meaningful from expanding the capacity set for developers or was it sort of more just a good example of what you can do if you increase the context window but you know that's something that might ultimately become table Stakes later on yeah I could I could maybe speak through this a little bit um so it is significant but not earth shattering or clearly I think it is the first time that Claude as a whole has just been a generally publicly available you used to be on a weakness um yes it has a longer context window but to me more significantly it is anthropic finding its its footholds uh in the very competitive CI landscape you know um anthopics message used to be that we're yes we're number two to open the eye but we're safer you know and that's that's not a super appealing uh thing to to many uh Engineers it is it is very appealing to some uh uh corporations by the way um but uh you know I think I think having the 100K contest window makes them state-of-the-art in one dimension which is very useful uh the ability to upload multiple files I think is super useful as well um and I and actually I have met a number of businesses I'm closer as a source graph who are actually choosing to build with claw 2 API over and above open AI just because they are better at latency better reliability in in better in some form of code synthesis um so I think it's anthropic finding it's foothold finally after a long while uh of being in open the eyeshadow yeah and we use cloud for the uh the transcript and timestamps and the buckets so shout out the 100K context window you know we couldn't do that when we first started the podcast we were like okay how do we trunk this stuff or like gpd4 and and all of that and then Bob was like just put the whole thing in here man and works great so uh that's a good start but I feel like they're always yeah a second second fiddle you know it's like every time there really something people are like cool okay some people like it must be more like okay fine I I feel bad for them because it's like it's really good stuff you know but they just need they just need some uh some help on the marketing side and the community buy-in so I just spent this past weekend at uh the club hackathon which is as far as I know anthropics first hackathon I I treated a pretty well received video where I was I was just eating the hackathon venue at 2 am in the morning and there was just a ton of people hacking there there were like 300 people uh participating uh for Claude And I think it's just the first real developer excitement I've ever seen for enthalpy kid Claude um so I think they're on their way up I think this paves the way for a multi-model future um that is something that a lot of people are betting on um it's just the the odds are stacked against entropic but they're making some Headway um I I do think that you should always be running all your chat side by side against uh tragicia and Claude and maybe mama two um so I I immediately I have a little uh many of our app that does that that uh save all the all the chats across and uh and yeah I can say I can legitimately say that Claude wins about 30 of the time uh as far as any time I give it a task to do I ask it a question um which is not you know doesn't make it number one but it actually is very additive to your overall toolkit of yeah I think you shouldn't use yeah it's certainly the first time that you're if you go on Twitter on any given day you will see people saying things like if you haven't used uh Claude you know for writing you have to try it now or so you know like people who are really who have made a switch who are have no affiliation who are very convinced that it is now part of the the suite of tools that people should really be paying attention to which I think is great where we shouldn't be at a stage yet where we're you know total totally in on one just one tool set I'll also mention I think this month or at least July was when the first inspection of where whether like is too much context not actually a good thing um so there's a there's a pretty famously product I forget the actual title a bit uh that shows a very pronounced new curve in the retrieval abilities of large context models um and so basically if you if if you if the item that is being retrieved is at the start or the end of the context window then it has the best chance of being received but if it's in the middle it has a high chance of being lost um and so is 100k context a good thing are you systematically testing its ability to um to retrieve the correct factual information or are you just looking at a summary and growing yeah it looks good to me you know um I think we will be testing like whether or not it's worth extending it to 100K or a million tokens or infinite tokens uh or do you want to blend uh a short window like 8 000 tokens or 4 000 tokens uh in couple that together with a proper semantic search system uh like the retrieval augmented generation and Vector database companies are doing so I think that that discussion has come up in open source a lot um and basically it I think it matches human memory right like you want to have a short working memory hahaha you know the I was thinking about it the one other obviously big sort of company update that we haven't spoken about yet was around the middle of the month Google bard had a a big set of updates a lot of it was sort of business focused right so it was available in more languages uh it was you know whatever the the sort of from a feature perspective the biggest thing that they were sort of hanging their hat on was around image recognition and sort of this push towards uh towards multimodality but you know did did you have any guys did you guys have any thoughts about that or was that sort of like you know not sort of on the the high priority list as a as an announcement or development this month I I think going back to the point before we're getting to the maturity level of the industry we're like doing like model updates and all this stuff like it's fine but like people need more you know people need more and like that's why I call it interpreter it's like so good right it's not just like oh we made the model A little better like we added this thing it's like this is like a whole new thing if you're playing the model game if not you got to go to the product level and I think Google should start thinking about how to make that work because when I search on Google Maps for certain stuff it's like completely does not work so maybe they should use models to like make that better and then say we're using Bard in Google Maps search uh but yeah I don't know I've kind of I'm kind of tuning off a lot of the single just model announcements so uh so Bart's updates I think the the multi-modality they actually beat gpt4 to releasing a generally available multimodal wall right you can upload an image and have Bard describe it and that's pretty interesting pretty cool um I think uh one of our earliest guests Robo flow uh Brad their CTO was actually doing some comparisons because they have access to a lot of division models and and Bart came up a little bit short but it was pretty good it was it was like close to the state of the art um I would say the problem with Bard is that you can't rely on them having reliable updates because they had a June update I don't actually remember of implicit code execution where they started to ship uh the code interpreter type functionality but in a more limited format if you run the same code the same questions that but advertising the June blog post it's sundarkai advertise in in a video that and tweet it out they no longer worked in the heart so they had a regression that's that was very embarrassing um obviously unintended but uh it's and it shows that it's hard to keep model progress up to date but I think Google has this checkered history riff its products being reliable you know they also killed off Google Adobe rip um and uh and I think that's something that they have to combat which is like yes they're they're trying to ship model progress I've met the bar people they're you know good artist people um but they have struggled to to ship uh products even more than open AI which is frankly embarrassing for a couple of the size of Google outside of the the biggies are there any other sort of key trends or or you know maybe not even key trends but sort of bubbling interest that you guys are noticing in the developer community that aren't necessarily super widely uh seen outside you know one of the things that I keep an eye on is all the auto GPT like things you know in this month we had gbt engineer and we had multi-on who held a hackathon and you know there's a few few things like that but you know not necessarily in the agent space but are there any other themes that you guys are are keeping an eye on let's say uh I I'm sure Alessio can chime in but on on I do keep a relative uh close eye on that agent stuff uh it has not uh died down in terms of the the heat uh even the other GPT team who by the way I work uh on the first floor the building that I work on uh they're hard at work uh shipping the next version and so I think a lot of people are engaging in the dream of agents and um I think like scoping them down to something usable is still a task that uh has not as it has so far eluded every single team so far and uh and it is what it is I think I think uh all these very ambitious goals we are at the very start of of this journey uh the same Journey that maybe self-driving cars took uh in 2012 when when they started doing the darker challenge um and I think the other thing I'll point out interest in terms of uh just overall interest uh I am definitely seeing a lot of uh eval type companies being formed and winning hackathons too um so what what at Utah companies they're they're basically uh companies in that you uh monitor the uh the success of your prompts or your agents and version them and um and and just share them potentially um I I I feel like I can't be more descriptive just because it's hard to um to really describe what they do it's just because they are not very clear about what they do yet um Lang chain launch Lang Smith um and I think that is the first commercial product that nine chain probably you know the the top one or two developer oriented AI projects out there um and that's more observability but also local uh tensorous ebal as well because they Aqua hired in an AI eval projects as well so I was I'll just call out just the general domain of how to eval models um is a very big focus of the developers here again yep yeah we've done um two seats and companies doing agents but they're both verticalized agents so I think the open source motion has been Auto gbt do anything um and now we're seeing a lot of Founders is like hey you know if you take that and then you combine it with like deep industry expertise you can get so many improvements to it and then the other piece of it is how do you do information retrieval so you know in general knowledge like documents everything is kind of flat but when you're in specific vertical say Finance for example um you know if you're looking at the earnings from this quarter like 10 quarters ago like the latest ones are like much more important so how do you start to create this like information hierarchy between documents and then how do you use that instead of doing simple like retrieval from like an embedding store it's like how do you also start to score these things that's another area of of research from from founders oh I'll call out two more things um one more thing that happened this week this month was sdxl uh you know text to image doesn't seem as sexy anymore even though like last year with all the raids um I but I do think like it's it's coming along um I I definitely wish that Google was putting up more of a fight because they actually at the start of the Year released some very interesting Capers that they never followed up on uh that show some really interesting Transformers based uh text image models that I thought was super interesting and then this the other uh element which uh you know I'm just like very fascinated by a lot of the I don't know like the uh uh I I I hesitate to say this but it's actually like the the character and like the um um let's just call they call it character replica and and all the sort of work versions of that um I I do think that a lot of people are hacking on this kind of stuff um the retention metrics on character AI blows away um you know a lot of the uh the metrics that you might see in on traditional social media sites and basically AI native social media is something that is something that that is there's something there that I think people haven't really explored yet and and people are exploring it you know like uh is this company and like you know he's always a few years ahead of it so uh not to keep returning to this theme but I I just think like it's it's definitely coming for a lot of like a lot of the ways that we we deal with things like right now we think co-pilot and we right now we think um uh we've been chat gbt but like uh what what we what we really want to speak to is is uh a way of serializing personality and intelligence um and and potentially that is a that is a leading form of Mind upload um so that Becca is into science fiction but I do see a lot of people working on that yeah I mean we just got a Financial Times report that says that AI personas uh from meta from Facebook could be coming next month they were talking about uh yeah they were talking about airport was there's one one that's Abraham Lincoln one that's like a surfer dude who gives you travel advice so it's it's it's you know the sourcing is three people with knowledge of the project or whatever um and it you know no obviously no confirmation from meta but it's no secret that Zuckerberg has been interested in this stuff and uh you know the the ftp's is actually it's a good overview of why a company like Meadow would care about it in very dollars and cents terms yeah something like and I want to State like the first version of this is very very me like when I first looked at character AI it was like okay I want to talk to Genghis Khan if I'm doing a history class but it's like not it's like what if what a 10 year old would enjoy you know um but I think the the various iterations of this professionally would be very interesting so on the developer side of this I have been calling for the development of agent clouds which are clouds that are specifically uh optimized not for uh human use but for uh EI agent teams and that is a form of character right it's a character is it with the different environments uh with the different dependencies pre-installed uh that can be programmatically controlled can get programmatic feedback to agents um and uh and there's a protocol for me um that some of the leading figures like Auto gbt and e2b are creating that um lets agents run clouds um this would this would definitely terrify the AI safety people because we have gone from like running them on a single machine towards running you know clusters originally um but it's happening all right so so let's talk about what comes next do you guys have any predictions for August or if not predictions just things that you're watching most closely go ahead Alice uh let me let me think and I think Sean is usually good at like the super long term prediction some more uh pragmatic I don't know you know yeah he's more like he he like minimum like 12 to 24 months um I I think like for me probably starting to see more public talk about open source models in production with people using that as a differentiator I think right now a lot of it is kind of like oh these models are there but nobody's really saying oh I moved away from opening I'm using this but in our we run a early adopters Community with about 1500 kind of like a Fortune 500 large companies leaders and some of them were like oh we deployed dolly in production and we're using it we're not writing a blog post about it um so I think right now the perception is still everybody's using open Ai and the open source models are like really toys but I think we're gonna get into September and you know you're not going to see a lot of announcements in August proper but I think a lot of people are gonna spend August getting these models ready and then going into end of the year and say hey we're here too you know we're using the open models like we don't need open AI um I think right now there's still not not a lot of a lot of public talk about that so excited to to see more uh yeah I'm a little bit uh as for myself uh this is very self-interested obviously but we had to edit an agenda you know I wrote about the the rise of the AI engineer I mean I think it's definitely happening as we speak um I I have seen multiple tags like people tag me multiple times a day on like uh how they're reorienting their careers I think people professionalizing around this and going from essentially like informal groups and slack channels and meetups and stuff towards uh certifications and courses and job titles and actual AI teams in every single company I think is happening um I I just got notification like two days ago that the uh you know in meta apparently you can sort of name your name a job site title whatever you want internally uh and so they emerged as the first AI engineer within meta uh has has been announced and uh so I think I think as far as you know the near-term I do see this career this profession come into place um that I've been forecasting for uh for a little bit and I'm excited to help it along awesome well guys great conversation tons of interesting stuff happening obviously um I do think it you know ironically I think it's a relatively more quiet time in some ways than than it even was and you know my my prediction for August is that we're going to see the extension of that we're going to see sort of the the biggest breath that we've had at least from a from a feeling perspective maybe since Chachi PT but then we're gonna rage back in in September you got Facebook connects in September you've got sort of just the return to business that everyone does after August um but of course I think you know the hackathons aren't going to stop in the Bay Area so people are going to keep building and it's entirely possible that something you know hits in the next four weeks that that totally changes that be exciting to see looking forward
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Podcast: Latent Space: The AI Engineer Podcast — Practitioners talking LLMs, CodeGen, Agents, Multimodality, AI UX, GPU Infra and all things Software 3.0 (LS 33 · TOP 5% what is this?)
Episode: The Busy Person's Intro to Finetuning & Open Source AI - Wing Lian, Axolotl
Pub date: 2023-12-08
The Latent Space crew will be at NeurIPS on Tuesday! Reach out with any parties and papers of interest. We have also been incubating a smol daily AI Newsletter and Latent Space University is making progress.
Good open models like Llama 2 and Mistral 7B (which has just released an 8x7B MoE model) have enabled their own sub-industry of finetuned variants for a myriad of reasons:
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Ownership & Control - you take responsibility for serving the models
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Privacy - not having to send data to a third party vendor
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Customization - Improving some attribute (censorship, multiturn chat and chain of thought, roleplaying) or benchmark performance (without cheating)
Related to improving benchmark performance is the ability to use smaller (7B, 13B) models, by matching the performance of larger models, which have both cost and inference latency benefits.
Core to all this work is finetuning, and the emergent finetuning library of choice has been Wing Lian’s Axolotl.
Axolotl
Axolotl is an LLM fine-tuner supporting SotA techniques and optimizations for a variety of common model architectures:
It is used by many of the leading open source models:
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Teknium: OpenHermes, Trismigestus, CollectiveCognition
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OpenOrca: Mistral-OpenOrca, Mistral-SlimOrca
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Nous Research: Puffin, Capybara, NousHermes
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Pygmalion: Mythalion, Pygmalion
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Eric Hartford: Dolphin, Samantha
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DiscoResearch: DiscoLM 120B & 70B
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OpenAccess AI Collective: Manticore, Minotaur, Jackalope, Hippogriff
As finetuning is very formatting dependent, it also provides prompt interfaces and formatters between a range of popular model formats from Stanford’s Alpaca and Steven Tey’s ShareGPT (which led to Vicuna) to the more NSFW Pygmalion community.
Nous Research Meetup
We last talked about Nous at the DevDay Recap at the e/acc “banger rave”. We met Wing at the Nous Research meetup at the a16z offices in San Francisco, where they officially announced their company and future plans:
Including Nous Forge:
Show Notes
We’ve already covered the nuances of Dataset Contamination and the problems with “Open Source” in AI, so we won’t rehash those topics here but do read/listen to those if you missed it.
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Axolotl GitHub and Discord
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The Flan paper and dataset
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StackLlama model and blogpost
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Multipack paper
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Our episode with Tri Dao
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Mamba state space models - Tri Dao and Albert Gu
Timestamps
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[00:00:00] Introducing Wing
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[00:02:34] SF Open Source AI Meetup
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[00:04:09] What is Axolotl?
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[00:08:01] What is finetuning?
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[00:08:52] Open Source Model Zoo
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[00:10:53] Benchmarks and Contamination
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[00:14:29] The Case for Open Source AI
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[00:17:34] Orca and OpenOrca
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[00:23:36] DiscoLM and Model Stacking
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[00:25:07] Datasets and Evals over Models
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[00:29:15] Distilling from GPT4
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[00:33:31] Finetuning - LoRA, QLoRA, ReLoRA, GPTQ
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[00:41:55] Axolotl vs HF Transformers
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[00:48:00] 20x efficiency with StackLlama and Multipack
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[00:54:47] Tri Dao and Mamba
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[00:59:08] Roadmap for Axolotl
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[01:01:20] The Open Source AI Community
Transcript
[00:00:00] Introducing Wing Lian
[00:00:00]
[00:00:00] swyx: Welcome to Latent Space, a special edition with Wing Lien, but also with our new guest host, Alex. Hello, hello. Welcome, welcome. Again, needs no introduction. I think it's like your sixth time on Latent Space already. I think so, yeah. And welcome, Wing. We just met, but you've been very prolific online. Thanks for having me.
[00:00:30] Yeah. So you are in town. You're not local. You're in town. You're from Minneapolis?
[00:00:35] Wing Lian: Annapolis. Annapolis. It's funny because a lot of people think it's Indianapolis. It's I've got Minneapolis, but I used to live out at least in the San Francisco Bay Area years ago from like 2008 to 2014. So it's fairly familiar here.
[00:00:50] swyx: Yep. You're the maintainer of Axolotl now, which we'll get into. You're very, very prolific in the open source AI community, and you're also the founder of the Open Access AI Collective. Yeah. Cool. Awesome. Maybe we can go over a little bit of your backgrounds into tech and then coming into AI, and then we'll cover what
[00:01:06] Wing Lian: happens and why you're here.
[00:01:08] Yeah. So. Back on tech, so I started years ago, I started way back when I was scraping, Apartment websites for listings and then, and then building like SEO optimized pages and then just throwing Google AdSense on it.
[00:01:24] And that got me through like college basically. Is
[00:01:27] swyx: that decent money? And what year
[00:01:28] Wing Lian: was this? Like 2004, 2005. Yeah, that's decent money. It's like thousand bucks a month. But as a college student, that's like. Gravy. Really good money, right? So, and then there's just too much competition It's just sort of like died off. I was writing stuff in like Perl back then using like like who nobody hosted anything on Perl anymore, right? Still did a little bit more like computer tech support and then software, and web more professionally.
[00:01:54] So I spent some time working on applications in the blood industry. I came out to San Francisco for, I was at SGN, so Social Gaming Network, as a startup. They started doing, with Facebook apps, and then they pivoted into doing mobile apps. And then, from there, I spent time.
[00:02:14] I've quite a few more startups since then and in the last few years I've been in the music space So like I was at United Masters for a while and then past year I've been at SoundCloud, but not doing that anymore and now that I have a lot more time It's just like all right.
[00:02:30] We're going full bore on axolotl and we're gonna we're gonna crush AI So yeah,
[00:02:34] SF Open Source AI Meetup
[00:02:34] swyx: totally you so you're here in town for the open source. Yeah, I meet up that we had yesterday Yep, yeah, that was amazing. Yeah, it was a big collection. Olama, Noose Research, Alignment Lab, Anyone else that I missed? I mean, Jeremy Howard is his own thing.
[00:02:47] Yeah.
[00:02:49] And Alex, you're also there. You love to bring SF to the world. Your takes?
[00:02:55] Alex Volkov: It's incredible that we recorded a Thursday Eye episode after that one. And LDJ, who's usually co hosts Thursday Eye, just like briefly mentioned, Oh yeah, I talked about it.
[00:03:04] Like, I saw Karpathy, and then I talked to Jeremy Howard, and the guy from Mistral came in, and it's like, He's talking about all these, titans of industry, basically, that outside of SF, You just don't meet casually hanging out in the same space. You can't, pull somebody. He ran into the Laylow from Mistral, he ran into him while, drinking water.
[00:03:20] He didn't even know he was there. It's just, that type of stuff is really hard to find outside of SF. So, absolutely, absolutely great. And also, presentations from Alignment Labs, presentations from News Research, news issues, talked about. Forge, and some of
[00:03:33] swyx: the other stuff they announced. We can say now they're officially a company.
[00:03:36] I met Technium.
[00:03:37] He
[00:03:37] Alex Volkov: came over here. He didn't want to get recorded. But maybe.
[00:03:41] Wing Lian: We'll wear him down at some point. Yeah, I'm excited for Forge. They've positioned it as this agentic sort of framework where it's just Drag and drop things and, fill in text with where you want to inject different variables and it opens up all of these potentials for data pipelines now, right?
[00:03:56] And using your own local LLMs and not relying on GPT 4 or anything like that. Yeah, yeah,
[00:04:02] swyx: good stuff. Okay, so let's maybe go into the Axolotl origin story and then we have, we have some intro or background.
[00:04:09] What is Axolotl?
[00:04:09] swyx: To do on like the open source model universe and also on fine tuning, but maybe just, since you're talking about your personal journey, what was your personal journey into
[00:04:18] Wing Lian: axolotl?
[00:04:19] Yeah, so my personal journey started like back in mid March, completely unrelated to AI and axolotl. And it really started, I fell while skiing, I torqued. Great 3 MCL sprain and being sort of like an active person that can no longer be active because the two, couldn't play soccer, because that is requires to have having knees until I, it's healed.
[00:04:42] So I. I decided I needed to find something to do to take up my free time. And that became, well, let's learn how to train in, these language models. It was everywhere. So I was like, all right, I'm just going to sit down, learn. I think I used like other, I think I was using like Alpacalora.
[00:05:00] Cause I think the Alpaca paper had just came out, come out then. So I was like using Alpacalora repo and sort of like learning how to use like. None of us were like GPU rich back then, and none of us, most of us still we're still all GPU poor, but I was doing what was it, like 4 bit, Alpaca Lord, there was like a 4 bit version where we were doing quant, or 8, no, 8 bit quantizations, and then I think they had released QLOR a little bit later, and I think right when, before QLOR came out, I was already starting to do fine tunes, but having this need to sort of like mix data sets together, and If you've ever looked at all the various different datasets available on HuggingFace, they all have various different prompt formats, and, it's sort of a nightmare, and then I think the other piece is if you've ever tried to fine tune, at least Back then probably the ecosystem's a little better now.
[00:05:54] Everybody required that you say, alright, you put your hyperparameters as command line arguments. And so it's always like, well, I now have to go copy and paste my previous thing and to change things out. And I really wanted it. to be in a YAML file because it was more portable and reproducible.
[00:06:09] So I was doing that and then the QLOR paper came out. Tim Dettmer announced that and then somebody looked it up for me yesterday and it's like between that announcement it took us seven days to get that integrated into Axolotl, right? Which is like, it's not. I wouldn't say it's really fast, but in a manner that, is in a, a reusable framework, I think it was quite the accomplishment then.
[00:06:33] And so we started, picking up traction with people there. And then it's just been building models, and then just iterating what my needs are. So, yeah. Excellent. Yeah. I
[00:06:44] Alex Volkov: want to ask, for folks who are listening who never heard of Axolotl, now do you describe how you got there?
[00:06:49] Can you, how do you summarize this for folks who maybe haven't fine tuned anything. They know about open source LLM exists, they maybe know like LLAML, what's XLR for somebody who doesn't know. I've never heard of a data set curation
[00:07:01] Wing Lian: creation before. We sort of have to take a step back and understand that, when you've got these language models, you have what I think most people refer to as like base models, also known as like foundational models, right?
[00:07:15] Where some benefactor, whether it's Meta or Mistral or whoever, has gone and spent all this money. To train these models on huge corpuses of text, right? And these, these corpuses, they're generally good across lots of different things, but they're really good at just saying, talking on and on and on, but they're not good at, following instructions or having chats or anything like that.
[00:07:40] So, when you think about fine tuning, it's like Saying, all right, we have this really sort of good generalized, text completion thing, and I want to turn it into something that I can talk to or have, follow instructions. So, I think fine tuning is probably best defined in like that.
[00:07:58] swyx: Okay, got it.
[00:07:59] And we actually
[00:08:01] What is finetuning?
[00:08:01] swyx: Do want to make sure that we have like an overall introduction to fine tuning for people because again like trying to make sure that we bring everyone along in this, in this journey. We already went into Loras and QLoras without explaining what
[00:08:12] Wing Lian: they are. Oh yes, yes, sorry.
[00:08:14] swyx: And so I will put things in my words and you can correct me as, as, as my I'll be the village idiot here.
[00:08:21] So, so fine tuning is basically sort of grabbing an open source model off the shelf, and then basically doing further training on it with a custom dataset of your own. Primarily, people use it, think about it as fine tuning for JSON output, or fine tuning for a style of response. Let's say you wanted to tell jokes, or be funny, or be short, or whatever.
[00:08:43] Just the open source AI community has really fine tuned in all sorts of different manner. I think we'll go over those those things now. Let's go over those things now, and then we'll talk about fine tuning methods.
[00:08:52] Open Source Model Zoo
[00:08:52] swyx: So there's a universe of people who fine tune stuff. Yesterday in your slides, you had, I'll just list some of these and then we'll maybe go through some of them, right?
[00:08:59] So Technium is personally leading Open Hermes, which is I think the sort of premier model out of the news. news community. There's OpenOrca, which you had a hand in. News, the news research itself also has Capybara and Puffin and all the others. There's Pygmalion, which I've never messed with.
[00:09:14] Eric Hartford, I am aware of his Uncensored Models and his Samantha Models. Disco Research with Disco LM. And then you personally have done Manticore, Minotaur, Jackalope, and Hippogriff. What should people know about all these names? Being part of AI Twitter is seeing all these things and going dude, I'm being DDoS'ed by all these things and I don't know how different they are.
[00:09:32] What should people know? Yeah, so
[00:09:34] Wing Lian: I think on a lot of these models, generally, we like to think of those as sort of general models, so If you think about it, what is GPT 4, what is Chad GPT? It's a good general model, and then, One of the services I think that OpenAI offers is like these fine tunings where you're a business and you have very specific business use cases and you might fine tune for that use case.
[00:10:00] All of these models are really just general use case that you can then go and maybe Fine tune another lore over it for your use cases, but they tend to be good. With good being relative, it's open source. Open source AI is still sort of is infancy. So, good is, it's pretty reasonable.
[00:10:18] It's probably still better than most, high schoolers at answering questions and being able to like figure things out and, and reasoning skills and math and those sorts of things, right?
[00:10:27] swyx: And also as measured on the Hugging
[00:10:29] Wing Lian: Face leaderboard. Yes, well, that's like a whole other discussion, right, there's a whole other, group of people who, and I, I mostly agree with them that, benchmarks can be, are pretty bogus these days, LM says, I think they published something recently where, even if you think the dataset's not contaminated, you can go and, find contamination And maybe we should step back and say what contamination is, right?
[00:10:53] Benchmarks and Contamination
[00:10:53] Wing Lian: So we have all of these data, when you go and do these benchmarks, there's a specific data set where there are these questions and usually it's multiple choice. And what can happen is, well, sometimes someone It puts the question, maybe maliciously, maybe accidentally, into the training dataset, and now the, the, your model knows how to answer the test questions really well, but it doesn't, it hasn't generalized the ability to actually do that
[00:11:20] Alex Volkov: right.
[00:11:21] We've seen some folks competitively announce models that are like the best at that leaderboard, but then it's, it's quite obvious that, In open source? Yeah, and in that leaderboard, for Hugging Face specific, I don't know if LMCs, if that had suffered, but we, there's been some models that seem to have been competitively trained and some leakage happened into their,
[00:11:41] swyx: like, supposal.
[00:11:43] I understand, once there's been a credible assertion, Hugging Face actually does take them down, right? Yeah, yeah,
[00:11:48] Alex Volkov: which is really hard to know, right?
[00:11:50] swyx: It's really hard to know, sometimes it's like a pure accident,
[00:11:52] Alex Volkov: it's oh, oops. You're going through a mixer. I think, a responsible So acknowledgement, that this kind of happened to you is also important.
[00:11:58] I saw LDJ from news research can acknowledge that. Because many of these datasets are collections of other datasets. There's a bunch of people are baking, basically. It's alchemy. Right. And so sometimes you don't know. Sometimes you pull an open source dataset and they announce, oh, you know what, actually, the MMLU benchmark which we used to Specifically identify models that did go into this data set, that then went into that data set.
[00:12:22] So sometimes it's actually an accident and folks take it down. But I've seen some competitive folks who want to put their name out there because people are starting to notice which is the top
[00:12:30] swyx: model. For those who want a fun take on this so the file one dataset. FindOne model from Microsoft was accused of being contaminated.
[00:12:37] And I saw this joke paper that was fantastic. It was called, training on the test set is all you need. It's a super small model that just memorizes everything. It was fantastic. So yeah, contamination, I think we've actually covered it in a previous episode before. So we're good. But again, I want to give people a map into the open source AI model, the universe.
[00:12:57] And Alex, you can also jump in here because you guys have spent a lot more time with them than I have. So, what should people know about Technium? What should people know about Noose? And then we can go down the list. Yeah,
[00:13:05] Wing Lian: I think so. I think if we start with, Technium. When you talk to him, he's gonna say, I think, I think his response is that he wants to build GP4 on his laptop, right?
[00:13:14] So, very, very good at building general models. I think with Noose, Noose Research, they're looking at more, sort of, More, more research focused things, like their Yarn models, I don't, I don't, they didn't actually train their, they have their own trainer for their Yarn models, but So they did not use Xlato for that one?
[00:13:30] They didn't use that, but like Is that, you don't have support for it? I think we do support Yarn, I think, I'd have to double check that answer. Yeah, I'm just kind of curious what you can and cannot support, and Yeah, I mean, Yarn is supportable, it's basically, I think it's just replacing, I think, the rope part of that, so Yeah, not, not a big deal.
[00:13:48] Yeah, it's not a big deal, it's just I haven't gotten to it, not enough people have asked, I think a lot of people have asked for other things, so it's just, squeaky wheel, right? I think at the end of the day, people are like building these data sets and I think if you sort of map things chronologically, these make more sense because it's like, how do we incrementally improve all of these models?
[00:14:07] So a lot of these models are just incremental improvements over the last thing, right? Whether it is sort of through methods of how do we, how did we curate the data set? How did we improve the quality of the data set? So, you maybe LDJ talked about it right on I think for, for Capybara and Puffin, like how those, those were very specific dataset curation techniques that he works on.
[00:14:29] The Case for Open Source AI
[00:14:29] Alex Volkov: So there's, folks are doing this for dataset curation. Folks are doing this for skillset building as well. Definitely people understand that open source is like very important, especially after the, the, the, the, the march, the debacle, the OpenAI weekend that we all had. And people started noticing that even after developer day in OpenAI, the APIs went out.
[00:14:48] And then after that, the whole leadership of the company is swiftly changed and people, there was worries about, you know. How can people continue building AI products based on these like shaky grounds that turned attention definitely to Technium at least in open RMS I started seeing this more and more on Twitter, but also other models and many companies They're gonna start with open AI just to get there quick, and then they they think about okay Maybe I don't want to share my knowledge.
[00:15:13] Maybe I don't want to sign up for Microsoft. Maybe they will change their terms and conditions so What else is out there? They turned to other companies. Up until yesterday, Google was nowhere to be found. We've talked about Gemini a little bit before in a previous And you can tune in
[00:15:26] swyx: to
[00:15:26] Alex Volkov: Thursday Eye.
[00:15:26] Yeah, you can tune in to Thursday Eye. We covered the Gemini release a little bit. And but many are turning into the open source community and seeing that Meta released and continues to release and commit to open source AI. Mistral came out and the model is way smaller than LLAMA and performs Significantly better.
[00:15:43] People play with OpenRMS, which is currently techniums based, news researched, sourced, axolotl trained OpenRMS, I assume, right? And then they play with this and they see that, okay, this is like GPT 3. 5 quality. We had GPT 4. 5 birthday just a week ago. A week ago, a year ago, a week ago, we never, interacted with these models of this caliber.
[00:16:04] And now there's one open source, one that's on my laptop, completely offline, that, I can continue improving for my use cases. So enterprises, companies are also noticing this. And the open source community folks are building the skill set, not only the data sets. They're building the actual kind of, here's how we're going to do this, with Axelotl, with these data sets.
[00:16:21] The curation pieces. Now. Interesting. There's like recipes of curation. The actual model training is kind of a competitive thing where people go and compete on these leaderboards that we talked about, the LMC arena, and that recently added open air and recently added open chat and a bunch of other stuff that are super cool.
[00:16:37] The hug and face open source leaderboard. And so there's a competitive aspect to this. There's the open source. Aspect to this, like Technium says, I want GPT 4 on my laptop. There's the, let me build a skill set that potentially turns into a company, like we saw with Noose. Noose just, started organizing, a bunch of people on Discord, and suddenly, they're announcing their company.
[00:16:54] It's happening across all these modalities, and suddenly all these people who saw these green pastures and a fairly quick way to, hey, here's a cool online community I can, start doing cool stuff with. You mentioned the same in the beginning, right? Like, after your accident, what's cool, let me try this out.
[00:17:08] Suddenly I start noticing that there's a significant movement of interest in enterprising companies into these areas. And, this skill set, these data sets, and this community is now very Very important, important enough to create an event which pulls in Andrei Karpathy from OpenAI to come and see what's new Jeremy Howard, like the event that we just talked about, people are flying over and this is just a meetup.
[00:17:28] So, definitely, the community is buzzing right now and I think Axelot is a big piece as well.
[00:17:34] Orca and OpenOrca
[00:17:34] Wing Lian: Cool. Maybe we can talk about like Orca real quick, Orca, OpenOrca rather, I think there was a lot of buzz when, the first Orca paper came out. And just briefly, what is Orca? Yeah, Orca was basically having traces of like chain of thought reasoning, right?
[00:17:48] So they go and they, they distill sort of GPT 4. They take, they take a sampling of data from the Flan dataset. Maybe we can like add some show notes in the Flan dataset. Yeah, but we've covered it. Okay, cool. Use GPT 4 to say, all right, explain this in a step by step reasoning, right?
[00:18:06] And then you take that and you, they train the model and it showed, very good improvements across a lot of benchmarks. So OpenOrca was sort of the open reproduction of that since Microsoft Research never released that particular data set. And going back to sort of the Hugging Face leaderboard thing, those models did really well.
[00:18:23] And then I think, so sort of the follow up to that was SlimOrca, right? I think Going into and building the OpenOrca dataset, we never really went in and, validated the actual answers that GPT 4 gave us, so what we did was one from OpenChat actually cross referenced the original Flan, the original Flan response, the human responses, the correct answers with the dataset, and then I went and took it and sent all of, both of them to GPT 4 and said, is this answer mostly correct, right?
[00:18:54] Yeah. And then we were able to filter the dataset from, At least of the GPT 4 only answers from like 800, 000 to like 500, 000 answers or rows and then, and then retrain the model and it had the same performance as the original model to within I think, 0. 1 percent here about, and 30 percent less data.
[00:19:13] So, yeah. Okay.
[00:19:15] swyx: Interesting. So, I mean, there's, there's so much there that I want to highlight, but yeah. Orca is interesting. I do want people to know about it. Putting chain of thought into the data set like it's just makes a ton of sense one thing I think it would be helpful for people to scope thing these things out is how much data are we talking about when when you When people are fine tuning and then how much time or resources or money does it take to train to fine
[00:19:36] Wing Lian: tune?
[00:19:37] Yeah, so I think there's a little bit of overlap there with sort of like fine tuning techniques, but let's say Orca and I think even Hermes, they're both relatively large data sets like 10 billion tokens. Yeah. So large data sets being or the original Orca was, or the original open Orca was 800,000 rows.
[00:19:55] I believe it was somewhere in the ballpark of like a gigabyte of data, of gigabyte, of text data. And I, I don't. I believe, Hermes was, is like a quarter million rows of data, I don't know the actual byte size on that particular one. So, going and training a, let's, let's say everybody's training 7 billion Mistral right now, right?
[00:20:15] So, to tri I, I believe to fine tune 7 billion Mistral on, let's say, 8 A6000s, which have 48 gigabytes of VRAM, I believe, It takes about 40 hours, so 40, and then that's, depending on where you get your compute, 40 times 6, so it's like 500 to fine tune that model, so, and, and that's assuming you get it right the first time, right?
[00:20:44] So, you know.
[00:20:45] swyx: Is, is that something that X. Lotto handles, like, getting it right the first
[00:20:48] Wing Lian: time? If you talk to anybody, it's like you've probably tried at least three or four runs or experiments to like find the right hyperparameters. And after a while you sort of have a feel for like which, where you need your hyperparameters to be.
[00:21:04] Usually you might do like a partial training run, do some benchmark. So I guess for Al Farouk, whether you're going by his. This is Jeremy, he's, his actual name, or his twitter handle. He released the Dharma dataset, which is basically a subset of all the benchmarks. And Axolotl actually supports, you know taking that subset and then just running many benchmarks across your model every time you're doing an evaluation so you can sort of like see sort of relative it's not going to be the actual benchmark score, but you can get ideas alright, is this benchmark improving, is this benchmark decreasing, based on, you know Wait,
[00:21:39] swyx: why don't you run the full benchmark?
[00:21:41] What, what, what The
[00:21:42] Wing Lian: full benchmarks take Take a long time. Significant, yeah, significant amount of time. Yeah. And Okay, so that's like
[00:21:48] swyx: mini MMLU. Yeah. Like,
[00:21:49] Wing Lian: mini BigBench or whatever. Yep, exactly.
[00:21:51] Alex Volkov: It's really cool. We, when I joined Web2Masters just recently, and one of the things that I try to do is hey I'm not, I'm a software engineer by trade, I don't have an MLE background, But I joined a company that does primarily MLE, and I wanted to learn from the community, Because a lot of the open source community, they use weights and biases, And the benchmark that you said that Pharrell did, remind me of the name, sorry.
[00:22:13] Dharma? Dharma, yeah, yeah. So Luigi showed me how Dharma shows inside the dashboard. In Wi and Biases dashboard and so you can actually kinda see the trending run and then you can see per each kind of iteration or, or epoch or you can see the model improving trending so you can on top of everything else.
[00:22:29] The wi and biases gives like hyper parameter tracking, which like you, you started with common line and that's really hard to like remember. Also the Dharma data set, like the quick, the mini orca mini, you mini many different things. It's pretty cool to like visualize them as well. And I, I heard that he's working on a new version of, of Dharma, so Dharma 2, et cetera.
[00:22:47] So hopefully, hopefully we'll see that soon, but definitely it's hard, right? You start this training around, it said like 40, 50 hours. Sometimes, sometimes it's like your SSHing into this machine. You, you start a process, you send it with God and you just go about your day, collecting data sets, and then you have to return.
[00:23:04] And the whole process of instrumentation of this is still a little bit like squeaky but definitely. Tuning performance, or like grabbing performance in the middle of this, like with Dharma and some other tools, is very helpful to know that you're not wasting precious resources going somewhere you shouldn't go.
[00:23:21] Yeah.
[00:23:22] swyx: Yeah. Very cool. Maybe I'll, I'll, before we go into like sort of more details on fine tuning stuff, I just wanted to round out the rest of the Excel autoverse. There's, there's still Eric Hartford stuff. I don't know if you want to talk about Pygmalion, Disco, anything that you know about
[00:23:35] Wing Lian: those, those things.
[00:23:36] DiscoLM and Model Stacking
[00:23:36] Wing Lian: Yeah, I think like one of the, definitely one of the more interesting ones was like the Disco 120b, right? Yeah, I know nothing about it. Yeah. So, so. Alpen from Pygmalion AI, right, so they, so Pygmalion is a sort of a, it's, it's, they have their own community, a lot of it is based around, roleplay models, those sorts of things, and Alpen, like, put together, merged together Llama270B, so, and Alpen, like, put together, merged together Llama270B, so, I don't remember how he stacked them together, whether he merged the layers in between. There's a whole, there's a whole toolkit for that by Charles Goddard, where you can like take a single model and like stack them together or multiple models merge.
[00:24:18] That's like a whole other talk and a whole other tool set, but was able to create this 120. Billion parameter model out of a LAMA two 70 B. And then I believe the, yeah, disco is a fine tune of, of the, the, the sort of the base one 20 B is, I believe Goliath one 20 B. So, and, and what are the
[00:24:37] swyx: headline results that people should know about
[00:24:39] Wing Lian: disco?
[00:24:39] I think for the headline results, I, I've, I haven't played with it personally because it's. It's a very large model and there's a lot of GPU, right? But, like, from what I've heard anecdotally, it performs really well. The responses are very good. Even with, like, just, even the base model is a lot better than, Llama70b.
[00:24:57] So, and we, I think generally everybody's like, we would all love to fine tune Llama70b, but it's just, it's so much, it's so much memory, so much compute, right?
[00:25:07] Datasets and Evals over Models
[00:25:07] Wing Lian: I
[00:25:07] Alex Volkov: want to touch on this point because the interesting thing That comes up out of being in this ecosphere and being friends with open source folks, tracking week to week state of the art performance on different models.
[00:25:19] First of all, a lot of the stuff that the folks do a couple of weeks ago, and then something like Mistral comes out, and a lot of the stuff back then, Doesn't technically make sense anymore. Like the artifacts of that work, the actual artifacts, they don't no longer make sense. They're like lower on the on, on the hug and face leaderboard or lower on LM CS leaderboard.
[00:25:36] But some of the techniques that people use, definitely the datasets. The datasets keep traveling, right? So open airmen, for example, is the dataset. The tum cleaned up for only. Open sourceable data that previously was just Hermes. And that, it was previously used to train Lama. And then once Mistral came out, it was used to train Mistral.
[00:25:54] And then it became significantly better on the 7b base Mistral. So the data sets keep traveling, keep getting better a little bit here and there. And so the techniques improve as well. It looks like both things are simultaneously true. The artifacts of a month and a half ago. The, the actual models themselves, it's great the hug and face has them, because not every company can keep up with the next weeks', oh, I, I'll install this model instead, sell this model instead.
[00:26:19] But the, the techniques and the, the dataset keep improving as we go further, and I think that's really cool. However, the outcome of this is that for a long time. For many, many people, including us, that we do this every week. We literally talk with people who release these models every week. It's really hard to know.
[00:26:36] So, there's a few aspects of this. One, I think, like you said, the bigger model, the 70B models, you actually have to have somebody like Perplexity, for example, giving you access to the 70B really fast. Or you have to, like, Actually, find some compute, and it's expensive, especially for the bigger models. For example Falcon 180B came out, like the hugest open source model.
[00:26:56] How do you evaluate this if you can't run it? Nobody liked it. It's really, so first of all, nobody liked it, but secondly, only the people who were able to find compute enough to run inference on this, they only had like, I can't run this on my laptop, and so that's why it's much easier, something like OpenRMS 7 to be, 7B, it's much easier, because you can run this on your MacBook.
[00:27:14] It's much easier to evaluate. It's much easier to figure out the vibes, right? Everybody talks about the vibes as an evaluation check. If you're plugged in enough, if you follow the right people, if they say pretty much the same things all independently, then you run into a problem of whether they're repeating, and their stochastic parents are repeating the same thing, or they actually evaluated themselves.
[00:27:31] Yeah, you never know. But, you never know, but like, I think on a large enough scale on Twitter, you start getting the feel. And we all know that like, OpenRMS is one of the top performing models, benchmarks, but also vibes. And I just wanted to highlight this vibes checks thing because you can have the benchmarks, you can have the evaluations, they potentially have contamination in them, potentially they not necessarily tell you the whole story because some models are good on benchmarks, but then you talk to them, they're not super helpful.
[00:28:00] And I think it's a combination of the benchmarks, the leaderboards, the chatbot, because LMSys, remember, their ranking is not only based on benchmarks, it's also people playing with their arena stuff. People actually like humans, like, get two answers. I think they completely ignore benchmarks. Yeah, and then They only do ELO.
[00:28:18] Oh, they do ELO completely, right? So that, for example, is just like people playing with both models and say, Hey, I prefer this one, I prefer that one. But also there's like some selection bias. The type of people who will go to LMCs to play with the models, they're a little bit specific in terms of like who they are.
[00:28:33] It's very interesting. There's so many models. People are doing this in this way, that way. Some people are doing this for academic rigor only to test out new ideas. Some people are actually doing this like the Intel fine tunes of Mistral. Intel wanted to come out and show that their hardware approach is possible, Mistral, etc.
[00:28:51] And it's really hard to know, like, what to pick, what to use. And especially on the bigger models, like you said, like the Llama 70B, the Falcon 180B. It's really because, like, who has the compute to validate those? So I would mention that, like, use with caution. Like, go and research and see if the biggest model that just released was actually worth the tokens and the money you spend on it.
[00:29:12] To try and, if you're a business, to integrate it.
[00:29:15] Distilling from GPT4
[00:29:15] swyx: Since you said use of caution, I'll bring in one issue that has always been in the back of my mind whenever I look at the entire universe of open source AI models, which is that 95 percent of the data is derived from GPC 4, correct?
[00:29:30] Which technically you can't use for commercial licenses,
[00:29:34] Wing Lian: right?
[00:29:35] swyx: What is the community's stance on this kind of stuff?
[00:29:40] Wing Lian: I think from the community stance, like I feel like a lot of us are just experimenting, so for us, it's like, we're not going and building a product that we're trying to sell, right?
[00:29:49] We're just building a product because we think it's interesting and we want to use it in our day to day lives, whether or not we try and integrate it. Personal use, yeah. Yeah, personal use, so like, as long as we're not selling it, yeah, it's fine. But
[00:30:01] swyx: like, I as a company cannot just take OpenHermes and start serving
[00:30:05] Alex Volkov: it and make money on it.
[00:30:06] OpenHermes you can. Because the opening of OpenHermes, I think, is a clean up. That did after the regular Hermes, please folks, check your licenses before you listen to podcasts and say, Hey, I will tell you though, you could say the same thing about OpenAI. You could say the same thing kind of makes sense, where OpenAI or StabilityAI trains their diffusion model on a bunch of pictures on the internet, and then the court kind of doesn't strike down Sarah Silverman, I think, or somebody else, who came and said, hey, this has my work in it, because of the way how it processes, and the model eventually builds this knowledge into the model, and then it doesn't actually reproduce one to one what happened in the dataset.
[00:30:45] You could claim the same thing for open source. Like, we're using And by we, I mean the, the open source community that I like happily report on uses GPT 4 to rank, for example, which is the better answer you, you, that's how you build one, one type of data set, right? Or DPO or something like this, you, you basically generate data set of like a question and four answers, for example, and then you go to GPT 4 and say, Hey, smartest model in the world right now, up to Gemini Ultra, that we should mention as well.
[00:31:11] Which one of those choices is better? But the choices themselves are not necessarily written with GPT 4. Some of them may be, so there's like full syntactic datasets. But there's also, datasets are just ranked with GPT 4. But they're actually generated with a sillier model, or like the less important model.
[00:31:25] The lines are very blurry as to what type of stuff is possible or not possible. And again, when you use this model that's up on Hug Face, the license says you can use this. OpenAI is not going to come after you, the user. If anything, OpenAI will try to say, hey, let's prevent this, this type of thing happening, and the brain, but I honestly don't think that they could know even, not that it makes it okay, it's just like, They also kind of do this with the Internet's archive, and also, I think that some of it is for use.
[00:31:55] You use models to help you augment tasks, which is what GPT 4 lets you do.
[00:32:00] swyx: Yeah, the worst thing that OpenAI can do is just kick you off OpenAI. That's because it's only enforced in the terms of service.
[00:32:05] Alex Volkov: Sure, but just like to make sure, to clarify who they're going to kick out, they could kick out like News, for example, if news are abusing their service, a user of the open source, fully Apache 2 open source, for example, They won't get kicked out if they use both, just because they use both.
[00:32:22] I don't believe so. I don't think OpenAI has a claim for that.
[00:32:25] swyx: Well, we're not lawyers, but I just want to mention it for people to know it's an issue.
[00:32:30] Wing Lian: And one of the things, like, I talked to someone recently, and I think that they also are like interested in it, but also to the point of like, right, if I use a model trained on data, using GPT for data, But I use that model to then regenerate new data.
[00:32:46] Is that model, is that data okay? So like you start going down this whole rabbit hole. So yeah. All right.
[00:32:53] swyx: Fantastic. Cool. Well, I think that's roughly highlights most of the open source universe. You also have your own models. Do you want to shout out any one of them? Yeah.
[00:33:01] Wing Lian: I mean, I think like, I think Early on, Manicore got a lot of love.
[00:33:04] I think it was mostly popular in, like, the roleplay communities. It was, it tended to be pretty truthful. It tended to be, like, have relatively good answers, depending on who you ask, right? But, I think for me, it was just, Releasing models was a way to try and, like, continue to build out the product, figure out what I needed to put into the product, how do I make it faster, and, if you've got to, like, go and debug your product, you may as well have it do something useful.
[00:33:29] Awesome. So, yeah.
[00:33:31] Finetuning - LoRA, QLoRA, ReLoRA, GPTQ
[00:33:31] swyx: Okay, and then maybe we'll talk about just fine tuning techniques. So this is going to be a little bit more technical than just talking about model names and datasets. So we started off talking about LoRa, QLoRa. I just learned from your readme there's ReLoRa. Which I've never heard about.
[00:33:45] Could you maybe talk about, like, just parameter efficient fine tuning that whole, that
[00:33:50] Wing Lian: whole journey, like, what people should know. Yeah, so with parameter efficient fine tuning, I think the popular ones, again, being, let's, we'll start with lore, right? So, usually what you do is you freeze all the layers on your base, on the base model, and then you, at the same time, you sort of introduce additional Oh, this is tight.
[00:34:08] No. You introduce, another set of layers over it, and then you train those, and it is done in a way that is mathematically possible, particularly with LORs that you can, then you, you, When you, when you train the model, you, you run your inputs through the base model, whose weights are frozen, but you, then you also run it through the additional weights, and then at the end you combine the weights, and then, and then, or you combine the weights to get your outputs, and then at the end, and when you're done training, you're left with this other set of weights, right, that are completely independent, and And then from that, what you can do is, some person smarter than I figured out, well, oh, they've done it in such a way that now I can merge these weights back into the original model without changing the architecture of the model, right?
[00:35:03] So, so, that tends to be, like, the go to, and You're training much fewer parameters so that when you do that, yes, you still need to have all of the original weights, but you have a smaller gradient, you have a smaller optimizer state, and you're just training less weights, so you can tend to train those models on, like, much smaller GPUs.
[00:35:27] swyx: Yeah. And it's roughly like, what I've seen, what I've seen out there is roughly like 1 percent the number of parameters that you're trading. Yeah, that sounds about right. Which is that much cheaper. So Axelotl supports full fine tune, LoRa, QLoRa,
[00:35:40] Wing Lian: Q. Yes. So, so QLoRa is, is very similar to LoRa. The paper was, if I remember correctly, the paper was Rather, traditionally, most people who did Loras were, were, they were quant, they were putting the model weights in 8 bit, and then fine tune, parameter efficient fine tuning over the Lora weights, and then with QLora, they were quantizing all of those, they were then quantizing the weights down to 4 bit, right, and then I believe they were also training on all of the linear layers in the model.
[00:36:15] And then with ReLore, that was an interesting paper, and then, I think, like, it got implemented. Some people in the community tried it, tried it out, and it showed that it didn't really have the impact that the paper indicated that it would. And from what I was told recently, that they re I guess they re released something for Relora, like, a few weeks ago, and that it's possibly better.
[00:36:44] I personally haven't had the time. What was the
[00:36:46] swyx: main difference,
[00:36:47] Wing Lian: apart from quantization? I don't know. Okay. What was the main difference, sorry?
[00:36:49] swyx: Apart from quantization, right? Like,
[00:36:50] Wing Lian: Qlora's thing was, like, we'll just drop off some bits. With Relora, what they did was, you would go through, you would define some number of steps that you would train, like, your Lora with, or your Qlora.
[00:37:01] Like, you could do Like, ReqLore, if you really wanted to, you would, you would train your LoRa for some number of steps, And then you would merge those weights into your base model, and then you would start over. So by starting, so, then by starting over, The optimizer has to find, like, sort of, re optimize again, and find what's the best direction to move in, and then do it all again, and then merge it in, do it all again, and theoretically, according to the paper, doing ReLore, you can do parameter efficient fine tuning, but still have sort of, like, the performance gains of doing a full fine tuning, so.
[00:37:38] swyx: Yeah, and
[00:37:39] Wing Lian: GPTQ? And GPTQ, so it's, I think with GPTQ, it's very similar to, more similar to QLore, where you're, it's mostly a quantization of the weights down to like 4 bit, where GPTQ is a very, is a specific methodology or implementation of quantization, so. Got it.
[00:37:57] Alex Volkov: Wang, for, for folks who use Axolotl, your users, some people who maybe, Want to try it out?
[00:38:03] And do they need to know the differences? Do they need to know the implementation details of QLora versus ReLora? Or is it okay for them to just know that Axolotl is the place that already integrated them? And if that's true, if that's all they need to know, how do they choose which method to use? Yeah,
[00:38:22] Wing Lian: so I think like, I think most people aren't going to be using ReLora.
[00:38:25] I think most people are going to be using either Lora or QLora. And I think they should have it. They should have an understanding of why they might want to use one over the other. Most people will say that with Qlora, the quality of the final model is not quite as good as like if you were to do a LoRa or a full fine tune, right?
[00:38:44] Just because, you've quantized these down, so your accuracy is probably a little off, and so that by the time you've done the Qlora, you're not moving the weights how you would on a full fine tune with the full parameter weights.
[00:38:56] Interesting.
[00:38:57] swyx: Okay, cool. For people who are more interested, obviously, read the papers. I just wanted to give people, like, a high level overview of what these things are. And you've done people a service by making it easy for people to try it out. I'm going to, I'm going to also ask a question which I know to be wrong, but I'm curious because I get asked this all the time.
[00:39:15] What is the difference between all these kinds of fine tunes
[00:39:17] Wing Lian: and RLHF? Okay, between all of these sorts of fine tunes and RLHF. So all of these sorts of fine tunes are based, are, ideally, this, they are taking knowledge that the base model already knows about, and presenting it in a way to the model that you're having the model answer like, Use what it already knows to sort of answer in a particular way, whether it's, you're extracting general knowledge, a particular task, right?
[00:39:44] Instruct, tune, chat, those sorts of things. And then generally with RLHF, so what is, let's go back, what is it? Reinforcement Learning with Human Feedback. So if we start with the human feedback part, What you're doing is you generally have, you have like a given prompt and then you, maybe you have one, maybe you have two, I think, like if you look at with Starling, you have like up to what, seven different, seven different possible responses, and you're sort of ranking those responses on, on some sort of metric, right, whether the metric is how much I, I might like that answer versus or I think with like starling is like how how how helpful was the answer how accurate was the answer how toxic was the answer those sorts of things on some sort of scale right and then using that to go back and like sort of Take a model and nudge it in the direction of giving that feedback, to be able to answer questions based on those preferences.
[00:40:42] swyx: Yeah, so you can apply, and is it commutative? Can you apply fine tuning after and onto an RLHF model? Or should the RLHF apply, come in afterwards,
[00:40:54] Wing Lian: after the fine tune? Um, I, yeah, I don't know that there's There's been enough research for one way or another, like, I don't know.
[00:41:02] That's a question that's been asked on Discord. Yeah, like, I definitely would say I don't know the answer. Go and try it and report back to me and let me know so I can answer for the next guy.
[00:41:10] swyx: It's shocking how much is still unknown about all these things. Well, I mean, that's what research is for, right?
[00:41:16] Wing Lian: So actually I, I think I saw on the top of a leaderboard, it was a, it was a mytral base model, and they didn't actually fine tune it. They, or they, they just did RLH, they did like an RLHF fine tune on it using like, I don't, I don't recall which dataset, but it was like, and it benchmarked really well.
[00:41:37] But yeah, you'd have to go and look at it. But, so it is interesting, like going back to that, it's like. Traditionally, most people will fine tune the model and then do like a DPO, PPO, some sort of reinforcement learning over that, but that particular model was, it seemed like they skipped like the supervised fine tuning or Scott.
[00:41:55] Axolotl vs HF Transformers
[00:41:55] swyx: Cool. One thing I did also want to comment about is the overall, like, landscape, competitive landscape, I don't know. Hugging Face Transformers, I think, has a PFT module.
[00:42:05] Wing Lian: Yeah, yeah, the PEFT, the Parameter Efficient Fine Tuning, yep. Is that a competitor to you? No, no, so we actually use it. We're just a wrapper over sort of, sort of the HuggingFace stuff.
[00:42:15] So, so that is their own sort of module where They have, taken the responsibility or yeah, the responsibility of like where you're doing these parameter efficient fine tuning methods and just sort of like, it is in that particular package where transformers is mostly responsible for sort of like the modeling code and, and the trainer, right.
[00:42:35] And then sort of, there's an integration between the two and, there's like a variety of other fine tuning packages, I think like TRL, TRLX, that's the stability AI one. Yeah, I think TRL likes the stability, yeah, Carper, and TRL is a hugging face trainer. Even that one's just another wrapper over, over the transformers library and the path library, right?
[00:43:00] But what we do is we have taken sort of those, yes, we've We also use that, but we also have more validation, right? So, there are some of us who have done enough fine tunes where like, Oh, this and this just don't go together, right? But most people don't know that, so like Example?
[00:43:19] Like, people want to One and one doesn't go together. I don't have an example offhand, but if you turn this knob and this knob, right? You would think, all right, maybe this will work, but you don't know until you try. And then by the time you find out it doesn't work, it's like maybe five minutes later, it's failed.
[00:43:34] It's failed in the middle of training or it's failed during the evaluation step. And you're like, ah, so we've, we've added a lot of, we've added a lot more validation in it. So that like, when you've, you've created your configuration, you run it through and now you say. The validation code says this is probably not right or probably not what you don't, not what you want.
[00:43:52] So are you like a, you
[00:43:53] swyx: do some linting of your YAML file?
[00:43:56] Wing Lian: There, I guess you could call it linting, it's sort of like Is there a set of rules out
[00:44:00] swyx: there somewhere? Yeah, there's a set of rules in there. That's amazing, you should write documentation like This rule is because, this user at this time, like, ran into this bug and that's what we invested in.
[00:44:10] It's like a good collection
[00:44:11] Wing Lian: of knowledge. Yeah, it is, and I guess like, if you really wanted to, like, figure it out, I guess you could, like, git blame everything, and But, yeah, it's, so, I think that's always a useful thing, it's like Because people want to experiment but they don't, people will get frustrated when you've experiment, you're experimenting and it breaks and you don't know why or you know why and you've just gone down the rabbit hole, right?
[00:44:37] So, so I think that's one of the big features that's, that I think I find important because it's It prevents you from doing things you probably shouldn't have, and it, and sometimes we will let you do those things, but we'll try and warn, warn you that you've done that.
[00:44:50] I
[00:44:51] Alex Volkov: have a follow up question on this, actually, because yesterday we hung out to this open source event, and I spent time by you a couple times, like when people told you, oh, XLR, I use XLR, it's super cool, and then the first thing you asked is, like, immediately, like, what can we improve?
[00:45:04] And yes, from multiple folks, and I think we talked about this a little bit, where there's It's a developer tool. It's like a machine learning slash developer tool. Your purpose in this is to help and keep people, as much as possible, like, Hey, here's the best set of things that you can use right now. The bear libraries are, or the bear trainer, for example, is a bear trainer.
[00:45:28] And also, maybe we should talk about how fast you're implementing these things. So you mentioned the first implementation took a week or so. Now there's a core maintainer group, right? There's like, features are landing, like Qlora, for example. Neftune, I don't know if that's one example of something that people potentially said that it's going to be cool, and then eventually, like, one of those things that didn't really shake out, like, people quickly tested this out.
[00:45:48] So, there's a ton of Wait, Neftune is cancelled? I don't know if it's fully canceled, but based on vibes, I heard that it's not that great. So like, but the whole point that I'm trying to make with Neftune as well is that being existing in the community of like XLR or like, I don't know, even following the, the GitHub options or following the Discord, it's a fairly good way to like, learn these, Kind of gut feelings that you just, you just said, right?
[00:46:14] Like where this, maybe this knob, that knob doesn't work. Some of these are not written down. Some of these are like tribal knowledge that passes from place to place. Axel is like a great collection of many of them. And so, do you get That back also from community of folks who just use, like, how do you know who uses this?
[00:46:30] I think that's still an issue, like, knowing if they trained with XLR or should they add this to things? Talk about, how do you get feedback and how else you should get feedback?
[00:46:38] Wing Lian: Yeah, I mean, most of the feedback comes from the Discord, so people come in and , they don't get a training running, they run into, like, obscure errors or, errors that That's a lot of things that maybe, maybe as a product we could catch, but like, there's a lot of things that at some point we need to go and do and it's just on the list somewhere.
[00:46:58] Right that's why when people come up, I'm like, what, what were your pain points? Because like, as a developer tool, if you're not happy with it, or you come in and in the first, Takes you 30 minutes and you're still not happy. You leave the tool and you may, you might move on maybe to a better tool, maybe to, one with less frustration, but it may not be as good, right?
[00:47:17] So I'm trying to like, figure out, all right, how can I reduce all this frustration? Because like for me, I use it every day for the most part, right? And so I am blind to that, right? Mm-Hmm. . Mm-Hmm. . I just know, I, I go do this, this, and this. It pretty much mostly works, right? But, so I don't have sort of that, alright, that learning curve that other people are seeing and don't understand their pain points.
[00:47:40] Yeah,
[00:47:40] Alex Volkov: you don't have the The ability to onboard yourself as a new user completely new to the whole paradigm to like get into the doors of like, Oh, no, I don't even know how to like ask about this problem or error.
[00:47:53] swyx: Cool. The last few things I wanted to cover was also just the more advanced stuff that you covered yesterday.
[00:48:00] 20x efficiency with StackLlama and Multipack
[00:48:00] swyx: So I'll just, caution this as like, yeah, this is more advanced. But you mentioned Stackllama and Multipack. What are they
[00:48:06] Wing Lian: and what should people know? Yeah, so, so, Stack Llama was, that paper came out, so Stack Llama I think was like, two, two, two separate, two separate concepts that they announced, so the first one was They being hugging face.
[00:48:20] Yeah, sorry, yes, they being hugging face, so the first one being sort of like, this idea of packing, like some packing sequences together, so like, if we think about training data, right, your training data is, let's say, to keep the math easy, let's say your training data is 500, We, we, we, we will use the terminology words.
[00:48:39] Let's say your training data is 500 words long, and let's say your, your context length, you know how much data your, that your model can accept is like, or that you want feed into your model. It's, let's say, we won't use tokens again, we'll we'll use it is it's 4,000 tokens, right? So if you're training at 4K Con or four 4,000 4K contacts and you're only using 500 of it, you're sitting like with the other 1500.
[00:49:05] 3, 500 words that you're not using, right? And typically that's either filled with these PAD tokens, so I think I made the analogy last night that it's like having sort of like a glass here you fill it up with a shot of liquor and then you're and that's your training data and then you just fill it up with more water and those are your PAD tokens and it's just, it doesn't do much, right?
[00:49:27] It's still the same thing, but you still have to go through all of that to go through all your training data. And then, so what Stack Llama showed was you could just sort of take your training data, append the next row of training data until you filled that entire 4k context, so in this example, right, with 500 words to 4k, that's 8 rows of training data.
[00:49:48] But, the problem with that is, is that with a lot of these transformer models, they're very much relying on attention, right? So, like, if you now have this sequence of words that now, in order for the, the model has seen all of these other words before, right? And then it sees another set of words, another set of words, but it's learning everything in context of all the words that it's seen before.
[00:50:13] We haven't corrected the attention for that. And just real quickly, since I said that that paper was two concepts, the other one was, I believe it was like a reinforcement learning, but outside the scope of this. So going from that, I implemented that early on because I was like, Oh, wow, this is really great.
[00:50:29] And. Yes, because it saves you a bunch of time, but the trade off is a little bit of accuracy, ultimately, but it still did pretty well. I think when I did Manicore, I think it used sort of that concept from Stack Llama of just sort of appending these sequences together, right? And then sort of the next evolution of that is Multipack, right?
[00:50:51] So, there was a separate paper on that, it was, I believe it was referenced, it got referenced in the Orca paper, where you could, you could properly mask those out using like a, I think it was like a lower block triangular attention mask, and then sort of, so, So, there's that. I did try implementing that, manually recreating that mask, but then one from the OpenChat, so he was helping with OpenOrca as well, and he had done an implementation of Multipack, and where he used FlashAttention, so FlashAttention So that was released by TreeDAO, and it was this huge performance gain.
[00:51:35] Everybody uses it now, even the Transformers library now, they've taken all of these, like, people are taking all of these models and sort of like, making it compatible with FlashAttention. But in Flash Tension, there is one particular implementation that lets you say, Well, I'm sending you all of these sequences like you would in Stack Llama, But let me send you another, another, Set of information about, this is where this set of sequences is, this is where the second set of sequences is.
[00:52:06] So like, if it was like, 500 words long, and you stacked them all together, you would just send it a row of information that was like, 0, 500, 1000, 1500, etc, etc, out to 4000. And it would know, alright, I need to break this up, and then run the forward pass with it. And then it would be able to, and it was much more, much more performant.
[00:52:29] And I think you end up seeing like 10x, 20x improvements over sort of, I mean, I think FlashAttention was like a 2x improvement, and then adding that with the Multipack, you start to see like, depending on, how much data you have, up to like a 20x improvement sometimes. 20x. 20x. Wow. Yeah.
[00:52:48] And I only know the 20x because I, like, before last night, I was like, I re ran the alpaca, I looked up the alpaca paper because it was like, I just need a frame of reference where somebody did it, and I think they used eight A100s for three hours, and they said it cost them 100. I don't, I don't think eight A100s cost, I don't know how much it costs right now.
[00:53:14] But I ended up rerunning it. Usually a dollar an hour, right? Yeah, so eight. The cheapest is like a
[00:53:18] Alex Volkov: dollar, a dollar an hour for one.
[00:53:20] Wing Lian: Yeah, so that's still like 24, 25. But maybe if you're going on Azure, maybe it's like, maybe it's 100 on Azure. I mean, it used to be more expensive, like, a year ago.
[00:53:31] Yeah, and then, so I re ran it with sort of like, I turned on all of the optimizations just to see what it would be. And like, and usually Multipack is the biggest optimization, so Multipack with Flash Detention. And it, I think I spun it up on 8 L40s, and it ran, and I didn't let it run all the way through, I just grabbed the time, the estimated completion time, and it was like 30 minutes, so it would have cost like 4 or 5 to run the entire, like, reproduce the alpaca paper, right?
[00:54:00] Which is crazy. It's crazy. 20x,
[00:54:02] Alex Volkov: yeah. I want to ask about, like, you said you turned on all the optimization. Is that the yaml file with xlodl, you just go and like check off, like, I want this, I want that? Yeah, yeah,
[00:54:10] Wing Lian: so there's like one particular yaml file in there, That, there's one particular YAML file in there that's like, it's under examples, llama2, fft, optimize.
[00:54:20] So, I think someone had created one where they just turned, they put in all of the optimizations and turned them on. I mean, it actually, it does run, which is like, sort of surprising sometimes, because sometimes, you optimize this, optimize this, and sometimes they just don't work together, but, yeah.
[00:54:36] Just turn the knobs on, and like, fine tuning should really just be that easy, right? I just want to flip the knob and move on with my life and not figure out how to implement it.
[00:54:47] Tri Dao and Mamba
[00:54:47] Alex Volkov: Specifically, the guy behind FlashAttention came up with something new. You want to talk about this a little bit? You want to briefly cover Mamba?
[00:54:53] Yeah, let's talk about Mamba. Let's talk about Mamba. So, what is Mamba?
[00:54:57] Wing Lian: Oh, gosh. I mean, I have not read the paper end to end. Like, I think you need to find someone smarter to tell you what Mamba is. But I think in a nutshell, it's sort of this, like, attentionless, attentionless model architecture. So I think it was, like, using a lot of his learnings from, like, I think Stanford did a lot of like sort of attentionless models with like I think Hyena several months ago as well so it is sort of this evolution of that of these of this research they've done and Apparently I believe it is what 5x faster for inference But the memory requirements are sub quadratic, so like I think, so with models that have attention, as you scale the context length out, the memory and the inference and training time goes up, quadratically, like Or squared, right?
[00:55:50] Whereas this one is closer, much closer to linear. So it's, it's really exciting. And there's a lot of like, I think a lot of people in the community are excited about it because especially I was talking with LGJ yesterday and he was saying it showed think with the perplexity curves and given the same exact, like comparing a, I think it was like a 140 million parameter model with the Pythea 140 million parameter model trained on the exact same data set as that model that there was a, that I believe the perplexity curves were a little bit lower than the Pythea model.
[00:56:26] So yeah. Yeah.
[00:56:28] Alex Volkov: I think one thing LDJ also is the guy behind, he was super excited to get like us to talk on Thursday about Mamba as well. He mentioned to me that the significant improvements in performance, it could be like 2x in the beginning where like lower tokens are, but then as you scale more with longer, longer tokens, because the non quadratic, the almost linear type scale, it's the performance improvements for larger and bigger and like more models are significant, like in the 10x to maybe 20x.
[00:56:57] Yeah, I think he said 10 yeah. At the larger models. And that's where we want to go. We want to get to the bigger sizes, the longer trains.
[00:57:06] Wing Lian: Yeah, yeah. So in particular, the longer context links. So like, if you're talking like 50, 60, like, or 128k context, like what is it, GPT turbo now? Or 4 turbo?
[00:57:19] 128, yes. So, like, getting out to that because it's no longer, yeah, it's like, it's, it's just as fast. I believe it should be just as fast, like, generating those tokens as it is, like, on a short, on a short
[00:57:34] Alex Volkov: prop. So, this came out just recently, and then between running to this open source AI, driving here in Uber, like, you already put out something that I saw that, that you started.
[00:57:44] Wait, what? Something today? Yeah,
[00:57:47] Wing Lian: what did you do? Well, I mean, so like tree and I forget who the other author is on that paper. They had released sort of the modeling code on, on GitHub. And then sort of like, it wasn't, they hadn't quite put it like made it like transform or, transformers library native.
[00:58:04] So, and it, it didn't quite drop in. Like cleanly into like Axel lot to get it, so that you could fine tune it. So like, it was one of the things I actually wanted to try and get done before the, before the meetup yesterday, and just demo that because that would be awesome, right? That'd be awesome.
[00:58:20] I think it dropped on Thursday and you know No. What day? No, today is Thursday. Thursday. I keep. I keep thinking today was Friday, that's what I said. I think, so it dropped on what, Tuesday, the meetup was Wednesday, I wanted to get it done for that, but I was getting it where it would like, the loss would just go to zero, and just fail.
[00:58:40] So, but yeah, right before coming here, I was working on it this morning and I think we finally got it working. So, I think Pharrell's training something on it. I'm pretty sure like Tenuim is going to be training something on it soon. So,
[00:58:52] Alex Volkov: yeah. So, we'll see, but I wanted to highlight the speed because you started with like within a week the first alpaca or, implementation and change in Axelot came and now like you're talking about like three days and that's with you flying and that's with you like presenting and talking on podcasts.
[00:59:08] Roadmap for Axolotl
[00:59:08] swyx: Very productive. Yeah. Yeah, excellent. Well, so, we're going to start wrapping up soon, but I always wanted to give you space to also talk about what you're working on next, and, on the
[00:59:17] Wing Lian: roadmap for Axelotl. Yeah, I think so, the roadmap for Axelotl is really like, I think, trying to stabilize sort of the feature set.
[00:59:26] Like, so the first thing on the roadmap is to write the roadmap, and then sort of going from there, it's, I think, So for me the sort of the vision is like it's it's a developer first platform right and as a developer You you're maybe you're more than likely doing it this sort of this side hustle side project trying to figure out like how do I build?
[00:59:45] LLMs and you know how do I build you know? How do I use a trainer that sort of thing and then you're you get comfortable with this tool? And then you maybe you take it to your company and you're training Models for where you work, right? So, and then, ultimately, you're saying, I want to use this because it's easy and I know how to use it.
[01:00:03] So, for me Given that sort of like, if I follow that through, that thought through, it's like, well, companies don't want to use this if it's hard for them to like, if given their specific use cases, right, they might need something specific in the workflow that they, and I, what I don't want is to have is them having to fork it, like, to Like, fork it in a way that is, like, hard to maintain, that if they want to get features, they then have to, like, rebase it and all of that.
[01:00:32] So, for me, and I actually have, like, a issue in GitHub that's about three or four months old at this point of exactly, yet, expose, like, create a plugin system, expose sort of, like, these hooks where companies can go in and build their own plugins and sort of, like, Modify, like, hyperparameters on the fly, or modify various, like attributes of training.
[01:00:57] Yeah, it's becoming a platform. Yeah, exactly. So, I need to, provide a way for, for them to be able to, like, use it in, in a, in a reliable manner and something that, that they can go invent and feel comfortable using, right? Yeah,
[01:01:10] swyx: awesome. You are working independently? You left SoundCloud a few months ago, and you have a non profit, the Open Access AI Collective.
[01:01:20] The Open Source AI Community
[01:01:20] swyx: It has a Discord people can join. How else can people support
[01:01:22] Wing Lian: you? I think really, like, for me, the biggest thing is, like, I'm looking, I'm always looking for contributors. Like, we have a great, set of core contributors, Nanobit, Amin slash TMM1, Casper Hansen, and then, and there are probably a few others who, I've Don't have the names offhand for, but we do see some like smaller PRs trickle through, but like A lot of the, sort of like, if I had somebody that could have gone and done Mamba for me, that would make my life a hundred times easier, right?
[01:01:51] I wouldn't have to be scrambling between, Ubers and meetings and those sorts of things to try and, like, get that implemented. So, there's definitely this, like, roadmap of, Things to do and nice to have, right? And like Nano is great at being a community manager and answering questions and sort of fueling all of that and being technical and you know It's really technical and can stole open PR's and fix things and like so and he's a graduate So he's a graduate student in Japan Working, doing research, and somehow he finds time to like, support this community, right?
[01:02:25] He's amazing, I love him, and I think everybody should like, show him some love, and then, but yeah, like, ultimately, the, I think the, the big, yeah, the biggest thing that I could ask for would be just, yeah, more core contributors.
[01:02:38] swyx: Cool. All right, well if you're interested in checking it out check out XLotto.
[01:02:42] Alex, anything else to, to
[01:02:43] Alex Volkov: add? Yeah, I will say folks who are listening to us, open source doesn't just happen. It happens because there's a bunch of great people. Giving their life, basically, to these things. So, first of all, be nice in comments. Like, that's obvious. Like, if you want to come in and complain about something, be productive and do the work as much as possible so the person who's, like, giving out of their life to help you will actually find it, like, easier.
[01:03:06] It usually gets to a point where, like, a small project becomes a platform, the platform then has rules, and then it's making it hard for some people to just go in and kind of say, Hey, this thing or that thing. Remember, there's people contributing without necessarily a lot of gain from it, just because they're contributing to the community.
[01:03:24] And also, come in and contribute. If you're using axolotl, and I heard many people, commercial people, come up to you, A16z folks come up to you, like many people, if they use axolotl, Give back. Give back to the community. I think it's always great. So I just like, if you're listening to this, and you've used Excelato, it helped you, there is a way to also contribute, not necessarily as the only core contributor, as a sponsorship, reach out, reach out to you as well, but definitely talk about this and give feedback as well.
[01:03:51] That's also very helpful. Sometimes people get stuck, and it's like, ah, okay, we'll do something else. No, just give feedback, talk about this. I think everybody else will generally benefit from that. Excellent.
[01:04:01] Wing Lian: Thank you. That's it. Yeah. Alright.
[01:04:04] Alex Volkov: Cool. Thanks for coming. Everybody should try Axolotl and tell us what
[01:04:08] swyx: they
[01:04:09] Wing Lian: think.
[01:04:11] Yeah.
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Podcast: Latent Space: The AI Engineer Podcast — Practitioners talking LLMs, CodeGen, Agents, Multimodality, AI UX, GPU Infra and all things Software 3.0 (LS 33 · TOP 5% what is this?)
Episode: The "Normsky" architecture for AI coding agents — with Beyang Liu + Steve Yegge of SourceGraph
Pub date: 2023-12-14
We are running an end of year survey for our listeners. Let us know any feedback you have for us, what episodes resonated with you the most, and guest requests for 2024!
RAG has emerged as one of the key pieces of the AI Engineer stack. Jerry from LlamaIndex called it a “hack”, Bryan from Hex compared it to “a recommendation system from LLMs”, and even LangChain started with it.
RAG is crucial in any AI coding workflow. We talked about context quality for code in our Phind episode. Today’s guests, Beyang Liu and Steve Yegge from SourceGraph, have been focused on code indexing and retrieval for over 15 years. We locked them in our new studio to record a 1.5 hours masterclass on the history of code search, retrieval interfaces for code, and how they get SOTA 30% completion acceptance rate in their Cody product by being better at the “bin packing problem” of LLM context generation.
Google Grok → SourceGraph → Cody
While at Google in 2008, Steve built Grok, which lives on today as Google Kythe. It allowed engineers to do code parsing and searching across different codebases and programming languages. (You might remember the infamous Google Platforms Rant from Steve’s time at Google, and his 2021 followup on GCP).
Beyang was an intern at Google at the same time, and Grok became the inspiration to start SourceGraph in 2013. The two didn’t know eachother personally until Beyang brought Steve out of retirement 9 years later to join him as VP Engineering. Fast forward 10 years, SourceGraph has become to best code search tool out there and raised $223M along the way.
Nine months ago, they open sourced SourceGraph Cody, their AI coding assistant. All their code indexing and search infrastructure allows them to get SOTA results by having better RAG than competitors:
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Code completions as you type that achieve an industry-best Completion Acceptance Rate (CAR) as high as 30% using a context-enhanced open-source LLM (StarCoder)
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Context-aware chat that provides the option of using GPT-4 Turbo, Claude 2, GPT-3.5 Turbo, Mistral 7x8B, or Claude Instant, with more model integrations planned
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Doc and unit test generation, along with AI quick fixes for common coding errors
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AI-enhanced natural language code search, powered by a hybrid dense/sparse vector search engine
There are a few pieces of infrastructure that helped Cody achieve these results:
Dense-sparse vector retrieval system
For many people, RAG = vector similarity search, but there’s a lot more that you can do to get the best possible results. From their release:
"Sparse vector search" is a fancy name for keyword search that potentially incorporates LLMs for things like ranking and term expansion (e.g., "k8s" expands to "Kubernetes container orchestration", possibly weighted as in SPLADE):
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Dense vector retrieval makes use of embeddings, the internal representation that LLMs use to represent text. Dense vector retrieval provides recall over a broader set of results that may have no exact keyword matches but are still semantically similar.
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Sparse vector retrieval is very fast, human-understandable, and yields high recall of results that closely match the user query.
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We've found the approaches to be complementary.
There’s a very good blog post by Pinecone on SPLADE for sparse vector search if you’re interested in diving in. If you’re building RAG applications in areas that have a lot of industry-specific nomenclature, acronyms, etc, this is a good approach to getting better results.
SCIP
In 2016, Microsoft announced the Language Server Protocol (LSP) and the Language Server Index Format (LSIF). This protocol makes it easy for IDEs to get all the context they need from a codebase to get things like file search, references, “go to definition”, etc.
SourceGraph developed SCIP, “a better code indexing format than LSIF”:
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Simpler and More Efficient Format: SCIP utilizes Protobuf instead of JSON, which is used by LSIF. Protobuf is more space-efficient, simpler, and more suitable for systems programming.
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Better Performance and Smaller Index Sizes: SCIP indexers, such as scip-clang, show enhanced performance and reduced index file sizes compared to LSIF indexers (10%-20% smaller)
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Easier to Develop and Debug: SCIP's design, centered around human-readable string IDs for symbols, makes it faster and more straightforward to develop new language indexers.
Having more efficient indexing is key to more performant RAG on code.
Show Notes
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Sourcegraph
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Cody
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Copilot vs Cody
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Steve’s Stanford seminar on Grok
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Steve’s blog
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Grab
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Fireworks
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Peter Norvig
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Noam Chomsky
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Code search
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Kelly Norton
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Zoekt
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v0.dev
See also our past episodes on Cursor, Phind, Codeium and Codium as well as the GitHub Copilot keynote at AI Engineer Summit.
Timestamps
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[00:00:00] Intros & Backgrounds
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[00:05:20] How Steve's work on Grok inspired SourceGraph for Beyang
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[00:08:10] What's Cody?
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[00:11:22] Comparison of coding assistants and the capabilities of Cody
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[00:16:00] The importance of context (RAG) in AI coding tools
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[00:21:33] The debate between Chomsky and Norvig approaches in AI
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[00:30:06] Normsky: the Norvig + Chomsky models collision
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[00:36:00] The death of the DSL?
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[00:40:00] LSP, Skip, Kythe, BFG, and all that fun stuff
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[00:53:00] The SourceGraph internal stack
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[00:58:46] Building on open source models
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[01:02:00] SourceGraph for engineering managers?
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[01:12:00] Lightning Round
Transcript
Alessio: Hey everyone, welcome to the Latent Space podcast. This is Alessio, partner and CTO-in-Residence at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol AI. [00:00:16]
Swyx: Hey, and today we're christening our new podcast studio in the Newton, and we have Beyang and Steve from Sourcegraph. Welcome. [00:00:25]
Beyang: Hey, thanks for having us. [00:00:26]
Swyx: So this has been a long time coming. I'm very excited to have you. We also are just celebrating the one year anniversary of ChatGPT yesterday, but also we'll be talking about the GA of Cody later on today. We'll just do a quick intros of both of you. Obviously, people can research you and check the show notes for more. Beyang, you worked in computer vision at Stanford and then you worked at Palantir. I did, yeah. You also interned at Google. [00:00:48]
Beyang: I did back in the day where I get to use Steve's system, DevTool. [00:00:53]
Swyx: Right. What was it called? [00:00:55]
Beyang: It was called Grok. Well, the end user thing was Google Code Search. That's what everyone called it, or just like CS. But the brains of it were really the kind of like Trigram index and then Grok, which provided the reference graph. [00:01:07]
Steve: Today it's called Kythe, the open source Google one. It's sort of like Grok v3. [00:01:11]
Swyx: On your podcast, which you've had me on, you've interviewed a bunch of other code search developers, including the current developer of Kythe, right? [00:01:19]
Beyang: No, we didn't have any Kythe people on, although we would love to if they're up for it. We had Kelly Norton, who built a similar system at Etsy, it's an open source project called Hound. We also had Han-Wen Nienhuys, who created Zoekt, which is, I think, heavily inspired by the Trigram index that powered Google's original code search and that we also now use at Sourcegraph. Yeah. [00:01:45]
Swyx: So you teamed up with Quinn over 10 years ago to start Sourcegraph and you were indexing all code on the internet. And now you're in a perfect spot to create a code intelligence startup. Yeah, yeah. [00:01:56]
Beyang: I guess the backstory was, I used Google Code Search while I was an intern. And then after I left that internship and worked elsewhere, it was the single dev tool that I missed the most. I felt like my job was just a lot more tedious and much more of a hassle without it. And so when Quinn and I started working together at Palantir, he had also used various code search engines in open source over the years. And it was just a pain point that we both felt, both working on code at Palantir and also working within Palantir's clients, which were a lot of Fortune 500 companies, large financial institutions, folks like that. And if anything, the pains they felt in dealing with large complex code bases made our pain points feel small by comparison. So that was really the impetus for starting Sourcegraph. [00:02:42]
Swyx: Yeah, excellent. Steve, you famously worked at Amazon. And you've told many, many stories. I want every single listener of Latent Space to check out Steve's YouTube because he effectively had a podcast that you didn't tell anyone about or something. You just hit record and just went on a few rants. I'm always here for your Stevie rants. And then you moved to Google, where you also had some interesting thoughts on just the overall Google culture versus Amazon. You joined Grab as head of eng for a couple of years. I'm from Singapore, so I have actually personally used a lot of Grab's features. And it was very interesting to see you talk so highly of Grab's engineering and sort of overall prospects. [00:03:21]
Steve: Because as a customer, it sucked? [00:03:22]
Swyx: Yeah, no, it's just like, being from a smaller country, you never see anyone from our home country being on a global stage or talked about as a startup that people admire or look up to, like on the league that you, with all your legendary experience, would consider equivalent. Yeah. [00:03:41]
Steve: Yeah, no, absolutely. They actually, they didn't even know that they were as good as they were, in a sense. They started hiring a bunch of people from Silicon Valley to come in and sort of like fix it. And we came in and we were like, Oh, we could have been a little better operational excellence and stuff. But by and large, they're really sharp. The only thing about Grab is that they get criticized a lot for being too westernized. Oh, by who? By Singaporeans who don't want to work there. [00:04:06]
Swyx: Okay. I guess I'm biased because I'm here, but I don't see that as a problem. If anything, they've had their success because they were more westernized than the Sanders Singaporean tech company. [00:04:15]
Steve: I mean, they had their success because they are laser focused. They copy to Amazon. I mean, they're executing really, really, really well for a giant. I was on a slack with 2,500 engineers. It was like this giant waterfall that you could dip your toe into. You'd never catch up. Actually, the AI summarizers would have been really helpful there. But yeah, no, I think Grab is successful because they're just out there with their sleeves rolled up, just making it happen. [00:04:43]
Swyx: And for those who don't know, it's not just like Uber of Southeast Asia, it's also a super app. PayPal Plus. [00:04:48]
Steve: Yeah. [00:04:49]
Swyx: In the way that super apps don't exist in the West. It's one of the enduring mysteries of B2C that super apps work in the East and don't work in the West. We just don't understand it. [00:04:57]
Beyang: Yeah. [00:04:58]
Steve: It's just kind of curious. They didn't work in India either. And it was primarily because of bandwidth reasons and smaller phones. [00:05:03]
Swyx: That should change now. It should. [00:05:05]
Steve: And maybe we'll see a super app here. [00:05:08]
Swyx: You retired-ish? I did. You retired-ish on your own video game? Mm-hmm. Any fun stories about that? And that's also where you discovered some need for code search, right? Mm-hmm. [00:05:16]
Steve: Sure. A need for a lot of stuff. Better programming languages, better databases. Better everything. I mean, I started in like 95, right? Where there was kind of nothing. Yeah. Yeah. [00:05:24]
Beyang: I just want to say, I remember when you first went to Grab because you wrote that blog post talking about why you were excited about it, about like the expanding Asian market. And our reaction was like, oh, man, how did we miss stealing it with you? [00:05:36]
Swyx: Hiring you. [00:05:37]
Beyang: Yeah. [00:05:38]
Steve: I was like, miss that. [00:05:39]
Swyx: Tell that story. So how did this happen? Right? So you were inspired by Grok. [00:05:44]
Beyang: I guess the backstory from my point of view is I had used code search and Grok while at Google, but I didn't actually know that it was connected to you, Steve. I knew you from your blog posts, which were always excellent, kind of like inside, very thoughtful takes from an engineer's perspective on some of the challenges facing tech companies and tech culture and that sort of thing. But my first introduction to you within the context of code intelligence, code understanding was I watched a talk that you gave, I think at Stanford, about Grok when you're first building it. And that was very eye opening. I was like, oh, like that guy, like the guy who, you know, writes the extremely thoughtful ranty like blog posts also built that system. And so that's how I knew, you know, you were involved in that. And then, you know, we always wanted to hire you, but never knew quite how to approach you or, you know, get that conversation started. [00:06:34]
Steve: Well, we got introduced by Max, right? Yeah. It was temporal. Yeah. Yeah. I mean, it was a no brainer. They called me up and I had noticed when Sourcegraph had come out. Of course, when they first came out, I had this dagger of jealousy stabbed through me piercingly, which I remember because I am not a jealous person by any means, ever. But boy, I was like, but I was kind of busy, right? And just one thing led to another. I got sucked back into the ads vortex and whatever. So thank God Sourcegraph actually kind of rescued me. [00:07:05]
Swyx: Here's a chance to build DevTools. Yeah. [00:07:08]
Steve: That's the best. DevTools are the best. [00:07:10]
Swyx: Cool. Well, so that's the overall intro. I guess we can get into Cody. Is there anything else that like people should know about you before we get started? [00:07:18]
Steve: I mean, everybody knows I'm a musician. I can juggle five balls. [00:07:24]
Swyx: Five is good. Five is good. I've only ever managed three. [00:07:27]
Steve: Five is hard. Yeah. And six, a little bit. [00:07:30]
Swyx: Wow. [00:07:31]
Beyang: That's impressive. [00:07:32]
Alessio: So yeah, to jump into Sourcegraph, this has been a company 10 years in the making. And as Sean said, now you're at the right place. Phase two. Now, exactly. You spent 10 years collecting all this code, indexing, making it easy to surface it. Yeah. [00:07:47]
Swyx: And also learning how to work with enterprises and having them trust you with their code bases. Yeah. [00:07:52]
Alessio: Because initially you were only doing on-prem, right? Like a lot of like VPC deployments. [00:07:55]
Beyang: So in the very early days, we're cloud only. But the first major customers we landed were all on-prem, self-hosted. And that was, I think, related to the nature of the problem that we're solving, which becomes just like a critical, unignorable pain point once you're above like 100 devs or so. [00:08:11]
Alessio: Yeah. And now Cody is going to be GA by the time this releases. So congrats to your future self for launching this in two weeks. Can you give a quick overview of just what Cody is? I think everybody understands that it's a AI coding agent, but a lot of companies say they have a AI coding agent. So yeah, what does Cody do? How do people interface with it? [00:08:32]
Beyang: Yeah. So how is it different from the like several dozen other AI coding agents that exist in the market now? When we thought about building a coding assistant that would do things like code generation and question answering about your code base, I think we came at it from the perspective of, you know, we've spent the past decade building the world's best code understanding engine for human developers, right? So like it's kind of your guide as a human dev if you want to go and dive into a large complex code base. And so our intuition was that a lot of the context that we're providing to human developers would also be useful context for AI developers to consume. And so in terms of the feature set, Cody is very similar to a lot of other assistants. It does inline autocompletion. It does code base aware chat. It does specific commands that automate, you know, tasks that you might rather not want to do like generating unit tests or adding detailed documentation. But we think the core differentiator is really the quality of the context, which is hard to kind of describe succinctly. It's a bit like saying, you know, what's the difference between Google and Alta Vista? There's not like a quick checkbox list of features that you can rattle off, but it really just comes down to all the attention and detail that we've paid to making that context work well and be high quality and fast for human devs. We're now kind of plugging into the AI coding assistant as well. Yeah. [00:09:53]
Steve: I mean, just to add my own perspective on to what Beyang just described, RAG is kind of like a consultant that the LLM has available, right, that knows about your code. RAG provides basically a bridge to a lookup system for the LLM, right? Whereas fine tuning would be more like on the job training for somebody. If the LLM is a person, you know, and you send them to a new job and you do on the job training, that's what fine tuning is like, right? So tuned to our specific task. You're always going to need that expert, even if you get the on the job training, because the expert knows your particular code base, your task, right? That expert has to know your code. And there's a chicken and egg problem because, right, you know, we're like, well, I'm going to ask the LLM about my code, but first I have to explain it, right? It's this chicken and egg problem. That's where RAG comes in. And we have the best consultants, right? The best assistant who knows your code. And so when you sit down with Cody, right, what Beyang said earlier about going to Google and using code search and then starting to feel like without it, his job was super tedious. Once you start using these, do you guys use coding assistants? [00:10:53]
Swyx: Yeah, right. [00:10:54]
Steve: I mean, like we're getting to the point very quickly, right? Where you feel like almost like you're programming without the internet, right? Or something, you know, it's like you're programming back in the nineties without the coding assistant. Yeah. Hopefully that helps for people who have like no idea about coding systems, what they are. [00:11:09]
Swyx: Yeah. [00:11:10]
Alessio: I mean, going back to using them, we had a lot of them on the podcast already. We had Cursor, we have Codium and Codium, very similar names. [00:11:18]
Swyx: Yeah. Find, and then of course there's Copilot. [00:11:22]
Alessio: You had a Copilot versus Cody blog post, and I think it really shows the context improvement. So you had two examples that stuck with me. One was, what does this application do? And the Copilot answer was like, oh, it uses JavaScript and NPM and this. And it's like, but that's not what it does. You know, that's what it's built with. Versus Cody was like, oh, these are like the major functions. And like, these are the functionalities and things like that. And then the other one was, how do I start this up? And Copilot just said NPM start, even though there was like no start command in the package JSON, but you know, most collapse, right? Most projects use NPM start. So maybe this does too. How do you think about open source models? Because Copilot has their own private thing. And I think you guys use Starcoder, if I remember right. Yeah, that's correct. [00:12:09]
Beyang: I think Copilot uses some variant of Codex. They're kind of cagey about it. I don't think they've like officially announced what model they use. [00:12:16]
Swyx: And I think they use a range of models based on what you're doing. Yeah. [00:12:19]
Beyang: So everyone uses a range of model. Like no one uses the same model for like inline completion versus like chat because the latency requirements for. Oh, okay. Well, there's fill in the middle. There's also like what the model's trained on. So like we actually had completions powered by Claude Instant for a while. And but you had to kind of like prompt hack your way to get it to output just the code and not like, hey, you know, here's the code you asked for, like that sort of text. So like everyone uses a range of models. We've kind of designed Cody to be like especially model, not agnostic, but like pluggable. So one of our kind of design considerations was like as the ecosystem evolves, we want to be able to integrate the best in class models, whether they're proprietary or open source into Cody because the pace of innovation in the space is just so quick. And I think that's been to our advantage. Like today, Cody uses Starcoder for inline completions. And with the benefit of the context that we provide, we actually show comparable completion acceptance rate metrics. It's kind of like the standard metric that folks use to evaluate inline completion quality. It's like if I show you a completion, what's the chance that you actually accept the completion versus you reject it? And so we're at par with Copilot, which is at the head of that industry right now. And we've been able to do that with the Starcoder model, which is open source and the benefit of the context fetching stuff that we provide. And of course, a lot of like prompt engineering and other stuff along the way. [00:13:40]
Alessio: And Steve, you wrote a post called cheating is all you need about what you're building. And one of the points you made is that everybody's fighting on the same axis, which is better UI and the IDE, maybe like a better chat response. But data modes are kind of the most important thing. And you guys have like a 10 year old mode with all the data you've been collecting. How do you kind of think about what other companies are doing wrong, right? Like, why is nobody doing this in terms of like really focusing on RAG? I feel like you see so many people. Oh, we just got a new model. It's like a bit human eval. And it's like, well, but maybe like that's not what we should really be doing, you know? Like, do you think most people underestimate the importance of like the actual RAG in code? [00:14:21]
Steve: I think that people weren't doing it much. It wasn't. It's kind of at the edges of AI. It's not in the center. I know that when ChatGPT launched, so within the last year, I've heard a lot of rumblings from inside of Google, right? Because they're undergoing a huge transformation to try to, you know, of course, get into the new world. And I heard that they told, you know, a bunch of teams to go and train their own models or fine tune their own models, right? [00:14:43]
Swyx: Both. [00:14:43]
Steve: And, you know, it was a s**t show. Nobody knew how to do it. They launched two coding assistants. One was called Code D with an EY. And then there was, I don't know what happened in that one. And then there's Duet, right? Google loves to compete with themselves, right? They do this all the time. And they had a paper on Duet like from a year ago. And they were doing exactly what Copilot was doing, which was just pulling in the local context, right? But fundamentally, I thought of this because we were talking about the splitting of the [00:15:10]
Swyx: models. [00:15:10]
Steve: In the early days, it was the LLM did everything. And then we realized that for certain use cases, like completions, that a different, smaller, faster model would be better. And that fragmentation of models, actually, we expected to continue and proliferate, right? Because we are fundamentally, we're a recommender engine right now. Yeah, we're recommending code to the LLM. We're saying, may I interest you in this code right here so that you can answer my question? [00:15:34]
Swyx: Yeah? [00:15:34]
Steve: And being good at recommender engine, I mean, who are the best recommenders, right? There's YouTube and Spotify and, you know, Amazon or whatever, right? Yeah. [00:15:41]
Swyx: Yeah. [00:15:41]
Steve: And they all have many, many, many, many, many models, right? For all fine-tuned for very specific, you know. And that's where we're heading in code, too. Absolutely. [00:15:50]
Swyx: Yeah. [00:15:50]
Alessio: We just did an episode we released on Wednesday, which we said RAG is like Rexis or like LLMs. You're basically just suggesting good content. [00:15:58]
Swyx: It's like what? Recommendations. [00:15:59]
Beyang: Recommendations. [00:16:00]
Alessio: Oh, got it. [00:16:01]
Steve: Yeah, yeah, yeah. [00:16:02]
Swyx: So like the naive implementation of RAG is you embed everything, throw it in a vector database, you embed your query, and then you find the nearest neighbors, and that's your RAG. But actually, you need to rank it. And actually, you need to make sure there's sample diversity and that kind of stuff. And then you're like slowly gradient dissenting yourself towards rediscovering proper Rexis, which has been traditional ML for a long time. But like approaching it from an LLM perspective. Yeah. [00:16:24]
Beyang: I almost think of it as like a generalized search problem because it's a lot of the same things. Like you want your layer one to have high recall and get all the potential things that could be relevant. And then there's typically like a layer two re-ranking mechanism that bumps up the precision and tries to get the relevant stuff to the top of the results list. [00:16:43]
Swyx: Have you discovered that ranking matters a lot? Oh, yeah. So the context is that I think a lot of research shows that like one, context utilization matters based on model. Like GPT uses the top of the context window, and then apparently Claude uses the bottom better. And it's lossy in the middle. Yeah. So ranking matters. No, it really does. [00:17:01]
Beyang: The skill with which models are able to take advantage of context is always going to be dependent on how that factors into the impact on the training loss. [00:17:10]
Swyx: Right? [00:17:10]
Beyang: So like if you want long context window models to work well, then you have to have a ton of data where it's like, here's like a billion lines of text. And I'm going to ask a question about like something that's like, you know, embedded deeply into it and like, give me the right answer. And unless you have that training set, then of course, you're going to have variability in terms of like where it attends to. And in most kind of like naturally occurring data, the thing that you're talking about right now, the thing I'm asking you about is going to be something that we talked about recently. [00:17:36]
Swyx: Yeah. [00:17:36]
Steve: Did you really just say gradient dissenting yourself? Actually, I love that it's entered the casual lexicon. Yeah, yeah, yeah. [00:17:44]
Swyx: My favorite version of that is, you know, how we have to p-hack papers. So, you know, when you throw humans at the problem, that's called graduate student dissent. That's great. It's really awesome. [00:17:54]
Alessio: I think the other interesting thing that you have is this inline assist UX that I wouldn't say async, but like it works while you can also do work. So you can ask Cody to make changes on a code block and you can still edit the same file at the same time. [00:18:07]
Swyx: Yeah. [00:18:07]
Alessio: How do you see that in the future? Like, do you see a lot of Cody's running together at the same time? Like, how do you validate also that they're not messing each other up as they make changes in the code? And maybe what are the limitations today? And what do you think about where the attack is going? [00:18:21]
Steve: I want to start with a little history and then I'm going to turn it over to Bian, all right? So we actually had this feature in the very first launch back in June. Dominic wrote it. It was called nonstop Cody. And you could have multiple, basically, LLM requests in parallel modifying your source [00:18:37]
Swyx: file. [00:18:37]
Steve: And he wrote a bunch of code to handle all of the diffing logic. And you could see the regions of code that the LLM was going to change, right? And he was showing me demos of it. And it just felt like it was just a little before its time, you know? But a bunch of that stuff, that scaffolding was able to be reused for where we're inline [00:18:56]
Swyx: sitting today. [00:18:56]
Steve: How would you characterize it today? [00:18:58]
Beyang: Yeah, so that interface has really evolved from a, like, hey, general purpose, like, request anything inline in the code and have the code update to really, like, targeted features, like, you know, fix the bug that exists at this line or request a very specific [00:19:13]
Swyx: change. [00:19:13]
Beyang: And the reason for that is, I think, the challenge that we ran into with inline fixes, and we do want to get to the point where you could just fire and forget and have, you know, half a dozen of these running in parallel. But I think we ran into the challenge early on that a lot of people are running into now when they're trying to construct agents, which is the reliability of, you know, working code generation is just not quite there yet in today's language models. And so that kind of constrains you to an interaction where the human is always, like, in the inner loop, like, checking the output of each response. And if you want that to work in a way where you can be asynchronous, you kind of have to constrain it to a domain where today's language models can generate reliable code well enough. So, you know, generating unit tests, that's, like, a well-constrained problem. Or fixing a bug that shows up as, like, a compiler error or a test error, that's a well-constrained problem. But the more general, like, hey, write me this class that does X, Y, and Z using the libraries that I have, that is not quite there yet, even with the benefit of really good context. Like, it definitely moves the needle a lot, but we're not quite there yet to the point where you can just fire and forget. And I actually think that this is something that people don't broadly appreciate yet, because I think that, like, everyone's chasing this dream of agentic execution. And if we're to really define that down, I think it implies a couple things. You have, like, a multi-step process where each step is fully automated. We don't have to have a human in the loop every time. And there's also kind of like an LM call at each stage or nearly every stage in that [00:20:45]
Swyx: chain. [00:20:45]
Beyang: Based on all the work that we've done, you know, with the inline interactions, with kind of like general Codyfeatures for implementing longer chains of thought, we're actually a little bit more bearish than the average, you know, AI hypefluencer out there on the feasibility of agents with purely kind of like transformer-based models. To your original question, like, the inline interactions with CODI, we actually constrained it to be more targeted, like, you know, fix the current error or make this quick fix. I think that that does differentiate us from a lot of the other tools on the market, because a lot of people are going after this, like, shnazzy, like, inline edit interaction, whereas I think where we've moved, and this is based on the user feedback that we've gotten, it's like that sort of thing, it demos well, but when you're actually coding day to day, you don't want to have, like, a long chat conversation inline with the code base. That's a waste of time. You'd rather just have it write the right thing and then move on with your life or not have to think about it. And that's what we're trying to work towards. [00:21:37]
Steve: I mean, yeah, we're not going in the agent direction, right? I mean, I'll believe in agents when somebody shows me one that works. Yeah. Instead, we're working on, you know, sort of solidifying our strength, which is bringing the right context in. So new context sources, ways for you to plug in your own context, ways for you to control or influence the context, you know, the mixing that happens before the request goes out, etc. And there's just so much low-hanging fruit left in that space that, you know, agents seems like a little bit of a boondoggle. [00:22:03]
Beyang: Just to dive into that a little bit further, like, I think, you know, at a very high level, what do people mean when they say agents? They really mean, like, greater automation, fully automated, like, the dream is, like, here's an issue, go implement that. And I don't have to think about it as a human. And I think we are working towards that. Like, that is the eventual goal. I think it's specifically the approach of, like, hey, can we have a transformer-based LM alone be the kind of, like, backbone or the orchestrator of these agentic flows? Where we're a little bit more bearish today. [00:22:31]
Swyx: You want the human in the loop. [00:22:32]
Beyang: I mean, you kind of have to. It's just a reality of the behavior of language models that are purely, like, transformer-based. And I think that's just like a reflection of reality. And I don't think people realize that yet. Because if you look at the way that a lot of other AI tools have implemented context fetching, for instance, like, you see this in the Copilot approach, where if you use, like, the at-workspace thing that supposedly provides, like, code-based level context, it has, like, an agentic approach where you kind of look at how it's behaving. And it feels like they're making multiple requests to the LM being like, what would you do in this case? Would you search for stuff? What sort of files would you gather? Go and read those files. And it's like a multi-hop step, so it takes a long while. It's also non-deterministic. Because any sort of, like, LM invocation, it's like a dice roll. And then at the end of the day, the context it fetches is not that good. Whereas our approach is just like, OK, let's do some code searches that make sense. And then maybe, like, crawl through the reference graph a little bit. That is fast. That doesn't require any sort of LM invocation at all. And we can pull in much better context, you know, very quickly. So it's faster. [00:23:37]
Swyx: It's more reliable. [00:23:37]
Beyang: It's deterministic. And it yields better context quality. And so that's what we think. We just don't think you should cargo cult or naively go like, you know, agents are the [00:23:46]
Swyx: future. [00:23:46]
Beyang: Let's just try to, like, implement agents on top of the LM that exists today. I think there are a couple of other technologies or approaches that need to be refined first before we can get into these kind of, like, multi-stage, fully automated workflows. [00:24:00]
Swyx: It makes sense. You know, we're very much focused on developer inner loop right now. But you do see things eventually moving towards developer outer loop. Yeah. So would you basically say that they're tackling the agent's problem that you don't want to tackle? [00:24:11]
Beyang: No, I would say at a high level, we are after maybe, like, the same high level problem, which is like, hey, I want some code written. I want to develop some software and can automate a system. Go build that software for me. I think the approaches might be different. So I think the analogy in my mind is, I think about, like, the AI chess players. Coding, in some senses, I mean, it's similar and dissimilar to chess. I think one question I ask is, like, do you think producing code is more difficult than playing chess or less difficult than playing chess? More. [00:24:41]
Swyx: I think more. [00:24:41]
Beyang: Right. And if you look at the best AI chess players, like, yes, you can use an LLM to play chess. Like, people have showed demos where it's like, oh, like, yeah, GPT-4 is actually a pretty decent, like, chess move suggester. Right. But you would never build, like, a best in class chess player off of GPT-4 alone. [00:24:57]
Swyx: Right. [00:24:57]
Beyang: Like, the way that people design chess players is that you have kind of like a search space and then you have a way to explore that search space efficiently. There's a bunch of search algorithms, essentially. We were doing tree search in various ways. And you can have heuristic functions, which might be powered by an LLM. [00:25:12]
Swyx: Right. [00:25:12]
Beyang: Like, you might use an LLM to generate proposals in that space that you can efficiently explore. But the backbone is still this kind of more formalized tree search based approach rather than the LLM itself. And so I think my high level intuition is that, like, the way that we get to more reliable multi-step workflows that do things beyond, you know, generate unit test, it's really going to be like a search based approach where you use an LLM as kind of like an advisor or a proposal function, sort of your heuristic function, like the ASTAR search algorithm. But it's probably not going to be the thing that is the backbone, because I guess it's not the right tool for that. Yeah. [00:25:50]
Swyx: I can see yourself kind of thinking through this, but not saying the words, the sort of philosophical Peter Norvig type discussion. Maybe you want to sort of introduce that in software. Yeah, definitely. [00:25:59]
Beyang: So your listeners are savvy. They're probably familiar with the classic like Chomsky versus Norvig debate. [00:26:04]
Swyx: No, actually, I wanted, I was prompting you to introduce that. Oh, got it. [00:26:08]
Beyang: So, I mean, if you look at the history of artificial intelligence, right, you know, it goes way back to, I don't know, it's probably as old as modern computers, like 50s, 60s, 70s. People are debating on like, what is the path to producing a sort of like general human level of intelligence? And kind of two schools of thought that emerged. One is the Norvig school of thought, which roughly speaking includes large language models, you know, regression, SVN, basically any model that you kind of like learn from data. And it's like data driven. Most of machine learning would fall under this umbrella. And that school of thought says like, you know, just learn from the data. That's the approach to reaching intelligence. And then the Chomsky approach is more things like compilers and parsers and formal systems. So basically like, let's think very carefully about how to construct a formal, precise system. And that will be the approach to how we build a truly intelligent system. I think Lisp was invented so that you could create like rules-based systems that you would call AI. As a language. Yeah. And for a long time, there was like this debate, like there's certain like AI research labs that were more like, you know, in the Chomsky camp and others that were more in the Norvig camp. It's a debate that rages on today. And I feel like the consensus right now is that, you know, Norvig definitely has the upper hand right now with the advent of LMs and diffusion models and all the other recent progress in machine learning. But the Chomsky-based stuff is still really useful in my view. I mean, it's like parsers, compilers, basically a lot of the stuff that provides really good context. It provides kind of like the knowledge graph backbone that you want to explore with your AI dev tool. Like that will come from kind of like Chomsky-based tools like compilers and parsers. It's a lot of what we've invested in in the past decade at Sourcegraph and what you build with Grok. Basically like these formal systems that construct these very precise knowledge graphs that are great context providers and great kind of guard rails enforcers and kind of like safety checkers for the output of a more kind of like data-driven, fuzzier system that uses like the Norvig-based models. [00:28:03]
Steve: Jang was talking about this stuff like it happened in the middle ages. Like, okay, so when I was in college, I was in college learning Lisp and prologue and planning and all the deterministic Chomsky approaches to AI. And I was there when Norvig basically declared it dead. I was there 3,000 years ago when Norvig and Chomsky fought on the volcano. When did he declare it dead? [00:28:26]
Swyx: What do you mean he declared it dead? [00:28:27]
Steve: It was like late 90s. [00:28:29]
Swyx: Yeah. [00:28:29]
Steve: When I went to Google, Peter Norvig was already there. He had basically like, I forget exactly where. It was some, he's got so many famous short posts, you know, amazing. [00:28:38]
Swyx: He had a famous talk, the unreasonable effectiveness of data. Yeah. [00:28:41]
Steve: Maybe that was it. But at some point, basically, he basically convinced everybody that deterministic approaches had failed and that heuristic-based, you know, data-driven statistical approaches, stochastic were better. [00:28:52]
Swyx: Yeah. [00:28:52]
Steve: The primary reason I can tell you this, because I was there, was that, was that, well, the steam-powered engine, no. The reason was that the deterministic stuff didn't scale. [00:29:06]
Swyx: Yeah. Right. [00:29:06]
Steve: They're using prologue, man, constraint systems and stuff like that. Well, that was a long time ago, right? Today, actually, these Chomsky-style systems do scale. And that's, in fact, exactly what Sourcegraph has built. Yeah. And so we have a very unique, I love the framing that Bjong's made, that the marriage of the Chomsky and the Norvig, you know, sort of models, you know, conceptual models, because we, you know, we have both of them and they're both really important. And in fact, there, there's this really interesting, like, kind of overlap between them, right? Where like the AI or our graph or our search engine could potentially provide the right context for any given query, which is, of course, why ranking is important. But what we've really signed ourselves up for is an extraordinary amount of testing. [00:29:45]
Swyx: Yeah. [00:29:45]
Steve: Because in SWIGs, you were saying that, you know, GPT-4 tends to the front of the context window and maybe other LLMs to the back and maybe, maybe the LLM in the middle. [00:29:53]
Swyx: Yeah. [00:29:53]
Steve: And so that means that, you know, if we're actually like, you know, verifying whether we, you know, some change we've made has improved things, we're going to have to test putting it at the beginning of the window and at the end of the window, you know, and maybe make the right decision based on the LLM that you've chosen. Which some of our competitors, that's a problem that they don't have, but we meet you, you know, where you are. Yeah. And we're, just to finish, we're writing tens of thousands. We're generating tests, you know, fill in the middle type tests and things. And then using our graph to basically sort of fine tune Cody's behavior there. [00:30:20]
Swyx: Yeah. [00:30:21]
Beyang: I also want to add, like, I have like an internal pet name for this, like kind of hybrid architecture that I'm trying to make catch on. Maybe I'll just say it here. Just saying it publicly kind of makes it more real. But like, I call the architecture that we've developed the Normsky architecture. [00:30:36]
Swyx: Yeah. [00:30:36]
Beyang: I mean, it's obviously a portmanteau of Norvig and Chomsky, but the acronym, it stands for non-agentic, rapid, multi-source code intelligence. So non-agentic because... Rolls right off the tongue. And Normsky. But it's non-agentic in the sense that like, we're not trying to like pitch you on kind of like agent hype, right? Like it's the things it does are really just developer tools developers have been using for decades now, like parsers and really good search indexes and things like that. Rapid because we place an emphasis on speed. We don't want to sit there waiting for kind of like multiple LLM requests to return to complete a simple user request. Multi-source because we're thinking broadly about what pieces of information and knowledge are useful context. So obviously starting with things that you can search in your code base, and then you add in the reference graph, which kind of like allows you to crawl outward from those initial results. But then even beyond that, you know, sources of information, like there's a lot of knowledge that's embedded in docs, in PRDs or product specs, in your production logging system, in your chat, in your Slack channel, right? Like there's so much context is embedded there. And when you're a human developer, and you're trying to like be productive in your code base, you're going to go to all these different systems to collect the context that you need to figure out what code you need to write. And I don't think the AI developer will be any different. It will need to pull context from all these different sources. So we're thinking broadly about how to integrate these into Codi. We hope through kind of like an open protocol that like others can extend and implement. And this is something else that should be accessible by December 14th in kind of like a preview stage. But that's really about like broadening this notion of the code graph beyond your Git repository to all the other sources where technical knowledge and valuable context can live. [00:32:21]
Steve: Yeah, it becomes an artifact graph, right? It can link into your logs and your wikis and any data source, right? [00:32:27]
Alessio: How do you guys think about the importance of, it's almost like data pre-processing in a way, which is bring it all together, tie it together, make it ready. Any thoughts on how to actually make that good? Some of the innovation you guys have made. [00:32:40]
Steve: We talk a lot about the context fetching, right? I mean, there's a lot of ways you could answer this question. But, you know, we've spent a lot of time just in this podcast here talking about context fetching. But stuffing the context into the window is, you know, the bin packing problem, right? Because the window is not big enough, and you've got more context than you can fit. You've got a ranker maybe. But what is that context? Is it a function that was returned by an embedding or a graph call or something? Do you need the whole function? Or do you just need, you know, the top part of the function, this expression here, right? You know, so that art, the golf game of trying to, you know, get each piece of context down into its smallest state, possibly even summarized by another model, right, before it even goes to the LLM, becomes this is the game that we're in, yeah? And so, you know, recursive summarization and all the other techniques that you got to use to like stuff stuff into that context window become, you know, critically important. And you have to test them across every configuration of models that you could possibly need. [00:33:32]
Beyang: I think data preprocessing is probably the like unsexy, way underappreciated secret to a lot of the cool stuff that people are shipping today. Whether you're doing like RAG or fine tuning or pre-training, like the preprocessing step matters so much because it's basically garbage in, garbage out, right? Like if you're feeding in garbage to the model, then it's going to output garbage. Concretely, you know, for code RAG, if you're not doing some sort of like preprocessing that takes advantage of a parser and is able to like extract the key components of a particular file of code, you know, separate the function signature from the body, from the doc string, what are you even doing? Like that's like table stakes. It opens up so much more possibilities with which you can kind of like tune your system to take advantage of the signals that come from those different parts of the code. Like we've had a tool, you know, since computers were invented that understands the structure of source code to a hundred percent precision. The compiler knows everything there is to know about the code in terms of like structure. Like why would you not want to use that in a system that's trying to generate code, answer questions about code? You shouldn't throw that out the window just because now we have really good, you know, data-driven models that can do other things. [00:34:44]
Steve: Yeah. When I called it a data moat, you know, in my cheating post, a lot of people were confused, you know, because data moat sort of sounds like data lake because there's data and water and stuff. I don't know. And so they thought that we were sitting on this giant mountain of data that we had collected, but that's not what our data moat is. It's really a data pre-processing engine that can very quickly and scalably, like basically dissect your entire code base in a very small, fine-grained, you know, semantic unit and then serve it up. Yeah. And so it's really, it's not a data moat. It's a data pre-processing moat, I guess. [00:35:15]
Beyang: Yeah. If anything, we're like hypersensitive to customer data privacy requirements. So it's not like we've taken a bunch of private data and like, you know, trained a generally available model. In fact, exactly the opposite. A lot of our customers are choosing Cody over Copilot and other competitors because we have an explicit guarantee that we don't do any of that. And that we've done that from day one. Yeah. I think that's a very real concern in today's day and age, because like if your proprietary IP finds its way into the training set of any model, it's very easy both to like extract that knowledge from the model and also use it to, you know, build systems that kind of work on top of the institutional knowledge that you've built up. [00:35:52]
Alessio: About a year ago, I wrote a post on LLMs for developers. And one of the points I had was maybe the depth of like the DSL. I spent most of my career writing Ruby and I love Ruby. It's so nice to use, but you know, it's not as performant, but it's really easy to read, right? And then you look at other languages, maybe they're faster, but like they're more verbose, you know? And when you think about efficiency of the context window, that actually matters. [00:36:15]
Swyx: Yeah. [00:36:15]
Alessio: But I haven't really seen a DSL for models, you know? I haven't seen like code being optimized to like be easier to put in a model context. And it seems like your pre-processing is kind of doing that. Do you see in the future, like the way we think about the DSL and APIs and kind of like service interfaces be more focused on being context friendly, where it's like maybe it's harder to read for the human, but like the human is never going to write it anyway. We were talking on the Hacks podcast. There are like some data science things like spin up the spandex, like humans are never going to write again because the models can just do very easily. Yeah, curious to hear your thoughts. [00:36:51]
Steve: Well, so DSLs, they involve, you know, writing a grammar and a parser and they're like little languages, right? We do them that way because, you know, we need them to compile and humans need to be able to read them and so on. The LLMs don't need that level of structure. You can throw any pile of crap at them, you know, more or less unstructured and they'll deal with it. So I think that's why a DSL hasn't emerged for sort of like communicating with the LLM or packaging up the context or anything. Maybe it will at some point, right? We've got, you know, tagging of context and things like that that are sort of peeking into DSL territory, right? But your point on do users, you know, do people have to learn DSLs like regular expressions or, you know, pick your favorite, right? XPath. I think you're absolutely right that the LLMs are really, really good at that. And I think you're going to see a lot less of people having to slave away learning these things. They just have to know the broad capabilities and the LLM will take care of the rest. [00:37:42]
Swyx: Yeah, I'd agree with that. [00:37:43]
Beyang: I think basically like the value profit of DSL is that it makes it easier to work with a lower level language, but at the expense of introducing an abstraction layer. And in many cases today, you know, without the benefit of AI cogeneration, like that totally worth it, right? With the benefit of AI cogeneration, I mean, I don't think all DSLs will go away. I think there's still, you know, places where that trade-off is going to be worthwhile. But it's kind of like how much of source code do you think is going to be generated through natural language prompting in the future? Because in a way, like any programming language is just a DSL on top of assembly, right? And so if people can do that, then yeah, like maybe for a large portion of the code [00:38:21]
Swyx: that's written, [00:38:21]
Beyang: people don't actually have to understand the DSL that is Ruby or Python or basically any other programming language that exists. [00:38:28]
Steve: I mean, seriously, do you guys ever write SQL queries now without using a model of some sort? At least a draft. [00:38:34]
Swyx: Yeah, right. [00:38:36]
Steve: And so we have kind of like, you know, past that bridge, right? [00:38:39]
Alessio: Yeah, I think like to me, the long-term thing is like, is there ever going to be, you don't actually see the code, you know? It's like, hey, the basic thing is like, hey, I need a function to some two numbers and that's it. I don't need you to generate the code. [00:38:53]
Steve: And the following question, do you need the engineer or the paycheck? [00:38:56]
Swyx: I mean, right? [00:38:58]
Alessio: That's kind of the agent's discussion in a way where like you cannot automate the agents, but like slowly you're getting more of the atomic units of the work kind of like done. I kind of think of it as like, you know, [00:39:09]
Beyang: do you need a punch card operator to answer that for you? And so like, I think we're still going to have people in the role of a software engineer, but the portion of time they spend on these kinds of like low-level, tedious tasks versus the higher level, more creative tasks is going to shift. [00:39:23]
Steve: No, I haven't used punch cards. [00:39:25]
Swyx: Yeah, I've been talking about like, so we kind of made this podcast about the sort of rise of the AI engineer. And like the first step is the AI enhanced engineer. That is that software developer that is no longer doing these routine, boilerplate-y type tasks, because they're just enhanced by tools like yours. So you mentioned OpenCodeGraph. I mean, that is a kind of DSL maybe, and because we're releasing this as you go GA, you hope for other people to take advantage of that? [00:39:52]
Beyang: Oh yeah, I would say so OpenCodeGraph is not a DSL. It's more of a protocol. It's basically like, hey, if you want to make your system, whether it's, you know, chat or logging or whatever accessible to an AI developer tool like Cody, here's kind of like the schema by which you can provide that context and offer hints. So I would, you know, comparisons like LSP obviously did this for kind of like standard code intelligence. It's kind of like a lingua franca for providing fine references and codefinition. There's kind of like analogs to that. There might be also analogs to kind of the original OpenAI, kind of like plugins, API. There's all this like context out there that might be useful for an LM-based system to consume. And so at a high level, what we're trying to do is define a common language for context providers to provide context to other tools in the software development lifecycle. Yeah. Do you have any critiques of LSP, by the way, [00:40:42]
Swyx: since like this is very much, very close to home? [00:40:45]
Steve: One of the authors wrote a really good critique recently. Yeah. I don't think I saw that. Yeah, yeah. LSP could have been better. It just came out a couple of weeks ago. It was a good article. [00:40:54]
Beyang: Yeah. I think LSP is great. Like for what it did for the developer ecosystem, it was absolutely fantastic. Like nowadays, like it's much easier now to get code navigation up and running in a bunch of editors by speaking this protocol. I think maybe the interesting question is like looking at the different design decisions comparing LSP basically with Kythe. Because Kythe has more of a... How would you describe it? [00:41:18]
Steve: A storage format. [00:41:20]
Beyang: I think the critique of LSP from a Kythe point of view would be like with LSP, you don't actually have an actual symbolic model of the code. It's not like LSP models like, hey, this function calls this other function. LSP is all like range-based. Like, hey, your cursor's at line 32, column 1. [00:41:35]
Swyx: Yeah. [00:41:35]
Beyang: And that's the thing you feed into the language server. And then it's like, okay, here's the range that you should jump to if you click on that range. So it kind of is intentionally ignorant of the fact that there's a thing called a reference underneath your cursor, and that's linked to a symbol definition. [00:41:49]
Steve: Well, actually, that's the worst example you could have used. You're right. But that's the one thing that it actually did bake in is following references. [00:41:56]
Swyx: Sure. [00:41:56]
Steve: But it's sort of hardwired. [00:41:58]
Swyx: Yeah. [00:41:58]
Steve: Whereas Kythe attempts to model [00:42:00]
Beyang: like all these things explicitly. [00:42:02]
Swyx: And so... [00:42:02]
Steve: Well, so LSP is a protocol, right? And so Google's internal protocol is gRPC-based. And it's a different approach than LSP. It's basically you make a heavy query to the back end, and you get a lot of data back, and then you render the whole page, you know? So we've looked at LSP, and we think that it's a little long in the tooth, right? I mean, it's a great protocol, lots and lots of support for it. But we need to push into the domain of exposing the intelligence through the protocol. Yeah. [00:42:29]
Beyang: And so I would say we've developed a protocol of our own called Skip, which is at a very high level trying to take some of the good ideas from LSP and from Kythe and merge that into a system that in the near term is useful for Sourcegraph, but I think in the long term, we hope will be useful for the ecosystem. Okay, so here's what LSP did well. LSP, by virtue of being like intentionally dumb, dumb in air quotes, because I'm not like ragging on it, allowed language servers developers to kind of like bypass the hard problem of like modeling language semantics precisely. So like if all you want to do is jump to definition, you don't have to come up with like a universally unique naming scheme for each symbol, which is actually quite challenging because you have to think about like, okay, what's the top scope of this name? Is it the source code repository? Is it the package? Does it depend on like what package server you're fetching this from? Like whether it's the public one or the one inside your... Anyways, like naming is hard, right? And by just going from kind of like a location to location based approach, you basically just like throw that out the window. All I care about is jumping definition, just make that work. And you can make that work without having to deal with like all the complex global naming things. The limitation of that approach is that it's harder to build on top of that to build like a true knowledge graph. Like if you actually want a system that says like, okay, here's the web of functions and here's how they reference each other. And I want to incorporate that like semantic model of how the code operates or how the code relates to each other at like a static level. You can't do that with LSP because you have to deal with line ranges. And like concretely the pain point that we found in using LSP for source graph is like in order to do like a find references [00:44:04]
Swyx: and then jump definitions, [00:44:04]
Beyang: it's like a multi-hop process because like you have to jump to the range and then you have to find the symbol at that range. And it just adds a lot of latency and complexity of these operations where as a human, you're like, well, this thing clearly references this other thing. Why can't you just jump me to that? And I think that's the thing that Kaith does well. But then I think the issue that Kaith has had with adoption is because it is more sophisticated schema, I think. And so there's basically more things that you have to implement to get like a Kaith implementation up and running. I hope I'm not like, correct me if I'm wrong about any of this. [00:44:35]
Steve: 100%, 100%. Kaith also has a problem, all these systems have the problem, even skip, or at least the way that we implemented the indexers, that they have to integrate with your build system in order to build that knowledge graph, right? Because you have to basically compile the code in a special mode to generate artifacts instead of binaries. And I would say, by the way, earlier I was saying that XREFs were in LSP, but it's actually, I was thinking of LSP plus LSIF. [00:44:58]
Swyx: Yeah. That's another. [00:45:01]
Steve: Which is actually bad. We can say that it's bad, right? [00:45:04]
Steve: It's like skip or Kaith, it's supposed to be sort of a model serialization, you know, for the code graph, but it basically just does what LSP needs, the bare minimum. LSIF is basically if you took LSP [00:45:16]
Beyang: and turned that into a serialization format. So like you build an index for language servers to kind of like quickly bootstrap from cold start. But it's a graph model [00:45:23]
Steve: with all of the inconvenience of the API without an actual graph. And so, yeah. [00:45:29]
Beyang: So like one of the things that we try to do with skip is try to capture the best of both worlds. So like make it easy to write an indexer, make the schema simple, but also model some of the more symbolic characteristics of the code that would allow us to essentially construct this knowledge graph that we can then make useful for both the human developer through SourceGraph and through the AI developer through Cody. [00:45:49]
Steve: So anyway, just to finish off the graph comment, we've got a new graph, yeah, that's skip based. We call it BFG internally, right? It's a beautiful something graph. A big friendly graph. [00:46:00]
Swyx: A big friendly graph. [00:46:01]
Beyang: It's a blazing fast. [00:46:02]
Steve: Blazing fast. [00:46:03]
Swyx: Blazing fast graph. [00:46:04]
Steve: And it is blazing fast, actually. It's really, really interesting. I should probably have to do a blog post about it to walk you through exactly how they're doing it. Oh, please. But it's a very AI-like iterative, you know, experimentation sort of approach. We're building a code graph based on all of our 10 years of knowledge about building code graphs, yeah? But we're building it quickly with zero configuration, and it doesn't have to integrate with your build. And through some magic tricks that we have. And so what just happens when you install the plugin, that it'll be there and indexing your code and providing that knowledge graph in the background without all that build system integration. This is a bit of secret sauce that we haven't really like advertised it very much lately. But I am super excited about it because what they do is they say, all right, you know, let's tackle function parameters today. Cody's not doing a very good job of completing function call arguments or function parameters in the definition, right? Yeah, we generate those thousands of tests, and then we can actually reuse those tests for the AI context as well. So fortunately, things are kind of converging on, we have, you know, half a dozen really, really good context sources, and we mix them all together. So anyway, BFG, you're going to hear more about it probably in the holidays? [00:47:12]
Beyang: I think it'll be online for December 14th. We'll probably mention it. BFG is probably not the public name we're going to go with. I think we might call it like Graph Context or something like that. [00:47:20]
Steve: We're officially calling it BFG. [00:47:22]
Swyx: You heard it here first. [00:47:24]
Beyang: BFG is just kind of like the working name. And so the impetus for BFG was like, if you look at like current AI inline code completion tools and the errors that they make, a lot of the errors that they make, even in kind of like the easy, like single line case, are essentially like type errors, right? Like you're trying to complete a function call and it suggests a variable that you defined earlier, but that variable is the wrong type. [00:47:47]
Swyx: And that's the sort of thing [00:47:47]
Beyang: where it's like a first year, like freshman CS student would not make that error, right? So like, why does the AI make that error? And the reason is, I mean, the AI is just suggesting things that are plausible without the context of the types or any other like broader files in the code. And so the kind of intuition here is like, why don't we just do the basic thing that like any baseline intelligent human developer would do, which is like click jump to definition, click some fine references and pull in that like Graph Context into the context window and then have it generate the completion. So like that's sort of like the MVP of what BFG was. And turns out that works really well. Like you can eliminate a lot of type errors that AI coding tools make just by pulling in that context. Yeah, but the graph is definitely [00:48:32]
Steve: our Chomsky side. [00:48:33]
Swyx: Yeah, exactly. [00:48:34]
Beyang: So like this like Chomsky-Norvig thing, I think pops up in a bunch of different layers. And I think it's just a very useful and also kind of like nicely nerdy way to describe the system that we're trying to build. [00:48:46]
Steve: By the way, I remembered the point I was trying to make earlier to your question, Alessio, about is AI going to replace programmers? And I was talking about how compilers, they thought, oh, are compilers going to replace programming? And what it did was just change [00:48:57]
Beyang: kind of what programmers [00:48:58]
Steve: had to focus on. And I think AI is just going to level us at the game, right? Programmers are still in the middle of stuff and, you know, Intel agents come along, but I don't believe. And so, yeah. [00:49:09]
Beyang: Yeah, I mean, to be clear, again, like with the agent stuff at a high level, I think we will get there. [00:49:14]
Swyx: I think that's still [00:49:14]
Beyang: the kind of long-term target. And I think also with Cody, it's like you can have Cody like draft up an execution plan. It's just not going to be the sort of thing where you can't attend to what it's doing. Like we think that like with Cody, it's like, yes, Cody, like, hey, I have this bug, [00:49:30]
Swyx: help me solve it. [00:49:30]
Beyang: It would do a reasonable job of fetching context and saying, like, here are the files you should modify. And if you prompt it further, you can actually suggest like code changes to make to those files. And that's a very nice way to like resolve issues because you're kind of like on the rails for most of the time. But then, you know, now and then you have to intervene as a human. I just think that like [00:49:48]
Swyx: if we're trying to get [00:49:48]
Beyang: to complete automation, where it's like the sort of thing where like a non-software engineer, like someone who has no technical expertise can just like speak a non-trivial feature into existence. [00:49:59]
Swyx: You know, that is still, [00:50:00]
Beyang: I think, several key innovations away from happening right now. And I don't think the pure like transformer based LLM orchestrator modeled agents that is kind of like dominant today is going to get us there. Yeah. [00:50:14]
Swyx: What you're talking about triggered a thread I've been working on for a little bit, which is, you know, we're very much reacting to developments in models on a month-to-month basis. We had a post about we're going to need a bigger moat, which is great JAWS reference for those who didn't catch it. I forgot all about that. How quickly models are evolving. But I think if you like kind of look out, I actually caught Sam Altman on the podcast yesterday talking about GPT-10. I know. Wow. [00:50:40]
Beyang: Things are accelerating. [00:50:42]
Swyx: And actually there's a pretty good cadence from GPT-2, 3 and 4 that you can, if you project out, 4 is based on George Hotz's concept of like 20 petaflops being a human's worth of compute. GPT-4 took about 100 years in terms of human years to train in terms of the amount of compute. So that's one living person. And every generation of GPT increases two orders of magnitude. So 5 is, you know, 100 people. And if you just project it out, 9 is every human on earth and 10 is every human ever. And he thinks he'll reach there by the end of the decade. George Hotz does? No, Sam Altman. Oh, Sam Altman. Okay. [00:51:19]
Beyang: Yeah. [00:51:20]
Swyx: So I just like setting those high level, you have dots on the line. We're at the start of the curve with Moore's law. George Moore, I think, thought it would last like 10 years. Yeah. And he just kept drawing for another 50. Yeah. And I think we have all these data points and we're just trying to draw, extrapolate the curve to where this goes. All I'm saying is, the agent stuff that we dealt might come here by 2030. And I don't know how you plan when things are not possible today and you're like, it's not worth doing. But like, you know, I mean, we're going to be here in 2030. [00:51:50]
Swyx: And what do we do then? [00:51:54]
Beyang: So is the question like, you know... There's no question. [00:51:57]
Swyx: It's like sharing of a comment just because like at the back of my head, anytime we hear things like things are not practical today. Yeah. I'm just like, all right, but how do we... [00:52:06]
Beyang: So here's like a question maybe, like I get the whole like scaling argument. I do think that there will be something like a Moore's law for AI inference. I mean, definitely, I think at like the hardware level, like GPUs, I think it gets a little fuzzier the higher you move up in the stack. But for instance, like going back to the chess analogy, right? At what point do we think that, you know, GPDX or whatever, you know, a pure, a transformer based LM model will be like state of the art or outperform the best like chess playing algorithm today? Because I think that is one milestone on... Where you completely overlap search. [00:52:41]
Swyx: Yeah, exactly. [00:52:42]
Beyang: Because I think that would be, I mean, just to put my cards on the table, I think that would kind of disprove the thesis that I just stated, which is, you know, kind of like the pure transformer, just scale the transformer based approach. That would be a proof point where like, hey, like maybe that is the right approach versus, oh, we actually have to take a step back and think, you get what I'm saying, right? Like is the transformer going to be like, is that the end all be all of architectures and it's just a matter of scaling that? [00:53:04]
Swyx: Yeah. [00:53:04]
Beyang: Or are there other algorithms and like that is going to be one piece of a system of intelligence that will have to take advantage of like many other algorithms and approaches. Yeah, we shall see. [00:53:14]
Swyx: Maybe John Carmack will find it. Yeah. All right. Sorry for that digression. I'm just very curious. So one thing I did actually want to check in on because we talked a little bit about code graphs and like reference graphs and all that. Do you actually use a graph database? No, right? No. [00:53:29]
Beyang: How would you find graph database? [00:53:31]
Steve: We use Postgres. And yeah, I saw a paper actually right after I joined Sourcegraph. There was some joint study between IBM and some other company that basically showed that Postgres was performing as well as most of the graph databases for most graph workloads. [00:53:43]
Swyx: Wow. [00:53:45]
Beyang: In V0 of Sourcegraph, we're like, we're building a code graph. Let's use a graph database. I won't name the database because I mean, it was like 10 years ago. So they're probably much better now. But like we basically tried to dump like a non-trivially sized like dataset, but also like not the whole universe of code, right? Like it was a relatively small dataset compared to what we're indexing now [00:54:05]
Swyx: into the database. [00:54:05]
Beyang: And it was just, we let it run for like a week. And I think it like seg faulted or something. And we're like, okay, let's try another approach. Let's just put everything in Postgres. And these days, like the graph data, I mean, it's partially in Postgres. It's partially just, I mean, you could store them as like flat files. [00:54:21]
Swyx: Yep. [00:54:21]
Beyang: I mean, at the end of the day, all the databases like just get me the data I want. Like answer the queries that I need, right? Like if all your queries are like, you know, single hops. [00:54:30]
Steve: Which they will be if you denormalize from other use cases. [00:54:33]
Beyang: Exactly. [00:54:34]
Swyx: Interesting. [00:54:34]
Beyang: So yeah. [00:54:35]
Swyx: Set of normal form is just a bunch of files. Yeah, yeah. And I don't know, like, [00:54:40]
Beyang: I feel like there's a bunch of stuff like that where it's like, if you look past the marketing and think about like the actual query load or like the traffic patterns or the end user use cases you need to serve, just go with like the tried and true, kind of like dumb classic tools over kind of like the new agent stuff. Yeah. I mean, there's a bunch of stuff like that in the search domain too. Especially right now with like, you know, embeddings and vector search and all that. But, you know, like classic search techniques still go very far. And I don't know, I think in the next year or two, maybe as we get past like the peak AI hype, we'll start to see the gap emerge or become more obvious to more people about like how many of like the newfangled techniques actually work in practice and yield a better product experience day to day. Yeah. [00:55:25]
Swyx: So speaking of which, like, you know, obviously there's a bunch of other people trying to build AI tooling. What can you say about your AI stack? Obviously you build a lot proprietary in-house, but like what approaches, you know, like so prompt engineering, do you have a prompt engineering management tool? You know, what approaches there do you do? Pre-processing orchestration, like do you use Airflow? Do you use something else? Like, you know, that kind of stuff. Yeah. [00:55:46]
Beyang: Ours is very like duct taped together at the moment. So in terms of stack, it's essentially go in TypeScript and now Rust. There's the code knowledge graph that we built, which is using indexers, many of which are open source, that speak the skip protocol. And we have the code search backend. You know, traditionally we supported regular expression search and a string literal search with like a trigram index. And we're also building more like fuzzy search on top of that now, kind of like natural language or keyword based search on top of that. We use a variety of open source and proprietary models. We try to be like pluggable with respect to different models so we can easily swap the latest model in and out as they come online. I'm just hunting for like, [00:56:26]
Swyx: is there anything out there that you're like, these guys are really good. Everyone else should check them out. So for example, you talked about recursive summarization, which is something that LangChain and Llama indexed. I presume you wrote your own. Yeah, we wrote our own. [00:56:37]
Beyang: I think like the stuff that Llama indexed and LangChain are doing are like super interesting. I think from our point of view, it's like we're still in the application, like end user use case discovery phase. And so adopting like an external infrastructure or middleware kind of tool just seems like overly constraining right now. Yeah, we need full control. Yeah, we need full control because we need to be able to iterate rapidly up and down the stack. But maybe at some point there'll be like a convergence and we can actually merge some of our stuff into theirs and turn that into a common resource. In terms of like other vendors that we use, I mean, obviously like nothing but good things to say about Anthropic and OpenAI, which we both kind of partner with and use. Also Plug for Fireworks as an inference platform. Their team was kind of like ex-meta people who basically know all like the bag of tricks for making inference fast. Yeah, I met Lynn. [00:57:25]
Swyx: So she was like with Sumith. She was like the co-manager of PyTorch for five years. Yeah, yeah, yeah. [00:57:31]
Beyang: But like is their main thing [00:57:32]
Swyx: that we just do fastest inference on earth? Is that what it is or? I think that's the pitch. [00:57:37]
Beyang: And it keeps getting faster somehow. Like we run Starcoder on top of Fireworks and that's made it so that we just don't have to think about building up an inference stack. And so that's great for us because it allows us to focus more on the kind of like data fetching, the knowledge graph and model fine tuning, which we've also invested a bit in. [00:57:55]
Swyx: That's right. [00:57:55]
Steve: We've got multiple AI work streams in progress now because we hired a head of AI finally. We spent close to a year actually. I think I talked to probably 75 candidates. And the guy we hired, Rashab, is absolutely world-class. And he immediately started multiple work streams, including he's fine-tuned Starcoder already. He's got prompt engineering work stream. He's got bettings work stream. He's got evaluation and experimentation. Benchmarking, wouldn't it be nice if Cody was on Hugging Face with a benchmark that we could just, anybody could say, well, we'll run against the benchmark or we'll make our own benchmark if we don't like yours. But we'll be forcing people into the sort of quantitative comparisons. And that's all happening under the AI program that he's building for us. [00:58:35]
Swyx: I should mention, by the way, I've heard that there's a V2 of Starcoder coming on. So you guys should talk to Hugging Face. Cool. Awesome. Great. I actually visited their offices in Paris, which is where I heard it. That's awesome. [00:58:47]
Steve: Can you guys believe how amazing it is that the open source models are competitive with GPT and Anthropic? I mean, it's nuts, right? I mean, that one Googler that was predicting that open source would catch up. At least he was right for completions. [00:59:03]
Beyang: Yeah. I mean, for completions, open source is state-of-the-art. [00:59:06]
Swyx: You were on OpenAI, then you went to Claude, and now you've ripped it up. Yeah. Yeah, for completions. [00:59:10]
Beyang: I mean, we still use Claude and GPT-4 for chat and also commands. Like, the ecosystem is going to continue to devolve. We obviously love the open source ecosystem and, like, huge shout out to Hugging Face. And also, like, meta research. We love the work that they're doing and kind of driving the ecosystem forward. [00:59:26]
Swyx: Yeah, you didn't mention Code Llama. [00:59:27]
Beyang: We're not using Code Llama currently. It's always kind of like a constant evaluation process. So, like, I don't want to come out and say, like, hey, this model is the best because we chose it. Basically, like, we did a bunch of, like, tests for the sorts of, like, contexts that we're fetching now and given the way that our prompts constructed now. And at the end of the day, it was like a judgment call. Like, starcoder seemed to work the best, and that's why we adopted it. But it's sort of like a continual process of revisitation. Like, if someone comes up with, like, a neat new, like, context fetching mechanism, and we have a couple coming online soon, then it's always like, okay, let's try that against the array of models that are available and see how this moves the needle across that set. [01:00:01]
Swyx: Yeah. What do you wish someone else built? This is a request for startups. [01:00:04]
Beyang: I mean, if someone could just provide, like, a very nice, clean data set of both naturally occurring and synthetic code data. [01:00:15]
Steve: Yeah. Could someone please give us their data mode? [01:00:17]
Swyx: Well, not even the data mode. [01:00:19]
Beyang: It's just like, I feel like most models today, they still use, like, combination of, like, the stack and the pile as, like, their training corpus. But you can only stretch that so far. At some point, you need more data. And I think there's still more alpha in, like, synthetic data. Like, we have a couple of efforts where, like, we think fine tuning some models on specific coding tasks will yield more kind of, like, reliable code generation of the sort where it's, like, reliable enough that we can fully automate it, at least, like, the one hop thing. And synthetic data is playing a part of that. But, I mean, if there were, like, a synthetic data provider, I don't think you could construct a provider that has access to, like, some proprietary code base. Like, no company in the world would be able to, like, sell that to you. But, like, anyone who's just, like, providing clean data sets off of the publicly available data. That would be nice. I don't know if there's a business around that, but, like, that's something that we definitely, like, [01:01:09]
Swyx: love to use. [01:01:09]
Beyang: Oh, for sure. [01:01:10]
Steve: My God. I mean, but that's also, like, the secret weapon, right, for any AI, you know, is the data that you've curated. So I doubt people are going to be, oh, we'll see, you know. But we can maybe contribute, you know, if we want to have a benchmark of our own. [01:01:25]
Swyx: Yeah. I would say, like, that would be the bull case for Repl.it, that, like, you want to be a coding platform where you also offer bounties. Like, then you eventually bootstrap your own proprietary set of coding data. I don't think they'll ever share it. The rumor is, this is from nobody at Repl.it that I'm hearing, but, like, they're just not leveraging that actively. Like, they're actually just betting on OpenAI to do a lot of that, which banking on OpenAI, you know, has been a winning strategy so far. [01:01:50]
Beyang: Yeah, they're definitely great at executing. [01:01:55]
Steve: Executing their CEO. [01:01:56]
Swyx: And then bring him back in four days. Yeah. [01:02:01]
Steve: That was a whole, like... [01:02:03]
Swyx: It was a company, like, just obsessed by the drama. Like, we were unable to work. I just walked in after it happened, and this whole room in the new room was just like, everyone's just staring at their phones. [01:02:12]
Beyang: Yeah, it's a bit difficult to ignore. I mean, it would have real implications for us, too, because, like, we're using them. And so there's a very real question of, like, do we have to, like, do it quick? [01:02:21]
Swyx: Yeah, Microsoft. Like, you just move to Microsoft, right? [01:02:23]
Beyang: Yeah, I mean, that would have been, like, the break glass plan. If the worst case played out, then I think we'd have a lot of customers, you know, the day after being like, you know, how can you guarantee the reliability of your services if the company itself isn't stable? But I'm really happy they got things sorted out and things are stable now because, like, they build really cool stuff and we love using their tech. [01:02:43]
Swyx: Yeah, awesome. [01:02:44]
Alessio: So we kind of went through everything, right? Sourcecraft, Cody, why agents don't work, why inline completion is better, all of these things. How does that bubble up to who manages the people, right? Because as engineering managers, I didn't write much code. I was mostly helping people write their own code, you know, so even if you have the best inline completion, it doesn't help me do my job. [01:03:08]
Swyx: Yeah. [01:03:08]
Alessio: What's kind of the future of Sourcecraft in the engineering org? [01:03:13]
Beyang: That's a really interesting question. And I think it sort of gets at this, like, issue, which is basically, like, every AI DevTools creator or producer these days, I think us included, we're kind of, like, focusing on the wrong problem in a way. Because, like, the real problem of modern software development, I think, is not how quickly can you write more lines of code. It's really about managing the emergent complexity of codebases as they evolve and grow and how to make, like, efficient development tractable again. Because the bulk of your time becomes more about understanding how the system works and how the pieces fit together currently so that you can update it in a way that gets you your added functionality, doesn't break anything, and doesn't introduce a lot of additional complexity that will slow you down in the future. And if anything, like, the Interloop developer tools that are all about, like, generating lines of code, yes, they help you get your feature done faster. They generate a lot of boilerplate for you. But they might make this problem of, like, managing large, complex codebases more challenging, just because instead of having, like, a pistol, you'll have a machine gun in terms of, like, being able to write code. And there's going to be a bunch of, like, natural language prompted code that is generated in the future that was produced by someone who doesn't even have an understanding of source code. And so, like, how are you going to verify the quality of that and make sure it not only checks the kind of, like, low-level boxes, but also fits architecturally in a way that's sensible into your codebase. And so I think as we look forward to the future of the next year, we have a lot of ideas around how to make codebases, as they evolve, more understandable and manageable to the people who really care about the codebase as a whole. You know, tech leads, engineering leaders, folks like that. It is kind of like a return to our ultimate mission at Sourcegraph, which is to make code accessible to all. It's not really about, you know, enabling people to write code. And if anything, like, the original version of Sourcegraph was a rejection of, like, hey, let's stop trying to build, like, the next best editor, because, like, there's already enough people doing that. The real problem that we're facing, I mean, Quinn, myself, and you, Steve at Google, was like, how do we make sense of the code that exists so that we can understand enough to know what code needs to be written? Mm-hmm. [01:05:25]
Steve: Yeah. Well, I'll tell you what customers want, right? And what they're going to get. What they want is for Cody to have a monitor for developer productivity. And any developer who falls below a threshold, a button lights up where the admin can fire them. Or Cody will even press that button for you as time passes. But I'm kind of only half tongue-in-cheek here. We've got some prospects who are kind of, like, sniffing down that avenue. And we're like, no. But what they're going to get is a much greater whole code-based understanding, which is actually something that Cody is, I would argue, the best at today in the coding assistance space, right? Because of our search engine and the techniques that we're using. And that whole code-based understanding is so important, you know, for any sort of a manager who just wants to get a feel for the architecture or potential security vulnerabilities or whether, you know, people are writing code that's well-tested and et cetera, et cetera, right? And solving that problem is tricky, right? This is not the developer inner loop or outer loop. It's like the manager inner loop? [01:06:21]
Swyx: No, outer loop. [01:06:21]
Steve: The manager inner loop is staring at your belly button, I guess. So in any case... [01:06:27]
Beyang: Waiting for the next Slack message to arrive? [01:06:29]
Steve: Yes. What they really want is a batch mode for these assistants where you can actually take the coding assistant and shove its face into your code base, you know, and six billion lines of code later, right? It's told you all the security vulnerabilities. That's what they really actually want. It's insanely expensive proposition, right? You know, just the GPU costs, especially if you're doing it on a regular basis. So it's better to do it at the point the code enters the system. And so now we're starting to get into developer outer loop stuff. And I think that's where a lot of the... To your question, right? A lot of the admins and managers and so, you know, the decision makers, anybody who just like kind of isn't coding [01:07:03]
Swyx: but is involved, [01:07:03]
Steve: they're going to have a set of tools, right? [01:07:06]
Swyx: And a set of... [01:07:06]
Steve: Just like with CodeSearch today. Our CodeSearch actually serves that audience as well. The CIO types, right? Because they're just like, oh, hey, I want to see how we do, you know, Samaloth. And they use our search engine and they go find it. And AI is just going to make that so much easier for them. [01:07:20]
Swyx: Yeah, this is my perfect place to put my anecdote of how I used Cody yesterday. I was actually trying to build this sort of Twitter scraper thing. And Twitter is notoriously very challenging to work with because they don't want to work with you, with anyone. There's a repo that I wanted to inspect. It was really big that had the Twitter scraper thing in it. And I pulled it into Copilot, didn't work. But then I noticed that on your landing page, you had a web version. Like, I typically think of Cody as a VS Code extension, but you have a web version where you just plug in any repo in there and just talk to it. And that's what I used to figure it out. So yeah. [01:07:54]
Steve: Wow, Cody web is wild. [01:07:57]
Beyang: Yeah, I mean, we've done a very poor job of making the existence of that feature. It's not easy to find. [01:08:02]
Swyx: It's not easy to find. You don't have to go through the search thing. It's like, oh, this is old source graph. You don't want to look at old source graph. I mean, you can use source graph, all the AI stuff. Old source graph has AI stuff and it's Cody web. Yeah, yeah. [01:08:13]
Beyang: There's a little ask Cody button that's hidden in the upper right-hand corner. We should make that more visible. It's definitely one of those aha moments when you can ask a question of Cody. Of any repo, right? [01:08:22]
Swyx: Because you already indexed it. Well, you didn't embed it, but you indexed it. Yeah. [01:08:26]
Beyang: And there's actually some use cases that have emerged among power users where they kind of do... You're familiar with v0.dev. You can kind of replicate that, but for arbitrary frameworks and libraries with Cody web. Because there's also an equally hidden toggle, which you may not have discovered yet, where you can actually tag in multiple repositories as context. [01:08:44]
Swyx: Yeah. [01:08:44]
Beyang: And so you can do things like, we have a demo path where it's like, okay, let's say you want to build a stock ticker [01:08:50]
Swyx: that's React-based, [01:08:50]
Beyang: but uses this one tick data fetching API. It's like you tag both repositories in, you ask it, it's like two sentences, like build a stock tick app, track the tick data of Bank of America, Wells Fargo over the past week, and then generates a code. You can paste that in and it just works magically. We'll probably invest in that more just because the wow factor of that is just pretty incredible. It's like, what if you can speak apps into existence that use the frameworks and packages that you want to use? Yeah. [01:09:19]
Swyx: It's not even fine-tuning. It's just taking advantage of your RAG pipeline. [01:09:22]
Beyang: Yeah. It's just RAG. RAG is all you need for many things. [01:09:25]
Steve: It's not just RAG. It's RAG, right? RAG's good. Not a fallback. [01:09:33]
Swyx: Yeah. [01:09:33]
Beyang: But I guess getting back to the original question, I think there's a couple of things I think would be interesting for engineering leaders. One is the use case that you called out is all the stuff that you currently don't do that you really ought to be doing with respect to ensuring code quality or updating dependencies or keeping things up to date. The things that humans find toilsome and tedious and just don't want to do but would really help up-level the quality, security, and robustness of your code base, now we potentially have a way to do that with machines. I think there's also this other thing, and this gets back to the point of how do you measure developer productivity? It's the perennial age-old question. Every CFO in the world would love to do it in the same way that you can measure marketing or sales or other parts of the organization. And I think what is the actual way you would do this that is good? And if you had all the time in the world, I think as an engineering manager or an engineering leader, what you would do is you would go read through the Git log, maybe line by line, be like, you, Sean, these are the features that you built over the past six months or a year. These are the things that delivered that you helped drive. Here's the stuff that you did to help your teammates. Here are the reviews that you did that helped ensure that we maintain a coherent and a high-quality code base. Now connect that to the things that matter to the business. What were we trying to drive this? Was it engagement? Was it revenue? Was it adoption of some new product line? And really weave that story together. The work that you did had this impact on the metrics that moved the needle for the business and ultimately show up in revenue or stock price or whatever it is that's at the very top of any for-profit organization. And you could, in theory, do all that today if you had all the time in the world. [01:11:22]
Swyx: Yeah. [01:11:22]
Beyang: But as an engineering leader- It's a busy building. Yeah, you're too busy building, you're too busy with a bunch of other stuff. Plus it's also tedious. Reading through a Git log and trying to understand what a change does and summarizing that, it's not the most exciting work in the world. But with the benefit of AI, I think you could conceive of a system that actually does a lot of the tedium and helps you actually tell that story. And I think that is maybe the ultimate answer to how we get at developer productivity in a way that a CFO would be like, okay, I can buy that. The work that you did impacted these core metrics because these features were tied to those and therefore we can afford to invest more in this part of the organization. And that's what we really want to drive towards. That's what we've been trying to build all along in a way with Sourcegraph. It's this kind of code-based level of understanding and the availability of LLMs and AI now just puts that much sooner in reach, I think. [01:12:14]
Swyx: Yeah. [01:12:15]
Steve: But I mean, we have to focus also, small company, our short-term focus is lovability, right? [01:12:21]
Swyx: Yeah. [01:12:21]
Steve: We absolutely have to make Cody, like everybody wants it, right? [01:12:25]
Swyx: Absolutely. [01:12:26]
Steve: Sourcegraph is all about enabling non-engineering roles, decision makers and so on. As Bianca says, I mean, I think there's just a lot of opportunity there once we've built a lovable Cody. [01:12:37]
Swyx: Awesome. [01:12:37]
Alessio: We want to jump into lightning round? [01:12:40]
Swyx: Lightning round. [01:12:40]
Alessio: Okay. [01:12:41]
Swyx: So we usually have three, [01:12:42]
Alessio: one around acceleration, exploration, and then a final takeaway. So the acceleration one is what's something that already happened in AI that is possible today that you thought would take much longer? [01:12:54]
Beyang: I mean, just LLMs and how good the vision models are now. Like I got my start. Okay. [01:13:00]
Swyx: Yeah. [01:13:00]
Beyang: Back in the day, I got my start machine learning in computer vision, but circa like 2009, 2010. [01:13:07]
Swyx: And in those days, [01:13:07]
Beyang: everything was like statistical based. Neural nets had not yet made their comeback. And so nothing really worked. And so I was very bearish after that experience on the future of computer vision. But like, man, the progress that's been made just in the past, like three, four years has just been absolutely astounding. Came up faster than I expected it to. Yeah. [01:13:27]
Steve: Multimodal in general, [01:13:28]
Swyx: I think is, [01:13:28]
Steve: I think there's a lot more capability there that we're not tapping into. Potentially even in the coding assistant space. You know, honestly, I think that the form factor that coding assistants have today is probably not the steady state that we're seeing, you know, long-term. You'll always have completions and you always have chat and commands and so on. But I think we're going to discover a lot more. And I think multimodal potentially opens up some kind of new ways to, you know, get your stuff done. So yeah, I think the capabilities are there today. And they're just, it's just shocking. I mean, like, I still am astonished when I sit down, you know, and I have a conversation with the LLM, with the context, and it's like, I'm talking to a, you know, a senior engineer or an architect or somebody, right? I think that people have very different working models with these assistants today. You know, some people are just completion, completion, completion, that's it. And if they want some code generated, they write a comment and then, you know what I mean? Telling them what to do. But I truly think that there are other modalities that we're going to stumble across. Just kind of latently, you know, inherently built into the LLMs today that we just haven't found them yet. They're more of a discovery than invention, you know? [01:14:31]
Swyx: Like other usage patterns? [01:14:34]
Steve: Absolutely. I mean, the one that we talked about earlier, nonstop coding is one, right? Where you could just kick off a whole bunch of, you know, requests to refactor and so on. But, you know, there could be any number of others. You know, we talk about agents, you know, that's kind of out there. But I think there are kind of more inner loop type ones to be found. And we haven't looked at all at multimodal yet. [01:14:52]
Swyx: Yeah, for sure. Like there's two that come to mind, just off the top of my head. One, which is effectively architecture diagrams and entity relationship diagrams. There's probably more alpha in like synthesizing them for management to see. Ooh, yeah. Which is like, you don't need AI for that. You can just use your reference graph. Yeah. But then also doing it the other way around when like someone draws stuff on a whiteboard and actually generating code. [01:15:14]
Steve: Well, you can generate the diagram and then, you know, explanations as well. [01:15:18]
Swyx: Yeah. And then the other one is, there was a demo that went pretty viral like two, three weeks ago about how someone just had an always on script, just screenshotting and sending it to GPT Vision on some kind of time interval. And it would just autonomously suggest stuff. Yeah. So like no trigger, just watching your screen and just like being a real co-pilot rather than having you initiate with a chat. Yeah. [01:15:39]
Beyang: It's like the return of Clippy, right? But actually good. [01:15:42]
Swyx: The reason I know this is that we actually did a hackathon where we wrote that project, but it roasted you while you did it. So it's like, hey, you're on Twitter right now. You should be coding. Yeah. That can be a fun co-pilot thing as well. Yeah, yeah. Okay. So I'll jump on. Exploration. What do you think is the most interesting unsolved question in AI? I mean, I think- [01:16:01]
Steve: It used to be scaling, right? With CNNs and RNNs and Transformer solved that. Yeah. So what's the next big hurdle? It's keeping GPT-10 from emerging. [01:16:09]
Beyang: I mean, do you mean that like- Oh, is this like a safetyist argument? I feel like, do you mean like the pure model, like AI layer or- [01:16:17]
Swyx: No, it doesn't have to be. [01:16:18]
Beyang: For me personally, it's like, how do you get reliable, like first try working code generation? Even like the single hop, like write a function that does this. Because I think like if you want to get to the point where you can actually be truly agentic or like multi-step automated, a necessary part of that is like the single step has to be robust and reliable. And so I think that's the problem that we're focused on solving right now. Because once you have that, it's a building block that you can then compose into longer chains. [01:16:47]
Alessio: And just to wrap things up, what's one message takeaway that you want people to remember and think about? I mean, I think for me, [01:16:55]
Beyang: it's just like the best dev tools in the future are going to have to leverage many different forms of intelligence. You know, calling back to that like Normsky architecture, trying to make catch on. [01:17:06]
Swyx: You should have called it something cool, like S star or R star. [01:17:09]
Beyang: Yes, yes, yes. [01:17:10]
Swyx: Just one letter and then just let people speculate. Yeah, yeah. What could he mean? [01:17:14]
Beyang: I don't know, like in terms of like trying to describe what we're building, we try to be a little bit more like down to earth and like straightforward. And I think like Normsky kind of like encapsulates like the two big technology areas that we're investing in that we think will be very important for producing really good dev tools. And I think it's a big differentiator that we view that Cody has right now. [01:17:35]
Steve: Yeah, and mine would be, I know for a fact that not all developers today are using coding systems. Yeah, and that's probably because they tried it and it didn't, you know, immediately write a bunch of beautiful code for them and they were like, oh, too much effort and they left, right? Well, my big takeaway from this talk would be if you're one of those engineers, you better start like planning another career, okay? Because this stuff is in the future and honestly, it takes some effort to actually make coding assistance work today, right? You have to, you know, just like talking to GPT, they'll give you the runaround, just like doing a Google search sometimes. But if you're not putting that effort in and learning the sort of footprint, and the characteristics of how LLMs behave under different query conditions and so on, if you're not getting a feel for the coding assistant, then you're letting this whole train just like pull out of the station and leave you behind. [01:18:26]
Swyx: Yeah, absolutely. [01:18:28]
Alessio: Yeah, thank you guys so much for coming on and being the first guest in the new studio. [01:18:32]
Swyx: Our pleasure. [01:18:34]
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Podcast: Latent Space: The AI Engineer Podcast — Practitioners talking LLMs, CodeGen, Agents, Multimodality, AI UX, GPU Infra and all things Software 3.0 (LS 33 · TOP 5% what is this?)
Episode: NeurIPS 2023 Recap — Best Papers
Pub date: 2023-12-23
We are running an end of year listener survey! Please let us know any feedback you have, what episodes resonated with you, and guest requests for 2024! Survey link here.
NeurIPS 2023 took place from Dec 10–16 in New Orleans. The Latent Space crew was onsite for as many of the talks and workshops as we could attend (and more importantly, hosted cocktails and parties after hours)!
Picking from the 3586 papers accepted to the conference (available online, full schedule here) is an impossible task, but we did our best to present an audio guide with brief commentary on each. We also recommend MLContests.com NeurIPS recap and Seb Ruder’s NeurIPS primer and Jerry Liu’s paper picks. We also found the VizHub guide useful for a t-SNE clustering of papers. Lots also happened in the arxiv publishing world outside NeurIPS, as highlighted by Karpathy, especially DeepMind’s Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models.
Jan 2024 update: we also strongly recommend ‘s pick of the year’s 10 best papers, including Pythia.
We’ll start with the NeurIPS Best Paper Awards, and then go to a selection of non-awarded but highly influential papers, and then arbitrary personal picks to round out the selection. Where we were able to do a poster session interview, please scroll to the relevant show notes for images of their poster for discussion. We give Chris Ré the last word due to the Mamba and StripedHyena state space models drawing particular excitement but still being too early to assess impact.
Timestamps
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[0:01:19] Word2Vec (Jeff Dean, Greg Corrado)
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[0:15:28] Emergence Mirage (Rylan Schaeffer)
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[0:28:48] DPO (Rafael Rafailov)
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[0:41:36] DPO Poster Session (Archit Sharma)
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[0:52:03] Datablations (Niklas Muennighoff)
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[1:00:50] QLoRA (Tim Dettmers)
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[1:12:23] DataComp (Samir Gadre)
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[1:25:38] DataComp Poster Session (Samir Gadre, Alex Dimakis)
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[1:35:25] LLaVA (Haotian Liu)
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[1:47:21] LLaVA Poster Session (Haotian Liu)
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[1:59:19] Tree of Thought (Shunyu Yao)
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[2:11:27] Tree of Thought Poster Session (Shunyu Yao)
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[2:20:09] Toolformer (Jane Dwivedi-Yu)
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[2:32:26] Voyager (Guanzhi Wang)
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[2:45:14] CogEval (Ida Momennejad)
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[2:59:41] State Space Models (Chris Ré)
Papers covered
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Distributed Representations of Words and Phrases and their Compositionality (Word2Vec) Tomas Mikolov · Ilya Sutskever · Kai Chen · Greg Corrado · Jeff Dean. The recently introduced continuous Skip-gram model is an efficient method for learning high-quality distributed vector representations that capture a large number of precise syntactic and semantic word relationships. In this paper we present several improvements that make the Skip-gram model more expressive and enable it to learn higher quality vectors more rapidly. We show that by subsampling frequent words we obtain significant speedup, and also learn higher quality representations as measured by our tasks. We also introduce Negative Sampling, a simplified variant of Noise Contrastive Estimation (NCE) that learns more accurate vectors for frequent words compared to the hierarchical softmax. An inherent limitation of word representations is their indifference to word order and their inability to represent idiomatic phrases. For example, the meanings of Canada'' and "Air'' cannot be easily combined to obtain "Air Canada''. Motivated by this example, we present a simple and efficient method for finding phrases, and show that their vector representations can be accurately learned by the Skip-gram model.
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Some notable reflections from Tomas Mikolov - and debate over the Seq2Seq paper credit with Quoc Le
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Are Emergent Abilities of Large Language Models a Mirage? (Schaeffer et al.). Emergent abilities are abilities that are present in large-scale models but not in smaller models and are hard to predict. Rather than being a product of models’ scaling behavior, this paper argues that emergent abilities are mainly an artifact of the choice of metric used to evaluate them. Specifically, nonlinear and discontinuous metrics can lead to sharp and unpredictable changes in model performance. Indeed, the authors find that when accuracy is changed to a continuous metric for arithmetic tasks where emergent behavior was previously observed, performance improves smoothly instead. So while emergent abilities may still exist, they should be properly controlled and researchers should consider how the chosen metric interacts with the model.
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Direct Preference Optimization: Your Language Model is Secretly a Reward Model (Rafailov et al.)
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While large-scale unsupervised language models (LMs) learn broad world knowledge and some reasoning skills, achieving precise control of their behavior is difficult due to the completely unsupervised nature of their training. Existing methods for gaining such steerability collect human labels of the relative quality of model generations and fine-tune the unsupervised LM to align with these preferences, often with reinforcement learning from human feedback (RLHF). However, RLHF is a complex and often unstable procedure, first fitting a reward model that reflects the human preferences, and then fine-tuning the large unsupervised LM using reinforcement learning to maximize this estimated reward without drifting too far from the original model.
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In this paper, we leverage a mapping between reward functions and optimal policies to show that this constrained reward maximization problem can be optimized exactly with a single stage of policy training, essentially solving a classification problem on the human preference data. The resulting algorithm, which we call Direct Preference Optimization (DPO), is stable, performant, and computationally lightweight, eliminating the need for fitting a reward model, sampling from the LM during fine-tuning, or performing significant hyperparameter tuning.
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Our experiments show that DPO can fine-tune LMs to align with human preferences as well as or better than existing methods. Notably, fine-tuning with DPO exceeds RLHF's ability to control sentiment of generations and improves response quality in summarization and single-turn dialogue while being substantially simpler to implement and train.
See also on DPO: and recent Twitter discussions
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Scaling Data-Constrained Language Models (Muennighoff et al.)
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The current trend of scaling language models involves increasing both parameter count and training dataset size. Extrapolating this trend suggests that training dataset size may soon be limited by the amount of text data available on the internet. Motivated by this limit, we investigate scaling language models in data-constrained regimes. Specifically, we run a large set of experiments varying the extent of data repetition and compute budget, ranging up to 900 billion training tokens and 9 billion parameter models. We find that with constrained data for a fixed compute budget, training with up to 4 epochs of repeated data yields negligible changes to loss compared to having unique data. However, with more repetition, the value of adding compute eventually decays to zero. We propose and empirically validate a scaling law for compute optimality that accounts for the decreasing value of repeated tokens and excess parameters. Finally, we experiment with approaches mitigating data scarcity, including augmenting the training dataset with code data or removing commonly used filters. Models and datasets from our 400 training runs are freely available at https://github.com/huggingface/datablations.
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2 minute poster session presentation video
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QLoRA: Efficient Finetuning of Quantized LLMs (Dettmers et al.).
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This paper proposes QLoRA, a more memory-efficient (but slower) version of LoRA that uses several optimization tricks to save memory. They train a new model, Guanaco, that is fine-tuned only on a single GPU for 24h and outperforms previous models on the Vicuna benchmark. Overall, QLoRA enables using much fewer GPU memory for fine-tuning LLMs. Concurrently, other methods such as 4-bit LoRA quantization have been developed that achieve similar results.
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DataComp: In search of the next generation of multimodal datasets (Gadre et al.)
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Multimodal datasets are a critical component in recent breakthroughs such as CLIP, Stable Diffusion and GPT-4, yet their design does not receive the same research attention as model architectures or training algorithms. To address this shortcoming in the machine learning ecosystem, we introduce DataComp, a testbed for dataset experiments centered around a new candidate pool of 12.8 billion image-text pairs from Common Crawl. Participants in our benchmark design new filtering techniques or curate new data sources and then evaluate their new dataset by running our standardized CLIP training code and testing the resulting model on 38 downstream test sets.
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Our benchmark consists of multiple compute scales spanning four orders of magnitude, which enables the study of scaling trends and makes the benchmark accessible to researchers with varying resources. Our baseline experiments show that the DataComp workflow leads to better training sets. Our best baseline, DataComp-1B, enables training a CLIP ViT-L/14 from scratch to 79.2% zero-shot accuracy on ImageNet, outperforming OpenAI's CLIP ViT-L/14 by 3.7 percentage points while using the same training procedure and compute. We release \datanet and all accompanying code at www.datacomp.ai.
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Visual Instruction Tuning (Liu et al)
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Instruction tuning large language models (LLMs) using machine-generated instruction-following data has improved zero-shot capabilities on new tasks, but the idea is less explored in the multimodal field. In this paper, we present the first attempt to use language-only GPT-4 to generate multimodal language-image instruction-following data.
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By instruction tuning on such generated data, we introduce LLaVA: Large Language and Vision Assistant, an end-to-end trained large multimodal model that connects a vision encoder and LLM for general-purpose visual and language understanding.
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Our early experiments show that LLaVA demonstrates impressive multimodel chat abilities, sometimes exhibiting the behaviors of multimodal GPT-4 on unseen images/instructions, and yields a 85.1% relative score compared with GPT-4 on a synthetic multimodal instruction-following dataset. When fine-tuned on Science QA, the synergy of LLaVA and GPT-4 achieves a new state-of-the-art accuracy of 92.53%. We make GPT-4 generated visual instruction tuning data, our model and code base publicly available.
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Tree of Thoughts: Deliberate Problem Solving with Large Language Models (Yao et al)
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Language models are increasingly being deployed for general problem solving across a wide range of tasks, but are still confined to token-level, left-to-right decision-making processes during inference. This means they can fall short in tasks that require exploration, strategic lookahead, or where initial decisions play a pivotal role.
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To surmount these challenges, we introduce a new framework for language model inference, Tree of Thoughts (ToT), which generalizes over the popular Chain of Thought approach to prompting language models, and enables exploration over coherent units of text (thoughts) that serve as intermediate steps toward problem solving.
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ToT allows LMs to perform deliberate decision making by considering multiple different reasoning paths and self-evaluating choices to decide the next course of action, as well as looking ahead or backtracking when necessary to make global choices.
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Our experiments show that ToT significantly enhances language models’ problem-solving abilities on three novel tasks requiring non-trivial planning or search: Game of 24, Creative Writing, and Mini Crosswords. For instance, in Game of 24, while GPT-4 with chain-of-thought prompting only solved 4\% of tasks, our method achieved a success rate of 74\%.
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Code repo with all prompts: https://github.com/princeton-nlp/tree-of-thought-llm.
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Toolformer: Language Models Can Teach Themselves to Use Tools (Schick et al)
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LMs exhibit remarkable abilities to solve new tasks from just a few examples or textual instructions, especially at scale. They also, paradoxically, struggle with basic functionality, such as arithmetic or factual lookup, where much simpler and smaller specialized models excel.
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In this paper, we show that LMs can teach themselves to use external tools via simple APIs and achieve the best of both worlds.
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We introduce Toolformer, a model trained to decide which APIs to call, when to call them, what arguments to pass, and how to best incorporate the results into future token prediction.
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This is done in a self-supervised way, requiring nothing more than a handful of demonstrations for each API. We incorporate a range of tools, including a calculator, a Q&A system, a search engine, a translation system, and a calendar.
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Toolformer achieves substantially improved zero-shot performance across a variety of downstream tasks, often competitive with much larger models, without sacrificing its core language modeling abilities.
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Voyager: An Open-Ended Embodied Agent with Large Language Models (Wang et al)
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We introduce Voyager, the first LLM-powered embodied lifelong learning agent in Minecraft that continuously explores the world, acquires diverse skills, and makes novel discoveries without human intervention. Voyager consists of three key components:
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1) an automatic curriculum that maximizes exploration,
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2) an ever-growing skill library of executable code for storing and retrieving complex behaviors, and
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3) a new iterative prompting mechanism that incorporates environment feedback, execution errors, and self-verification for program improvement.
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Voyager interacts with GPT-4 via blackbox queries, which bypasses the need for model parameter fine-tuning. The skills developed by Voyager are temporally extended, interpretable, and compositional, which compounds the agent's abilities rapidly and alleviates catastrophic forgetting. Empirically, Voyager shows strong in-context lifelong learning capability and exhibits exceptional proficiency in playing Minecraft. It obtains 3.3x more unique items, travels 2.3x longer distances, and unlocks key tech tree milestones up to 15.3x faster than prior SOTA. Voyager is able to utilize the learned skill library in a new Minecraft world to solve novel tasks from scratch, while other techniques struggle to generalize.
Voyager discovers new Minecraft items and skills continually by self-driven exploration, significantly outperforming the baselines.
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Evaluating Cognitive Maps and Planning in Large Language Models with CogEval (Momennejad et al)
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Recently an influx of studies claims emergent cognitive abilities in large language models (LLMs). Yet, most rely on anecdotes, overlook contamination of training sets, or lack systematic Evaluation involving multiple tasks, control conditions, multiple iterations, and statistical robustness tests. Here we make two major contributions.
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First, we propose CogEval, a cognitive science-inspired protocol for the systematic evaluation of cognitive capacities in LLMs. The CogEval protocol can be followed for the evaluation of various abilities.
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Second, here we follow CogEval to systematically evaluate cognitive maps and planning ability across eight LLMs (OpenAI GPT-4, GPT-3.5-turbo-175B, davinci-003-175B, Google Bard, Cohere-xlarge-52.4B, Anthropic Claude-1-52B, LLaMA-13B, and Alpaca-7B). We base our task prompts on human experiments, which offer both established construct validity for evaluating planning, and are absent from LLM training sets.
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We find that, while LLMs show apparent competence in a few planning tasks with simpler structures, systematic evaluation reveals striking failure modes in planning tasks, including hallucinations of invalid trajectories and falling in loops. These findings do not support the idea of emergent out-of-the-box planning ability in LLMs. This could be because LLMs do not understand the latent relational structures underlying planning problems, known as cognitive maps, and fail at unrolling goal-directed trajectories based on the underlying structure. Implications for application and future directions are discussed.
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Mamba: Linear-Time Sequence Modeling with Selective State Spaces (Albert Gu, Tri Dao)
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Foundation models, now powering most of the exciting applications in deep learning, are almost universally based on the Transformer architecture and its core attention module. Many subquadratic-time architectures such as linear attention, gated convolution and recurrent models, and structured state space models (SSMs) have been developed to address Transformers' computational inefficiency on long sequences, but they have not performed as well as attention on important modalities such as language. We identify that a key weakness of such models is their inability to perform content-based reasoning, and make several improvements.
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First, simply letting the SSM parameters be functions of the input addresses their weakness with discrete modalities, allowing the model to selectively propagate or forget information along the sequence length dimension depending on the current token.
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Second, even though this change prevents the use of efficient convolutions, we design a hardware-aware parallel algorithm in recurrent mode. We integrate these selective SSMs into a simplified end-to-end neural network architecture without attention or even MLP blocks (Mamba).
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Mamba enjoys fast inference (5x higher throughput than Transformers) and linear scaling in sequence length, and its performance improves on real data up to million-length sequences. As a general sequence model backbone, Mamba achieves state-of-the-art performance across several modalities such as language, audio, and genomics. On language modeling, our Mamba-1.4B model outperforms Transformers of the same size and matches Transformers twice its size, both in pretraining and downstream evaluation.
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Podcast: Latent Space: The AI Engineer Podcast — Practitioners talking LLMs, CodeGen, Agents, Multimodality, AI UX, GPU Infra and all things Software 3.0 (LS 33 · TOP 5% what is this?)
Episode: NeurIPS 2023 Recap — Top Startups
Pub date: 2023-12-30
We are running an end of year listener survey! Please let us know any feedback you have, what episodes resonated with you, and guest requests for 2024! Survey link here.
We can’t think of a more Latent-Space-y way to end 2023 than with a mega episode featuring many old and new friends recapping their biggest news, achievements, and themes and memes of the year!
We previously covered the Best Papers of NeurIPS 2023, but the other part of NeurIPS being an industry friendly conference is all the startups that show up to hire and promote their latest and greatest products and papers! As a startup-friendly podcast, we of course were ready with our mics to talk to everyone we could track down.
In lieu of an extended preamble, we encourage you to listen and click through all the interviews and show notes, all of which have been curated to match the references mentioned in the episode.
Timestamps & Show Notes
-
[00:01:26] Jonathan Frankle - Chief Scientist, MosaicML/Databricks
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see also the Mosaic/MPT-7B episode
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$1.3B MosaicML x Databricks acquisition
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[00:22:11] Lin Qiao - CEO, Fireworks AI
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Fireworks Mixtral
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[00:38:24] Aman Sanger - CEO, Anysphere (Cursor)
-
see also the Cursor episode
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$8m seed from OpenAI
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Tweet: Request-level memory-based KV caching
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Tweet: GPT-4 grading and Trueskill ratings for rerankers
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[00:51:14] Aravind Srinivas - CEO, Perplexity
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1m app installs on iOS and Android
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pplx-online api 7b and 70b models
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Shaan Puri/Paul Graham Fierce Nerds story
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[01:04:26] Will Bryk - CEO, Metaphor
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“Andrew Huberman may have singlehandedly ruined the SF social scene”
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[01:12:49] Jeremy Howard - CEO, Answer.ai
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see also the End of Finetuning episode
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Jeremy’s podcast with Tanishq Abraham, Jess Leao
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Announcing Answer.ai with $10m from Decibel VC
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Laundry Buddy, Nov 2023 AI Meme of the Month
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[01:37:13] Joel Hestness - Principal Scientist, Cerebras
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CerebrasGPT, all the Cerebras papers we discussed
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[01:56:34] Jason Corso - CEO, Voxel51
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Open Source FiftyOne project
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CVPR Survival Guide
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[02:02:39] Brandon Duderstadt - CEO, Nomic.ai
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GPT4All, Atlas, Demo
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[02:12:39] Luca Antiga - CTO, Lightning.ai
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Pytorch Lightning, Lightning Studios, LitGPT
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[02:29:46] Jay Alammar - Engineering Fellow, Cohere
-
The Illustrated Transformer
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Podcast: Canción Exploder (LS 40 · TOP 2% what is this?)
Episode: Ibeyi - Sangoma
Pub date: 2022-10-12
Ibeyi es el duo de las hermanas gemelas Lisa-Kaindé Díaz y Naomi Díaz. Al ser hijas del percusionista cubano Anga Díaz, del grupo Buena Vista Social Club, se criaron con fuertes influencias musicales. Vivieron gran parte de sus adolescencias en Francia y desarrollaron sus carreras en Inglaterra. En Ibeyi, Lisa-Kaindé escribe la letra y toca el piano. Naomi hace la percusión y participa en la producción. Ambas son vocalistas también. Ellas escriben en inglés, español, francés, y yoruba.
En este episodio Lisa y Naomi hablan sobre “Sangoma,” la primera canción de su álbum “Spell 31”. Nos cuentan que durante la pandemia exploraron cómo la música puede curar, una reflexión que capturan en esta canción.
Encuentra más información sobre Sangoma y contenido extra visitando cancionexploder.com/ibeyi.
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Podcast: The Blindboy Podcast (LS 70 · TOP 0.05% what is this?)
Episode: Johnny Marr
Pub date: 2023-12-06
I chat with legendary songwriter and musician Johnny Marr
Hosted on Acast. See acast.com/privacy for more information.
The podcast and artwork embedded on this page are from Blindboyboatclub, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Danger Close with Jack Carr (LS 61 · TOP 0.1% what is this?)
Episode: Mark ‘Billy’ Billingham: Adapt, Survive and Win
Pub date: 2021-10-20
Today’s guest on Danger Close is former SAS operator Mark “Billy” Billingham.
From 1983 to 1991 Billy served in the UK’s Parachute Regiment before joining SAS where he was a Mountain Troop specialist conducting operations in Iraq, Afghanistan, South America and Africa. He is a certified SF and Counter Terrorist Sniper Instructor, an Advanced Evasive Driving Instructor, a Combat Survival/RTI Instructor, a Tracking/Jungle Warfare/Navigation Instructor, a Demolition/Sabotage Instructor, a Ski Mountaineering/Rock Climbing/Abseiling/Ice climbing Instructor, a Counter Terrorist Instructor. He is also a philanthropist and highly sought-after speaker.
He is the author of The Hard Way: Adapt, Survive and Win and the new book Call to Kill: The Enemy Is Everywhere. Following his career in the military, Billy served as a personal bodyguard for high-profile clients including Brad Pitt, Angelina Jolie, Sir Michael Caine, Jude Law, Hulk Hogan, Kate Moss, Russell Crowe and Tom Cruise. Since 2015, Billy has been one of the lead instructors on the television series SAS: Who Dares Wins.
You can learn more about Billy at markbillybillingham.com or follow him on social @billingham22b.
Featured Gear:
Ten Thousand Tactical Short
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Kevin Aerolite 30 Sleeping Bag
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SIG Sauer: Today’s episode is presented by SIG Sauer.
Ten Thousand: Today's featured gear segment is brought to you by Ten Thousand. Ten Thousand is offering our listeners 15% of their purchase! Go to tenthousand.cc and enter code DANGERCLOSE15 to receive 15% off.
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Podcast: Stompcast (LS 49 · TOP 0.5% what is this?)
Episode: Pt 2: Billy Billingham, on being an SAS Soldier
Pub date: 2022-11-21
In part 2 of this stomp, Dr Alex asks Billy about life as a soldier in the armed forces and the challenges he faced when readjusting back to civilian life.
Billy shares with Dr Alex how his family had to deal with the uncertainty of his tours - recalling the time when a 10 day tour became a 9 month tour without any form of contact or communication, and why his daughter felt like she didn’t ‘know’ her dad until she read his books.
Billy also discusses the treatment of veterans of the British Armed Forces and how we can better support those who have served and their families.
You can follow Billy here and pick up a copy of ‘Survive to Fight’, his new fiction book that’s based on his life and career.
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The podcast and artwork embedded on this page are from Dr Alex George, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Saturday Live (LS 46 · TOP 1% what is this?)
Episode: Billy Billingham, Louise Minchin, Shaun Escoffery and George Asprey, Dr Alex George
Pub date: 2023-05-20
We’re all standing to attention, as the paragon of discipline, former SAS member Billy Billingham and chief instructor on Channel 4’s SAS: Who Dares Wins talks about how he's gone from a life of crime to towing the line. Louise Minchin is a broadcaster who spent twenty years on the BBC Breakfast sofa, but did you know she is passionate about endurance sports? Her latest book "Fearless" sees her taking on physical challenges with inspirational women.
And completing the Circle of Life are the leonine brothers and longest serving cast members of The Lion King, actors Shaun Escoffery and George Asprey.
Former A & E doctor, Love Island star and mental health campaigner Dr Alex George shares his Inheritance Tracks.
Presenters: Nikki Bedi and Huw Stephens
Producer: Ben Mitchell
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Podcast: Combat Story (LS 56 · TOP 0.5% what is this?)
Episode: UK SAS Sergeant Major | 7/7 SAS Ground CDR | Who Dares Wins TV | The Hard Way | Billy Billingham
Pub date: 2022-09-24
EXCLUSIVE NordVPN Deal ➼ https://nordvpn.com/combatstory Try it risk-free now with a 30-day money-back guarantee! I use NordVPN myself so you're in good company (and I know security). If you sign up, it will also help the show so thank you in advance!
Today we hear an incredible Combat Story from across the pond from a legendary UK SAS operator Billy Billingham who served over 30 years with deployments to Iraq, Afghanistan, South America, Africa, and many other places we can’t discuss.
Billy came from an extremely challenging childhood drinking and fighting on the streets of Birmingham to being appointed a Member of the British Empire by Her Majesty the Queen for his efforts on select hostage rescues and serving as the SAS Ground Commander on what is known as 7/7 or the deadly coordinated attacks in London on July 7th, 2005.
Billy went from being the absolute elite leader, working alongside our own Delta and DEVGRU operators for years in the post-9/11 world, to hanging up his uniform and finding a new path. He has know written an autobiography aptly titled ‘The Hard Way’ and two fiction novels based on his own exploits titled ‘Call to Kill’ and ‘Survive to Fight’ (think SAS meets 007).
He is also a lead on two incredibly successful programs in the UK and Australia titled “SAS: Who Dares Wins” where he and other special operators take civilians and celebrities through punishing special ops training; he shared that the show is coming to the US soon! He and his wife run a charity organization and he does public speaking engagements across the UK.
I hope you enjoy this incredibly humble story that takes us inside the “Interest Room” (which I learned is the term for the SAS Team Room) and the highs and lows of a life well-lived at the tip of the spear from our UK brothers and sisters as much as I did.
#military #veteran
Find Billy Online:
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- Instagram @billingham22b https://www.instagram.com/billingham22b/
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- Twitter @billingham229b https://twitter.com/billingham229b
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- Facebook https://www.facebook.com/MarkBillinghamSAS/
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- Deux Mains Academy @rebuildglobally https://www.instagram.com/rebuildglobally/
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- Billy’s Website https://www.markbillybillingham.com/about
Find Ryan Online:
-
- Ryan’s Linktree https://linktr.ee/combatstory
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- Merch https://www.bonfire.com/store/combatstory/
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- Instagram @combatstory https://www.instagram.com/combatstory
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- Facebook @combatstoryofficial https://fb.me/combatstoryofficial
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- Send us messages at https://m.me/combatstoryofficial
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- Learn more about Ryan www.combatstory.com/aboutus
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- Intro Song: Sport Rock from Audio Jungle
Show Notes
- 0:00 - Intro
- 0:41 - Guest Introduction (Billy Billingham)
- 2:36 - Interview begins
- 5:11 - Rough childhood in Peaky Blinders neighborhood in Birmingham, England
- 17:26 -Cadet School experience
- 22:12 - Overcoming getting burned by caustic soda at 15 yrs old and almost dying in a gang fight to still join the military
- 28:30 - Joining The Second Battalion, Parachute Regiment (2 PARA)
- 30:03 - Lessons from the PARAs and first jump in the jungle of Belize
- 42:48 - First time in a combat zone in Cypress
- 44:23 - Fighting terrorists in Ireland
- 48:15 - Why SAS route and the brutal training and selection process
- 57:50 - Path after SAS selection into Mountain Troupe
- 1:01:25 - Excitement of first op in first week as SAS
- 1:04:17 - Squad "Interest rooms"
- 1:05:45 - The squadron dynamic and respect for those who came before
- 1:09:21 - Why the B Squadron are characterized as the "rogues"
- 1:11:12 - Combat Story - A hostage rescue during a bloody time in Bosnia
- 1:19:36 - The sixth sense that comes with experience
- 1:2102 - Emotions after a mission and after getting out
- 1:24:25 - Where were you on 7/7 during London bombings?
- 1:27:56 - TV show "SAS: Who Dares Wins" and what is coming to the US
- 1:30:40 - "The Hard Way" autobiography and "Call to Kill" novel
- 1:35:46 - What did you carry into combat?
- 1:39:40 - Would you do it again?
- 1:42:55 - Listener comments and shout outs
This video covers the following subjects:
-
SAS Sergeant Major
-
7/7 SAS Ground CDR
-
SAS Ground Commander
-
UK SAS operator Billy Billingham
Billy is a certified SF and Counter Terrorist Sniper Instructor, Advanced Evasive Driving Instructor, Tracking/Jungle Warfare/Navigation Instructor, Demolition/Sabotage Instructor, Ski Mountaineering/Rock Climbing/Abseiling/Ice climbing Instructor, Combat Survival/RTI Instructor, Counter Terrorist Instructor (all options) and has worked as a Patrol Medic/Trauma Life Support agent for 5 hospital attachments.
If you would like to learn more about SAS Sergeant Major, Billy Billingham I suggest you look into our various other video clips: https://www.youtube.com/channel/UCCyApoJr-mNmdMNwdk22xEQ
_________________________
Have I responded to all of your questions about SAS Sergeant Major, Billy Billingham?
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Podcast: Stompcast (LS 49 · TOP 0.5% what is this?)
Episode: Pt 1: Billy Billingham in Malvern
Pub date: 2022-11-21
SAS: Who Dares Wins team member and Sunday Times best-selling author, Mark ‘Billy’ Billingham, is putting Dr Alex through his paces on a stomp through the hills of Malvern this week.
Billy had an 18 year career in the military and is a highly decorated SAS soldier. He’s completed missions in both the UK and some of the most hostile countries in the world, which has earned him an MBE and the Queens commendation for Bravery.
Billy shares with Dr Alex how he coped with fear while on tour; what it’s really like to serve and survive in environments like the jungle; how a soldier's sixth sense becomes finetuned while in survival mode and why you should never take the easy route….
You can follow Billy here and pick up a copy of ‘Survive to Fight’, his new novel that’s based on his life and career.
Hosted on Acast. See acast.com/privacy for more information.
The podcast and artwork embedded on this page are from Dr Alex George, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Origin Story (LS 51 · TOP 0.5% what is this?)
Episode: Centrism: Stuck in the middle with you
Pub date: 2022-06-06
Centrism has become an all-purpose term of abuse but what does it actually mean? And what does Centrism want? Dorian and Ian journey to the centre of the middle, dropping in on Tony Benn, William Rees-Mogg, the crises of the 70s, Trotsky, fascism, communism, Clinton, Blair, and the guillotine.…
Help Ian and Dorian move NOT LEFT, NOT RIGHT, BUT FORWARD by supporting their Origin Story research on Patreon: www.Patreon.com/originstorypod
––––––––
Centrism: A Reading List
From Ian:
The Oxford History of the French Revolution by William Doyle. The single best all-in-one history of the French revolution. And one of my favourite history books of all time – a rare instance in which the author combines pace, thoroughness and impeccable research.
John Stuart Mill, Victorian Firebrand by Richard Reeves. Decent, if slightly pedestrian biography of the great liberal philosopher.
John Maynard Keynes trilogy by Robert Skidelsky. The best work on Keynes.
The Third Way by Anthony Giddens. Nowhere near as good as it should be, nor as I expected it to be. Surprisingly vacuous.
From Dorian:
The Vital Centre by Arthur M. Schlesinger Jr. Fascinating post-war argument for the importance of the radical centre
Trotsky on centrism
Independent Nation: How Centrists Can Change American Politics by John Avlon. Solid history of those who sought to occupy the centre of American politics.
Toward a Radical Middle by Renata Adler. New Yorker writer’s 1969 manifesto for radical centrism in a fractious time.
Life in the Centre by Roy Jenkins. The arch-centrist’s juicy memoir.
Safety First: The Making of New Labour by Paul Anderson and Nyta Mann. A first-draft history of New Labour from 1997.
Blair and Brown: The New Labour Revolution. Satisfying BBC documentary series on iPlayer, with contributions from all the key players.
––––––––
- “When centrism is so hard to define, like nailing jelly to the wall, you have to ask does it even deserve to be called an ism at all?” – Ian
- “Trotsky says Centrism is parasitic, opportunistic, vain, uninterested in theory, and harder on the left than the right… and those criticisms are still levelled at centrists today.” – Dorian
- “The thing is, Centrism is often popular with voters but unpopular with people who are very interested in politics. Because it’s not passionate.” – Ian
- “I myself am an ideologue, an ideologue for liberalism, so it’s possible I feel threatened by something which essentially isn’t ideological.” – Ian
––––––––
Written and presented by Dorian Lynskey and Ian Dunt. Audio production by Alex Rees. Music by Jade Bailey. Logo art by Mischa Welsh. Group Editor: Andrew Harrison. Origin Story is a Podmasters production
Learn more about your ad choices. Visit podcastchoices.com/adchoices
The podcast and artwork embedded on this page are from Podmasters, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Transfert (LS 54 · TOP 0.5% what is this?)
Episode: Un étranger qui ressemble beaucoup à mon mari
Pub date: 2023-11-02
Qu'est-ce que le grand amour? Est-ce celui qui est évident, fulgurant, éternel? Celui où l'on promet d'être ensemble pour le meilleur et pour le pire, jusqu'à ce que la mort nous sépare? A-t-on vraiment conscience de la portée de cette promesse?
Le temps passe, la vie s'écoule plus ou moins tranquillement. On traverse des épreuves. On traverse des joies. Le meilleur et le pire, à 20 ans, 30 ans, 50 ans, 60 ans. Chaque jour qui passe, chaque décennie nous rappelle combien cette promesse est réelle.
Brigitte et Marc, Marc et Brigitte, ç'a toujours été une seule et même entité. L'un ne va pas sans l'autre. Et pourtant...
L'histoire de Brigitte a été recueillie par Nina Pareja.
Transfert est produit et réalisé par Slate.fr.
Direction éditoriale: Christophe Carron
Direction de la production: Sarah Koskievic
Direction artistique et habillage musical: Benjamin Saeptem Hours
Production éditoriale: Sarah Koskievic et Benjamin Saeptem Hours
Prise de son: Nina Pareja
Montage: Victor Benhamou
Musique: Sable Blanc
L'introduction a été écrite à quatre mains par Sarah Koskievic et Benjamin Saeptem Hours. Elle est lue par Aurélie Rodrigues.
Retrouvez Transfert tous les jeudis sur Slate.fr et sur votre application d'écoute. Découvrez aussi Transfert Club, l'offre premium de Transfert. Deux fois par mois, Transfert Club donne accès à du contenu exclusif, des histoires inédites et les coulisses de vos épisodes préférés. Pour vous abonner, rendez-vous sur slate.fr/transfertclub.
Pour proposer une histoire, vous pouvez nous envoyer un mail à l'adresse transfert@slate.fr
The podcast and artwork embedded on this page are from Slate.fr Podcasts, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Transfert (LS 54 · TOP 0.5% what is this?)
Episode: Une success-story ordinaire
Pub date: 2023-12-07
Bo Burnham en a fait une chanson, «White woman's Instagram»: Instagram comme reflet d'une vie parfaite que l'on peut étaler sur les réseaux sociaux pour montrer ses succès et ses réussites dans la vie comme au travail. Exactement comme la société nous le demande. Mais si, après tout, le succès, ce n'était pas ça? Si la vraie réussite n'était qu'une succession de petits bonheurs ordinaires? Si, comme dans la chanson, tout n'était pas aussi joli qu'on veut bien le montrer?
La vie d'Edith n'est «pas trop instagrammable, mais un peu quand même». Elle s'amuse, papillonne, elle a des amis et un bon job, mais elle avoue s'ennuyer un peu. Pas trop, mais un peu. Et puis, le confinement arrive et tout est chamboulé.
L'histoire d'Edith a été recueillie par Miren Garaicoechea.
Transfert est produit et réalisé par Slate Podcasts.
Direction éditoriale: Christophe Carron
Direction de la production: Sarah Koskievic
Direction artistique: Benjamin Saeptem Hours
Production éditoriale: Sarah Koskievic et Benjamin Saeptem Hours
Chargée de pré-production: Astrid Verdun
Prise de son et montage et habillage musical: Victor Benhamou
Musique: «A nice Getaway», de Thomas Gallicani
L'introduction a été écrite par Sarah Koskievic et Benjamin Saeptem Hours. Elle est lue par Aurélie Rodrigues.
Retrouvez Transfert tous les jeudis sur Slate.fr et sur votre application d'écoute. Découvrez aussi Transfert Club, l'offre premium de Transfert. Deux fois par mois, Transfert Club donne accès à du contenu exclusif, des histoires inédites et les coulisses de vos épisodes préférés. Pour vous abonner, rendez-vous sur slate.fr/transfertclub.
Pour proposer une histoire, vous pouvez nous envoyer un mail à l'adresse transfert@slate.fr
The podcast and artwork embedded on this page are from Slate.fr Podcasts, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Lex Fridman Podcast (LS 76 · TOP 0.01% what is this?)
Episode: #397 – Greg Lukianoff: Cancel Culture, Deplatforming, Censorship & Free Speech
Pub date: 2023-09-25
Greg Lukianoff is a free speech advocate, first-amendment attorney, president of FIRE – Foundation for Individual Rights and Expression, and co-author of The Coddling of the American Mind and a new book The Canceling of the American Mind. Please support this podcast by checking out our sponsors:
– Policygenius: https://www.policygenius.com/
– Babbel: https://babbel.com/lexpod and use code Lexpod to get 55% off
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Transcript: https://lexfridman.com/greg-lukianoff-transcript
EPISODE LINKS:
Greg’s Twitter: https://twitter.com/glukianoff
Greg’s Instagram: https://instagram.com/glukianoff
FIRE: https://thefire.org/
FIRE on Twitter: https://twitter.com/TheFIREorg
Greg’s Books
The Canceling of the American Mind: https://amzn.to/464yasg
The Coddling of the American Mind: https://amzn.to/3EL48hj
Freedom from Speech: https://amzn.to/3rhrdVN
Unlearning Liberty: https://amzn.to/3rlFnoN
Books Mentioned
The Closing of the American Mind: https://amzn.to/4638KuX
The Origins of Political Order: https://amzn.to/464zkE8
So You’ve Been Publicly Shamed: https://amzn.to/48nm1Af
Racial Paranoia: https://amzn.to/3RzyY3U
Why Buddhism Is True: https://amzn.to/3t4R5Vk
Speaking Freely: https://amzn.to/3Zr64oG
PODCAST INFO:
Podcast website: https://lexfridman.com/podcast
Apple Podcasts: https://apple.co/2lwqZIr
Spotify: https://spoti.fi/2nEwCF8
RSS: https://lexfridman.com/feed/podcast/
YouTube Full Episodes: https://youtube.com/lexfridman
YouTube Clips: https://youtube.com/lexclips
SUPPORT & CONNECT:
– Check out the sponsors above, it’s the best way to support this podcast
– Support on Patreon: https://www.patreon.com/lexfridman
– Twitter: https://twitter.com/lexfridman
– Instagram: https://www.instagram.com/lexfridman
– LinkedIn: https://www.linkedin.com/in/lexfridman
– Facebook: https://www.facebook.com/lexfridman
– Medium: https://medium.com/@lexfridman
OUTLINE:
Here’s the timestamps for the episode. On some podcast players you should be able to click the timestamp to jump to that time.
(00:00) – Introduction
(10:49) – Cancel culture & freedom of speech
(25:21) – Left-wing vs right-wing cancel culture
(34:06) – Religion
(36:46) – College rankings by freedom of speech
(42:54) – Deplatforming
(57:29) – Whataboutism
(1:02:32) – Steelmanning
(1:10:08) – How the left argues
(1:20:48) – Diversity, equity, and inclusion
(1:32:39) – Why colleges lean left
(1:40:17) – How the right argues
(1:44:52) – Hate speech
(1:53:39) – Platforming
(2:03:10) – Social media
(2:24:17) – Depression
(2:35:48) – Hope
The podcast and artwork embedded on this page are from Lex Fridman, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Lex Fridman Podcast (LS 76 · TOP 0.01% what is this?)
Episode: #400 – Elon Musk: War, AI, Aliens, Politics, Physics, Video Games, and Humanity
Pub date: 2023-11-09
Elon Musk is CEO of X, xAI, SpaceX, Tesla, Neuralink, and The Boring Company.
Thank you for listening ❤ Please support this podcast by checking out our sponsors:
– LMNT: https://drinkLMNT.com/lex to get free sample pack
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Transcript: https://lexfridman.com/elon-musk-4-transcript
EPISODE LINKS:
Elon’s X: https://x.com/elonmusk
xAI: https://x.com/xai
Tesla: https://x.com/tesla
Tesla Optimus: https://x.com/tesla_optimus
Tesla AI: https://x.com/Tesla_AI
SpaceX: https://x.com/spacex
Neuralink: https://x.com/neuralink
The Boring Company: https://x.com/boringcompany
PODCAST INFO:
Podcast website: https://lexfridman.com/podcast
Apple Podcasts: https://apple.co/2lwqZIr
Spotify: https://spoti.fi/2nEwCF8
RSS: https://lexfridman.com/feed/podcast/
YouTube Full Episodes: https://youtube.com/lexfridman
YouTube Clips: https://youtube.com/lexclips
SUPPORT & CONNECT:
– Check out the sponsors above, it’s the best way to support this podcast
– Support on Patreon: https://www.patreon.com/lexfridman
– Twitter: https://twitter.com/lexfridman
– Instagram: https://www.instagram.com/lexfridman
– LinkedIn: https://www.linkedin.com/in/lexfridman
– Facebook: https://www.facebook.com/lexfridman
– Medium: https://medium.com/@lexfridman
OUTLINE:
Here’s the timestamps for the episode. On some podcast players you should be able to click the timestamp to jump to that time.
(00:00) – Introduction
(10:25) – War and human nature
(14:51) – Israel-Hamas war
(20:59) – Military-Industrial Complex
(25:16) – War in Ukraine
(29:59) – China
(44:15) – xAI Grok
(55:13) – Aliens
(1:03:13) – God
(1:05:41) – Diablo 4 and video games
(1:14:48) – Dystopian worlds: 1984 and Brave New World
(1:20:59) – AI and useful compute per watt
(1:26:40) – AI regulation
(1:33:32) – Should AI be open-sourced?
(1:40:54) – X algorithm
(1:52:15) – 2024 presidential elections
(2:05:14) – Politics
(2:08:16) – Trust
(2:13:47) – Tesla’s Autopilot and Optimus robot
(2:22:46) – Hardships
The podcast and artwork embedded on this page are from Lex Fridman, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Poetry Magazine Podcast (LS 45 · TOP 1% what is this?)
Episode: Lena Khalaf Tuffaha and Cindy Juyoung Ok on the Renowned and Rebellious Palestinian Poet Zakaria Mohammed
Pub date: 2023-09-12
On this week’s episode, Cindy Juyoung Ok speaks with poet, essayist, and translator Lena Khalaf Tuffaha about the life and work of the renowned Palestinian poet and writer Zakaria Mohammed. Born in Nablus, Palestine, Mohammed was a freelance journalist, editor, and poet who authored nine volumes of poetry. In 1994, after twenty-five years in exile, he returned to his homeland to live in Ramallah where he recently died at the age of seventy-three. Ok and Khalaf Tuffaha discuss Mohammed’s rebelliousness, his democratizing practice of posting early drafts of his poems to Facebook, and how he approached writing in the shadow of Mahmoud Darwish. They also talk about grief, the politics of translation, and the always tricky task of composing an email. Finally, Khalaf Tuffaha treats us to some of Mohammed’s poems in Arabic and English translation that appear in the September 2023 issue of Poetry.
The podcast and artwork embedded on this page are from Poetry Foundation, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: AI Lawyer Talking Tech
Episode: How AI Passes the Bar: GPT-4's Human-Level Performance
Pub date: 2023-03-15
GPT Takes the Bar Exam Again; This Time It Scores Among Top 10% of Test TakersDate: 14 Mar 2023
Source: LawSites
Illinois Environmental Protection Agency Seeks Student Worker and Technical Advisor IIDate: 14 Mar 2023
Source: Illinois State Bar Association
New GPT-4 Passes All Sections Of The Uniform Bar Exam. Maybe This Will Finally Kill The Bar Exam.Date: 14 Mar 2023
Source: Above The Law
Casetexts CoCounsel, the First AI Legal Assistant, Is Powered by OpenAIs GPT-4, the First Large Language Model to Pass Bar ExamDate: 14 Mar 2023
Source: Wavy
LiquidText Hires Three Tenured Legal Technology Experts and Announces Participation in Legalweek NY to Support Growth in Legal SectorDate: 14 Mar 2023
Source: ABC 4
Next-Gen Bar Exam Must Tackle Google Schools and the Digital Native Myth by Testing Basic Tech Skills for PracticeDate: 14 Mar 2023
Source: 3 Geeks and a Law Blog
Top 5 General Counsels to Follow in 2023Date: 14 Mar 2023
Source: ExecutiveBiz
Best Law Firms to Work For: White & Case LLPDate: 14 Mar 2023
Source: JDJournal
AI lawyers? Not likely, experts say. But dont discount AI in the legal field yetDate: 14 Mar 2023
Source: KSL-TV
Guest post: ChatGPT – What are the risks to law firms?Date: 14 Mar 2023
Source: Legal IT Insider
Harvey AI: What We Know So FarDate: 13 Mar 2023
Source: LexBlog
GPT Takes the Bar Exam Again; This Time It Scores Among Top 10% of Test TakersDate: 14 Mar 2023
Source: LawSites
ChatGPT: Navigating the Legal Loopholes of AIDate: 14 Mar 2023
Source: Law Technology Today
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Podcast: AI Lawyer Talking Tech
Episode: Legal Research Technology - Harvey AI - Change Management in Legal
Pub date: 2023-02-24
Legal research technologyDate: 24 Feb 2023Source: Legal.ThomsonReuters.comHow to Create a Law Firm WebsiteDate: 23 Feb 2023Source: RankingsStenograph® Announces Launch of CaseTestify®Date: 23 Feb 2023Source: 680 The FanMeet the Black Women Leading Illinois Law SchoolsDate: 23 Feb 2023Source: 2CivilityHarvey Not-So Marvy, Booze Lose, Fox ShoxDate: 23 Feb 2023Source: Above The LawHow can ARPA funds help your government legal department?Date: 23 Feb 2023Source: Legal.ThomsonReuters.comChange Management In Legal – Some Practical Tips For User-Facing Interactions/Technology Management Best PracticesDate: 23 Feb 2023Source: International Legal Technology Association RSS Feed2023 Research: High Growth Law Firms Grow 4.5X More Than Average Growth FirmsDate: 23 Feb 2023Source: Professional Services Marketing TodayChat GPT Risks and the Need for Corporate PoliciesDate: 23 Feb 2023Source: New Media and Technology Law BlogBurges Salmon goes live with iManage Knowledge UnlockedDate: 23 Feb 2023Source: Legal Technology News - Legal IT Professionals | Everything legal technologyRobot Lawyers: Sooner Than You ThinkDate: 23 Feb 2023Source: Richmond Journal of Law and TechnologyHow to handle security incidents/data breaches under the LGPDDate: 23 Feb 2023Source: Legal IT group
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Podcast: Education Research Reading Room (LS 47 · TOP 1% what is this?)
Episode: ERRR #083a. Sam Gibbs on Concept-led Curriculum
Pub date: 2023-10-01
Ollie Lovell · ERRR083a. Sam Gibbs on Concept-led Curriculum Sam Gibbs is Trust Lead for Curriculum and Development at The…
The podcast and artwork embedded on this page are from Ollie Lovell: Teacher, author, podcaster, blogger, PhD candidate. @ollie_lovell, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Education Research Reading Room (LS 47 · TOP 1% what is this?)
Episode: ERRR #083b. Sam Gibbs on Concept-led Curriculum
Pub date: 2023-10-26
Ollie Lovell · ERRR083b Sam Gibbs on Concept-led Curriculum (Part 2) Sam Gibbs continues to speak with Ollie about the…
The podcast and artwork embedded on this page are from Ollie Lovell: Teacher, author, podcaster, blogger, PhD candidate. @ollie_lovell, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Lenny's Podcast: Product | Growth | Career (LS 58 · TOP 0.5% what is this?)
Episode: Unpacking Amazon’s unique ways of working | Bill Carr (author of Working Backwards)
Pub date: 2023-11-02
Bill Carr is the co-author of Working Backwards: Insights, Stories, and Secrets from Inside Amazon. With a background at Amazon of over 15 years, Bill played a pivotal role in shaping the company’s global digital music and video ventures, including Amazon Music, Prime Video, and Amazon Studios. After Amazon, Bill was an Executive in Residence with Maveron, an early-stage, consumer-only venture capital firm. He later served as the chief operating officer of OfferUp, the largest mobile marketplace for local buyers and sellers in the U.S. Today he’s the co-founder of Working Backwards LLC, where he helps companies implement Amazon’s time-tested management strategies. In this episode, we discuss:
• What exactly “working backwards” is, and how you do it
• Why having “single-threaded leaders” is so effective
• Inside Amazon’s intense product review process
• How to actually follow the “disagree and commit” principle
• The thinking behind the principle “Leaders are right, a lot”
• Input vs. output metrics
• Fostering a culture of risk-taking and innovation
• The role and responsibilities of a “bar raiser” in your hiring, and how it significantly improves the success rate of new hires
—
Brought to you by AssemblyAI—Production-ready AI models to transcribe and understand speech | Coda—Meet the evolution of docs | Wix Studio—The web creation platform built for agencies
—
Find the full transcript at: https://www.lennyspodcast.com/unpacking-amazons-unique-ways-of-working-bill-carr-author-of-working-backwards/
—
Where to find Bill Carr:
• X: https://twitter.com/BillCarr89
• LinkedIn: https://www.linkedin.com/in/bill-carr/
• Website: https://www.workingbackwards.com/
—
Where to find Lenny:
• Newsletter: https://www.lennysnewsletter.com
• X: https://twitter.com/lennysan
• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/
—
In this episode, we cover:
(00:00) Bill’s background
(04:26) Amazon’s workplace evolution
(09:54) Amazon’s “fitness function”
(11:44) Single-threaded leadership
(18:07) Implementing a program orientation with single-threaded leadership
(20:16) The GM model vs. single-threaded leadership
(21:31) Functional countermeasures needed for single-threaded leadership
(25:22) Embracing the “disagree and commit” principle
(30:22) Understanding disagreements
(32:41) Deciphering Amazon’s “Leaders are right, a lot” principle
(35:25) An explanation of the working backwards framework
(41:16) PR FAQ process: Amazon’s innovation engine
(44:47) Deconstructing the PR FAQ structure
(43:49) The concentric circle model for sharing PR FAQs
(44:55) The customer problem-solution statement
(47:52) Create a product funnel, not a product tunnel
(51:19) How Amazon promotes action vs. talk
(54:35) Amazon’s flywheel and input metrics
(1:00:51) Signs you’ve got a good input metric
(1:04:23) How mistakes can still be made with working backwards
(1:06:54) Why disagreements aren’t necessarily signs products will fail
(1:08:02) Examples of failed Amazon projects
(1:09:55) Cultivating risk-taking and accepting failure
(1:13:57) Amazon’s “bar-raiser” practice for hiring
(1:18:21) Selecting Amazon’s bar raisers
(1:20:41) Advice on implementing practices from Working Backwards
(1:23:10) Bill’s work as an advisor
(1:26:05) Lightning round
—
Referenced:
• Working Backwards: Insights, Stories, and Secrets from Inside Amazon: https://www.amazon.com/Working-Backwards-Insights-Stories-Secrets/dp/1250267595
• Jeff Bezos on X: https://twitter.com/jeffbezos
• D.E. Shaw: https://www.deshaw.com/
• Eric Ries’s website: https://theleanstartup.com/
• GM business model: https://fourweekmba.com/general-motors-business-model/
• Rick Dalzell on LinkedIn: https://www.linkedin.com/in/richarddalzell/
• The Effective Decision by Peter F. Drucker: https://hbr.org/1967/01/the-effective-decision
• Template: Working Backwards PR FAQ: https://www.workingbackwards.com/resources/working-backwards-pr-faq
• Good to Great: Why Some Companies Make the Leap and Others Don’t: https://www.amazon.com/Good-Great-Some-Companies-Others/dp/0066620996
• The Amazon flywheel: https://feedvisor.com/resources/amazon-trends/amazon-flywheel-explained/
• Sixsigma: https://www.6sigma.us/
• Loonshots: How to Nurture the Crazy Ideas That Win Wars, Cure Diseases, and Transform Industries: https://www.amazon.com/Loonshots-Nurture-Diseases-Transform-Industries/dp/1250185963
• Andy Jassy on LinkedIn: https://www.linkedin.com/in/andy-jassy-8b1615/
• Implementing Amazon’s Bar Raiser Process in Hiring: A Quick Guide: https://www.barraiser.com/blogs/implementing-amazons-bar-raiser-process-in-hiring
• Microspeak: The As-Appropriate (AA) interviewer: https://devblogs.microsoft.com/oldnewthing/20231017-00/?p=108897
• The Practice of Management: https://www.amazon.com/Practice-Management-Peter-F-Drucker/dp/0060878975
• The Effective Executive: The Definitive Guide to Getting the Right Things Done: https://www.amazon.com/Effective-Executive-Definitive-Harperbusiness-Essentials/dp/0060833459
• Steve Jobs: https://www.amazon.com/Steve-Jobs-Walter-Isaacson/dp/1451648537
• Seveneves: https://www.amazon.com/Seveneves-Neal-Stephenson/dp/0062334514
• A Gentleman in Moscow: https://www.amazon.com/A-Gentleman-in-Moscow/dp/0143110438
• Dune on Prime Video: https://www.amazon.com/Dune-Timoth%C3%A9e-Chalamet/dp/B09LJXY4PH
• A Spy Among Friends: https://www.imdb.com/title/tt15565872/
• Zipp 303 Firecrest tubeless disc brake: https://www.sram.com/en/zipp/models/wh-303-ftld-a1
• The Fifth Discipline: The Art & Practice of the Learning Organization: https://www.amazon.com/Fifth-Discipline-Practice-Learning-Organization/dp/0385517254
—
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Podcast: Lenny's Podcast: Product | Growth | Career (LS 58 · TOP 0.5% what is this?)
Episode: Brian Chesky’s new playbook
Pub date: 2023-11-12
Brian Chesky is the co-founder and CEO of Airbnb. Under Brian’s leadership, Airbnb has grown into a community of over 4 million hosts who have welcomed more than 1.5 billion guests across over 220 countries and regions. I had the privilege of working under his leadership, so it is a great honor to have him on the show. We discuss:
• How Airbnb has shifted their thinking on product management
• Why bureaucracy happens in companies, and how to avoid it
• The importance of founders diving into the details
• Why Airbnb moved away from traditional growth channels and what they are doing instead
• Airbnb’s newly released features
• How and why Brian encourages his team to set ambitious goals
• Why he says he still has a lot to prove
—
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Find the full transcript at: https://www.lennyspodcast.com/brian-cheskys-new-playbook/#transcript
—
Where to find Brian Chesky:
• X: https://twitter.com/bchesky
• LinkedIn: https://www.linkedin.com/in/brianchesky/
—
Where to find Lenny:
• Newsletter: https://www.lennysnewsletter.com
• X: https://twitter.com/lennysan
• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/
—
In this episode, we cover:
(00:00) Brian’s background
(05:18) The current structure of product management at Airbnb
(09:21) How fast-moving companies become slow-moving bureaucracies
(12:20) Brian’s thoughts on performance marketing
(13:50) Airbnb’s rolling two-year roadmap
(15:30) Brian’s journey as CEO in a growing company
(18:34) Best practices for A/B testing
(20:30) Who inspired Airbnb’s new direction
(23:18) The first changes Brian implemented at the onset of the pandemic
(24:51) Why founders should be “in the details”
(30:15) Airbnb’s marketing, communication, and creative functions
(31:38) Advice for founders on how to lead
(34:15) Tips for implementing Airbnb’s business methodology
(38:48) Airbnb’s winter release
(41:47) Why Airbnb no longer has separate guest and host teams
(42:38) Brian’s thoughts on design trends
(45:36) The importance of empowering hosts with great tools
(45:57) How setting ambitious goals improves team performance
(50:05) Tips for preventing burnout
(56:02) Tips for personal and professional growth
(58:19) Why Brian says he still has a lot to prove
(1:02:58) Paying it forward
(1:05:03) A fun fact about Brian
(1:09:26) Airbnb’s origin story
—
Referenced:
• Localmind: https://www.crunchbase.com/organization/localmind
• Config 2023 in review: https://www.figma.com/blog/config-2023-recap/
• Why Founders Fail: The Product CEO Paradox: https://techcrunch.com/2013/08/10/why-founders-fail-the-product-ceo-paradox/
• Hiroki Asai on LinkedIn: https://www.linkedin.com/in/hiroki-asai-a44137110/
• Jony Ive on Crunchbase: https://www.crunchbase.com/person/jonathan-ive
• Charles Eames: https://en.wikipedia.org/wiki/Charles_Eames
• Airbnb 2023 Winter Release: https://news.airbnb.com/en-in/airbnb-2023-winter-release-introducing-guest-favorites-a-collection-of-the-2-million-most-loved-homes-on-airbnb/
• Airbnb 2023 winter release reel: https://x.com/bchesky/status/1722243847751970861?s=20
• John Wooden’s website: https://coachwooden.com/
• An 85-year Harvard study found the No. 1 thing that makes us happy in life: It helps us ‘live longer’: https://www.cnbc.com/2023/02/10/85-year-harvard-study-found-the-secret-to-a-long-happy-and-successful-life.html
• Sam Altman on X: https://twitter.com/sama
• Alfred P. Sloan: https://en.wikipedia.org/wiki/Alfred_P._Sloan
• Bob Dylan quote: https://quotefancy.com/quote/950807/Bob-Dylan-An-artist-has-got-to-be-careful-never-really-to-arrive-at-a-place-where-he
• OpenAI: https://openai.com/
• Michael Seibel’s website: https://www.michaelseibel.com/
• Y Combinator: https://www.ycombinator.com/
• The Norman Rockwell Museum: https://www.nrm.org/
• Rhode Island School of Design: https://www.risd.edu/
• Joe Gebbia on LinkedIn: https://www.linkedin.com/in/jgebbia/
• Nathan Blecharczyk on LinkedIn: https://www.linkedin.com/in/blecharczyk/
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.
—
Lenny may be an investor in the companies discussed.
Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe
The podcast and artwork embedded on this page are from Lenny Rachitsky, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Syntax - Tasty Web Development Treats (LS 61 · TOP 0.1% what is this?)
Episode: 676: Google IDX - VS Code in the Browser with David East
Pub date: 2023-10-06
In this episode of Syntax, Wes and Scott talk with David East about Google’s new cloud based full-stack, multiplatform app development workflow, Project IDX.
Show Notes * 00:22 Welcome * 01:31 Browser in the car * 02:16 Syntax Brought to you by Sentry * 02:24 Who is David East? * David East * David East * David East (@_davideast) / X * Learn from David East’s courses | Frontend Masters * Firebase | Google’s Mobile and Web App Development Platform * 04:32 What is IDX? * Project IDX * Flutter - Build apps for any screen * Welcome to nix.dev — nix.dev documentation * 13:15 What’s the experience of IDX? * Nx: Smart, Fast and Extensible Build System * 16:42 IDX isn’t just a toy - it’s a dev machine * 20:29 What’s the offline mode like? * 23:30 How are VS Code extensions handled? * 27:03 Is multiplayer or project sharing on the road map? * 28:45 How is latency taken care of? * 31:43 This could be faster than local dev environment * 36:18 Portability of your projects * 42:25 What do you think about iPad coding? * 44:28 Phone testing with IDX * Firebase Test Lab * 46:59 How is AI being integrated? * 50:23 Supper Club questions * Introducing Operator | Fonts by Hoefler&Co. * MD IO by Mass-Driver - Future Fonts * 55:25 SIIIIICK ××× PIIIICKS ×××
××× SIIIIICK ××× PIIIICKS ××× CSS
Shameless Plugs * The Bad At Css Podcast
Tweet us your tasty treats * Scott’s Instagram * LevelUpTutorials Instagram * Wes’ Instagram * Wes’ Twitter * Wes’ Facebook * Scott’s Twitter * Make sure to include @SyntaxFM in your tweets * Wes Bos on Bluesky * Scott on Bluesky * Syntax on Bluesky
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Podcast: Syntax - Tasty Web Development Treats (LS 61 · TOP 0.1% what is this?)
Episode: Design Systems with Brad Frost
Pub date: 2023-10-20
In this supper club episode of Syntax, Wes and Scott talk with Brad Frost about how to implement design systems in small and large scale projects, best practices around naming things, keeping everything in sync across different codebases, and how design systems help projects.
Show Notes * 00:32 Welcome * 01:02 Syntax Brought to you by Sentry * 01:34 Introducing Brad Frost * Brad Frost.com * Atomic Design by Brad Frost * Brad (@brad_frost) on Twitter * Brad on LinkedIn * Brad on Mastodon * Brad on YouTube * Brad on GitHub * Brad Frost on CodePen * Big Medium | Design for What’s Next * 06:43 What is a design system? * 12:12 How do you keep design and code in sync? * Material Design * Shopify Polaris * Carbon Design System * The Design System Ecosystem | Brad Frost * 16:13 How do you use Shopify, WordPress, React, etc. through a design system? * 19:41 How is CSS handled? * 25:40 What’s the benefit of going all in on web components? * 29:13 Do small startups need to worry about design systems? * 33:03 How do design tokens work? * 38:17 How do you deal with pushback on design systems? * 41:46 How do you go outside the guidelines? * 45:24 What system do you use for naming things? * 49:34 How do you best document your language choices? * 51:09 Supper Club questions * Thinking in Systems: International Bestseller: Donella H. Meadows, Diana Wright: 9781603580557: Amazon.com: Books * Miriam Eric Suzanne * Zeldman on Web and Interaction Design - Famous for stating the obvious. * 57:54 SIIIIICK ××× PIIIICKS ×××
××× SIIIIICK ××× PIIIICKS ××× * Rubblebucket
Shameless Plugs * Frostapalooza! | Brad Frost * FROSTAPALOOZA - A Concert Party Happening On August 17th 2024
Tweet us your tasty treats * Scott’s Instagram * LevelUpTutorials Instagram * Wes’ Instagram * Wes’ Twitter * Wes’ Facebook * Scott’s Twitter * Make sure to include @SyntaxFM in your tweets * Wes Bos on Bluesky * Scott on Bluesky * Syntax on Bluesky
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Podcast: Syntax - Tasty Web Development Treats (LS 61 · TOP 0.1% what is this?)
Episode: 691: Cloudflare Workers Are Next Level With Rita Kozlov And Brendan Irvine-Broque
Pub date: 2023-11-10
In this supper club episode of Syntax, Wes and Scott talk with Rita Kozlov And Brendan Irvine-Broque about Cloudflare Workers, Cloudflare AI, browser rendering API, Cloudflare’s D1 database, WinterCG, miniflare, and more!
Show Notes * 00:32 Welcome * 01:53 Syntax Brought to you by Sentry * 02:20 What are Cloudflare Workers? * Announcing WinterJS * Cloudflare Workers® * Puppeteer | Puppeteer * 06:23 How long did Workers take to ship? * 07:31 Can you run your entire business on Cloudflare Workers? * 10:52 Interesting use cases for Cloudflare Workers * 12:33 What makes the edge important? * 18:05 Managing GDPR compliance * 19:02 What are the tradeoffs of building with Cloudflare Workers? * Cloudflare Queues * 20:22 How does Workers pricing work? * 26:54 What are situations where you might need longer times? * 28:50 Browser rendering API * Browser Rendering docs * 29:43 What is Cloudflare D1 database product? * Cloudflare D1 * 31:05 Cloudflare Hyperdrive * Hyperdrive * “Serverless” Databases * 34:27 Cloudflare Workers don’t use a Node.js runtime * Introducing workerd: the Open Source Workers runtime * 37:13 What is WinterCG? * WinterCG * 45:09 Will we ever see a standard for server routing? * TCP sockets · Cloudflare Workers docs * 49:30 What is miniflare? * 🔥 Miniflare · Miniflare * 54:05 Can I run Python on Cloudflare? * 55:49 Cloudflare AI * Partnering with Hugging Face to make deploying AI easier * Cloudflare + AI * WebGPU API * Cache · Cloudflare Workers docs * 57:04 Supper Club questions * 59:38 Sick Picks
Sick Picks * Get a bench scrape
Shameless Plugs * Cloudflare Discord
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The podcast and artwork embedded on this page are from Wes Bos & Scott Tolinski - Full Stack JavaScript Web Developers, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Syntax - Tasty Web Development Treats (LS 61 · TOP 0.1% what is this?)
Episode: 693: Lessons Learned & Bugs Fixed from Launching Syntax.fm
Pub date: 2023-11-15
In this episode of Syntax, Wes and Scott talk about the lessons they learned while launching the new Syntax website including launching now, transcription bugs, error monitoring, black text on black backgrounds, and more.
Show Notes * 00:10 Welcome to Syntax * 01:41 Syntax Brought to you by Sentry * 02:43 Don’t wait. Launch! * 04:28 Transcript bug * Most Powerful Speech-to-Text API | Deepgram * 09:01 Error monitoring is a must * 12:36 Timestamp error * 16:20 Black text on black background might hide things * 17:33 WASM Vercel file system * 21:18 Things have gotten easier to launch * PlanetScale: The world’s most advanced database platform — PlanetScale * 23:36 Switching from OpenAI to Anthropic Claude and AI Responses aren’t always JSON * 25:34 Local dev is fast * Navigation API * 31:37 Mind your payloads * 32:41 GitHub Milestones * 33:57 Almost forgot the Robots.txt * 36:17 Chron job timeout * Inngest * 40:06 TypeScript errors don’t need to be zero to launch * 42:25 GitHub Actions pipeline bug * 43:23 Basic testing will do * Playwright * 44:56 Have a designer to work with * Airbase * 52:07 Sick Picks
Sick Picks * Scott: Dog Poop Bags With Dispenser * Wes: Resistance band
Shameless Plugs * Scott: Sentry * Wes: Wes Bos Courses
Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads
Wes: X Instagram Tiktok LinkedIn Threads
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The podcast and artwork embedded on this page are from Wes Bos & Scott Tolinski - Full Stack JavaScript Web Developers, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Syntax - Tasty Web Development Treats (LS 61 · TOP 0.1% what is this?)
Episode: 695: 5 New CSS Features You Should Know
Pub date: 2023-11-20
In this episode of Syntax, Wes and Scott talk about 5 new CSS features :nth-child(4 of .neat), CSS Motion Path, Scroll Snap, Scroll Driven Animations, and Margin Trim.
Show Notes * 00:25 Welcome * 01:10 :nth-child(4 of .neat) * selector list argument of :nth-child and :nth-last-child CSS pseudo-classes | Can I use * 06:43 CSS Motion Path * 10:38 Scroll Snap * Practical CSS Scroll Snapping * 14:36 Scroll Driven Animations * Scroll-driven Animations * Supper Club × Bramus Van Damme on CSS * 16:58 Margin Trim
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The podcast and artwork embedded on this page are from Wes Bos & Scott Tolinski - Full Stack JavaScript Web Developers, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Syntax - Tasty Web Development Treats (LS 61 · TOP 0.1% what is this?)
Episode: 696: How to Build a Website or App
Pub date: 2023-11-22
In this episode of Syntax, Wes and Scott answer a listener’s question about their process for building a website in 2023. Do you start with design? With code? How do you decide on which CMS or if you need a CMS? How do you choose a backend framework? And where do you host it?
Show Notes * 00:10 Welcome * 02:12 Syntax Brought to you by Sentry * 03:06 How do you build a website? * 04:57 Start with the design * Figma * Design Systems with Brad Frost * 11:12 Choose a Frontend / Components * Pug * EJS * React * Svelte * Remix * Storybook * 25:16 Real data or fake data? * Polypane * DrizzleORM * 29:34 Do you need a CMS or not? * Statamic * Syntax 254: Headless CMS Break Down & Roundup * WordPress.org * Astro * 35:16 Choosing a backend language or framework * 39:56 Testing * 44:50 Where do you host your website? * Vercel * Netlify Drop * Glitch * CodePen * 50:04 Sick Picks
Sick Picks * Scott: Chip clips * Wes: Soft close used toilet seat Amazon Warehouse Deals
Shameless Plugs * Scott: Sentry * Wes: Wes Bos Courses
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Podcast: Latent Space: The AI Engineer Podcast — Practitioners talking LLMs, CodeGen, Agents, Multimodality, AI UX, GPU Infra and all things Software 3.0 (LS 33 · TOP 5% what is this?)
Episode: Powering your Copilot for Data – with Artem Keydunov of Cube.dev
Pub date: 2023-10-26
The first workshops and talks from the AI Engineer Summit are now up! Join the >20k viewers on YouTube, find clips on Twitter (we’re also clipping @latentspacepod), and chat with us on Discord!
Text-to-SQL was one of the first applications of NLP. Thoughtspot offered “Ask your data questions” as their core differentiation compared to traditional dashboarding tools. In a way, they provide a much friendlier interface with your own structured (aka “tabular”, as in “SQL tables”) data, the same way that RLHF and Instruction Tuning helped turn the GPT-3 of 2020 into the ChatGPT of 2022.
Today, natural language queries on your databases are a commodity. There are 4 different ChatGPT plugins that offer this, as well as a bunch of startups like one of our previous guests, Seek.ai. Perplexity originally started with a similar product in 2022:
In March 2023 LangChain wrote a blog post on LLMs and SQL highlighting why they don’t consistently work:
-
“LLMs can write SQL, but they are often prone to making up tables, making up field”
-
“LLMs have some context window which limits the amount of text they can operate over”
-
“The SQL it writes may be incorrect for whatever reason, or it could be correct but just return an unexpected result.”
For example, if you ask a model to “return all active users in the last 7 days” it might hallucinate a is\_active column, join to an activity table that doesn’t exist, or potentially get the wrong date (especially in leap years!).
We previously talked to Shreya Rajpal at Guardrails AI, which also supports Text2SQL enforcement. Their approach was to run the actual SQL against your database and then use the error messages to improve the query:
Semantic Layers to the rescue
Cube is an open source semantic layer which recently integrated with LangChain to solve these issues in a different way. You can use YAML, Javascript, or Python to create definitions of different metrics, measures and dimensions for your data:
Creating these metrics and passing them in the model context limits the possibility for errors as the model just needs to query the active\_users view, and Cube will then expand that into the full SQL in a reliable way. The downside of this approach compared to the Guardrails one for example is that it requires more upfront work to define metrics, but on the other hand it leads to more reliable and predictable outputs.
The promise of adding a great semantic layer to your LLM app is irresistible - you greatly minimize hallucinations, make much more token efficient prompts, and your data stays up to date without any retraining or re-indexing. However, there are also difficulties with implementing semantic layers well, so we were glad to go deep on the topic with Artem as one of the leading players in this space!
Timestamps
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[00:00:00] Introductions
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[00:01:28] Statsbot and limitations of natural language processing in 2017
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[00:04:27] Building Cube as the infrastructure for Statsbot
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[00:08:01] Open sourcing Cube in 2019
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[00:09:09] Explaining the concept of a semantic layer/Cube
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[00:11:01] Using semantic layers to provide context for AI models working with tabular data
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[00:14:47] Workflow of generating queries from natural language via semantic layer
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[00:21:07] Using Cube to power customer-facing analytics and natural language interfaces
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[00:22:38] Building data-driven AI applications and agents
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[00:25:59] The future of the modern data stack
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[00:29:43] Example use cases of Slack bots powered by Cube
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[00:30:59] Using GPT models and limitations around math
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[00:32:44] Tips for building data-driven AI apps
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[00:35:20] Challenges around monetizing embedded analytics
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[00:36:27] Lightning Round
Transcript
Swyx: Hey everyone, welcome to the Latent Space podcast. This is Swyx, writer, editor of Latent Space and founder of Smol.ai and Alessio, partner and CTO in residence at Decibel Partners. [00:00:15]
Alessio: Hey everyone, and today we have Artem Keydunov on the podcast, co-founder of Cube. Hey Artem. [00:00:21]
Artem: Hey Alessio, hi Swyx. Good to be here today, thank you for inviting me. [00:00:25]
Alessio: Yeah, thanks for joining. For people that don't know, I've known Artem for a long time, ever since he started Cube. And Cube is actually a spin-out of his previous company, which is Statsbot. And this kind of feels like going both backward and forward in time. So the premise of Statsbot was having a Slack bot that you can ask, basically like text to SQL in Slack, and this was six, seven years ago, something like that. A lot ahead of its time, and you see startups trying to do that today. And then Cube came out of that as a part of the infrastructure that was powering Statsbot. And Cube then evolved from an embedded analytics product to the semantic layer and just an awesome open source evolution. I think you have over 16,000 stars on GitHub today, you have a very active open source community. But maybe for people at home, just give a quick like lay of the land of the original Statsbot product. You know, what got you interested in like text to SQL and what were some of the limitations that you saw then, the limitations that you're also seeing today in the new landscape? [00:01:28]
Artem: I started Statsbot in 2016. The original idea was to just make sort of a side project based off my initial project that I did at a company that I was working for back then. And I was working for a company that was building software for schools, and we were using Slack a lot. And Slack was growing really fast, a lot of people were talking about Slack, you know, like Slack apps, chatbots in general. So I think it was, you know, like another wave of, you know, bots and all that. We have one more wave right now, but it always comes in waves. So we were like living through one of those waves. And I wanted to build a bot that would give me information from different places where like a data lives to Slack. So it was like developer data, like New Relic, maybe some marketing data, Google Analytics, and then some just regular data, like a production database, so it sells for sometimes. And I wanted to bring it all into Slack, because we were always chatting, you know, like in Slack, and I wanted to see some stats in Slack. So that was the idea of Statsbot, right, like bring stats to Slack. I built that as a, you know, like a first sort of a side project, and I published it on Reddit. And people started to use it even before Slack came up with that Slack application directory. So it was a little, you know, like a hackish way to install it, but people are still installing it. So it was a lot of fun. And then Slack kind of came up with that application directory, and they reached out to me and they wanted to feature Statsbot, because it was one of the already being kind of widely used bots on Slack. So they featured me on this application directory front page, and I just got a lot of, you know, like new users signing up for that. It was a lot of fun, I think, you know, like, but it was sort of a big limitation in terms of how you can process natural language, because the original idea was to let people ask questions directly in Slack, right, hey, show me my, you know, like opportunities closed last week or something like that. My co founder, who kind of started helping me with this Slack application, him and I were trying to build a system to recognize that natural language. But it was, you know, we didn't have LLMs right back then and all of that technology. So it was really hard to build the system, especially the systems that can kind of, you know, like keep talking to you, like maintain some sort of a dialogue. It was a lot of like one off requests, and like, it was a lot of hit and miss, right? If you know how to construct a query in natural language, you will get a result back. But you know, like, it was not a system that was capable of, you know, like asking follow up questions to try to understand what you actually want. And then kind of finally, you know, like, bring this all context and go to generate a SQL query, get the result back and all of that. So that was a really missing part. And I think right now, that's, you know, like, what is the difference? So right now, I kind of bullish that if I would start Statsbot again, probably would have a much better shot at it. But back then, that was a big limitation. We kind of build a queue, right, as we were working on Statsbot, because we needed it. [00:04:27]
Alessio: What was the ML stack at the time? Were you building, trying to build your own natural language understanding models, like were there open source models that were good that you were trying to leverage? [00:04:38]
Artem: I think it was mostly combination of a bunch of things. And we tried a lot of different approaches. The first version, which I built, like was Regex. They were working well. [00:04:47]
Swyx: It's the same as I did, I did option pricing when I was in finance, and I had a natural language pricing tool thing. And it was Regex. It was just a lot of Regex. [00:04:59]
Artem: Yeah. [00:05:00]
Artem: And my co-founder, Pavel, he's much smarter than I am. He's like PhD in math, all of that. And he started to do some stuff. I was like, no, you just do that stuff. I don't know. I can do Regex. And he started to do some models and trying to either look at what we had on the market back then, or try to build a different sort of models. Again, we didn't have any foundation back in place, right? We wanted to try to use existing math, obviously, right? But it was not something that we can take the model and try and run it. I think in 2019, we started to see more of stuff, like ecosystem being built, and then it eventually kind of resulted in all this LLM, like what we have right now. But back then in 2016, it was not much available for just the people to build on top. It was some academic research, right, kind of been happening. But it was very, very early for something to actually be able to use. [00:05:58]
Alessio: And then that became Cube, which started just as an open source project. And I think I remember going on a walk with you in San Mateo in 2020, something like that. And you had people reaching out to you who were like, hey, we use Cube in production. I just need to give you some money, even though you guys are not a company. What's the story of Cube then from Statsbot to where you are today? [00:06:21]
Artem: We built a Cube at Statsbot because we needed it. It was like, the whole Statsbot stack was that we first tried to translate the initial sort of language query into some sort of multidimensional query. It's like we were trying to understand, okay, people wanted to get active opportunities, right? What does it mean? Is it a metric? Is it what a dimension here? Because usually in analytics, you always, you know, like, try to reduce everything down to the sort of, you know, like a multidimensional framework. So that was the first step. And that's where, you know, like it didn't really work well because all this limitation of us not having foundational technologies. But then from the multidimensional query, we wanted to go to SQL. And that's what was SemanticLayer and what was Cube essentially. So we built a framework where you would be able to map your data into this concept, into this metrics. Because when people were coming to Statsbot, they were bringing their own datasets, right? And the big question was, how do we tell the system what is active opportunities for that specific users? How we kind of, you know, like provide that context, how we do the training. So that's why we came up with the idea of building the SemanticLayer so people can actually define their metrics and then kind of use them as a Statsbot. So that's how we built a Cube. At some point, we saw people started to see more value in the Cube itself, you know, like kind of building the SemanticLayer and then using it to power different types of the application. So in 2019, we decided, okay, it feels like it might be a standalone product and a lot of people want to use it. Let's just try to open source it. So we took it out of Statsbot and open-sourced. [00:08:01]
Swyx: Can I make sure that everyone has the same foundational knowledge? The concept of a cube is not something that you invented. I think, you know, not everyone has the same background in analytics and data that all three of us do. Maybe you want to explain like OLAP Cube, HyperCube, the brief history of cubes. Right. [00:08:17]
Artem: I'll try, you know, like a lot of like Wikipedia pages and like a lot of like a blog post trying to go into academics of it. So I'm trying to like... [00:08:25]
Swyx: Cube's according to you. Yeah. [00:08:27]
Artem: So when we think about just a table in a database, the problem with the table, it's not a multidimensional, meaning that in many cases, if we want to slice the data, we kind of need to result with a different table, right? Like think about when you're writing a SQL query to answer one question, SQL query always ends up with a table, right? So you write one SQL, you got one. And then you write to answer a different question, you write a second query. So you're kind of getting a bunch of tables. So now let's imagine that we can kind of bring all these tables together into multidimensional table. And that's essentially Cube. So it's just like the way that we can have measures and dimension that can potentially be used at the same time from a different angles. [00:09:09]
Alessio: So initially, a lot of your use cases were more BI related, but you recently released a LangChain integration. There's obviously more and more interest in, again, using these models to answer data questions. So you've seen the chat GPT code interpreter, which is renamed as like advanced data analysis. What's kind of like the future of like the semantic layer in AI? You know, what are like some of the use cases that you're seeing and why do you think it's a good strategy to make it easier to do now the text to SQL you wanted to do seven years ago? [00:09:39]
Artem: Yeah. So, I mean, you know, when it started to happen, I was just like, oh my God, people are now building Statsbot with Cube. They just have a better technology for, you know, like natural language. So it kind of, it made sense to me, you know, like from the first moment I saw it. So I think it's something that, you know, like happening right now and chat bot is one of the use cases. I think, you know, like if you try to generalize it, the use case would be how do we use structured or tabular data with, you know, like AI models, right? Like how do we turn the data and give the context as a data and then bring it to the model and then model can, you know, like give you answers, make a questions, do whatever you want. But the question is like how we go from just the data in your data warehouse, database, whatever, which is usually just a tabular data, right? Like in a SQL based warehouses to some sort of, you know, like a context that system can do. And if you're building this application, you have to do it. It's like no way you can get away around not doing this. You either map it manually or you come up with some framework or something else. So our take is that and my take is that semantic layer is just really good place for this context to leave because you need to give this context to the humans. You need to give that context to the AI system anyway, right? So that's why you define metric once and then, you know, like you teach your AI system what this metric is about. [00:11:01]
Alessio: What are some of the challenges of using tabular versus language data and some of the ways that having the semantic layer kind of makes that easier maybe? [00:11:09]
Artem: Imagine you're a human, right? And you're going into like your new data analyst at a company and just people give you a warehouse with a bunch of tables and they tell you, okay, just try to make sense of this data. And you're going through all of these tables and you're really like trying to make sense without any, you know, like additional context or like some columns. In many cases, they might have a weird names. Sometimes, you know, if they follow some kind of like a star schema or, you know, like a Kimball style dimensions, maybe that would be easier because you would have facts and dimensions column, but it's still, it's hard to understand and kind of make sense because it doesn't have descriptions, right? And then there is like a whole like industry of like a data catalogs exist because the whole purpose of that to give context to the data so people can understand that. And I think the same applies to the AI, right? Like, and the same challenge is that if you give it pure tabular data, it doesn't have this sort of context that it can read. So you sort of needed to write a book or like essay about your data and give that book to the system so it can understand it. [00:12:12]
Alessio: Can you run through the steps of how that works today? So the initial part is like the natural language query, like what are the steps that happen in between to do model, to semantic layer, semantic layer, to SQL and all that flow? [00:12:26]
Artem: The first key step is to do some sort of indexing. That's what I was referring to, like write a book about your data, right? Describe in a text format what your data is about, right? Like what metrics it has, dimensions, what is the structures of that, what a relationship between those metrics, what are potential values of the dimensions. So sort of, you know, like build a really good index as a text representation and then turn it into embeddings into your, you know, like a vector storage. Once you have that, then you can provide that as a context to the model. I mean, there are like a lot of options, like either fine tune or, you know, like sort of in context learning, but somehow kind of give that as a context to the model, right? And then once this model has this context, it can create a query. Now the query I believe should be created against semantic layer because it reduces the room for the error. Because what usually happens is that your query to semantic layer would be very simple. It would be like, give me that metric group by that dimension and maybe that filter should be applied. And then your real query for the warehouse, it might have like a five joins, a lot of different techniques, like how to avoid fan out, fan traps, chasm traps, all of that stuff. And the bigger query, the more room that the model can make an error, right? Like even sometimes it could be a small error and then, you know, like your numbers is going to be off. But making a query against semantic layer, that sort of reduces the error. So the model generates a SQL query and then it executes us again, semantic layer. And semantic layer executes us against your warehouse and then sends result all the way back to the application. And then can be done multiple times because what we were missing was both this ability to have a conversation, right? With the model. You can ask question and then system can do a follow-up questions, you know, like then do a query to get some additional information based on this information, do a query again. And sort of, you know, like it can keep doing this stuff and then eventually maybe give you a big report that consists of a lot of like data points. But the whole flow is that it knows the system, it knows your data because you already kind of did the indexing and then it queries semantic layer instead of a data warehouse directly. [00:14:47]
Alessio: Maybe just to make it a little clearer for people that haven't used a semantic layer before, you can add definitions like revenue, where revenue is like select from customers and like join orders and then sum of the amount of orders. But in the semantic layer, you're kind of hiding all of that away. So when you do natural language to queue, it just select revenue from last week and then it turns into a bigger query. [00:15:12]
Swyx: One of the biggest difficulties around semantic layer for people who've never thought about this concept before, this all sounds super neat until you have multiple stakeholders within a single company who all have different concepts of what a revenue is. They all have different concepts of what active user is. And then they'll have like, you know, revenue revision one by the sales team, you know, and then revenue revision one, accounting team or tax team, I don't know. I feel like I always want semantic layer discussions to talk about the not so pretty parts of the semantic layer, because this is where effectively you ship your org chart in the semantic layer. [00:15:47]
Artem: I think the way I think about it is that at the end of the day, semantic layer is a code base. And in Qubit, it's essentially a code base, right? It's not just a set of YAML files with pythons. I think code is never perfect, right? It's never going to be perfect. It will have a lot of, you know, like revisions of code. We have a version control, which helps it's easier with revisions. So I think we should treat our metrics and semantic layer as a code, right? And then collaboration is a big part of it. You know, like if there are like multiple teams that sort of have a different opinions, let them collaborate on the pull request, you know, they can discuss that, like why they think that should be calculated differently, have an open conversation about it, you know, like when everyone can just discuss it, like an open source community, right? Like you go on a GitHub and you talk about why that code is written the way it's written, right? It should be written differently. And then hopefully at some point you can come up, you know, like to some definition. Now if you still should have multiple versions, right? It's a code, right? You can still manage it. But I think the big part of that is that like, we really need to treat it as a code base. Then it makes a lot of things easier, not as spreadsheets, you know, like a hidden Excel files. [00:16:53]
Alessio: The other thing is like then having the definition spread in the organization, like versus everybody trying to come up with their own thing. But yeah, I'm sure that when you talk to customers, there's people that have issues with the product and it's really like two people trying to define the same thing. One in sales that wants to look good, the other is like the finance team that wants to be conservative and they all have different definitions. How important is the natural language to people? Obviously you guys both work in modern data stack companies either now or before. There's going to be the whole wave of empowering data professionals. I think now a big part of the wave is removing the need for data professionals to always be in the loop and having non-technical folks do more of the work. Are you seeing that as a big push too with these models, like allowing everybody to interact with the data? [00:17:42]
Artem: I think it's a multidimensional question. That's an example of, you know, like where you have a lot of inside the question. In terms of examples, I think a lot of people building different, you know, like agents or chatbots. You have a company that built an internal Slack bot that sort of answers questions, you know, like based on the data in a warehouse. And then like a lot of people kind of go in and like ask that chatbot this question. Is it like a real big use case? Maybe. Is it still like a toy pet project? Maybe too right now. I think it's really hard to tell them apart at this point because there is a lot of like a hype, you know, and just people building LLM stuff because it's cool and everyone wants to build something, you know, like even at least a pet project. So that's what happened in Krizawa community as well. We see a lot of like people building a lot of cool stuff and it probably will take some time for that stuff to mature and kind of to see like what are real, the best use cases. But I think what I saw so far, one use case was building this chatbot and we have even one company that are building it as a service. So they essentially connect into Q semantic layer and then offering their like chatbot So you can do it in a web, in a slack, so it can, you know, like answer questions based on data in your semantic layer, but also see a lot of things like they're just being built in house. And there are other use cases, sort of automation, you know, like that agent checks on the data and then kind of perform some actions based, you know, like on changes in data. But other dimension of your question is like, will it replace people or not? I think, you know, like what I see so far in data specifically, you know, like a few use cases of LLM, I don't see Q being part of that use case, but it's more like a copilot for data analyst, a copilot for data engineer, where you develop something, you develop a model and it can help you to write a SQL or something like that. So you know, it can create a boilerplate SQL, and then you can edit this SQL, which is fine because you know how to edit SQL, right? So you're not going to make a mistake, but it will help you to just generate, you know, like a bunch of SQL that you write again and again, right? Like boilerplate code. So sort of a copilot use case. I think that's great. And we'll see more of it. I think every platform that is building for data engineers will have some sort of a copilot capabilities and Cubectl, we're building this copilot capabilities to help people build semantic layers easier. I think that just a baseline for every engineering product right now to have some sort of, you know, like a copilot capabilities. Then the other use case is a little bit more where Cube is being involved is like, how do we enable access to data for non-technical people through the natural language as an interface to data, right? Like visual dashboards, charts, it's always has been an interface to data in every BI. Now I think we will see just a second interface as a just kind of a natural language. So I think at this point, many BI's will add it as a commodity feature is like Tableau will probably have a search bar at some point saying like, Hey, ask me a question. I know that some of the, you know, like AWS Squeak site, they're about to announce features like this in their like BI. And I think Power BI will do that, especially with their deal with open AI. So every company, every BI will have this some sort of a search capabilities built in inside their BI. So I think that's just going to be a baseline feature for them as well. But that's where Cube can help because we can provide that context, right? [00:21:07]
Alessio: Do you know how, or do you have an idea for how these products will differentiate once you get the same interface? So right now there's like, you know, Tableau is like the super complicated and it's like super sad. It's like easier. Yeah. Do you just see everything will look the same and then how do people differentiate? [00:21:24]
Artem: It's like they all have line chart, right? And they all have bar chart. I feel like it pretty much the same and it's going to be fragmented as well. And every major vendor and most of the vendors will try to have some sort of natural language capabilities and they might be a little bit different. Some of them will try to position the whole product around it. Some of them will just have them as a checkbox, right? So we'll see, but I don't think it's going to be something that will change the BI market, you know, like something that will can take the BI market and make it more consolidated rather than, you know, like what we have right now. I think it's still will remain fragmented. [00:22:04]
Alessio: Let's talk a bit more about application use cases. So people also use Q for kind of like analytics in their product, like dashboards and things like that. How do you see that changing and more, especially like when it comes to like agents, you know, so there's like a lot of people trying to build agents for reporting, building agents for sales. If you're building a sales agent, you need to know everything about the purchasing history of the customer. All of these things. Yeah. Any thoughts there? What should all the AI engineers listening think about when implementing data into agents? [00:22:38]
Artem: Yeah, I think kind of, you know, like trying to solve for two problems. One is how to make sure that agents or LLM model, right, has enough context about, you know, like a tabular data and also, you know, like how do we deliver updates to the context, which is also important because data is changing, right? So every time we change something upstream, we need to surely update that context in our vector database or something. And how do you make sure that the queries are correct? You know, I think it's obviously a big pain and that's all, you know, like AI kind of, you know, like a space right now, how do we make sure that we don't, you know, provide our own cancers, but I think, you know, like be able to reduce the room for error as much as possible that what I would look for, you know, like to try to like minimize potential damage. And then our use case for Qube, it's been using a lot to power sort of customer facing analytics. So I don't think much is going to change is that I feel like again, more and more products will adopt natural language interfaces as sort of a part of that product as well. So we would be able to power this business to not only, you know, like a chart, visuals, but also some sort of, you know, like a summaries, probably in the future, you're going to open the page with some surface stats and you will have a smart summary kind of generated by AI. And that summary can be powered by Qube, right, like, because the rest is already being powered by Qube. [00:24:04]
Alessio: You know, we had Linus from Notion on the pod and one of the ideas he had that I really like is kind of like thumbnails of text, kind of like how do you like compress knowledge and then start to expand it. A lot of that comes into dashboards, you know, where like you have a lot of data, you have like a lot of charts and sometimes you just want to know, hey, this is like the three lines summary of it. [00:24:25]
Artem: Exactly. [00:24:26]
Alessio: Makes sense that you want to power that. How are you thinking about, yeah, the evolution of like the modern data stack in quotes, whatever that means today. What's like the future of what people are going to do? What's the future of like what models and agents are going to do for them? Do you have any, any thoughts? [00:24:42]
Artem: I feel like modern data stack sometimes is not very, I mean, it's obviously big crossover between AI, you know, like ecosystem, AI infrastructure, ecosystem, and then sort of a data. But I don't think it's a full overlap. So I feel like when we know, like I'm looking at a lot of like what's happening in a modern data stack where like we use warehouses, we use BI's, you know, different like transformation tools, catalogs, like data quality tools, ETLs, all of that. I don't see a lot of being compacted by AI specifically. I think, you know, that space is being compacted as much as any other space in terms of, yes, we'll have all this copilot capabilities, some of AI capabilities here and there, but I don't see anything sort of dramatically, you know, being sort of, you know, a change or shifted because of, you know, like AI wave. In terms of just in general data space, I think in the last two, three years, we saw an explosion, right? Like we got like a lot of tools, every vendor for every problem. I feel like right now we should go through the cycle of consolidation. If Fivetran and DBT merge, they can be Alteryx of a new generation or something like that. And you know, probably some ETL tool there. I feel it might happen. I mean, it's just natural waves, you know, like in cycles. [00:25:59]
Alessio: I wonder if everybody is going to have their own copilot. The other thing I think about these models is like Swyx was at Airbyte and yeah, there's Fivetran. [00:26:08]
Swyx: Fivetran versus AirByte, I don't think it'll mix very well. [00:26:10]
Alessio: A lot of times these companies are doing the syntax work for you of like building the integration between your data store and like the app or another data store. I feel like now these models are pretty good at coming up with the integration themselves and like using the docs to then connect the two. So I'm really curious, like in the future, what that will look like. And same with data transformation. I mean, you think about DBT and some of these tools and right now you have to create rules to normalize and transform data. In the future, I could see you explaining the model, how you want the data to be, and then the model figuring out how to do the transformation. I think it all needs a semantic layer as far as like figuring out what to do with it. You know, what's the data for and where it goes. [00:26:53]
Artem: Yeah, I think many of this, you know, like workflows will be augmented by, you know, like some sort of a copilot. You know, you can describe what transformation you want to see and it can generate a boilerplate right, of transformation for you, or even, you know, like kind of generate a boilerplate of specific ETL driver or ETL integration. I think we're still not at the point where this code can be fully automated. So we still need a human and a loop, right, like who can be, who can use this copilot. But in general, I think, yeah, data work and software engineering work can be augmented quite significantly with all that stuff. [00:27:31]
Alessio: You know, the big thing with machine learning before was like, well, all of your data is bad. You know, the data is not good for anything. And I think like now, at least with these models, they have some knowledge of their own and they can also tell you if your data is bad, which I think is like something that before you didn't have. Any cool apps that you've seen being built on Qube, like any kind of like AI native things that people should think about, new experiences, anything like that? [00:27:54]
Artem: Well, I see a lot of Slack bots. They all remind me of Statsbot, but I know like I played with a few of them. They're much, much better than Statsbot. It feels like it's on the surface, right? It's just that use case that you really want, you know, think about you, a data engineer in your company, like everyone is like, and you're asking, hey, can you pull that data for me? And you would be like, can I build a bot to replace myself? You know, like, so they can both ping that bot instead. So it's like, that's why a lot of people doing that. So I think it's a first use case that actually people are playing with. But I think inside that use case, people get creative. So I see bots that can actually have a dialogue with you. So, you know, like you would come to that bot and say, hey, show me metrics. And the bot would be like, what kind of metrics? What do you want to look at? You will be like active users. And then it would be like, how do you define active users? You want to see active users sort of cohort, you want to see active users kind of changing behavior over time, like a lot of like a follow up questions. So it tries to sort of, you know, like understand what exactly you want. And that's how many data analysts work, right? When people started to ask you something, you always try to understand what exactly do you mean? Because many people don't know how to ask correct questions about your data. It's a sort of an interesting specter. On one side of the specter, you know, nothing is like, hey, show me metrics. And the other side of specter, you know how to write SQL, and you can write exact query to your data warehouse, right? So many people like a little bit in the middle. And the data analysts, they usually have the knowledge about your data. And that's why they can ask follow up questions and to understand what exactly you want. And I saw people building bots who can do that. That part is amazing. I mean, like generating SQL, all that stuff, it's okay, it's good. But when the bot can actually act like they know that your data and they can ask follow up questions. I think that's great. [00:29:43]
Swyx: Yeah. [00:29:44]
Alessio: Are there any issues with the models and the way they understand numbers? One of the big complaints people have is like GPT, at least 3.5, cannot do math. Have you seen any limitations and improvement? And also when it comes to what model to use, do you see most people use like GPT-4? Because it's like the best at this kind of analysis. [00:30:03]
Artem: I think I saw people use all kinds of models. To be honest, it's usually GPT. So inside GPT, it could be 3.5 or 4, right? But it's not like I see a lot of something else, to be honest, like, I mean, maybe some open source alternatives, but it feels like the market is being dominated by just chat GPT. In terms of the problems, I think chatting about it with a few people. So if math is required to do math, you know, like outside of, you know, like chat GPT itself, so it would be like some additional Python scripts or something. When we're talking about production level use cases, it's quite a lot of Python code around, you know, like your model to make it work. To be honest, it's like, it's not that magic that you just throw the model in and like it can give you all these answers. For like a toy use cases, the one we have on a, you know, like our demo page or something, it works fine. But, you know, like if you want to do like a lot of post-processing, do a mass on URL, you probably need to code it in Python anyway. That's what I see people doing. [00:30:59]
Alessio: We heard the same from Harrison and LangChain that most people just use OpenAI. We did a OpenAI has no moat emergency podcast, and it was funny to like just see the reaction that people had to that and how hard it actually is to break down some of the monopoly. What else should people keep in mind, Artem? You're kind of like at the cutting edge of this. You know, if I'm looking to build a data-driven AI application, I'm trying to build data into my AI workflows. Any mistakes people should avoid? Any tips on the best stack to use? What tools to use? [00:31:32]
Artem: I would just recommend going through to warehouse as soon as possible. I think a lot of people feel that MySQL can be a warehouse, which can be maybe on like a lower scale, but definitely not from a performance perspective. So just kind of starting with a good warehouse, a query engine, Lakehouse, that's probably like something I would recommend starting from a day zero. And there are good ways to do it, very cheap, with open source technologies too, especially in the Lakehouse architecture. I think, you know, I'm biased, obviously, but using a semantic layer, preferably Cube, and for, you know, like a context. And other than that, I just feel it's a very interesting space in terms of AI ecosystem. I see a lot of people using link chain right now, which is great, you know, like, and we build an integration. But I'm sure the space will continue to evolve and, you know, like we'll see a lot of interesting tools and maybe, you know, like some tools would be a better fit for a job. I'm not aware of any right now, but it's always interesting to see how it evolves. Also it's a little unclear, you know, like how all the infrastructure around actually developing, testing, documenting, all that stuff will kind of evolve too. But yeah, again, it's just like really interesting to see and observe, you know, what's happening in this space. [00:32:44]
Swyx: So before we go to the lightning round, I wanted to ask you on your thoughts on embedded analytics and in a sense, the kind of chatbots that people are inserting on their websites and building with LLMs is very much sort of end user programming or end user interaction with their own data. I love seeing embedded analytics, and for those who don't know, embedded analytics is basically user facing dashboards where you can see your own data, right? Instead of the company seeing data across all their customers, it's an individual user seeing their own data as a slice of the overall data that is owned by the platform that they're using. So I love embedded analytics. Well, actually, overwhelmingly, the observation that I've had is that people who try to build in this market fail to monetize. And I was wondering your insights on why. [00:33:31]
Artem: I think overall, the statement is true. It's really hard to monetize, you know, like in embedded analytics. That's why at Qube we're excited more about our internal kind of BI use case, or like a company's a building, you know, like a chatbots for their internal data consumption or like internal workflows. Embedded analytics is hard to monetize because it's historically been dominated by the BI vendors. And we still see a lot of organizations are using BI tools as vendors. And what I was talking about, BI vendors adding natural language interfaces, they will probably add that to the embedded analytics capabilities as well, right? So they would be able to embed that too. So I think that's part of it. Also, you know, if you look at the embedded analytics market, the bigger organizations are big GADs, they're really more custom, you know, like it becomes and at some point I see many organizations, they just stop using any vendor, and they just kind of build most of the stuff from scratch, which probably, you know, like the right way to do. So it's sort of, you know, like you got a market that is very kept at the top. And then you also in that middle and small segment, you got a lot of vendors trying to, you know, like to compete for the buyers. And because again, the BI is very fragmented, embedded analytics, therefore is fragmented also. So you're really going after the mid market slice, and then with a lot of other vendors competing for that. So that's why it's historically been hard to monetize, right? I don't think AI really going to change that just because it's using model, you just pay to open AI. And that's it, like everyone can do that, right? So it's not much of a competitive advantage. So it's going to be more like a commodity features that a lot of vendors would be able to leverage. [00:35:20]
Alessio: This is great, Artem. As usual, we got our lightning round. So it's three questions. One is about acceleration, one on exploration, and then take away. The acceleration thing is what's something that already happened in AI or maybe, you know, in data that you thought would take much longer, but it's already happening today. [00:35:38]
Artem: To be honest, all this foundational models, I thought that we had a lot of models that been in production for like, you know, maybe decade or so. And it was like a very niche use cases, very vertical use cases, it's just like in very customized models. And even when we're building Statsbot back then in 2016, right, even back then, we had some natural language models being deployed, like a Google Translate or something that was still was a sort of a model, right, but it was very customized with a specific use case. So I thought that would continue for like, many years, we will use AI, we'll have all these customized niche models. But there is like foundational model, they like very generic now, they can serve many, many different use cases. So I think that is a big change. And I didn't expect that, to be honest. [00:36:27]
Swyx: The next question is about exploration. What is one thing that you think is the most interesting unsolved question in AI? [00:36:33]
Artem: I think AI is a subset of software engineering in general. And it's sort of connected to the data as well. Because software engineering as a discipline, it has quite a history. We build a lot of processes, you know, like toolkits and methodologies, how we prod that, [00:36:50]
Swyx: right. [00:36:51]
Artem: But AI, I don't think it's completely different. But it has some unique traits, you know, like, it's quite not idempotent, right, and kind of from many dimensions and like other traits. So which kind of may require a different methodologies may require different approaches and a different toolkit. I don't think how much is going to deviate from a standard software engineering, I think many tools and practices that we develop our software engineering can be applied to AI. And some of the data best practices can be applied as well. But it's like we got a DevOps, right, like it's just a bunch of tools, like ecosystem. So now like AI is kind of feels like it's shaping into that with a lot of its own, you know, like methodologies, practices and toolkits. So I'm really excited about it. And I think it's a lot of unsolved still question again, how do we develop that? How do we test you know, like, what is the best practices? How what is a methodologist? So I think that would be an interesting to see. [00:37:44]
Alessio: Awesome. Yeah. Our final message, you know, you have a big audience of engineers and technical folks, what's something you want everybody to remember to think about to explore? [00:37:55]
Artem: I mean, it says being hooked to try to build a chatbot, you know, like for analytics, back then and kind of, you know, like looking at what people do right now, I think, yeah, just do that. I mean, it's working right now, with foundational models, it's actually now it's possible to build all those cool applications. I'm so excited to see, you know, like, how much changed in the last six years or so that we actually now can build a smart agents. So I think that sort of, you know, like a takeaways and yeah, we are, as humans in general, we like we really move technology forward. And it's fun to see, you know, like, it's just a first hand. [00:38:30]
Alessio: Well, thank you so much for coming on Artem. [00:38:32]
Swyx: This was great. [00:38:32]
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Podcast: Latent Space: The AI Engineer Podcast — Practitioners talking LLMs, CodeGen, Agents, Multimodality, AI UX, GPU Infra and all things Software 3.0 (LS 33 · TOP 5% what is this?)
Episode: Beating GPT-4 with Open Source LLMs — with Michael Royzen of Phind
Pub date: 2023-11-03
At the AI Pioneers Summit we announced Latent Space Launchpad, an AI-focused accelerator in partnership with Decibel. If you’re an AI founder of enterprise early adopter, fill out this form and we’ll be in touch with more details.
We also have a lot of events coming up as we wrap up the year, so make sure to check out our community events page and come say hi!
We previously interviewed the founders of many developer productivity startups embedded in the IDE, like Codium AI, Cursor, and Codeium. We also covered Replit’s (former) SOTA model, replit-code-v1-3b and most recently had Amjad and Michele announce replit-code-v1_5-3b at the AI Engineer Summit.
Much has been speculated about the StackOverflow traffic drop since ChatGPT release, but the experience is still not perfect. There’s now a new player in the “search for developers” arena: Phind.
Phind’s goal is to help you find answers to your technical questions, and then help you implement them. For example “What should I use to create a frontend for a Python script?” returns a list of frameworks as well as links to the sources. You can then ask follow up questions on specific implementation details, having it write some code for you, etc. They have both a web version and a VS Code integration
They recently were top of Hacker News with the announcement of their latest model, which is now the #1 rated model on the BigCode Leaderboard, beating their previous version:
TLDR Cheat Sheet:
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Based on CodeLlama-34B, which is trained on 500B tokens
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Further fine-tuned on 70B+ high quality code and reasoning tokens
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Expanded context window to 16k tokens
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5x faster than GPT-4 (100 tok/s vs 20 tok/s on single stream)
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74.7% HumanEval vs 45% for the base model
We’ve talked before about HumanEval being limited in a lot of cases and how it needs to be complemented with “vibe based” evals. Phind thinks of evals alongside two axis:
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Context quality: when asking the model to generate code, was the context high quality? Did we put outdated examples in it? Did we retrieve the wrong files?
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Result quality: was the code generated correct? Did it follow the instructions I gave it or did it misunderstand some of it?
If you have bad results with bad context, you might get to a good result by working on better RAG. If you have good context and bad result you might either need to work on your prompting or you have hit the limits of the model, which leads you to fine tuning (like they did).
Michael was really early to this space and started working on CommonCrawl filtering and indexing back in 2020, which led to a lot of the insights that now power Phind. We talked about that evolution, his experience at YC, how he got Paul Graham to invest in Phind and invite him to dinner at his house, and how Ron Conway connected him with Jensen Huang to get access to more GPUs!
Show Notes
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Phind
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BigScience T0
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InstructGPT Paper
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Inception-V3
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LMQL
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Marginalia Nu
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Mistral AI
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People:
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Paul Graham (pg)
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Ron Conway
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Yacine Jernite from HuggingFace
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Jeff Delaney
Timestamps
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[00:00:00] Intros & Michael's early interest in computer vision
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[00:03:14] Pivoting to NLP and natural language question answering models
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[00:07:20] Building a search engine index of Common Crawl and web pages
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[00:11:26] Releasing the first version of Hello based on the search index and BigScience T0 model
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[00:14:02] Deciding to focus the search engine specifically for programmers
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[00:17:39] Overview of Phind's current product and focus on code reasoning
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[00:21:51] The future vision for Phind to go from idea to complete code
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[00:24:03] Transitioning to using the GPT-4 model and the impact it had
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[00:29:43] Developing the Phind model based on CodeLlama and additional training
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[00:32:28] Plans to continue improving the Phind model with open source technologies
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[00:43:59] The story of meeting Paul Graham and Ron Conway and how that impacted the company
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[00:53:02] How Ron Conway helped them get GPUs from Nvidia
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[00:57:12] Tips on how Michael learns complex AI topics
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[01:01:12] Lightning Round
Transcript
Alessio: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, partner and CTO of Residence and Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol AI. [00:00:19]
Swyx: Hey, and today we have in the studio Michael Royzen from Phind. Welcome. [00:00:23]
Michael: Thank you so much. [00:00:24]
Alessio: It's great to be here. [00:00:25]
Swyx: Yeah, we are recording this in a surprisingly hot October in San Francisco. And sometimes the studio works, but the blue angels are flying by right now, so sorry about the noise. So welcome. I've seen Phind blow up this year, mostly, I think since your launch in Feb and V2 and then your Hacker News posts. We tend to like to introduce our guests, but then obviously you can fill in the blanks with the origin story. You actually were a high school entrepreneur. You started SmartLens, which is a computer vision startup in 2017. [00:00:59]
Michael: That's right. I remember when like TensorFlow came out and people started talking about, obviously at the time after AlexNet, the deep learning revolution was already in flow. Good computer vision models were a thing. And what really made me interested in deep learning was I got invited to go to Apple's WWDC conference as a student scholar because I was really into making iOS apps at the time. So I go there and I go to this talk where they added an API that let people run computer vision models on the device using far more efficient GPU primitives. After seeing that, I was like, oh, this is cool. This is going to have a big explosion of different computer vision models running locally on the iPhone. And so I had this crazy idea where it was like, what if I could just make this model that could recognize just about anything and have it run on the device? And that was the genesis for what eventually became SmartLens. I took this data set called ImageNet 22K. So most people, when they think of ImageNet, think of ImageNet 1K. But the full ImageNet actually has, I think, 22,000 different categories. So I took that, filtered it, pre-processed it, and then did a massive fine tune on Inception V3, which was, I think, the state of the art deep convolutional computer vision model at the time. And to my surprise, it actually worked insanely well. I had no idea what would happen if I give a single model. I think it ended up being 17,000 categories approximately that I collapsed them into. It worked so well that it actually worked better than Google Lens, which released its V1 around the same time. And on top of this, the model ran on the device. So it didn't need an internet connection. A big part of the issue with Google Lens at the time was that connections were slower. 4G was around, but it wasn't nearly as fast. So there was a noticeable lag having to upload an image to a server and get it back. But just processing it locally, even on the iPhones of the day in 2017, much faster. It was a cool little project. It got some traction. TechCrunch wrote about it. There was kind of like one big spike in usage, and then over time it tapered off. But people still pay for it, which is wild. [00:03:14]
Swyx: That's awesome. Oh, it's like a monthly or annual subscription? [00:03:16]
Michael: Yeah, it's like a monthly subscription. [00:03:18]
Swyx: Even though you don't actually have any servers? [00:03:19]
Michael: Even though we don't have any servers. That's right. I was in high school. I had a little bit of money. I was like, yeah. [00:03:25]
Swyx: That's awesome. I always wonder what the modern equivalents kind of "Be my eyes". And it would be actually disclosed in the GPT-4 Vision system card recently that the usage was surprisingly not that frequent. The extent to which all three of us have our sense of sight. I would think that if I lost my sense of sight, I would use Be My Eyes all the time. The average usage of Be My Eyes per day is 1.5 times. [00:03:49]
Michael: Exactly. I was thinking about this as well, where I was also looking into image captioning, where you give a model an image and then it tells you what's in the image. But it turns out that what people want is the exact opposite. People want to give a description of an image and then have the AI generate the image. [00:04:04]
Alessio: Oh, the other way. [00:04:06]
Michael: Exactly. And so at the time, I think there were some GANs, NVIDIA was working on this back in 2019, 2020. They had some impressive, I think, face GANs where they had this model that would produce these really high quality portraits, but it wasn't able to take a natural language description the way Midjourney or DALL-E 3 can and just generate you an image with exactly what you described in it. [00:04:32]
Swyx: And how did that get into NLP? [00:04:35]
Michael: Yeah, I released the SmartLens app and that was around the time I was a senior in high school. I was applying to college. College rolls around. I'm still sort of working on updating the app in college. But I start thinking like, hey, what if I make an enterprise version of this as well? At the time, there was Clarify that provided some computer vision APIs, but I thought this massive classification model works so well and it's so small and so fast, might as well build an enterprise product. And I didn't even talk to users or do any of those things that you're supposed to do. I was just mainly interested in building a type of backend I've never built before. So I was mainly just doing it for myself just to learn. I built this enterprise classification product and as part of it, I'm also building an invoice processing product where using some of the aspects that I built previously, although obviously it's very different from classification, I wanted to be able to just extract a bunch of structured data from an unstructured invoice through our API. And that's what led me to Hugnyface for the first time because that involves some natural language components. And so I go to Hugnyface and with various encoder models that were around at the time, I used the standard BERT and also Longformer, which came out around the same time. And Longformer was interesting because it had a much bigger context window than those models at the time, like BERT, all of the first gen encoder only models, they only had a context window of 512 tokens and it's fixed. There's none of this alibi or ROPE that we have now where we can basically massage it to be longer. They're fixed, 512 absolute encodings. Longformer at the time was the only way that you can fit, say, like a sequence length or ask a question about like 4,000 tokens worth of text. Implemented Longformer, it worked super well, but like nobody really kind of used the enterprise product and that's kind of what I expected because at the end of the day, it was COVID. I was building this kind of mostly for me, mostly just kind of to learn. And so nobody really used it and my heart wasn't in it and I kind of just shelved it. But a little later, I went back to HugMeFace and I saw this demo that they had, and this is in the summer of 2020. They had this demo made by this researcher, Yacine Jernite, and he called it long form question answering. And basically, it was this self-contained notebook demo where you can ask a question the way that we do now with ChatGPT. It would do a lookup into some database and it would give you an answer. And it absolutely blew my mind. The demo itself, it used, I think, BART as the model and in the notebook, it had support for both an Elasticsearch index of Wikipedia, as well as a dense index powered by Facebook's FAISS. I think that's how you pronounce it. It was very iffy, but when it worked, I think the question in the demo was, why are all boats white? When it worked, it blew my mind that instead of doing this few shot thing, like people were doing with GPT-3 at the time, which is all the rage, you could just ask a model a question, provide no extra context, and it would know what to do and just give you the answer. It blew my mind to such an extent that I couldn't stop thinking about that. When I started thinking about ways to make it better, I tried training, doing the fine tune with a larger BART model. And this BART model, yeah, it was fine tuned on this Reddit data set called Eli5. So basically... [00:08:02]
Alessio: Subreddit. [00:08:03]
Swyx: Yeah, subreddit. [00:08:04]
Alessio: Yeah. [00:08:05]
Michael: And put it into like a well-formatted, relatively clean data set of like human questions and human answers. And that was a really great bootstrap for that model to be able to answer these types of questions. And so Eli5 actually turned out to be a good data set for training these types of question answering models, because the question is written by a human, the answer is written by a human, and at least helps the model get the format right, even if the model is still very small and it can't really think super well, at least it gets the format right. And so it ends up acting as kind of a glorified summarization model, where if it's fed in high quality context from the retrieval system, it's able to have a reasonably high quality output. And so once I made the model as big as I can, just fine tuning on BART large, I started looking for ways to improve the index. So in the demo, in the notebook, there were instructions for how to make an Elasticsearch index just for Wikipedia. And I was like, why not do all of Common Crawl? So I downloaded Common Crawl, and thankfully, I had like 10 or $15,000 worth of AWS credits left over from the SmartLens project. And that's what really allowed me to do this, because there's no other funding. I was still in college, not a lot of money, and so I was able to spin up a bunch of instances and just process all of Common Crawl, which is massive. So it's roughly like, it's terabytes of text. I went to Alexa to get the top 1,000 websites or 10,000 websites in the world, then filtered only by those websites, and then indexed those websites, because the web pages were already included in Dump. [00:09:38]
Swyx: You mean to supplement Common Crawl or to filter Common Crawl? [00:09:41]
Michael: Filter Common Crawl. [00:09:42]
Alessio: Oh, okay. [00:09:43]
Michael: Yeah, sorry. So we filtered Common Crawl just by the top, I think, 10,000, just to limit this, because obviously there's this massive long tail of small sites that are really cool, actually. There's other projects like, shout out to Marginalia Nu, which is a search engine specialized on the long tail. I think they actually exclude the top 10,000. [00:10:03]
Swyx: That's what they do. [00:10:04]
Alessio: Yeah. [00:10:05]
Swyx: I've seen them around, I just don't really know what their pitch is. Okay, that makes sense. [00:10:08]
Michael: So they exclude all the top stuff. So the long tail is cool, but for this, that was kind of out of the question, and that was most of the data anyway. So we've removed that. And then I indexed the remaining approximately 350 million webpages through Elasticsearch. So I built this index running on AWS with these webpages, and it actually worked quite well. You can ask it general common knowledge, history, politics, current events, questions, and it would be able to do a fast lookup in the index, feed it into the model, and it would give a surprisingly good result. And so when I saw that, I thought that this is definitely doable. And it kind of shocked me that no one else was doing this. And so this was now the fall of 2020. And yeah, I was kind of shocked no one was doing this, but it costs a lot of money to keep it up. I was still in college. There are things going on. I got bogged down by classes. And so I ended up shelving this for almost a full year, actually. When I returned to it in fall of 2021, when BigScience released T0, when BigScience released the T0 models, that was a massive jump in the reasoning ability of the model. And it was better at reasoning, it was better at summarization, it was still a glorified summarizer basically. [00:11:26]
Swyx: Was this a precursor to Bloom? Because Bloom's the one that I know. [00:11:29]
Alessio: Yeah. [00:11:30]
Michael: Actually coming out in 2022. But Bloom had other problems where for whatever reason, the Bloom models just were never really that good, which is so sad because I really wanted to use them. But I think they didn't turn on that much data. I think they used like the original, they were trying to replicate GPT-3. So they just use those numbers, which we now know are like far below Chinchilla Optimal and even Chinchilla Optimal, which we can like talk about later, like what we're currently doing with MIMO goes, yeah, it goes way beyond that. But they weren't trying enough data. I'm not sure how that data was clean, but it probably wasn't super clean. And then they didn't really do any fine tuning until much later. So T0 worked well because they took the T5 models, which were closer to Chinchilla Optimal because I think they were trained on also like 300 something billion tokens, similar to GPT-3, but the models were much smaller. I think T0 is the first model that did large scale instruction tuning from diverse data sources in the fall of 2021. This is before Instruct GPT. This is before Flan T5, which came out in 2022. This is the very, very first, at least well-known example of that. And so it came out and then I did, on top of T0, I also did the Reddit Eli5 fine tune. And that was the first model and system that actually worked well enough to where I didn't get discouraged like I did previously, because the failure cases of the BART based system was so egregious. Sometimes it would just miss a question so horribly that it was just extremely discouraging. But for the first time, it was working reasonably well. Also using a much bigger model. I think the BART model is like 800 million parameters, but T0, we were using 3B. So it was T0, 3B, bigger model. And that was the very first iteration of Hello. So I ended up doing a show HN on Hacker News in January 2022 of that system. Our fine tune T0 model connected to our Elasticsearch index of those 350 million top 10,000 common crawl websites. And to the best of my knowledge, I think that's the first example that I'm aware of a LLM search engine model that's effectively connected to like a large enough index that I consider like an internet scale. So I think we were the first to release like an internet scale LLM powered rag search system In January 2022, around the time me and my future co-founder, Justin, we were like, this seems like the future. [00:14:02]
Alessio: This is really cool. [00:14:03]
Michael: I couldn't really sleep even like I was going to bed and I was like, I was thinking about it. Like I would say up until like 2.30 AM, like reading papers on my phone in bed, go to sleep, wake up the next morning at like eight and just be super excited to keep working. And I was also doing my thesis at the same time, my senior honors thesis at UT Austin about something very similar. We were researching factuality in abstractive question answering systems. So a lot of overlap with this project and the conclusions of my research actually kind of helped guide the development path of Hello. In the research, we found that LLMs, they don't know what they don't know. So the conclusion was, is that you always have to do a search to ensure that the model actually knows what it's talking about. And my favorite example of this even today is kind of with chat GPT browsing, where you can ask chat GPT browsing, how do I run llama.cpp? And chat GPT browsing will think that llama.cpp is some file on your computer that you can just compile with GCC and you're all good. It won't even bother doing a lookup, even though I'm sure somewhere in their internal prompts they have something like, if you're not sure, do a lookup. [00:15:13]
Alessio: That's not good enough. So models don't know what they don't know. [00:15:15]
Michael: You always have to do a search. And so we approached LLM powered question answering from the search angle. We pivoted to make this for programmers in June of 2022, around the time that we were getting into YC. We realized that what we're really interested in is the case where the models actually have to think. Because up until then, the models were kind of more glorified summarization models. We really thought of them like the Google featured snippets, but on steroids. And so we saw a future where the simpler questions would get commoditized. And I still think that's going to happen with like Google SGE and like it's nowadays, it's really not that hard to answer the more basic kind of like summarization, like current events questions with lightweight models that'll only continue to get cheaper over time. And so we kind of started thinking about this trade off where LLM models are going to get both better and cheaper over time. And that's going to force people who run them to make a choice. Either you can run a model of the same intelligence that you could previously for cheaper, or you can run a better model for the same price. So someone like Google, once the price kind of falls low enough, they're going to deploy and they're already doing this with SGE, they're going to deploy a relatively basic glorified summarizer model that can answer very basic questions about like current events, who won the Super Bowl, like, you know, what's going on on Capitol Hill, like those types of things. The flip side of that is like more complex questions where like you have to reason and you have to solve problems and like debug code. And we realized like we're much more interested in kind of going along the bleeding edge of that frontier case. And so we've optimized everything that we do for that. And that's a big reason of why we've built Phind specifically for programmers, as opposed to saying like, you know, we're kind of a search engine for everyone because as these models get more capable, we're very interested in seeing kind of what the emergent properties are in terms of reasoning, in terms of being able to solve complex multi-step problems. And I think that some of those emerging capabilities like we're starting to see, but we don't even fully understand. So I think there's always an opportunity for us to become more general if we wanted, but we've been along this path of like, what is the best, most advanced reasoning engine that's connected to your code base, that's connected to the internet that we can just provide. [00:17:39]
Alessio: What is Phind today, pragmatically, from a product perspective, how do people interact with it? Yeah. Or does it plug into your workflow? [00:17:46]
Michael: Yeah. [00:17:47]
Alessio: So Phind is really a system. [00:17:48]
Michael: Phind is a system for programmers when they have a question or when they're frustrated or when something's not working. [00:17:54]
Swyx: When they're frustrated. [00:17:55]
Alessio: Yeah. [00:17:56]
Michael: For them to get on block. I think like the single, the most abstract page for Phind is like, if you're experiencing really any kind of issue as a programmer, we'll solve that issue for you in 15 seconds as opposed to 15 minutes or longer. Phind has an interface on the web. It has an interface in VS code and more IDEs to come, but ultimately it's just a system where a developer can paste in a question or paste in code that's not working and Phind will do a search on the internet or they will find other code in your code base perhaps that's relevant. And then we'll find the context that it needs to answer your question and then feed it to a reasoning engine powerful enough to actually answer it. So that's really the philosophy behind Phind. It's a system for getting developers the answers that they're looking for. And so right now from a product perspective, this means that we're really all about getting the right context. So the VS code extension that we launched recently is a big part of this because you can just ask a question and it knows where to find the right code context in your code. It can do an internet search as well. So it's up to date and it's not just reliant on what the model knows and it's able to figure out what it needs by itself and answer your question based on that. If it needs some help, you can also get yourself kind of just, there's opportunities for you yourself to put in all that context in. But the issue is also like not everyone wants these VS code. Some people like are real Neovim sticklers or they're using like PyCharm or other IDEs, JetBrains. And so for those people, they're actually like okay with switching tabs, at least for now, if it means them getting their answer. Because really like there's been an explosion of all these like startups doing code, doing search, etc. But really who everyone's competing with is ChatGPT, which only has like that one web interface. Like ChatGPT is really the bar. And so that's what we're up against. [00:19:50]
Alessio: And so your idea, you know, we have Amman from Cursor on the podcast and they've gone through the we need to own the IDE thing. Yours is more like in order to get the right answer, people are happy to like go somewhere else basically. They're happy to get out of their IDE. [00:20:05]
Michael: That was a great podcast, by the way. But yeah, so part of it is that people sometimes perhaps aren't even in an IDE. So like the whole task of software engineering goes way beyond just running code, right? There's also like a design stage. There's a planning stage. A lot of this happens like on whiteboards. It happens in notebooks. And so the web part also exists for that where you're not even coding it and you're just trying to get like a more conceptual understanding of what you're trying to build first. The podcast with Amman was great, but somewhere where I disagree with him is that you need to own the IDE. I think like he made some good points about not having platform risk in the long term. But some of the features that were mentioned like suggesting diffs, for example, those are all doable with an extension. We haven't yet seen with VS Code in particular any functionality that we'd like to do yet in the IDE that we can't either do through directly supported VS Code functionality or something that we kind of hack into there, which we've also done a fair bit of. And so I think it remains to be seen where that goes. But I think what we're looking to be is like we're not trying to just be in an IDE or be an IDE. Like Phind is a system that goes beyond the IDE and like is really meant to cover the entire lifecycle of a developer's thought process in going about like, hey, like I have this idea and I want to get from that idea to a working product. And so then that's what the long term vision of Phind is really about is starting with that. In the future, I think programming is just going to be really just the problem solving. Like you come up with an idea, you come up with like the basic design for the algorithm in your head, and you just tell the AI, hey, just like just do it, just make it work. And that's what we're building towards. [00:21:51]
Swyx: I think we might want to give people an impression about like type of traffic that you have, because when you present it with a text box, you could type in anything. And I don't know if you have some mental categorization of like what are like the top three use cases that people tend to coalesce around. [00:22:08]
Alessio: Yeah, that's a great question. [00:22:09]
Michael: The two main types of searches that we see are how-to questions, like how to do X using Y tool. And this historically has been our bread and butter, because with our embeddings, like we're really, really good at just going over a bunch of developer documentation and figuring out exactly the part that's relevant and just telling you, OK, like you can use this method. But as LLMs have gotten better, and as we've really transitioned to using GPT-4 a lot in our product, people organically just started pasting in code that's not working and just said, fix it for me. [00:22:42]
Swyx: Fix this. [00:22:43]
Alessio: Yeah. [00:22:44]
Michael: And what really shocks us is that a lot of the people who do that, they're coming from chat GPT. So they tried it in chat GPT with chat GPT-4. It didn't work. Maybe it required like some multi-step reasoning. Maybe it required some internet context or something found in either a Stack Overflow post or some documentation to solve it. And so then they paste it into find and then find works. So those are really those two different cases. Like, how can I build this conceptually or like remind me of this one detail that I need to build this thing? Or just like, here's this code. Fix it. And so that's what a big part of our VS Code extension is, is like enabling a much smoother here just like fix it for me type of workflow. That's really its main benefits. Like it's in your code base. It's in the IDE. It knows how to find the relevant context to answer that question. But at the end of the day, like I said previously, that's still a relatively, not to say it's a small part, but it's a limited part of the entire mental life cycle of a programmer. [00:23:47]
Swyx: Yep. So you launched in Feb and then you launched V2 in August. You had a couple other pretty impactful posts slash feature launches. The web search one was massive. So you were mostly a GPT-4 wrapper. We were for a long time. [00:24:03]
Michael: For a long time until recently. Yeah. [00:24:05]
Alessio: Until recently. [00:24:06]
Swyx: So like people coming over from ChatGPT were saying, we're going to say model with your version of web search. Would that be the primary value proposition? [00:24:13]
Michael: Basically yeah. And so what we've seen is that any model plus web search is just significantly better than [00:24:18]
Alessio: that model itself. Do you think that's what you got right in April? [00:24:21]
Swyx: Like so you got 1500 points on Hacking News in April, which is like, if you live on Hacking News a lot, that is unheard of for someone so early on in your journey. [00:24:31]
Alessio: Yeah. [00:24:32]
Michael: We're super, super grateful for that. Definitely was not expecting it. So what we've done with Hacker News is we've just kept launching. [00:24:38]
Alessio: Yeah. [00:24:39]
Michael: Like what they don't tell you is that you can just keep launching. That's what we've been doing. So we launched the very first version of Find in its current incarnation after like the previous demo connected to our own index. Like once we got into YC, we scrapped our own index because it was too cumbersome at the time. So we moved over to using Bing as kind of just the raw source data. We launched as Hello Cognition. Over time, every time we like added some intelligence to the product, a better model, we just keep launching. And every additional time we launched, we got way more traffic. So we actually silently rebranded to Find in late December of last year. But like we didn't have that much traffic. Nobody really knew who we were. [00:25:18]
Swyx: How'd you pick the name out of it? [00:25:19]
Michael: Paul Graham actually picked it for us. [00:25:21]
Swyx: All right. [00:25:22]
Alessio: Tell the story. Yeah. So, oh boy. [00:25:25]
Michael: So this is the biggest side. Should we go for like the full Paul Graham story or just the name? [00:25:29]
Swyx: Do you want to do it now? Or do you want to do it later? I'll give you a choice. [00:25:32]
Alessio: Hmm. [00:25:33]
Michael: I think, okay, let's just start with the name for now and then we can do the full Paul Graham story later. But basically, Paul Graham, when we were lucky enough to meet him, he saw our name and our domain was at the time, sayhello.so and he's just like, guys, like, come on, like, what is this? You know? And we were like, yeah, but like when we bought it, you know, we just kind of broke college students. Like we didn't have that much money. And like, we really liked hello as a name because it was the first like conversational search engine. And that's kind of, that's the angle that we were approaching it from. And so we had sayhello.so and he's like, there's so many problems with that. Like, like, like the say hello, like, what does that even mean? And like .so, like, it's gotta be like a .com. And so we did some time just like with Paul Graham in the room. We just like looked at different domain names, like different things that like popped into our head. And one of the things that popped into like Paul Graham said was fine with the Phind spelling in particular. [00:26:33]
Swyx: Yeah. Which is not typical naming advice, right? Yes. Because it's not when people hear it, they don't spell it that way. [00:26:38]
Michael: Exactly. It's hard to spell. And also it's like very 90s. And so at first, like, we didn't like, I was like, like, ah, like, I don't know. But over time it kept growing on us. And eventually we're like, okay, we like the name. It's owned by this elderly Canadian gentleman who we got to know, and he was willing to sell it to us. [00:26:57]
Michael: And so we bought it and we changed the name. Yeah. [00:27:01]
Swyx: Anyways, where were you? [00:27:02]
Alessio: I had to ask. [00:27:03]
Swyx: I mean, you know, everyone who looks at you is wondering. [00:27:06]
Michael: And a lot of people actually pronounce it Phind, which, you know, by now it's part of the game. But eventually we want to buy Phind.com and then just have that redirect to Phind. So Phind is like definitely the right spelling. But like, we'll just, yeah, we'll have all the cases addressed. [00:27:23]
Swyx: Cool. So Bing web search, and then August you launched V2. Is V2 the Phind as a system pitch? Or have you moved, evolved since then? [00:27:31]
Michael: Yeah, so I don't, like the V2 moniker, like, I don't really think of it that way in my mind. There's like, there's the version we launched during, last summer during YC, which was the Bing version directed towards programmers. And that's kind of like, that's why I call it like the first incarnation of what we currently are. Because it was already directed towards programmers. We had like a code snippet search built in as well, because at the time, you know, the models we were using weren't good enough to generate code snippets. Even GPT, like the text DaVinci 2 was available at the time, wasn't that good at generating code and it would generate like very, very short, very incomplete code snippets. And so we launched that last summer, got some traction, but really like we were only doing like, I don't know, maybe like 10,000 searches a day. [00:28:15]
Alessio: Some people knew about it. [00:28:16]
Michael: Some people used it, which is impressive because looking back, the product like was not that good. And every time we've like made an improvement to the way that we retrieve context through better embeddings, more intelligent, like HTML parsers, and importantly, like better underlying models. Every major version after that was when we introduced a better underlying answering model. Like in February, we had to swallow a bit of our pride when we were like, okay, our own models aren't good enough. We have to go to open AI. And actually that did lead to kind of like our first decent bump of traffic in February. And people kept using it, like our attention was way better too. But we were still kind of running into problems of like more advanced reasoning. Some people tried it, but people were leaving because even like GPT 3.5, both turbo and non-turbo, like still not that great at doing like code related reasoning beyond the how do you do X, like documentation search type of use case. And so it was really only when GPT 4 came around in April that we were like, okay, like this is like our first real opportunity to really make this thing like the way that it should have been all along. And having GPT 4 as the brain is what led to that Hacker News post. And so what we did was we just let anyone use GPT 4 on Fyne for free without a login, [00:29:43]
Alessio: which I actually don't regret. [00:29:45]
Michael: So it was very expensive, obviously. But like at that stage, all we needed to do was show like, we just needed to like show people here's what Fyne can do. That was the main thing. And so that worked. That worked. [00:29:58]
Alessio: Like we got a lot of users. [00:29:59]
Michael: Do you know Fireship? [00:30:01]
Swyx: Yeah. YouTube, Jeff Delaney. [00:30:03]
Michael: Yeah. He made a short about Fyne. [00:30:06]
Alessio: Oh. [00:30:07]
Michael: And that's on top of the Hacker News post. And that's what like really, really made it blow up. It got millions of views in days. And he's just funny. Like what I love about Fireship is like he like you guys, yeah, like humor goes a long a long way towards like really grabbing people's attention. And so that blew up. [00:30:25]
Swyx: Something I would be anxious about as a founder during that period, so obviously we all remember that pretty closely. So there were a couple of people who had access to the GPT-4 API doing this, which is unrestricted access to GPT-4. And I have to imagine OpenAI wasn't that happy about that because it was like kind of de facto access to GPT-4 before they released it. [00:30:46]
Alessio: No, no. [00:30:47]
Michael: GPT-4 was in chat GPT from day one. I think. OpenAI actually came to our support because what happened was we had people building unofficial APIs around to try to get free access to it. And I think OpenAI actually has the right perspective on this where they're like, OK, people can do whatever they want with the API if they're paying for it, like they can do whatever they want, but it's like not OK if, you know, paying customers are being exploite by these other actors. They actually got in touch with us and they helped us like set up better Cloudflare bot monitoring controls to effectively like crack down on those unofficial APIs, which we're very happy about. But yeah, so we launched GPT-4. A lot of people come to the product and yeah, for a long time, we're just we're figuring out like what do we make of this, right? How do we a make it better, but also deal with like our costs, which have just like massively, massively ballooned. Over time, it's become more clear with the release of Llama 2 and Llama 3 on the horizon that we will once again see a return to vertical applications running their own models. As was true last year and before, I think that GPT-4, my hypothesis is that the jump from 4 to 4.5 or 4 to 5 will be smaller than the jump from 3 to 4. And the reason why is because there were a lot of different things. Like there was two plus, effectively two, two and a half years of research that went into going from 3 to 4. Like more data, bigger model, all of the instruction tuning techniques, RLHF, all of that is known. And like Meta, for example, and now there's all these other startups like Mistral too, like there's a bunch of very well-funded open source players that are now working on just like taking the recipe that's now known and scaling it up. So I think that even if a delta exists, the delta between in 2024, the delta between proprietary and open source won't be large enough that a startup like us with a lot of data that we've collected can take the data that we have, fine tune an open source model, and like be able to have it be better than whatever the proprietary model is at the time. That's my hypothesis.
Michael: But we'll once again see a return to these verticalized models. And that's something that we're super excited about because, yeah, that brings us to kind of the fine model because the plan from kind of the start was to be able to return to that if that makes sense. And I think now we're definitely at a point where it does make sense because we have requests from users who like, they want longer context in the model, basically, like they want to be able to ask questions about their entire code base without, you know, context and retrieval and taking a chance of that. Like, I think it's generally been shown that if you have the space to just put the raw files inside of a big context window, that is still better than chunking and retrieval. So there's various things that we could do with longer context, faster speed, lower cost. Super excited about that. And that's the direction that we're going with the fine model. And our big hypothesis there is precisely that we can take a really good open source model and then just train it on absolutely all of the high quality data that we can find. And there's a lot of various, you know, interesting ideas for this. We have our own techniques that we're kind of playing with internally. One of the very interesting ideas that I've seen, I think it's called Octopack from BigCode. I don't think that it made that big waves when it came out, I think in August. But the idea is that they have this data set that maps GitHub commits to a change. So basically there's all this really high quality, like human made, human written diff data out there on every time someone makes a commit in some repo. And you can use that to train models. Take the file state before and like given a commit message, what should that code look like in the future? [00:34:52]
Swyx: Got it. [00:34:53]
Alessio: Do you think your HumanEval is any good?
Michael: So we ran this experiment. We trained the Phind model. And if you go to the BigCode leaderboard, as of today, October 5th, all of our models are at the top of the BigCode leaderboard by far. It's not close, particularly in languages other than Python. We have a 10 point gap between us and the next best model on JavaScript. I think C sharp, multilingual. And what we kind of learned from that whole experience releasing those models is that human eval doesn't really matter. Not just that, but GPT-4 itself has been trained on human eval. And we know this because GPT-4 is able to predict the exact docstring in many of the problems. I've seen it predict like the specific example values in the docstring, which is extremely improbable. So I think there's a lot of dataset contamination and it only captures a very limited subset of what programmers are actually doing. What we do internally for evaluations are we have GPT-4 score answers. GPT-4 is a really good evaluator. I mean, obviously it's by really good, I mean, it's the best that we have. I'm sure that, you know, a couple of months from now, next year, we'll be like, oh, you know, like GPT-4.5, GPT-5, it's so much better. Like GPT-4 is terrible, but like right now it's the best that we have short of humans. And what we found is that when doing like temperature zero evals, it's actually mostly deterministic GPT-4 across runs in assigning scores to two different answers. So we found it to be a very useful tool in comparing our model to say, GPT-4, but yeah, on our like internal real world, here's what people will be asking this model dataset. And the other thing that we're running is just like releasing the model to our users and just seeing what they think. Because that's like the only thing that really matters is like releasing it for the application that it's intended for, and then seeing how people react. And for the most part, the incredible thing is, is that people don't notice a difference between our model and GPT-4 for the vast majority of searches. There's some reasoning problems that GPT-4 can still do better. We're working on addressing that. But in terms of like the types of questions that people are asking on find, there's not that much difference. And in fact, I've been running my own kind of side by side comparisons, shout out to GodMode, by the way. [00:37:16]
Michael: And I've like myself, I've kind of confirmed this to be the case. And even sometimes it gives a better answer, perhaps like more concise or just like better implementation than GPT-4, which that's what surprises me. And by now we kind of have like this reasoning is all you need kind of hypothesis where we've seen emerging capabilities in the find model, whereby training it on high quality code, it can actually like reason better. It went from not being able to solve world problems, where riddles were like with like temporal placement of objects and moving and stuff like that, that GPT-4 can do pretty well. We went from not being able to do those at all to being able to do them just by training on more code, which is wild. So we're already like starting to see like these emerging capabilities. [00:37:59]
Swyx: So I just wanted to make sure that we have the, I guess, like the model card in our heads. So you started from Code Llama? [00:38:07]
Alessio: Yes. [00:38:08]
Swyx: 65, 34? 34. [00:38:10]
Michael: So unfortunately, there's no Code Llama 70b. If there was, that would be super cool. But there's not. [00:38:15]
Swyx: 34. And then, which in itself was Llama 2, which is on 2 trillion tokens and the added 500 billion code tokens. Yes. [00:38:22]
Michael: And you just added a bunch more. [00:38:23]
Alessio: Yeah. [00:38:24]
Michael: And they also did a couple of things. So they did, I think they did 500 billion, like general pre-training and then they did an extra 20 billion long context pre-training. So they actually increased the like max position tokens to 16k up from 8k. And then they changed the theta parameter for the ROPE embeddings as well to give it theoretically better long context support up to 100k tokens. But yeah, but otherwise it's like basically Llama 2. [00:38:50]
Swyx: And so you just took that and just added data. [00:38:52]
Michael: Exactly. [00:38:53]
Swyx: You didn't do any other fundamental. [00:38:54]
Michael: Yeah. So we didn't actually, we haven't yet done anything with the model architecture and we just trained it on like many, many more billions of tokens on our own infrastructure. And something else that we're taking a look at now is using reinforcement learning for correctness. One of the interesting pitfalls that we've noticed with the Phind model is that in cases where it gets stuff wrong, it sometimes is capable of getting the right answer. It's just, there's a big variance problem. It's wildly inconsistent. There are cases when it is able to get the right chain of thought and able to arrive [00:39:25]
Alessio: at the right answer, but not always. [00:39:27]
Michael: And so like one of our hypotheses is something that we're going to try is that like we can actually do reinforcement learning on, for a given problem, generate a bunch of completions and then like use the correct answer as like a loss basically to try to get it to be more correct. And I think there's a high chance I think of this working because it's very similar to the like RLHF method where you basically show pairs of completions for a given question except the criteria is like which one is like less harmful. But here we have a different criteria. But if the model is already capable of getting the right answer, which it is, we're just, we just need to cajole it into being more consistent. [00:40:06]
Alessio: There were a couple of things that I noticed in the product that were not strange but unique. So first of all, the model can talk multiple times in a row, like most other applications is like human model, human model. And then you had outside of the thumbs up, thumbs down, you have things like have DLLM prioritize this message and its answers or then continue from this message to like go back. How does that change the flow of the user and like in terms of like prompting it, yeah, what are like some tricks or learnings you've had? [00:40:37]
Michael: So yeah, that's specifically in our pair programmer mode, which is a more conversational mode that also like asks you clarifying questions back if it doesn't fully understand what you're doing and it kind of it holds your hand a bit more. And so from user feedback, we had requests to make more of an auto GPT where you can kind of give it this problem that might take multiple searches or multiple different steps like multiple reasoning steps to solve. And so that's the impetus behind building that product. Being able to do multiple steps and also be able to handle really long conversations. Like people are really trying to use the pair programmer to go from like sometimes really from like basic idea to like complete working code. And so we noticed was is that we were having like these very, very long threads, sometimes with like 60 messages, like 100 messages. And like those become really, really challenging to manage the appropriate context window of what should go inside of the context and how to preserve the context so that the model can continue or the product can continue giving good responses, even if you're like 60 messages deep in a conversation. So that's where the prioritized user messages like comes from. It's like people have asked us to just like let them pin messages that they want to be left in the conversation. And yeah, and then that seems to have like really gone a long way towards solving that problem, yeah. [00:41:54]
Alessio: And then you have a run on Replit thing. Are you planning to build your own repl? Like learning some people trying to run the wrong code, unsafe code? [00:42:03]
Michael: Yes. Yes. So I think like in the long term vision of like being a place where people can go from like idea to like fully working code, having a code sandbox, like a natively integrated code sandbox makes a lot of sense. And replit is great and people use that feature. But yeah, I think there's more we can do in terms of like having something a bit closer to code interpreter where it's able to run the code and then like recursively iterate on it. Exactly. [00:42:31]
Swyx: So you're working on APIs to enable you to do that? Yep. So Amjad has specifically told me in person that he wants to enable that for people at the same time. He's also working on his own models, and Ghostwriter and you know, all the other stuff. So it's going to get interesting. Like he wants to power you, but also compete with you. Yeah. [00:42:47]
Michael: And like, and we love replit. I think that a lot of the companies in our space, like we're all going to converge to solving a very similar problem, but from a different angle. So like replit approaches this problem from the IDE side. Like they started as like this IDE that you can run in the browser. And they started from that side, making coding just like more accessible. And we're approaching it from the side of like an LLM that's just like connected to everything that it needs to be connected to, which includes your code context. So that's why we're kind of making inroads into IDEs, but we're kind of, we're approaching this problem from different sides. And I think it'll be interesting to see where things end up. But I think that in the long, long term, we have an opportunity to also just have like this general technical reasoning engine product that's potentially also not just for, not just for programmers. It's also powered in this web interface, like where there's potential, I think other things that we will build that eventually might go beyond like our current scope. [00:43:49]
Swyx: Exciting. We'll look forward to that. We're going to zoom out a little bit into sort of AI ecosystem stories, but first we got to get the Paul Graham, Ron Conway story. [00:43:59]
Alessio: Yeah. [00:44:00]
Michael: So flashback to last summer, we're in the YC batch. We're doing the summer batch, summer 22. So the summer batch runs from June to September, approximately. And so this was late July, early August, right around the time that many like YC startups start like going out, like during up, here's how we're going to pitch investors and everything. And at the same time, me and my co-founder, Justin, we were planning on moving to New York. So for a long time, actually, we were thinking about building this company in New York, mainly for personal reasons, actually, because like during the pandemic, pre-ChatGPT, pre last year, pre the AI boom, SF unfortunately really kind of, you know, like lost its luster. Yeah. Like no one was here. It was far from clear, like if there would be an AI boom, if like SF would be like... [00:44:49]
Alessio: Back. [00:44:50]
Michael: Yeah, exactly. Back. As everyone is saying these days, it was far from clear. And so, and all of our friends, we were graduating college because like we happened to just graduate college and immediately start YC, like we didn't even have, I think we had a week in between. [00:45:06]
Swyx: You didn't bother looking for jobs. You were just like, this is what we want to do. [00:45:08]
Michael: Well, actually both me and my co-founder, we had jobs that we secured in 2021 from previous internships, but we both, funny enough, when I spoke to my boss's boss at the company at where I reneged my offer, I told him we got into YC, they actually said, yeah, you should do YC. [00:45:27]
Swyx: Wow. [00:45:28]
Alessio: That's very selfless. [00:45:29]
Swyx: That was really great that they did that. But in San Francisco, they would have offered to invest as well. [00:45:33]
Michael: Yes, they would have. But yeah, but we were both planning to be in New York and all of our friends were there from college at this point, like we have this whole plan where like on August 1st, we're going to move to New York and we had like this Airbnb for the month of New York. We're going to stay there and we're going to work and like all of that. The day before we go to New York, I called Justin and I just, I tell him like, why are we doing this? Because in our batch, by the time August 1st rolled around, all of our mentors at YC were saying like, hey, like you should really consider staying in SF. [00:46:03]
Swyx: It's the hybrid batch, right? [00:46:04]
Michael: Yeah, it was the hybrid batch, but like there were already signs that like something was kind of like afoot in SF, even if like we didn't fully want to admit it yet. And so we were like, I don't know, I don't know. Something kind of clicked when the rubber met the road and it was time to go to New York. We're like, why are we doing this? And like, we didn't have any good reasons for staying in New York at that point beyond like our friends are there. So we still go to New York because like we have the Airbnb, like we don't have any other kind of place to go for the next few weeks. We're in New York and New York is just unfortunately too much fun. Like all of my other friends from college who are just, you know, basically starting their jobs, starting their lives as adults. They just stepped into these jobs, they're making all this money and they're like partying and like all these things are happening. And like, yeah, it's just a very distracting place to be. And so we were just like sitting in this like small, you know, like cramped apartment, terrible posture, trying to get as much work done as we can, too many distractions. And then we get this email from YC saying that Paul Graham is in town in SF and he is doing office hours with a certain number of startups in the current batch. And whoever signs up first gets it. And I happen to be super lucky. I was about to go for a run, but I just, I saw the email notification come across the street. I immediately clicked on the link and like immediately, like half the spots were gone, but somehow the very last spot was still available. And so I picked the very, very last time slot at 7 p.m. semi-strategically, you know, so we would have like time to go over. And also because I didn't really know how we're going to get to SF yet. And so we made a plan that we're going to fly from New York to SF and back to New York in one day and do like the full round trip. And we're going to meet with PG at the YC Mountain View office. And so we go there, we do that, we meet PG, we tell him about the startup. And one thing I love about PG is that he gets like, he gets so excited. Like when he gets excited about something, like you can see his eyes like really light up. And he'll just start asking you questions. In fact, it's a little challenging sometimes to like finish kind of like the rest of like the description of your pitch because like, he'll just like asking all these questions about how it works. And I'm like, you know, what's going on? [00:48:19]
Swyx: What was the most challenging question that he asked you? [00:48:21]
Michael: I think that like really how it worked. Because like as soon as like we told him like, hey, like we think that the future of search is answers, not links. Like we could really see like the gears turning in his head. I think we were like the first demo of that. [00:48:35]
Swyx: And you're like 10 minutes with him, right? [00:48:37]
Michael: We had like 45, yeah, we had a decent chunk of time. And so we tell him how it works. Like he's very excited about it. And I just like, I just blurted out, I just like asked him to invest and he hasn't even seen the product yet. We just asked him to invest and he says, yeah. And like, we're super excited about that. [00:48:55]
Swyx: You haven't started your batch. [00:48:56]
Michael: No, no, no. This is about halfway through the batch or two, two, no, two thirds of the batch. [00:49:02]
Swyx: And you're like not technically fundraising yet. We're about to start fundraising. Yeah. [00:49:06]
Michael: So we have like this demo and like we showed him and like there was still a lot of issues with the product, but I think like it must have like still kind of like blown his mind in some way. So like we're having fun. He's having fun. We have this dinner planned with this other friend that we had in SF because we were only there for that one day. So we thought, okay, you know, after an hour we'll be done, you know, we'll grab dinner with our friend and we'll fly back to New York. But PG was like, like, I'm having so much fun. Do you want to have dinner? Yeah. Come to my house. Or he's like, I gotta go have dinner with my wife, Jessica, who's also awesome, by the way. [00:49:40]
Swyx: She's like the heart of YC. Yeah. [00:49:42]
Michael: Jessica does not get enough credit as an aside for her role. [00:49:46]
Swyx: He tries. [00:49:47]
Michael: He understands like the technical side and she understands people and together they're just like a phenomenal team. But he's like, yeah, I got to go see Jessica, but you guys are welcome to come with. Do you want to come with? And we're like, we have this friend who's like right now outside of like literally outside the door who like we also promised to get dinner with. It's like, we'd love to, but like, I don't know if we can. He's like, oh, he's welcome to come too. So all of us just like hop in his car and we go to his house and we just like have this like we have dinner and we have this just chat about the future of search. Like I remember him telling Jessica distinctly, like our kids as kids are not going to know what like a search result is. Like they're just going to like have answers. That was really like a mind blowing, like inflection point moment for sure. [00:50:34]
Swyx: Wow, that email changed your life. [00:50:35]
Michael: Absolutely. [00:50:36]
Swyx: And you also just spoiled the booking system for PG because now everyone's just going to go after the last slot. Oh man. [00:50:42]
Michael: Yeah. But like, I don't know if he even does that anymore. [00:50:46]
Swyx: He does. He does. Yeah. I've met other founders that he did it this year. [00:50:49]
Michael: This year. Gotcha. But when we told him about how we did it, he was like, I am like frankly shocked that YC just did like a random like scheduling system. [00:50:55]
Alessio: They didn't like do anything else. But, um. [00:50:58]
Swyx: Okay. And then he introduces Duron Conway. Yes. Who is one of the most legendary angels in Silicon Valley. [00:51:04]
Michael: Yes.So after PG invested, the rest of our round came together pretty quickly. [00:51:10]
Swyx: I'm, by the way, I'm surprised. Like it's, it might feel like playing favorites right within the current batch to be like, yo, PG invested in this one. Right. [00:51:17]
Alessio: Too bad for the others. [00:51:18]
Swyx: Too bad for the others, I guess. [00:51:19]
Michael: I think this is a bigger point about YC and like these accelerators in general is like YC gets like a lot of criticism from founders who feel like they didn't get value out of it. But like, in my view, YC is what you make of it. And YC tells you this. They're like, you really got to grab this opportunity, like buy the balls and make the most of it. And if you do, then it could be the best thing in the world. And if you don't, and if you're just kind of like a passive, even like an average founder in YC, you're still going to fail. And they tell you that. They're like, if you're average in your batch, you're going to fail. Like you have to just be exceptional in every way. With that in mind, perhaps that's even part of the reason why we asked PG to invest. And so yeah, after PG invested, the rest of our round came together pretty quickly, which I'm very fortunate for. And yeah, he introduced us to Ron. And after he did, I get a call from Ron. And then Ron says like, hey, like PG tells me what you're working on. I'd love to come meet you guys. And I'm like, wait, no way. And then we're just holed up in this like little house in San Mateo, which is a little small, but you know, it had a nice patio. In fact, we had like a monitor set up outside on the deck out there. And so Ron Conway comes over, we go over to the patio where like our workstation is. And Ron Conway, he's known for having like this notebook that he goes around with where he like sits down with the notebook and like takes very, very detailed notes. So he never like forgets anything. So he sits down with his notebook and he asks us like, hey guys, like, what do you need? And we're like, oh, we need GPUs. Back then, the GPU shortage wasn't even nearly as bad as it is now. But like even then, it was still challenging to get like the quota that we needed. And he's like, okay, no problem. And then like he leaves a couple hours later, we get an email and we're CC'd on an email that Ron wrote to Jensen, the CEO of Nvidia, saying like, hey, these guys need GPUs. [00:53:02]
Swyx: You didn't say how much? It was just like, just give them GPUs. [00:53:04]
Alessio: Basically, yeah. [00:53:05]
Michael: Ron is known for writing these like one-liner emails that are like very short, but very to the point. And I think that's why like everyone responds to Ron. Everyone loves Ron. And so Jensen responds. He responds quickly, like tagging this VP of AI at Nvidia. And we start working with Nvidia, which is great. And something that I love about Nvidia, by the way, is that after that intro, we got matched with like a dedicated team. And at Nvidia, they know that they're going to win regardless. So they don't care where you get the GPUs from. They're like, they're truly neutral, unlike various sales reps that you might encounter at various like clouds and, you know, hardware companies, et cetera. They actually just want to help you because they know they don't care. Like regardless, they know that if you're getting Nvidia GPUs, they're still winning. So I guess that's a tip is that like if you're looking for GPUs like Nvidia, they'll help you do it. [00:53:54]
Swyx: So just to tie up this thing, because so first of all, that's a fantastic story. And I just wanted to let you tell that because it's special. That is a strategic shift, right? That you already decided to make by the time you met Ron, which is we are going to have our own hardware. We're going to rack him in a data center somewhere. [00:54:11]
Michael: Well, not even that we need our own hardware because actually we don't. Right. But we just we just need GPUs, period. And like every cloud loves like they have their own sales tactics and like they want to make you commit to long terms and like very non-flexible terms. And like there's a web of different things that you kind of have to navigate. Nvidia will kind of be to the point like, OK, you can do this on this cloud, this on this cloud. Like this is your budget. Maybe you want to consider buying as well. Like they'll help you walk through what the options are. And the reason why they're helpful is because like they look at the full picture. So they'll help you with the hardware. And in terms of software, they actually implemented a custom feature for us in Faster Transformer, which is one of their libraries.
Swyx: For you? [00:54:53]
Michael: For us. Yeah. Which is wild. I don't think they would have done it otherwise. They implemented streaming generation for T5 based models, which we were running at the time up until we switched to GPT in February, March of this year. So they implemented that just for us, actually, in Faster Transformer. And so like they'll help you like look at the complete picture and then just help you get done what you need to get done. I know one of your interests is also local models, open source models and hardware kind of goes hand in hand.
Alessio: Any fun projects, explorations in the space that you want to share with local llamas and stuff? [00:55:27]
Michael: Yeah, it's something that we're very interested in because something that kind of we're hearing a lot about is like people want something like find, especially companies, but they want to have it like within like their own sandbox. They want to have it like on hardware that they control. And so I'm super, super interested in how we can get big models to run efficiently on local hardware. And so like Ollama is great. Llama CPP is great. Very interested in like where the quantization thing is going. Because like obviously there are all these like great quantization libraries now that go to 4-bit, 8-bit, but specifically int8 and int4. [00:56:04]
Alessio: Which is the lowest it can go, right? [00:56:05]
Swyx: Yeah. [00:56:06]
Michael: So we have these great quantization libraries that for the most part are able to get the size down with not that much quality loss. But there is some like the quantized models currently are actually worse than the non-quantized ones. And so I'm very curious if the future is something like what NVIDIA is doing with their implementation of FP8, which they're implementing in their transformer engine library. Where basically once FP8 support is kind of more widespread and hardware can support it efficiently, you can kind of switch between the two different FP8 formats. One with greater precision, one with greater range. And then combine that with only not doing FP8 on every layer and doing like a mixed precision with like FP32 on some layers. And like NVIDIA claims that this strategy that they're kind of demoing with the H100 has no degradation. And so it remains to be seen whether that is really true in practice. But that's something that we're excited about and whether that can be applied to Macs and other hardware once they get FP8 support as well. [00:57:05]
Alessio: Cool. [00:57:06]
Swyx: One thing I wanted to do before we go into lightning round. Oh, we should also talk about hiring. How do you get your info? You seem self-taught. Yeah. [00:57:12]
Michael: I've always just, well, I'm fortunate to have like a decent systems background from UT Austin. And somewhat of a research background, even though like I didn't publish any papers, but like I went through all the motions. Like I didn't publish the thesis that I wrote, mainly out of time because I was doing both of that and the startup at the same time. And then I graduated and then it was YC and then everything was kind of one after another. But like I'm very fortunate to kind of have like the systems and like a bit of like a research background. But for the most part, outside of that foundation, like I've always just, whenever I've been interested in something, I just like. [00:57:43]
Swyx: Like give people tips, right? Like where do you, what fire hose do you drink from? Yeah, exactly. [00:57:48]
Michael: So like whenever I see something that blows my mind, the way that that initial hugging face demo did, that was like the start of everything. I'll start from the beginning. If I don't know anything, I'll start by just trying to get a mental model of what is happening. Like first I need to understand what, so I can understand like the why, the how and the why. And once I can understand that, then I can make my own hypotheses about like, okay, here are the assumptions that the authors of this made. I mean, here's why maybe they're correct. Maybe they're wrong. And here's how like I can improve on it and iterate on it. And I guess that's the mindset that I approach it from is like, once I understand something, like how can it be better? How can it be faster? How can it be like more accurate? And so I guess for anyone starting now, like I would have used find if I was starting now. Cause like I would have loved to just have been able to say like, Hey, like I have no idea what I'm doing. Can you just like be this like technical research assistant and kind of hold my hand and like ask me clarifying questions and like help me like formalize my assumptions like along the way. I would have loved that. But yeah, I just kind of did that myself. [00:58:50]
Swyx: Recording Looms of yourself using Phind actually would be pretty interesting. Yeah. Because I think you, you would use find differently than people would by themselves. [00:58:57]
Michael: I think so. Yeah. I generally use Phind for everything, which is definitely, yeah, it's like, no, no, even like non-technical questions as well. Cause that's just something I'm curious about, but that's less of a usage pattern nowadays. Like most people generally for the most part do technical questions on find. And that is completely understandable because of very deliberate decisions that we've made in how we've optimized the product. Like we've optimized the product very much in a quality first manner as opposed to a like speed first or like some balance of the two matters. So we're like, we have to run GPT-4 or some GPT-4 equivalent by default. And like, and it has to give like a good answer to like a very demanding technical audience where people will leave. So that's just the trade off. So like sometimes it's, it's slower for like simple questions, but like we did that on purpose. [00:59:46]
Alessio: So before we do a lightning round, call for hiring any roles you're looking for. What should people know about what can I find? Yeah. [00:59:55]
Michael: So we really straddled the line between product and research I find. For the past little while, a lot of the work that we've done has been solely product. But we also do, especially now with the find model, a very particular kind of applied research in trying to apply the very latest techniques and techniques that might not, that have not even been proven yet to training the very, very best model for our vertical. And the two go hand in hand because the product, the UI, the UX is kind of model agnostic. But when it has a better kernel, as Andrej Karpathy put it, plugged into it, it gets so much better. So we're doing really kind of both at the same time. And so someone who enjoys seeing both of those sides, like doing something very tangible that affects the user, high quality, reliable code that runs in production, but also having that chance to experiment with building these models. Yeah, we'd love to talk to you. [01:00:50]
Swyx: And the title is Applied AI Engineer. [01:00:52]
Michael: I don't know what the title is. Like that is one title, but I don't know if this really exists because I feel like we're too rigid about like bucketing people into categories. [01:01:02]
Swyx: Yeah, Founding Engineer is fine. [01:01:03]
Michael: Yeah, well, we already have a Founding Engineer technically. [01:01:06]
Swyx: Well, for what it's worth, OpenAI is adopting Applied AI Engineer. Really? So it's becoming a thing. We'll see. [01:01:12]
Alessio: We'll see. Lightning round. Yeah, we have three questions, acceleration, exploration, and then a takeaway. So the acceleration one is what's something that already happened in AI that you thought would take much longer? [01:01:24]
Michael: Yeah, the jump from these like models being glorified summarization models to actual powerful reasoning engines happened much faster than we thought because like our product itself transitioned from being kind of this glorified summarization product to now like mostly a reasoning heavy product. And we had no idea that this would happen this fast. Like we thought that there'd be a lot more time and like many more things that needed to happen before we could do some level of like intelligent reasoning on a low level about people's code. But it's already happened and it happened much faster than we could have thought. But I think that leads into your next point. [01:02:02]
Alessio: Which is exploration. [01:02:04]
Swyx: What do you think is the most interesting unsolved question in AI? [01:02:07]
Michael: I think solving hallucinations, being able to guarantee that the answer will be correct is I think super interesting. And it's particularly relevant to us because like we operate in a space where like everything needs to be correct. Like the code, like not just the logic, but like the implementation, everything has to be completely correct. And there's a lot of very interesting work that's going on in this space. Some of it is approaching it from the angle of formal grammars. There's a very interesting paper that came out recently. I forget where it came out of, but the paper is basically you can define a grammar that restricts and modifies the model's log probs, like decoding strategy to only conform to that grammar. And that helps it... [01:02:53]
Swyx: Is this LMQL? Because I feel like LMQL is a little bit too structured for... If the goal is avoiding hallucination, that's such a vague goal. Yeah. [01:03:02]
Michael: This is only something we've begun to take a look at. I haven't fully read the paper yet. Like I've only kind of skimmed the abstract, but it's something that like we're definitely interested in exploring further. But something that we are like a bit further along on is also like exploring reinforcement learning for correctness, as opposed to only harmfulness the way it has typically been used in my college. [01:03:23]
Swyx: I'm interested to see your paper on that. Just a quick follow-up. Do you have internal evals for what hallucination rate is on stock GPC4 and then maybe what yours is after fine-tuning? [01:03:34]
Michael: We don't measure hallucination directly in our internal benchmarks. We more measure like was the answer right or was it wrong? We measure hallucination indirectly by evaluating the context, like the RAG context fed into the model as well. So basically, if the context was bad and the answer was bad, then chances are like it's the context. But if the context was good and it just like misinterpreted that or had the wrong conclusion, then like we can take different steps there. Harrison from LangChain has been talking about this sort of two-by-two matrix with the RAG people. It's a pretty simple concept. [01:04:08]
Swyx: What's the source of error? [01:04:11]
Michael: Exactly. I've been talking to Harrison actually about like a more structured way perhaps within Linkchain to like do evals. Because I think that's a massive problem. Like every single eval is different for these big, large language models and doing them in a quantitative way is really hard. But it's possible with like a platform that I think harnesses GPT-4 in the right way. That and also perhaps a stricter prompting language like a prompting markup language for prompting models is something I'm also very interested in. Because we've written some very, very complex prompts particularly for a VS Code extension to like very fancy things with people's code. And like I wish there was a way that you could have like a more formal way like a Python for LLM prompting that you could activate desired things within like the model's execution flow through some other abstraction above language that has been like tested to do that some of the time. Perhaps like combined with like formal grammar limitations and stuff like that. Interesting. I have no idea what that looks like. These are all things these are all things that have kind of emerged directly from the issues we're facing ourselves internally. But yeah, definitely very abstract so far.
Alessio: And yeah, just to wrap what's one message idea you want people to remember and think about? [01:05:32]
Michael: I think pay attention to those moments that like really jump out at you. Like when you see like a crazy demo that you can't forget about like something that you just think is really, really cool. Because I see a lot of people trying to start startups from the angle of like, hey, I just want to start a startup or I'm just bored at my job or like I'm like generally interested in the space. And I personally disagree with that. My take is that it's much easier having been on both sides of that coin now, it's much easier to stay obsessed every single day when the genesis of your startup is something that really spoke to you in an incredibly meaningful way beyond just being some insight that you've noticed. And I guess that's what we're discovering now is that in the long, long term what you're really building is like you're building a group of people that believe this thing, that believe that the future of solving problems and making things will be just like focused more on the human thought process as opposed to the implementation part. And it's that belief that I think is what really gets you through the tough times and hopefully gets you to the other side someday. [01:06:47]
Swyx: Awesome. I kind of want to play Lose Yourself as the outro music. [01:06:52]
Alessio: Then we'll get DMCA strike. Thank you so much for coming on.
Michael: Yeah, thank you so much for having me. This was really fun. [01:06:59]
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Podcast: Real Dictators (LS 71 · TOP 0.05% what is this?)
Episode: Pol Pot Part 4: The Killing Fields
Pub date: 2022-07-05
Pol Pot constructs a secretive one-party state. Cambodia descends into hell as his twisted social experiment begins. Finally, the shifting sands of geopolitics will create challenges for the Khmer Rouge. Pol Pot will be forced back into the jungle. But even then, the man with untold blood on his hands will somehow evade justice.
A Noiser production, written by Dan Smith. Many thanks to Elizabeth Becker and University of Washington Libraries for archive audio.
This is Part 4 of 4.
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Podcast: Real Dictators (LS 71 · TOP 0.05% what is this?)
Episode: Pol Pot Part 3: Vietnam War, Cambodian Revolution
Pub date: 2022-06-28
The Khmer Rouge launch their revolution against Sihanouk… until Sihanouk himself is stabbed in the back. America’s action in Vietnam escalates, spilling over the border with catastrophic consequences. The enigmatic Pol Pot will emerge from the bloodshed to become the most powerful man in the land. At the outset of ‘Year Zero’, the true horror will begin...
A Noiser production, written by Dan Smith.
This is Part 3 of 4.
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Podcast: Real Dictators (LS 71 · TOP 0.05% what is this?)
Episode: Pol Pot Part 2: Welcome to the Jungle
Pub date: 2022-06-21
Saloth Sar, the young man who will become Pol Pot, arrives in France in 1949. In just a few short months, he will be transformed into a die-hard communist rebel. Meanwhile, in Cambodia, things are coming to the boil. Returning from Europe, Sar goes to ground with the Vietnamese communists. He begins to prepare his very own revolution…
A Noiser production, written by Dan Smith.
This is Part 2 of 4.
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Podcast: Real Dictators (LS 71 · TOP 0.05% what is this?)
Episode: Pol Pot Part 1: Tyrant in the Shadows
Pub date: 2022-06-21
Cambodia today is many people’s idea of heaven on earth. But in the 1970s it was a netherworld of death and despair. Under Pol Pot and the Khmer Rouge, in just four years around a quarter of the population perished. Pol Pot was a uniquely anonymous dictator - a man who preferred to operate in the shadows. So who was he? And how did he come to lead such a staggeringly bloody regime?
A Noiser production, written by Dan Smith.
This is Part 1 of 4.
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Podcast: Real Dictators (LS 71 · TOP 0.05% what is this?)
Episode: BONUS: Dictators’ Chefs
Pub date: 2023-05-09
For this special bonus episode, Noiser writer Duncan Barrett sat down for a chat with Witold Szabłowski, author of How to Feed a Dictator: Saddam Hussein, Idi Amin, Enver Hoxha, Fidel Castro and Pol Pot Through the Eyes of Their Cooks.
The role of personal chef to a dictator is a fascinating one. It’s an extremely intimate relationship. These cooks have literally nourished tyrants. They’ve satisfied their culinary cravings, altered their moods for better and worse, and even influenced their policies. So what can they tell us about dictators’ appetites?
Real Dictators will return soon for Season 5.
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Podcast: Latent Space: The AI Engineer Podcast — Practitioners talking LLMs, CodeGen, Agents, Multimodality, AI UX, GPU Infra and all things Software 3.0 (LS 33 · TOP 5% what is this?)
Episode: Why AI Agents Don't Work (yet) - with Kanjun Qiu of Imbue
Pub date: 2023-10-14
Thanks to the over 11,000 people who joined us for the first AI Engineer Summit! A full recap is coming, but you can 1) catch up on the fun and videos on Twitter and YouTube, 2) help us reach 1000 people for the first comprehensive State of AI Engineering survey and 3) submit projects for the new AI Engineer Foundation.
See our Community page for upcoming meetups in SF, Paris, NYC, and Singapore.
This episode had good interest on Twitter.
Last month, Imbue was crowned as AI’s newest unicorn foundation model lab, raising a $200m Series B at a >$1 billion valuation. As “stealth” foundation model companies go, Imbue (f.k.a. Generally Intelligent) has stood as an enigmatic group given they have no publicly released models to try out. However, ever since their $20m Series A last year their goal has been to “develop generally capable AI agents with human-like intelligence in order to solve problems in the real world”.
From RL to Reasoning LLMs
Along with their Series A, they announced Avalon, “A Benchmark for RL Generalization Using Procedurally Generated Worlds”. Avalon is built on top of the open source Godot game engine, and is ~100x faster than Minecraft to enable fast RL benchmarking and a clear reward with adjustable game difficulty.
After a while, they realized that pure RL isn’t a good path to teach reasoning and planning. The agents were able to learn mechanical things like opening complex doors, climbing, but couldn’t go to higher level tasks. A pure RL world also doesn’t include a language explanation of the agent reasoning, which made it hard to understand why it made certain decisions. That pushed the team more towards the “models for reasoning” path:
“The second thing we learned is that pure reinforcement learning is not a good vehicle for planning and reasoning. So these agents were able to learn all sorts of crazy things: They could learn to climb like hand over hand in VR climbing, they could learn to open doors like very complicated, like multiple switches and a lever open the door, but they couldn't do any higher level things. And they couldn't do those lower level things consistently necessarily. And as a user, I do not want to interact with a pure reinforcement learning end to end RL agent. As a user, like I need much more control over what that agent is doing.”
Inspired by Chelsea Finn’s work on SayCan at Stanford, the team pivoted to have their agents do the reasoning in natural language instead. This development parallels the large leaps in reasoning that humans have developed as the scientific method:
“We are better at reasoning now than we were 3000 years ago. An example of a reasoning strategy is noticing you're confused. Then when I notice I'm confused, I should ask:
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What was the original claim that was made?
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What evidence is there for this claim?
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Does the evidence support the claim?
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Is the claim correct?
This is like a reasoning strategy that was developed in like the 1600s, you know, with like the advent of science. So that's an example of a reasoning strategy. There are tons of them. We employ all the time, lots of heuristics that help us be better at reasoning. And we can generate data that's much more specific to them.“
The Full Stack Model Lab
One year later, it would seem that the pivot to reasoning has had tremendous success, and Imbue has now reached a >$1B valuation, with participation from Astera Institute, NVIDIA, Cruise CEO Kyle Vogt, Notion co-founder Simon Last, and others. Imbue tackles their work with a “full stack” approach:
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Models. Pretraining very large (>100B parameter) models, optimized to perform well on internal reasoning benchmarks, with a ~10,000 Nvidia H100 GPU cluster lets us iterate rapidly on everything from training data to architecture and reasoning mechanisms.
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Tools and Agents. Building internal productivity tools from coding agents for fixing type checking and linting errors, to sophisticated systems like CARBS (for hyperparameter tuning and network architecture search).
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Interface Invention. Solving agent trust and collaboration (not merely communication) with humans by creating better abstractions and interfaces — IDEs for users to program computers in natural language.
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Theory. Publishing research about the theoretical underpinnings of self-supervised learning, as well as scaling laws for machine learning research.
Kanjun believes we are still in the “bare metal phase” of agent development, and they want to take a holistic approach to building the “operating system for agents”. We loved diving deep into the Imbue approach toward solving the AI Holy Grail of reliable agents, and are excited to share our conversation with you today!
Timestamps
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[00:00:00] Introductions
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[00:06:07] The origin story of Imbue
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[00:09:39] Imbue's approach to training large foundation models optimized for reasoning
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[00:12:18] Imbue's goals to build an "operating system" for reliable, inspectable AI agents
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[00:15:37] Imbue's process of developing internal tools and interfaces to collaborate with AI agents
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[00:17:27] Imbue's focus on improving reasoning capabilities in models, using code and other data
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[00:19:50] The value of using both public benchmarks and internal metrics to evaluate progress
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[00:21:43] Lessons learned from developing the Avalon research environment
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[00:23:31] The limitations of pure reinforcement learning for general intelligence
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[00:28:36] Imbue's vision for building better abstractions and interfaces for reliable agents
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[00:31:36] Interface design for collaborating with, rather than just communicating with, AI agents
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[00:37:40] The future potential of an agent-to-agent protocol
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[00:39:29] Leveraging approaches like critiquing between models and chain of thought
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[00:45:49] Kanjun's philosophy on enabling team members as creative agents at Imbue
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[00:53:51] Kanjun's experience co-founding the communal co-living space The Archive
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[01:00:22] Lightning Round
Show Notes
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Imbue
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Avalon
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CARBS (hyperparameter optimizer)
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Series B announcement
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Kanjun/Imbue’s Podcast
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MIT Media Lab
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Research mentioned:
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Momentum Contrast
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SimClr
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Chelsea Finn - SayCan
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Agent Protocol - part of the AI Engineer Foundation
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Xerox PARC
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Michael Nielsen
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Jason Benn
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Outset Capital
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Scenius - Kevin Kelly
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South Park Commons
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The Archive
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Thursday Nights in AI
Transcript
Alessio: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, Partner and CTO at Residence at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol.ai. [00:00:19]
Swyx: Hey, and today in the studio we have Kanjun from Imbue. Welcome. So you and I have, I guess, crossed paths a number of times. You're formerly named Generally Intelligent and you've just announced your rename, rebrand in huge, humongous ways. So congrats on all of that. And we're here to dive in into deeper detail on Imbue. We like to introduce you on a high level basis, but then have you go into a little bit more of your personal side. So you graduated your BS at MIT and you also spent some time at the MIT Media Lab, one of the most famous, I guess, computer hacking labs in the world. Then you graduated MIT and you went straight into BizOps at Dropbox, where you're eventually chief of staff, which is a pretty interesting role we can dive into later. And then it seems like the founder bug hit you. You were basically a three times founder at Ember, Sorceress, and now at Generally Intelligent slash Imbue. What should people know about you on the personal side that's not on your LinkedIn? That's something you're very passionate about outside of work. [00:01:12]
Kanjun: Yeah. I think if you ask any of my friends, they would tell you that I'm obsessed with agency, like human agency and human potential. [00:01:19]
Swyx: That's work. Come on.
Kanjun: It's not work. What are you talking about?
Swyx: So what's an example of human agency that you try to promote? [00:01:27]
Kanjun: With all of my friends, I have a lot of conversations with them that's kind of helping figure out what's blocking them. I guess I do this with a team kind of automatically too. And I think about it for myself often, like building systems. I have a lot of systems to help myself be more effective. At Dropbox, I used to give this onboarding talk called How to Be Effective, which people liked. I think like a thousand people heard this onboarding talk, and I think maybe Dropbox was more effective. I think I just really believe that as humans, we can be a lot more than we are. And it's what drives everything. I guess completely outside of work, I do dance. I do partner dance. [00:02:03]
Swyx: Yeah. Lots of interest in that stuff, especially in the sort of group living houses in San Francisco, which I've been a little bit part of, and you've also run one of those. [00:02:12]
Kanjun: That's right. Yeah. I started the archive with two friends, with Josh, my co-founder, and a couple of other folks in 2015. That's right. And GPT-3, our housemates built. [00:02:22]
Swyx: Was that the, I guess, the precursor to Generally Intelligent, that you started doing more things with Josh? Is that how that relationship started? Yeah. [00:02:30]
Kanjun: This is our third company together. Our first company, Josh poached me from Dropbox for Ember. And there we built a really interesting technology, laser raster projector, VR headset. And then we were like, VR is not the thing we're most passionate about. And actually it was kind of early days when we both realized we really do believe that in our lifetimes, like computers that are intelligent are going to be able to allow us to do much more than we can do today as people and be much more as people than we can be today. And at that time, we actually, after Ember, we were like, work on AI research or start an AI lab. A bunch of our housemates were joining OpenAI, and we actually decided to do something more pragmatic to apply AI to recruiting and to try to understand like, okay, if we are actually trying to deploy these systems in the real world, what's required? And that was Sorceress. That taught us so much about maybe an AI agent in a lot of ways, like what does it actually take to make a product that people can trust and rely on? I think we never really fully got there. And it's taught me a lot about what's required. And it's kind of like, I think informed some of our approach and some of the way that we think about how these systems will actually get used by people in the real world. [00:03:42]
Swyx: Just to go one step deeper on that, you're building AI agents in 2016 before it was cool. You got some muscle and you raised $30 million. Something was working. What do you think you succeeded in doing and then what did you try to do that did not pan out? [00:03:56]
Kanjun: Yeah. So the product worked quite well. So Sorceress was an AI system that basically looked for candidates that could be a good fit and then helped you reach out to them. And this was a little bit early. We didn't have language models to help you reach out. So we actually had a team of writers that like, you know, customized emails and we automated a lot of the customization. But the product was pretty magical. Like candidates would just be interested and land in your inbox and then you can talk to them. As a hiring manager, that's such a good experience. I think there were a lot of learnings, both on the product and market side. On the market side, recruiting is a market that is endogenously high churn, which means because people start hiring and then we hire the role for them and they stop hiring. So the more we succeed, the more they... [00:04:39]
Swyx: It's like the whole dating business. [00:04:40]
Kanjun: It's the dating business. Exactly. Exactly. And I think that's the same problem as the dating business. And I was really passionate about like, can we help people find work that is more exciting for them? A lot of people are not excited about their jobs and a lot of companies are doing exciting things and the matching could be a lot better. But the dating business phenomenon like put a damper on that, like it's actually a pretty good business. But as with any business with like relatively high churn, the bigger it gets, the more revenue we have, the slower growth becomes because if 30% of that revenue you lose year over year, then it becomes a worse business. So that was the dynamic we noticed quite early on after our Series A. I think the other really interesting thing about it is we realized what was required for people to trust that these candidates were like well vetted and had been selected for a reason. And it's what actually led us, you know, a lot of what we do at Imbue is working on interfaces to figure out how do we get to a situation where when you're building and using agents, these agents are trustworthy to the end user. That's actually one of the biggest issues with agents that, you know, go off and do longer range goals is that I have to trust, like, did they actually think through this situation? And that really informed a lot of our work today. [00:05:52]
Alessio: Let's jump into GI now, Imbue. When did you decide recruiting was done for you and you were ready for the next challenge? And how did you pick the agent space? I feel like in 2021, it wasn't as mainstream. Yeah. [00:06:07]
Kanjun: So the LinkedIn says that it started in 2021, but actually we started thinking very seriously about it in early 2020, late 2019, early 2020. So what we were seeing is that scale is starting to work and language models probably will actually get to a point where like with hacks, they're actually going to be quite powerful. And it was hard to see that at the time, actually, because GPT-3, the early versions of it, there are all sorts of issues. We're like, oh, that's not that useful, but we could kind of see like, okay, you keep improving it in all of these different ways and it'll get better. What Josh and I were really interested in is how can we get computers that help us do bigger things? Like, you know, there's this kind of future where I think a lot about, you know, if I were born in 1900 as a woman, like my life would not be that fun. I'd spend most of my time like carrying water and literally like getting wood to put in the stove to cook food and like cleaning and scrubbing the dishes and, you know, getting food every day because there's no refrigerator, like all of these things, very physical labor. And what's happened over the last 150 years since the industrial revolution is we've kind of gotten free energy, like energy is way more free than it was 150 years ago. And so as a result, we've built all these technologies like the stove and the dishwasher and the refrigerator, and we have electricity and we have infrastructure, running water, all of these things that have totally freed me up to do what I can do now. And I think the same thing is true for intellectual energy. We don't really see it today, but because we're so in it, but our computers have to be micromanaged. You know, part of why people are like, oh, you're stuck to your screen all day. Well, we're stuck to our screen all day because literally nothing happens unless I'm doing something in front of my screen. I don't, you know, I can't send my computer off to do a bunch of stuff for me. And there is a future where that's not the case, where, you know, I can actually go off and do stuff and trust that my computer will pay my bills and figure out my travel plans and do the detailed work that I am not that excited to do so that I can like be much more creative and able to do things that I as a human, I'm very excited about and collaborate with other people. And there are things that people are uniquely suited for. So that's kind of always been the thing that has been really exciting to me. Like Josh and I have known for a long time, I think that, you know, whatever AI is, it would happen in our lifetimes. And the personal computer kind of started giving us a bit of free intellectual energy. And this is like really the explosion of free intellectual energy. So in early 2020, we were thinking about this and what happened was self-supervised learning basically started working across everything. So worked in language, SimClear came out, I think MoCo had come out, Momentum Contrast had come out earlier in 2019, SimClear came out in early 2020. And we're like, okay, for the first time, self-supervised learning is working really well across images and text and suspect that like, okay, actually it's the case that machines can learn things the way that humans do. And if that's true, if they can learn things in a fully self-supervised way, because like as people, we are not supervised. We like go Google things and try to figure things out. So if that's true, then like what the computer could be is much bigger than what it is today. And so we started exploring ideas around like, how do we actually go? We didn't think about the fact that we could actually just build a research lab. So we were like, okay, what kind of startup could we build to like leverage self-supervised learning? So that eventually becomes something that allows computers to become much more able to do bigger things for us. But that became General Intelligence, which started as a research lab. [00:09:39]
Alessio: So your mission is you aim to rekindle the dream of the personal computer. So when did it go wrong and what are like your first products and user facing things that you're building to rekindle it? [00:09:53]
Kanjun: Yeah. So what we do at Imbue is we train large foundation models optimized for reasoning. And the reason for that is because reasoning is actually, we believe the biggest blocker to agents or systems that can do these larger goals. If we think about something that writes an essay, like when we write an essay, we like write it. We put it and then we're done. We like write it and then we look at it and we're like, oh, I need to do more research on that area. I'm going to go do some research and figure it out and come back and, oh, actually it's not quite right. The structure of the outline. So I'm going to rearrange the outline, rewrite it. It's this very iterative process and it requires thinking through like, okay, what am I trying to do? Is the goal correct? Also like, has the goal changed as I've learned more? So as a tool, like when should I ask the user questions? I shouldn't ask them questions all the time, but I should ask them questions in higher risk situations. How certain am I about the like flight I'm about to book? There are all of these notions of like risk certainty, playing out scenarios, figuring out how to make a plan that makes sense, how to change the plan, what the goal should be. That are things that we lump under the bucket of reasoning and models today, they're not optimized for reasoning. It turns out that there's not actually that much explicit reasoning data on the internet as you would expect. And so we get a lot of mileage out of optimizing our models for reasoning in pre-training. And then on top of that, we build agents ourselves and we, I can get into, we really believe in serious use, like really seriously using the systems and trying to get to an agent that we can use every single day, tons of agents that we can use every single day. And then we experiment with interfaces that help us better interact with the agents. So those are some set of things that we do on the kind of model training and agent side. And then the initial agents that we build, a lot of them are trying to help us write code better because code is most of what we do every day. And then on the infrastructure and theory side, we actually do a fair amount of theory work to understand like, how do these systems learn? And then also like, what are the right abstractions for us to build good agents with, which we can get more into. And if you look at our website, we build a lot of tools internally. We have a like really nice automated hyperparameter optimizer. We have a lot of really nice infrastructure and it's all part of the belief of like, okay, let's try to make it so that the humans are doing the things humans are good at as much as possible. So out of our very small team, we get a lot of leverage. [00:12:18]
Swyx: And so would you still categorize yourself as a research lab now, or are you now in startup mode? Is that a transition that is conscious at all? [00:12:26]
Kanjun: That's a really interesting question. I think we've always intended to build, you know, to try to build the next version of the computer, enable the next version of the computer. The way I think about it is there's a right time to bring a technology to market. So Apple does this really well. Actually, iPhone was under development for 10 years, AirPods for five years. And Apple has a story where iPhone, the first multi-touch screen was created. They actually were like, oh wow, this is cool. Let's like productionize iPhone. They actually brought, they like did some work trying to productionize it and realized this is not good enough. And they put it back into research to try to figure out like, how do we make it better? What are the interface pieces that are needed? And then they brought it back into production. So I think of production and research as kind of like these two separate phases. And internally we have that concept as well, where like things need to be done in order to get to something that's usable. And then when it's usable, like eventually we figure out how to productize it. [00:13:20]
Alessio: What's the culture like to make that happen, to have both like kind of like product oriented, research oriented. And as you think about building the team, I mean, you just raised 200 million. I'm sure you want to hire more people. What are like the right archetypes of people that work at Imbue? [00:13:35]
Kanjun: I would say we have a very unique culture in a lot of ways. I think a lot about social process design. So how do you design social processes that enable people to be effective? I like to think about team members as creative agents, because most companies, they think of their people as assets and they're very proud of this. And I think about like, okay, what is an asset? It's something you own that provides you value that you can discard at any time. This is a very low bar for people. This is not what people are. And so we try to enable everyone to be a creative agent and to really unlock their superpowers. So a lot of the work I do, you know, I was mentioning earlier, I'm like obsessed with agency. A lot of the work I do with team members is try to figure out like, you know, what are you really good at? What really gives you energy and where can we put you such that, how can I help you unlock that and grow that? So much of our work, you know, in terms of team structure, like much of our work actually comes from people. Carbs, our hyperparameter optimizer came from Abe trying to automate his own research process doing hyperparameter optimization. And he actually pulled some ideas from plasma physics. He's a plasma physicist to make the local search work. A lot of our work on evaluations comes from a couple of members of our team who are like obsessed with evaluations. We do a lot of work trying to figure out like, how do you actually evaluate if the model is getting better? Is the model making better agents? Is the agent actually reliable? A lot of things kind of like, I think of people as making the like them shaped blob inside imbue and I think, you know, yeah, that's the kind of person that we're, we're hiring for. We're hiring product engineers and data engineers and research engineers and all these roles. We have projects, not teams. We have a project around data, data collection and data engineering. That's actually one of the key things that improve the model performance. We have a pre-training kind of project with some fine tuning as part of that. And then we have an agent's project that's like trying to build on top of our models as well as use other models in the outside world to try to make agents then we actually use as programmers every day. So all sorts of different, different projects. [00:15:37]
Swyx: As a founder, you're now sort of a capital allocator among all of these different investments effectively at different projects. And I was interested in how you mentioned that you were optimizing for improving reasoning and specifically inside of your pre-training, which I assume is just a lot of data collection. [00:15:55]
Kanjun: We are optimizing reasoning inside of our pre-trained models. And a lot of that is about data. And I can talk more about like what, you know, what exactly does it involve? But actually big, maybe 50% plus of the work is figuring out even if you do have models that reason well, like the models are still stochastic. The way you prompt them still makes, is kind of random, like makes them do random things. And so how do we get to something that is actually robust and reliable as a user? How can I, as a user, trust it? We have all sorts of cool things on the, like, you know, I was mentioning earlier when I talked to other people building agents, they have to do so much work, like to try to get to something that they can actually productize and it takes a long time and agents haven't been productized yet for, partly for this reason is that like the abstractions are very leaky. We can get like 80% of the way there, but like self-driving cars, like the remaining 20% is actually really difficult. We believe that, and we have internally, I think some things that like an interface, for example, that lets me really easily like see what the agent execution is, fork it, try out different things, modify the prompt, modify like the plan that it is making. This type of interface, it makes it so that I feel more like I'm collaborating with the agent as it's executing, as opposed to it's just like doing something as a black box. That's an example of a type of thing that's like beyond just the model pre-training, but on the model pre-training side, like reasoning is a thing that we optimize for. And a lot of that is about what data do we put in. [00:17:27]
Swyx: It's interesting just because I always think like, you know, out of the levers that you have, the resources that you have, I think a lot of people think that running foundation model company or a research lab is going to be primarily compute. And I think the share of compute has gone down a lot over the past three years. It used to be the main story, like the main way you scale is you just throw more compute at it. And now it's like, Flops is not all you need. You need better data, you need better algorithms. And I wonder where that shift has gone. This is a very vague question, but is it like 30-30-30 now? Is it like maybe even higher? So one way I'll put this is people estimate that Llama2 maybe took about three to $4 million of compute, but probably 20 to $25 million worth of labeling data. And I'm like, okay, well that's a very different story than all these other foundation model labs raising hundreds of millions of dollars and spending it on GPUs. [00:18:20]
Kanjun: Data is really expensive. We generate a lot of data. And so that does help. The generated data is close to actually good, as good as human labeled data. [00:18:34]
Swyx: So generated data from other models? [00:18:36]
Kanjun: From our own models. From your own models. Or other models, yeah. [00:18:39]
Swyx: Do you feel like there's certain variations of this? There's the sort of the constitutional AI approach from Anthropic and basically models sampling training on data from other models. I feel like there's a little bit of like contamination in there, or to put it in a statistical form, you're resampling a distribution that you already have that you already know doesn't match human distributions. How do you feel about that basically, just philosophically? [00:19:04]
Kanjun: So when we're optimizing models for reasoning, we are actually trying to like make a part of the distribution really spiky. So in a sense, like that's actually what we want. We want to, because the internet is a sample of the human distribution that's also skewed in all sorts of ways. That is not the data that we necessarily want these models to be trained on. And so when we're generating data, we're not really randomly generating data. We generate very specific things that are like reasoning traces and that help optimize reasoning. Code also is a big piece of improving reasoning. So generated code is not that much worse than like regular human written code. You might even say it can be better in a lot of ways. So yeah. So we are trying to already do that. [00:19:50]
Alessio: What are some of the tools that you thought were not a good fit? So you built Avalon, which is your own simulated world. And when you first started, the metagame was like using games to simulate things using, you know, Minecraft and then OpenAI is like the gym thing and all these things. And I think in one of your other podcasts, you mentioned like Minecraft is like way too slow to actually do any serious work. Is that true? Yeah. I didn't say it. [00:20:17]
Swyx: I don't know. [00:20:18]
Alessio: That's above my pay grade. But Avalon is like a hundred times faster than Minecraft for simulation. When did you figure that out that you needed to just like build your own thing? Was it kind of like your engineering team was like, Hey, this is too slow. Was it more a long-term investment? [00:20:34]
Kanjun: Yeah. At that time we built Avalon as a research environment to help us learn particular things. And one thing we were trying to learn is like, how do you get an agent that is able to do many different tasks? Like RL agents at that time and environments at that time. What we heard from other RL researchers was the like biggest thing keeping holding the field back is lack of benchmarks that let us explore things like planning and curiosity and things like that and have the agent actually perform better if the agent has curiosity. And so we were trying to figure out in a situation where, how can we have agents that are able to handle lots of different types of tasks without the reward being pretty handcrafted? That's a lot of what we had seen is that like these very handcrafted rewards. And so Avalon has like a single reward it's across all tasks. And it also allowed us to create a curriculum so we could make the level more or less difficult. And it taught us a lot, maybe two primary things. One is with no curriculum, RL algorithms don't work at all. So that's actually really interesting. [00:21:43]
Swyx: For the non RL specialists, what is a curriculum in your terminology? [00:21:46]
Kanjun: So a curriculum in this particular case is basically the environment Avalon lets us generate simpler environments and harder environments for a given tasks. What's interesting is that the simpler environments, what you'd expect is the agent succeeds more often. So it gets more reward. And so, you know, kind of my intuitive way of thinking about it is, okay, the reason why it learns much faster with a curriculum is it's just getting a lot more signal. And that's actually an interesting general intuition to have about training these things as like, what kind of signal are they getting? And like, how can you help it get a lot more signal? The second thing we learned is that reinforcement learning is not a good vehicle, like pure reinforcement learning is not a good vehicle for planning and reasoning. So these agents were not able to, they were able to learn all sorts of crazy things. They could learn to climb like hand over hand in VR climbing, they could learn to open doors like very complicated, like multiple switches and a lever open the door, but they couldn't do any higher level things. And they couldn't do those lower level things consistently necessarily. And as a user, I do not want to interact with a pure reinforcement learning end to end RL agent. As a user, like I need much more control over what that agent is doing. And so that actually started to get us on the track of thinking about, okay, how do we do the reasoning part in language? And we were pretty inspired by our friend Chelsea Finn at Stanford was I think working on SACAN at the time where it's basically an experiment where they have robots kind of trying to do different tasks and actually do the reasoning for the robot in natural language. And it worked quite well. And that led us to start experimenting very seriously with reasoning. [00:23:31]
Alessio: How important is the language part for the agent versus for you to inspect the agent? You know, like is it the interface to kind of the human on the loop really important or? [00:23:43]
Kanjun: Yeah, I personally think of it as it's much more important for us, the human user. So I think you probably could get end to end agents that work and are fairly general at some point in the future. But I think you don't want that. Like we actually want agents that we can like perturb while they're trying to figure out what to do. Because, you know, even a very simple example, internally we have like a type error fixing agent and we have like a test generation agent. Test generation agent goes off rails all the time. I want to know, like, why did it generate this particular test? [00:24:19]
Swyx: What was it thinking? [00:24:20]
Kanjun: Did it consider, you know, the fact that this is calling out to this other function? And the formatter agent, if it ever comes up with anything weird, I want to be able to debug like what happened with RL end to end stuff. Like we couldn't do that. Yeah. [00:24:36]
Swyx: It sounds like you have a bunch of agents operating internally within the company. What's your most, I guess, successful agent and what's your least successful one? [00:24:44]
Kanjun: The agents don't work. All of them? I think the only successful agents are the ones that do really small things. So very specific, small things like fix the color of this button on the website or like change the color of this button. [00:24:57]
Swyx: Which is now sweep.dev is doing that. Exactly. [00:25:00]
Kanjun: Perfect. Okay. [00:25:02]
Swyx: Well, we should just use sweep.dev. Well, I mean, okay. I don't know how often you have to fix the color of a button, right? Because all of them raise money on the idea that they can go further. And my fear when encountering something like that is that there's some kind of unknown asymptote ceiling that's going to prevent them, that they're going to run head on into that you've already run into. [00:25:21]
Kanjun: We've definitely run into such a ceiling. But what is the ceiling? [00:25:24]
Swyx: Is there a name for it? Like what? [00:25:26]
Kanjun: I mean, for us, we think of it as reasoning plus these tools. So reasoning plus abstractions, basically. I think actually you can get really far with current models and that's why it's so compelling. Like we can pile debugging tools on top of these current models, have them critique each other and critique themselves and do all of these, like spend more computer inference time, context hack, retrieve augmented generation, et cetera, et cetera, et cetera. Like the pile of hacks actually does get us really far. And a way to think about it is like the underlying language model is kind of like a noisy channel. Actually I don't want to use this analogy. It's actually a really bad analogy, but you kind of like trying to get more signal out of the channel. We don't like to think about it that way. It's what the default approach is, is like trying to get more signal out of this noising channel. But the issue with agents is as a user, I want it to be mostly reliable. It's kind of like self-driving in that way. Like it's not as bad as self-driving, like in self-driving, you know, you're like hurtling at 70 miles an hour. It's like the hardest agent problem. But one thing we learned from Sorceress and one thing we learned by using these things internally is we actually have a pretty high bar for these agents to work. You know, it's actually really annoying if they only work 50% of the time and we can make interfaces to make it slightly less annoying. But yeah, there's a ceiling that we've encountered so far and we need to make the models better. We also need to make the kind of like interface to the user better. And also a lot of the like critiquing. I hope what we can do is help people who are building agents actually like be able to deploy them. I think, you know, that's the gap that we see a lot of today is everyone who's trying to build agents to get to the point where it's robust enough to be deployable. It just, it's like an unknown amount of time. Okay. [00:27:12]
Swyx: So this goes back into what Embu is going to offer as a product or a platform. How are you going to actually help people deploy those agents? Yeah. [00:27:21]
Kanjun: So our current hypothesis, I don't know if this is actually going to end up being the case. We've built a lot of tools for ourselves internally around like debugging, around abstractions or techniques after the model generation happens. Like after the language model generates the text and like interfaces for the user and the underlying model itself, like models talking to each other, maybe some set of those things kind of like an operating system. Some set of those things will be helpful for other people. And we'll figure out what set of those things is helpful for us to make our agents. Like what we want to do is get to a point where we can like start making an agent, deploy it, it's reliable, like very quickly. And there's a similar analog to software engineering, like in the early days, in the seventies and the sixties, like to program a computer, like you have to go all the way down to the registers and write things and eventually we had assembly. That was like an improvement. But then we wrote programming languages with these higher levels of abstraction and that allowed a lot more people to do this and much faster. And the software created is much less expensive. And I think it's basically a similar route here where we're like in the like bare metal phase of agent building. And we will eventually get to something with much nicer abstractions. [00:28:36]
Alessio: We had this conversation with George Hotz and we were like, there's not a lot of reasoning data out there. And can the models really understand? And his take was like, look, with enough compute, you're not that complicated as a human. Like the model can figure out eventually why certain decisions are made. What's been your experience? Like as you think about reasoning data, like do you have to do a lot of like manual work or like is there a way to prompt models to extract the reasoning from actions that they [00:29:03]
Swyx: see? [00:29:03]
Kanjun: So we don't think of it as, oh, throw enough data at it and then it will figure out what the plan should be. I think we're much more explicit. You know, a way to think about it is as humans, we've learned a lot of reasoning strategies over time. We are better at reasoning now than we were 3000 years ago. An example of a reasoning strategy is noticing you're confused. Then when I notice I'm confused, I should ask like, huh, what was the original claim that was made? What evidence is there for this claim? Does the evidence support the claim? Is the claim correct? This is like a reasoning strategy that was developed in like the 1600s, you know, with like the advent of science. So that's an example of a reasoning strategy. There are tons of them. We employ all the time, lots of heuristics that help us be better at reasoning. And we didn't always have them. And because they're invented, like we can generate data that's much more specific to them. So I think internally, yeah, we have a lot of thoughts on what reasoning is and we generate a lot more specific data. We're not just like, oh, it'll figure out reasoning from this black box or like it'll figure out reasoning from the data that exists. Yeah. [00:30:04]
Alessio: I mean, the scientific method is like a good example. If you think about hallucination, right, people are thinking, how do we use these models to do net new, like scientific research? And if you go back in time and the model is like, well, the earth revolves around the sun and people are like, man, this model is crap. It's like, what are you talking about? Like the sun revolves around the earth. It's like, how do you see the future? Like if the models are actually good enough, but we don't believe them, it's like, how do we make the two live together? So you're like, you use Inbu as a scientist to do a lot of your research and Inbu tells you, hey, I think this is like a serious path you should go down. And you're like, no, that sounds impossible. Like how is that trust going to be built? And like, what are some of the tools that maybe are going to be there to inspect it? [00:30:51]
Kanjun: Really there are two answers to this. One element of it is as a person, like I need to basically get information out of the model such that I can try to understand what's going on with the model. Then the second question is like, okay, how do you do that? And that's kind of some of our debugging tools, they're not necessarily just for debugging. They're also for like interfacing with and interacting with the model. So like if I go back in this reasoning trace and like change a bunch of things, what's going to happen? Like, what does it conclude instead? So that kind of helps me understand like, what are its assumptions? And, you know, we think of these things as tools. And so it's really about like, as a user, how do I use this tool effectively? I need to be willing to be convinced as well. It's like, how do I use this tool effectively? And what can it help me with? [00:31:36]
Swyx: And what can it tell me? There's a lot of mention of code in your process. And I was hoping to dive in even deeper. I think we might run the risk of giving people the impression that you view code or you use code just as like a tool within InView just for coding assistance. But I think you actually train code models. And I think there's a lot of informal understanding about how adding code to language models improves their reasoning capabilities. I wonder if there's any research or findings that you have to share that talks about the intersection of code and reasoning. Hmm. Yeah. [00:32:08]
Kanjun: So the way I think about it intuitively is like code is the most explicit example of reasoning data on the internet. [00:32:15]
Swyx: Yeah. [00:32:15]
Kanjun: And it's not only structured, it's actually very explicit, which is nice. You know, it says this variable means this, and then it uses this variable. And then the function does this. As people, when we talk in language, it takes a lot more to extract that explicit structure out of our language. And so that's one thing that's really nice about code is I see it as almost like a curriculum for reasoning. I think we use code in all sorts of ways. The coding agents are really helpful for us to understand what are the limitations of the agents. The code is really helpful for the reasoning itself. But also code is a way for models to act. So by generating code, it can act on my computer. And, you know, when we talk about rekindling the dream of the personal computer, kind of where I see computers going is, you know, like computers will eventually become these much more malleable things where I, as a user today, I have to know how to write software code, like in order to make my computer do exactly what I want it to do. But in the future, if the computer is able to generate its own code, then I can actually interface with it in natural language. And so one way we think about agents is kind of like a natural language programming language. It's a way to program my computer in natural language that's much more intuitive to me as a user. And these interfaces that we're building are essentially IDEs for users to program our computers in natural language. Maybe I should say what we're doing that way. Maybe it's clearer. [00:33:47]
Swyx: I don't know. [00:33:47]
Alessio: That's a good pitch. What do you think about the different approaches people have, kind of like text first, browser first, like multi-on? What do you think the best interface will be? Or like, what is your, you know, thinking today? [00:33:59]
Kanjun: In a lot of ways, like chat as an interface, I think Linus, Linus Lee, you had on this. I really like how he put it. Chat as an interface is skeuomorphic. So in the early days, when we made word processors on our computers, they had notepad lines because that's what we understood these like objects to be. Chat, like texting someone is something we understand. So texting our AI is something that we understand. But today's word documents don't have notepad lines. And similarly, the way we want to interact with agents, like chat is a very primitive way of interacting with agents. What we want is to be able to inspect their state and to be able to modify them and fork them and all of these other things. And we internally have, think about what are the right representations for that? Like architecturally, like what are the right representations? What kind of abstractions do we need to build? And how do we build abstractions that are not leaky? Because if the abstractions are leaky, which they are today, like, you know, this stochastic generation of text is like a leaky abstraction. I cannot depend on it. And that means it's actually really hard to build on top of. But our experience and belief is actually by building better abstractions and better tooling, we can actually make these things non-leaky. And now you can build like whole things on top of them. So these other interfaces, because of where we are, we don't think that much about them. [00:35:17]
Swyx: Yeah. [00:35:17]
Alessio: I mean, you mentioned, this is kind of like the Xerox Spark moment for AI. And we had a lot of stuff come out of Parc, like the, what you see is what you got editors and like MVC and all this stuff. But yeah, but then we didn't have the iPhone at Parc. We didn't have all these like higher things. What do you think it's reasonable to expect in like this era of AI, you know, call it like five years or so? Like what are like the things we'll build today and what are things that maybe we'll see in kind of like the second wave of products? [00:35:46]
Kanjun: That's interesting. I think the waves will be much faster than before. Like what we're seeing right now is basically like a continuous wave. Let me zoom a little bit earlier. So people like the Xerox Parc analogy I give, but I think there are many different analogies. Like one is the like analog to digital computer is kind of an example, like another analogy to where we are today. The analog computer Vannevar Bush built in the 1930s, I think, and it's like a system of pulleys and it can only calculate one function. Like it can calculate like an integral. And that was so magical at the time because you actually did need to calculate this integral bunch, but it had a bunch of issues like in analog errors compound. And so there was actually a set of breakthroughs necessary in order to get to the digital computer, like Turing's decidability, Shannon. I think the like whole like relay circuits can be thought of as can be mapped to Boolean operators and a set of other like theoretical breakthroughs, which essentially were abstractions. They were like creating abstractions for these like very like lossy circuits. They were creating abstractions for these like very analog circuits and digital had this nice property of like being error correcting. And so when I talk about like less leaky abstractions, that's what I mean. That's what I'm kind of pointing a little bit to. It's not going to look exactly the same way. And then the Xerox PARC piece, a lot of that is about like, how do we get to computers that as a person, I can actually use well. And the interface actually helps it unlock so much more power. So the sets of things we're working on, like the sets of abstractions and the interfaces, like hopefully that like help us unlock a lot more power in these systems. Like hopefully that'll come not too far in the future. I could see a next version, maybe a little bit farther out. It's like an agent protocol. So a way for different agents to talk to each other and call each other. Kind of like HTTP. [00:37:40]
Swyx: Do you know it exists already? [00:37:41]
Kanjun: Yeah, there is a nonprofit that's working on one. I think it's a bit early, but it's interesting to think about right now. Part of why I think it's early is because the issue with agents, it's not quite like the internet where you could like make a website and the website would appear. The issue with agents is that they don't work. And so it may be a bit early to figure out what the protocol is before we really understand how these agents get constructed. But, you know, I think that's, I think it's a really interesting question. [00:38:09]
Swyx: While we're talking on this agent to agent thing, there's been a bit of research recently on some of these approaches. I tend to just call them extremely complicated chain of thoughting, but any perspectives on kind of meta-GPT, I think it's the name of the paper. I don't know if you care about at the level of individual papers coming out, but I did read that recently and TLDR, it beat GPT-4 and human eval by role-playing software agent development agency, instead of having sort of single shot or single role, you have multiple roles and how having all of them criticize each other as agents communicating with other agents. [00:38:45]
Kanjun: Yeah, I think this is an example of an interesting abstraction of like, okay, can I just plop in this like multi-role critiquing and see how it improves my agent? And can I just plop in chain of thought, tree of thought, plop in these other things and see how they improve my agent? One issue with this kind of prompting is that it's still not very reliable. It's like, there's one lens, which is like, okay, if you do enough of these techniques, you'll get to high reliability. And I think actually that's a pretty reasonable lens. We take that lens often. And then there's another lens that's like, okay, but it's starting to get really messy what's in the prompt and like, how do we deal with that messiness? And so maybe you need like cleaner ways of thinking about and constructing these systems. And we also take that lens. So yeah, I think both are necessary. Yeah. [00:39:29]
Swyx: Side question, because I feel like this also brought up another question I had for you. I noticed that you work a lot with your own benchmarks, your own evaluations of what is valuable. I would say I would contrast your approach with OpenAI as OpenAI tends to just lean on, hey, we played StarCraft or hey, we ran it on the SAT or the, you know, the AP bio test and that did results. Basically, is benchmark culture ruining AI? [00:39:55]
Swyx: Or is that actually a good thing? Because everyone knows what an SAT is and that's fine. [00:40:04]
Kanjun: I think it's important to use both public and internal benchmarks. Part of why we build our own benchmarks is that there are not very many good benchmarks for agents, actually. And to evaluate these things, you actually need to think about it in a slightly different way. But we also do use a lot of public benchmarks for like, is the reasoning capability in this particular way improving? So yeah, it's good to use both. [00:40:26]
Swyx: So for example, the Voyager paper coming out of NVIDIA played Minecraft and set their own benchmarks on getting the Diamond X or whatever and exploring as much of the territory as possible. And I don't know how that's received. That's obviously fun and novel for the rest of the engineer, the people who are new to the scene. But for people like yourselves, you build Avalon just because you already found deficiencies with using Minecraft. Is that valuable as an approach? Oh, yeah. I love Voyager. [00:40:57]
Kanjun: I mean, Jim, I think is awesome. And I really like the Voyager paper and I think it has a lot of really interesting ideas, which is like the agent can create tools for itself and then use those tools. [00:41:06]
Swyx: He had the idea of the curriculum as well, which is something that we talked about earlier. Exactly. [00:41:09]
Kanjun: And that's like a lot of what we do. We built Avalon mostly because we couldn't use Minecraft very well to like learn the things we wanted. And so it's like not that much work to build our own. [00:41:19]
Swyx: It took us, I don't know. [00:41:22]
Kanjun: We had like eight engineers at the time, took about eight weeks. So six weeks. [00:41:27]
Swyx: And OpenAI built their own as well, right? Yeah, exactly. [00:41:30]
Kanjun: It's just nice to have control over our environment. But if you're doing our own sandbox to really trying to inspect our own research questions. But if you're doing something like experimenting with agents and trying to get them to do things like Minecraft is a really interesting environment. And so Voyager has a lot of really interesting ideas in it. [00:41:47]
Swyx: Yeah. Cool. One more element that we had on this list, which is context and memory. I think that's kind of like the foundational, quote unquote, RAM of our era. I think Andrej Karpathy has already made this comparison. So there's nothing new here. And that's just the amount of working knowledge that we can fit into one of these agents. And it's not a lot, right? Especially if you need to get them to do long running tasks. If they need to self-correct from errors that they observe while operating in their environment. Do you see this as a problem? Do you think we're going to just trend to infinite context and that'll go away? Or how do you think we're going to deal with it? [00:42:22]
Kanjun: I think when you talked about what's going to happen in the first wave and then in the second wave, I think what we'll see is we'll get like relatively simplistic agents pretty soon. And they will get more and more complex. And there's like a future wave in which they are able to do these like really difficult, really long running tasks. And the blocker to that future, one of the blockers is memory. And that was true of computers too. You know, I think when von Neumann made the von Neumann architecture, he was like, the biggest blocker will be like, we need this amount of memory, which is like, I don't remember exactly like 32 kilobytes or something to store programs. And that will allow us to write software. He didn't say it this way because he didn't have these terms, but that only really was like happened in the seventies with the microchip revolution. It may be the case that we're waiting for some research breakthroughs or some other breakthroughs in order for us to have like really good long running memory. And then in the meantime, agents will be able to do all sorts of things that are a little bit smaller than that. I do think with the pace of the field, we'll probably come up with all sorts of interesting things like, you know, RAG is already very helpful. [00:43:26]
Swyx: Good enough, you think? [00:43:27]
Kanjun: Maybe good enough for some things. [00:43:29]
Swyx: How is it not good enough? I don't know. [00:43:31]
Kanjun: I just think about a situation where you want something that's like an AI scientist. As a scientist, I have learned so much about my fields and a lot of that data is maybe hard to fine tune or on, or maybe hard to like put into pre-training. Like a lot of that data, I don't have a lot of like repeats of the data that I'm seeing. You know, like if I'm a scientist, I've like accumulated so many little data points. And ideally I'd want to store those somehow, or like use those to fine tune myself as a model somehow, or like have better memory somehow. I don't think RAG is enough for that kind of thing. But RAG is certainly enough for like user preferences and things like that. Like what should I do in this situation? What should I do in that situation? That's a lot of tasks. We don't have to be a scientist right away. Awesome. [00:44:21]
Swyx: I have a hard question, if you don't mind me being bold. Yeah. I think the most comparable lab to InView is Adept. You know, a research lab with like some amount of product situation on the horizon, but not just yet, right? Why should people work for InView over Adept? And we can cut this if it's too like... Yeah. [00:44:40]
Kanjun: The way I think about it is I believe in our approach. The type of thing that we're doing is we're trying to like build something that enables other people to build agents and build something that really can be maybe something like an operating system for agents. I know that that's what we're doing. I don't really know what everyone else is doing. You know, I can kind of like talk to people and have some sense of what they're doing. And I think it's a mistake to focus too much on what other people are doing, because extremely focused execution on the right thing is what matters. To the question of like, why us? I think like strong focus on reasoning, which we believe is the biggest blocker, on inspectability, which we believe is really important for user experience and also for the power and capability of these systems. Building non-leaky, good abstractions, which we believe is solving the core issue of agents, which is around reliability and being able to make them deployable. And then really seriously trying to use these things ourselves, like every single day, and getting to something that we can actually ship to other people that becomes something that is a platform. Like, it feels like it could be Mac or Windows. I love the dogfooding approach. [00:45:49]
Swyx: That's extremely important. And you will not be surprised how many agent companies I talk to that don't use their own agent. Oh no, that's not good. That's a big surprise. [00:45:59]
Kanjun: Yeah, I think if we didn't use our own agents, then we would have all of these beliefs about how good they are. Wait, did you have any other hard questions you wanted to ask? [00:46:08]
Swyx: Yeah, mine was just the only other follow-up that you had based on the answer you just gave was, do you see yourself releasing models or do you see yourself, what is the artifacts that you want to produce that lead up to the general operating system that you want to have people use, right? And so a lot of people just as a byproduct of their work, just to say like, hey, I'm still shipping, is like, here's a model along the way. Adept took, I don't know, three years, but they released Persimmon recently, right? Like, do you think that kind of approach is something on your horizon? Or do you think there's something else that you can release that can show people, here's kind of the idea, not the end products, but here's the byproducts of what we're doing? [00:46:51]
Kanjun: Yeah, I don't really believe in releasing things to show people like, oh, here's what we're doing that much. I think as a philosophy, we believe in releasing things that will be helpful to other people. [00:47:02]
Swyx: Yeah. [00:47:02]
Kanjun: And so I think we may release models or we may release tools that we think will help agent builders. Ideally, we would be able to do something like that, but I'm not sure exactly what they look like yet. [00:47:14]
Swyx: I think more companies should get into the releasing evals and benchmarks game. Yeah. [00:47:20]
Kanjun: Something that we have been talking to agent builders about is co-building evals. So we build a lot of our own evals and every agent builder tells me, basically evals are their biggest issue. And so, yeah, we're exploring right now. And if you are building agents, please reach out to me because I would love to, like, figure out how we can be helpful based on what we've seen. Cool. [00:47:40]
Swyx: That's a good call to action. I know a bunch of people that I can send your way. Cool. Great. [00:47:43]
Kanjun: Awesome. [00:47:44]
Swyx: Yeah. We can zoom out to other interests now. [00:47:46]
Alessio: We got a lot of stuff. So we have Sherif from Lexicon, the podcast. He had a lot of interesting questions on his website. You similarly have a lot of them. Yeah. [00:47:55]
Swyx: I need to do this. I'm very jealous of people with personal websites right there. Like, here's the high level questions of goals of humanity that I want to set people on. And I don't have that. [00:48:04]
Alessio: It's never too late, Sean. [00:48:05]
Swyx: Yeah. [00:48:05]
Alessio: It's never too late. [00:48:06]
Kanjun: Exactly. [00:48:07]
Alessio: There were a few that stuck out as related to your work that maybe you're kind of learning [00:48:12]
Swyx: more about it. [00:48:12]
Alessio: So one is why are curiosity and goal orientation often at odds? And from a human perspective, I get it. It's like, you know, would you want to like go explore things or kind of like focus on your career? How do you think about that from like an agent perspective? Where it's like, should you just stick to the task and try and solve it as in the guardrails as possible? Or like, should you look for alternative solutions? [00:48:34]
Swyx: Yeah. [00:48:34]
Kanjun: I think one thing that's really interesting about agents actually is that they can be forked. Like, you know, we can take an agent that's executed to a certain place and said, okay, here, like fork this and do a bunch of different things. I try a bunch of different things. Some of those agents can be goal oriented and some of them can be like more curiosity driven. You can prompt them in slightly different ways. And something I'm really curious about, like what would happen if in the future, you know, we were able to actually go down both paths. As a person, why I have this question on my website is I really find that like I really can only take one mode at a time and I don't understand why. And like, is it inherent in like the kind of context that needs to be held? That's why I think from an agent perspective, like forking it is really interesting. Like I can't fork myself to do both, but I maybe could fork an agent to like add a certain point in a task. [00:49:26]
Swyx: Yeah. Explore both. Yeah. [00:49:28]
Alessio: How has the thinking changed for you as the funding of the company changed? That's one thing that I think a lot of people in the space think is like, oh, should I raise venture capital? Like, how should I get money? How do you feel your options to be curious versus like goal oriented has changed as you raise more money and kind of like the company has grown? [00:49:50]
Kanjun: Oh, that's really funny. Actually, things have not changed that much. So we raised our Series A $20 million in late 2021. And our entire philosophy at that time was, and still kind of is, is like, how do we figure out the stepping stones, like collect stepping stones that eventually let us build agents, kind of these new computers that help us do bigger things. And there was a lot of curiosity in that. And there was a lot of goal orientation in that. Like the curiosity led us to build CARBS, for example, this hyperparameter optimizer. Great name, by the way. [00:50:28]
Swyx: Thank you. [00:50:29]
Kanjun: Is there a story behind that name? [00:50:30]
Swyx: Yeah. [00:50:31]
Kanjun: Abe loves CARBS. It's also cost aware. So as soon as he came up with cost aware, he was like, I need to figure out how to make this work. But the cost awareness of it was really important. So that curiosity led us to this really cool hyperparameter optimizer. That's actually a big part of how we do our research. It lets us experiment on smaller models. And for those experiment results to carry to larger ones. [00:50:56]
Swyx: Which you also published a scaling laws, which is great. I think the scaling laws paper from OpenAI was like the biggest. And from Google, I think, was the greatest public service to machine learning that any research lab can do. Yeah, totally. [00:51:10]
Kanjun: What was nice about CARBS is it gave us scaling laws for all sorts of hyperparameters. So yeah, that's cool. It basically hasn't changed very much. So there's some curiosity. And then there's some goal oriented parts. Like Avalon, it was like a six to eight week sprint for all of us. And we got this thing out. And then now different projects do like more curiosity or more goal orientation at different times. Cool. [00:51:36]
Swyx: Another one of your questions that we highlighted was, how can we enable artificial agents to permanently learn new abstractions and processes? I think this is might be called online learning. [00:51:45]
Kanjun: Yeah. So I struggle with this because, you know, that scientist example I gave. As a scientist, I've like permanently learned a lot of new things. And I've updated and created new abstractions and learned them pretty reliably. And you were talking about like, okay, we have this RAM that we can store learnings in. But how well does online learning actually work? And the answer right now seems to be like, as models get bigger, they fine tune faster. So they're more sample efficient as they get bigger. [00:52:15]
Swyx: Because they already had that knowledge in there. You're just kind of unlocking it. [00:52:23]
Kanjun: Partly maybe because they already have like some subset of the representation. Partly they just memorize things more, which is good. So maybe this question is going to be solved, but I still don't know what the answer is. [00:52:36]
Swyx: As I've had a platform that continually fine tunes for you as you work on that domain, which is something I'm working on. Well, that's great. We would love to use that. We'll talk more. Two more questions just about your general activities. I think you've just been very active in the San Francisco tech scene. You're a founding member of Software Commons. [00:52:56]
Kanjun: Oh yeah, that's true. [00:52:57]
Swyx: Tell me more. By the time I knew about SPC, it was already a very established thing. But what was it like in the early days? What was the story there? [00:53:05]
Kanjun: Yeah, the story is Ruchi, who started it, was the VP of operations at Dropbox. And I was the chief of staff and we worked together very closely. She's actually one of the investors in Sorceress. And SPC is an investor in Vue. And at that time, Ruchi was like, you know, I would like to start a space for people who are figuring out what's next. And we were figuring out what's next post-Ember, those three months. And she was like, do you want to just like hang out in this space? And we're like, sure. And it was a really good group. Wasim and Jeff from Pilot, the folks from Zulip, and a bunch of other people at that time. It was a really good group. We just hung out. There was no programming. It's much more official than it was at that time. [00:53:44]
Swyx: Yeah, now it's like a YC before YC type of thing. That's right, yeah. [00:53:48]
Kanjun: At that time, we literally, it was a bunch of friends hanging out in the space together. [00:53:51]
Swyx: And was this concurrent with the Archive? [00:53:53]
Kanjun: Oh yeah, actually, I think we started the Archive around the same time. [00:53:56]
Swyx: You're just like really big into community. But also like, so, you know, I run a Hacker House and I'm also part of hopefully what becomes like the next Software Commons or whatever. What are the principles in organizing communities like that with really exceptional people that go on to do great things? Do you have to be really picky about who joins? Like all your friends just magically turn out super successful like that. You know, it's not normal, right? Like this is very special. And a lot of people want to do that and fail. And you had the co-authors of GPT-3 in your house. That's true. [00:54:32]
Kanjun: And a lot of other really cool people that you'll eventually hear about. [00:54:35]
Swyx: Co-founders of Pilot and anyone else. I don't want you to pick your friends, but there's some magic special sauce in getting people together and in one workspace, living space, whatever, right? And that's part of why I'm here in San Francisco. And I would love for more people to learn about it and also maybe get inspired to build their own. [00:54:52]
Kanjun: Your question is really more about like, how do you actually build a community that where people in it are like eventually are awesome? [00:54:59]
Swyx: Okay. [00:55:00]
Kanjun: Which is different than like why live in a co-living house. So one adage we had when we started the archive was you become the average of the five people closest to you. [00:55:08]
Swyx: Yes. [00:55:08]
Kanjun: And I think that's roughly true. And good people draw good people. So there are really two things. One, we were quite picky and it mattered a lot to us. Is this someone where if they're hanging out in the living room, we'd be really excited to come hang out. Yeah. Two is I think we did a really good job of creating a high growth environment and an environment where people felt really safe. We actually apply these things to our team and it works remarkably well as well. So I do a lot of basically how do I create safe spaces for people where it's not just like safe law, but like it's like a safe space where people really feel inspired by each other. And I think at the archive, we really made each other better. My friend, Michael Nielsen called it a self-actualization machine. [00:55:52]
Swyx: My goodness. Okay. [00:55:54]
Kanjun: And I think, yeah, people came in. Was he a part of the archive? He was not, but he hung out a lot. Honorary member. Friend of the archive. [00:56:02]
Swyx: Yeah. [00:56:02]
Kanjun: The culture was that we learned a lot of things from each other about like how to make better life systems and how to think about ourselves and psychological debugging. And a lot of us were founders. So having other founders going through similar things was really helpful. And a lot of us worked in AI. And so having other people to talk about AI with was really helpful. And so I think all of those things led to a form of idea flux and also kind of like, so I think a lot about like the idea flux and default habits or default impulses. It led to a set of idea flux and default impulses that led to some really interesting things and led to us doing much bigger things, I think, than we otherwise would have decided to do because it felt like taking risks was less risky. So that's something we do a lot of on the team. It's like, how do we make it so that taking risks is less risky? And there's a term called senious. [00:56:57]
Swyx: Yes. I was thinking Kevin Kelly. Kevin Kelly, senious. I was going to feed you that word, but I didn't want to like bias you. Yes. [00:57:02]
Kanjun: I think maybe like a lot of what I'm interested in is constructing a kind of senious. And the archive was definitely a senious in a particular, or like getting toward a senious in a particular way. And Jason Ben, my archive housemate and who now runs the neighborhood, [00:57:17]
Swyx: has a good way of putting it. [00:57:17]
Kanjun: If genius is from your genes, senious is from your scene. Yeah, I think like maybe a lot of the community building impulse is from this like interest in what kind of idea flux can be created. You know, there's a question of like, why did Xerox PARC come out with all of this interesting stuff? It's their senious. Why did Bell Labs come out with all this interesting stuff? Maybe it's their senious. Why didn't the transistor come out of Princeton? And the other people working on it at the time. [00:57:44]
Swyx: I just think it's remarkable how you hear a lot about Alan Kay. And I just read a bit. And apparently Alan Kay was like the most junior guy at Xerox PARC. Yeah, definitely. [00:57:53]
Kanjun: He's just the one who talks about it. He talks the most. [00:57:57]
Swyx: Yeah, exactly. Yeah. So I, you know, hopefully I'm also working towards contributing that senious. I called mine the more provocative name of the arena. Interesting. That's quite provocative. In the arena. [00:58:08]
Kanjun: So are you fighting other people in the arena? [00:58:11]
Swyx: No. You never know. [00:58:12]
Alessio: On any day in the mission, it's an adventure. [00:58:15]
Swyx: We're in the arena trying stuff, as they say. You are also a GP at Outset Capital, where you also co-organize the Thursday Nights in AI, where hopefully someday I'll eventually speak. You're on the roster. [00:58:28]
Kanjun: I'm on the roster. [00:58:29]
Swyx: Thank you so much. So why spend time being a VC and organizing all these events? You're also a very busy CEO and, you know, why spend time with that? Why is that an important part of your life? [00:58:39]
Kanjun: Yeah, for me personally, I really like helping founders. So Allie, my investing partner, is fortunately amazing and she does everything for the fund. So she like hosts the Thursday night events and she finds folks who we could invest in. And she does basically everything. Josh and I are her co-partners. So Allie was our former chief of staff at Sorceress. We just thought she was amazing. She wanted to be an investor. And Josh and I also like care about helping founders and kind of like giving back to the community. What we didn't realize at the time when we started the fund is that it would actually be incredibly helpful for Imbue. So talking to AI founders who are building agents and working on, you know, similar things is really helpful. They could potentially be our customers and they're trying out all sorts of interesting things. And I think being an investor, looking at the space from the other side of the table, it's just a different hat that I routinely put on. And it's helpful to see the space from the investor lens as opposed to from the founder lens. So I find that kind of like hat switching valuable. It maybe would lead us to do slightly different things. [00:59:44]
Swyx: Awesome. Appreciate that. [00:59:46]
Alessio: Yeah, you've been really generous with your time. Let's just wrap with the lightning round. Okay. So we have two questions, acceleration, exploration, and then a takeaway. So the acceleration question is, what's something that already happened in AI that you thought would take much longer to be here? [01:00:03]
Kanjun: I think the rate at which we discover new capabilities of existing models and kind of like build hacks on top of them to make them work better is something that has been surprising and awesome. And the research community building on its own ideas, that's probably, you want something very specific. Yeah, I think the rate at which we discovered capabilities probably. [01:00:22]
Swyx: Cool. Exploration slash requests for startups. If you weren't building Imbue, what AI company would you build? Hmm. Every founder has like their like number two. Really? Yeah, I don't know. [01:00:33]
Kanjun: Wow. I cannot imagine building any other thing than Imbue. [01:00:37]
Swyx: Wow. Well, that's a great answer too. [01:00:38]
Kanjun: It's like obviously the thing to build. [01:00:42]
Swyx: Okay. [01:00:42]
Kanjun: It's like obviously work on the fundamental platform. Yeah. [01:00:46]
Swyx: So that was my attempt at innovating this question, but the previous one was, but what was the most interesting unsolved question in AI? [01:00:53]
Kanjun: My answer is kind of boring, but the most interesting unsolved questions are these questions of, how do we make these stochastic systems into things that we can like reliably use and build on top of? [01:01:04]
Swyx: Yep. [01:01:05]
Alessio: And yeah, take away what's one message you want everyone to remember? [01:01:09]
Kanjun: Maybe two things. One is just the like, we're in a historic moment. I didn't think in my lifetime I would necessarily be in, like able to work on the things I'm excited to work on in this moment, but we're in a historic moment that where we'll look back and be like, oh my God, the future was invented in these years. And I think like, there may be a set of messages to take away from that. One is like, AI is a tool like any technology. And you know, when it comes to things like, what might the future look like? Like we like to think about it as, it's like just a better computer. It's like much more powerful computer that gives us a lot of free intellectual energy that we can now like solve so many problems with. You know, there are so many problems in the world [01:01:53]
Swyx: where we're like, [01:01:53]
Kanjun: oh, it's not worth a person thinking about that. And so things get worse and things get worse. No one wants to work on maintenance. And like this technology gives us the potential to actually be able to like allocate intellectual energy to all of those problems. And the world could be much better, like could be much more thoughtful because of that. I'm so excited about that. And there are definitely risks and dangers. And we actually do a fair, something I didn't talk about is we do a fair amount of work on the policy side. On the safety side, like we think about safety and policy in terms of engineering theory and also regulation. And kind of comparing to like the automobile or the airplane or any new technology, there's like a set of new possible capabilities and a set of new possible dangers that are unlocked with every new technology. And so on the engineering side, like we think a lot about engineering safety, like how do we actually engineer these systems so that they are inspectable and why we reason in natural language so that the systems are very inspectable so that we can like stop things if anything weird is happening. That's why we don't think end-to-end black boxes [01:02:58]
Swyx: are a good idea. [01:02:58]
Kanjun: On the theoretical side, we like really believe in like deeply understanding, like when we actually fine tune on individual examples, like what's going on, when we're pre-training, what's going on, like debugging tools for these agents to understand like what's going on. And then on the regulation side, I think there's actually a lot of regulation that already covers many of the dangers like that people are talking about. And there are areas where there's not much regulation. And so we focus on those areas where there's not much regulation. So some of our work is actually, we built an agent that helped us analyze the 20,000 pages of policy proposals submitted to the Department of Commerce request for AI policy proposals. We looked at what were the problems people brought up and what were the solutions they presented and then like did a summary analysis and kind of like, you know, build agents to do that. And now the Department of Commerce is like interested in using that as a tool to like analyze proposals. And so a lot of what we're trying to do on the regulation side is like actually figure out where is there regulation missing and how do we actually in a very targeted way try to solve those missing areas. So I guess if I were to say like, what are the takeaways? It's like the takeaway is like the future could be really exciting if we can actually get agents that are able to do these bigger things. Reasoning is the biggest blocker plus like these sets of abstractions to make things more robust and reliable. And there are, you know, things where we have to be quite careful and thoughtful about how do we deploy these and what kind of regulation should go along with it so that this is actually a technology that where we, when we deploy it, it is protective to people and not harmful. [01:04:36]
Swyx: Awesome, wonderful. [01:04:38]
Alessio: Thank you so much for your time, Kanjun. [01:04:40]
Kanjun: Thank you. [01:04:41]
Swyx: Thank you. [01:04:48]
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Podcast: Latent Space: The AI Engineer Podcast — Practitioners talking LLMs, CodeGen, Agents, Multimodality, AI UX, GPU Infra and all things Software 3.0 (LS 33 · TOP 5% what is this?)
Episode: The Point of LangChain — with Harrison Chase of LangChain
Pub date: 2023-09-06
As alluded to on the pod, LangChain has just launched LangChain Hub: “the go-to place for developers to discover new use cases and polished prompts.” It’s available to everyone with a LangSmith account, no invite code necessary. Check it out!
In 2023, LangChain has speedrun the race from 2:00 to 4:00 to 7:00 Silicon Valley Time. From the back to back $10m Benchmark seed and (rumored) $20-25m Sequoia Series A in April, to back to back critiques of “LangChain is Pointless” and “The Problem with LangChain” in July, to teaching with Andrew Ng and keynoting at basically every AI conference this fall (including ours), it has been an extreme rollercoaster for Harrison and his growing team creating one of the most popular (>60k stars at time of writing) building blocks for AI Engineers.
LangChain’s Origins
The first commit to LangChain shows its humble origins as a light wrapper around Python’s formatter.format for prompt templating. But as Harrison tells the story, even his first experience with text-davinci-002 in early 2022 was focused on chatting with data from their internal company Notion and Slack, what is now known as Retrieval Augmented Generation (RAG).
As the Generative AI meetup scene came to life post Stable Diffusion, Harrison saw a need for common abstractions for what people were building with text LLMs at the time:
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LLM Math, aka Riley Goodside’s “You Can’t Do Math” REPL-in-the-loop (PR #8)
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Self-Ask With Search, Ofir Press’ agent pattern (PR #9) (later ReAct, PR #24)
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NatBot, Nat Friedman’s browser controlling agent (PR #18)
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Adapters for OpenAI, Cohere, and HuggingFaceHub
All this was built and launched in a few days from Oct 16-25, 2022.
Turning research ideas/exciting usecases into software quickly and often has been in the LangChain DNA from Day 1 and likely a big driver of LangChain’s success, to date amassing the largest community of AI Engineers and being the default launch framework for every big name from Nvidia to OpenAI:
Dancing with Giants
But AI Engineering is built atop of constantly moving tectonic shifts:
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ChatGPT launched in November (“The Day the AGI Was Born”) and the API released in March. Before the ChatGPT API, OpenAI did not have a chat endpoint. In order to build a chatbot with history, you had to make sure to chain all messages and prompt for completion. LangChain made it easy to do that out of the box, which was a huge driver of usage.
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Today, OpenAI has gone all-in on the chat API and is deprecating the old completions models, essentially baking in the chat pattern as the default way most engineers should interact with LLMs… and reducing (but not eliminating) the value of ConversationChains.
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And there have been more updates since: Plugins released in API form as Functions in June (one of our top pods ever… reducing but not eliminating the value of OutputParsers) and Finetuning in August(arguably reducing some need for Retrieval and Prompt tooling).
With each update, OpenAI and other frontier model labs realign the roadmaps of this nascent industry, and Harrison credits the modular design of LangChain in staying relevant. LangChain has not been merely responsive either: LangChain added Agents in November, well before they became the hottest topic of the AI Summer, and now Agents feature as one of LangChain’s top two usecases.
LangChain’s problem for podcasters and newcomers alike is its sheer scope - it is the world’s most complete AI framework, but it also has a sprawling surface area that is difficult to fully grasp or document in one sitting. This means it’s time for the trademark Latent Space move (ChatGPT, GPT4, Auto-GPT, and Code Interpreter Advanced Data Analysis GPT4.5): the executive summary!
What isLangChain?
As Harrison explains, LangChain is an open source framework for building context-aware reasoning applications, available in Python and JS/TS.
It launched in Oct 2022 with the central value proposition of “composability”, aka the idea that every AI engineer will want to switch LLMs, and combine LLMs with other things into “chains”, using a flexible interface that can be saved via a schema.
Today, LangChain’s principal offerings can be grouped as:
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Components: isolated modules/abstractions
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Model I/O
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Models (for LLM/Chat/Embeddings, from OpenAI, Anthropic, Cohere, etc)
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Prompts (Templates, ExampleSelectors, OutputParsers)
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Retrieval (revised and reintroduced in March)
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Document Loaders (eg from CSV, JSON, Markdown, PDF)
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Text Splitters (15+ various strategies for chunking text to fit token limits)
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Retrievers (generic interface for turning an unstructed query into a set of documents - for self-querying, contextual compression, ensembling)
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Vector Stores (retrievers that search by similarity of embeddings)
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Indexers (sync documents from any source into a vector store without duplication)
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Memory (for long running chats, whether a simple Buffer, Knowledge Graph, Summary, or Vector Store)
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Use-Cases: compositions of Components
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Chains: combining a PromptTemplate, LLM Model and optional OutputParser
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with Router, Sequential, and Transform Chains for advanced usecases
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savable, sharable schemas that can be loaded from LangChainHub
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Agents: a chain that has access to a suite of tools, of nondeterministic length because the LLM is used as a reasoning engine to determine which actions to take and in which order. Notable 100LOC explainer here.
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Tools (interfaces that an agent can use to interact with the world - preset list here. Includes things like ChatGPT plugins, Google Search, WolframAlpha. Groups of tools are bundled up as toolkits)
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AgentExecutor (the agent runtime, basically the while loop, with support for controls, timeouts, memory sharing, etc)
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LangChain has also added a Callbacks system for instrumenting each stage of LLM, Chain, and Agent calls (which enables LangSmith, LangChain’s first cloud product), and most recently an Expression Language, a declarative way to compose chains.
LangChain the company incorporated in January 2023, announced their seed round in April, and launched LangSmith in July. At time of writing, the company has 93k followers, their Discord has 31k members and their weekly webinars are attended by thousands of people live.
The full-featuredness of LangChain means it is often the first starting point for building any mainstream LLM use case, because they are most likely to have working guides for the new developer. Logan (our first guest!) from OpenAI has been a notable fan of both LangChain and LangSmith (they will be running the first LangChain + OpenAI workshop at AI Eng Summit).
However, LangChain is not without its critics, with Aravind Srinivas, Jim Fan, Max Woolf, Mckay Wrigley and the general Reddit/HN community describing frustrations with the value of their abstractions, and many are attempting to write their own (the common experience of adding and then removing LangChain is something we covered in our Agents writeup). Harrison compares this with the timeless ORM debate on the value of abstractions.
LangSmith
Last month, Harrison launched LangSmith, their LLM observability tool and first cloud product. LangSmith makes it easy to monitor all the different primitives that LangChain offers (agents, chains, LLMs) as well as making it easy to share and evaluate them both through heuristics (i.e. manually written ones) and “LLM evaluating LLM” flows.
The top HN comment in the “LangChain is Pointless” thread observed that orchestration is the smallest part of the work, and the bulk of it is prompt tuning and data serialization. When asked this directly our pod, Harrison agreed:
“I agree that those are big pain points that get exacerbated when you have these complex chains and agents where you can't really see what's going on inside of them. And I think that's partially why we built Langsmith…” (48min mark)
You can watch the full launch on the LangChain YouTube:
It’s clear that the target audience for LangChain is expanding to folks who are building complex, production applications rather than focusing on the simpler “Q&A your docs” use cases that made it popular in the first place. As the AI Engineer space matures, there will be more and more tools graduating from supporting “hobby” projects to more enterprise-y use cases.
In this episode we run through some of the history of LangChain, how it’s growing from an open source project to one of the highest valued AI startups out there, and its future. We hope you enjoy it!
Show Notes
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LangChain
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LangChain’s Berkshire Hathaway Homepage
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Abstractions tweet
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LangSmith
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LangSmith Cookbooks repo
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LangChain Retrieval blog
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Evaluating CSV Question/Answering blog and YouTube
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MultiOn Partner blog
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Harvard Sports Analytics Collective
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Evaluating RAG Webinar
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awesome-langchain:
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LLM Math Chain
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Self-Ask
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LangChain Hub UI
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“LangChain is Pointless”
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Harrison’s links
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sports - estimating player compatibility in the NBA
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early interest in prompt injections
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GitHub
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Twitter
Timestamps
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[00:00:00] Introduction
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[00:00:48] Harrison's background and how sports led him into ML
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[00:04:54] The inspiration for creating LangChain - abstracting common patterns seen in other GPT-3 projects
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[00:05:51] Overview of LangChain - a framework for building context-aware reasoning applications
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[00:10:09] Components of LangChain - modules, chains, agents, etc.
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[00:14:39] Underappreciated parts of LangChain - text splitters, retrieval algorithms like self-query
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[00:18:46] Hiring at LangChain
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[00:20:27] Designing the LangChain architecture - balancing flexibility and structure
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[00:24:09] The difference between chains and agents in LangChain
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[00:25:08] Prompt engineering and LangChain
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[00:26:16] Announcing LangSmith
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[00:30:50] Writing custom evaluators in LangSmith
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[00:33:19] Reducing hallucinations - fixing retrieval vs generation issues
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[00:38:17] The challenges of long context windows
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[00:40:01] LangChain's multi-programming language strategy
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[00:45:55] Most popular LangChain blog posts - deep dives into specific topics
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[00:50:25] Responding to LangChain criticisms
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[00:54:11] Harrison's advice to AI engineers
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[00:55:43] Lightning Round
Transcript
Alessio: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, partner and CTO at Residence at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol.ai. [00:00:19]
Swyx: Welcome. Today we have Harrison Chase in the studio with us. Welcome Harrison. [00:00:23]
Harrison: Thank you guys for having me. I'm excited to be here. [00:00:25]
Swyx: It's been a long time coming. We've been asking you for a little bit and we're really glad that you got some time to join us in the studio. Yeah. [00:00:32]
Harrison: I've been dodging you guys for a while. [00:00:34]
Swyx: About seven months. You pulled me in here. [00:00:37]
Alessio: About seven months. But it's all good. I totally understand. [00:00:38]
Swyx: We like to introduce people through the official backgrounds and then ask you a little bit about your personal side. So you went to Harvard, class of 2017. You don't list what you did in Harvard. Was it CS? [00:00:48]
Harrison: Stats and CS. [00:00:50]
Swyx: That's awesome. I love me some good stats. [00:00:52]
Harrison: I got into it through stats, through doing sports analytics. And then there was so much overlap between stats and CS that I found myself doing more and more of that. [00:00:59]
Swyx: And it's interesting that a lot of the math that you learn in stats actually comes over into machine learning which you applied at Kensho as a machine learning engineer and Robust Intelligence, which seems to be the home of a lot of AI founders.
Harrison: It does. Yeah.
Swyx: And you started LangChain, I think around November 2022 and incorporated in January. Yeah. [00:01:19]
Harrison: I was looking it up for the podcast and the first tweet was on, I think October 24th. So just before the end of November or end of October. [00:01:26]
Swyx: Yeah. So that's your LinkedIn. What should people know about you on the personal side that's not obvious on LinkedIn? [00:01:33]
Harrison: A lot of how I got into this is all through sports actually. Like I'm a big sports fan, played a lot of soccer growing up and then really big fan of the NBA and NFL. And so freshman year at college showed up and I knew I liked math. I knew I liked sports. One of the clubs that was there was the Sports Analytics Collective. And so I joined that freshman year, I was doing a lot of stuff in like Excel, just like basic stats, but then like wanted to do more advanced stuff. So learn to code, learn kind of like data science and machine learning through that way. Kind of like just kept on going down that path. I think sports is a great entryway to data science and machine learning. There's a lot of like numbers out there. People like really care. Like I remember, I think sophomore, junior year, I was in the Sports Collective and the main thing we had was a blog. And so we wrote a blog. It wasn't me. One of the other people in the club wrote a blog predicting the NFL season. I think they made some kind of like with stats and I think their stats showed that like the Dolphins would end up beating the Patriots and New England got like pissed about it, of course. So people like really care and they'll give you feedback about whether you're like models doing well or poorly. And so you get that. And then you also get like instantaneous kind of like, well, not instantaneous, but really quick feedback. Like if you predict a game, the game happens that night. Like you don't have to wait a year to see what happens. So I think sports is a great kind of like entryway for kind of like data science. [00:02:43]
Alessio: There was actually my first article on the Twilio blog with a Python script to like predict pricing of like Daily Fantasy players based on my past week performance. Yeah, I don't know. It's a good getaway drug. [00:02:56]
Swyx: And on my end, the way I got into finance was through sports betting. So maybe we all have some ties in there. Was like Moneyball a big inspiration? The movie? [00:03:06]
Harrison: Honestly, not really. I don't really like baseball. That's like the big thing. [00:03:10]
Swyx: Let's call it a lot of stats. Cool. Well, we can dive right into LangChain, which is what everyone is excited about. But feel free to make all the sports analogies you want. That really drives home a lot of points. What was your GPT aha moment? When did you start working on GPT itself? Maybe not LangChain, just anything to do with the GPT API? [00:03:29]
Harrison: I think it probably started around the time we had a company hackathon. I think that was before I launched LangChain. I'm trying to remember the exact sequence of events, but I do remember that at the hackathon I worked with Will, who's now actually at LangChain as well, and then two other members of Robust. And we made basically a bot where you could ask questions of Notion and Slack. And so I think, yeah, RAG, basically. And I think I wanted to try that out because I'd heard that it was getting good. I'm trying to remember if I did anything before that to realize that it was good. So then I would focus on that on the hackathon. I can't remember or not, but that was one of the first times that I built something [00:04:06]
Swyx: with GPT-3. There wasn't that much opportunity before because the API access wasn't that widespread. You had to get into some kind of program to get that. [00:04:16]
Harrison: DaVinci-002 was not terrible, but they did an upgrade to get it to there, and they didn't really publicize that as much. And so I think I remember playing around with it when the first DaVinci model came out. I was like, this is cool, but it's not amazing. You'd have to do a lot of work to get it to do something. But then I think that February or something, I think of 2022, they upgraded it and it was it got better, but I think they made less of an announcement around it. And so I just, yeah, it kind of slipped under the radar for me, at least. [00:04:45]
Alessio: And what was the step into LangChain? So you did the hackathon, and then as you were building the kind of RAG product, you felt like the developer experience wasn't that great? Or what was the inspiration? [00:04:54]
Harrison: No, honestly, so around that time, I knew I was going to leave my previous job. I was trying to figure out what I was going to do next. I went to a bunch of meetups and other events. This was like the September, August, September of that year. So after Stable Diffusion, but before ChatGPT. So there was interest in generative AI as a space, but not a lot of people hacking on language models yet. But there were definitely some. And so I would go to these meetups and just chat with people and basically saw some common abstractions in terms of what they were building, and then thought it would be a cool side project to factor out some of those common abstractions. And that became kind of like LangChain. I looked up again before this, because I remember I did a tweet thread on Twitter to announce LangChain. And we can talk about what LangChain is. It's a series of components. And then there's some end-to-end modules. And there was three end-to-end modules that were in the initial release. One was NatBot. So this was the web agent by Nat Friedman. Another was LLM Math Chain. So it would construct- [00:05:51]
Swyx: GPT-3 cannot do math. [00:05:53]
Harrison: Yeah, exactly. And then the third was Self-Ask. So some type of RAG search, similar to React style agent. So those were some of the patterns in terms of what I was seeing. And those all came from open source or academic examples, because the people who were actually working on this were building startups. And they were doing things like question answering over your databases, question answering over SQL, things like that. But I couldn't use their code as kind of like inspiration to factor things out. [00:06:18]
Swyx: I talked to you a little bit, actually, roundabout, right after you announced LangChain. I'm honored. I think I'm one of many. This is your first open source project. [00:06:26]
Harrison: No, that's not actually true. I released, because I like sports stats. And so I remember I did release some really small, random Python package for scraping data from basketball reference or something. I'm pretty sure I released that. So first project to get a star on GitHub, let's say that. [00:06:45]
Swyx: Did you reference anything? What was the inspirations, like other frameworks that you look to when open sourcing LangChain or announcing it or anything like that? [00:06:53]
Harrison: I mean, the only main thing that I looked for... I remember reading a Hacker News post a little bit before about how a readme on the project goes a long way. [00:07:02]
Swyx: Readme's help. [00:07:03]
Harrison: Yeah. And so I looked at it and was like, put some status checks at the top and have the title and then one or two lines and then just right into installation. And so that's the main thing that I looked at in terms of how to structure it. Because yeah, I hadn't done open source before. I didn't really know how to communicate that aspect of the marketing or getting people to use it. I think I had some trouble finding it, but I finally found it and used that as a lot [00:07:25]
Swyx: of the inspiration there. Yeah. It was one of the subjects of my write-up how it was surprising to me that significant open source experience actually didn't seem to matter in the new wave of AI tooling. Most like auto-GPTs, Torrents, that was his first open source project ever. And that became auto-GPT. Yeah. I don't know. To me, it's just interesting how open source experience is kind of fungible or not necessary. Or you can kind of learn it on the job. [00:07:49]
Alessio: Overvalued. [00:07:50]
Swyx: Overvalued. Okay. You said it, not me. [00:07:53]
Alessio: What's your description of LangChain today? I think when I built the LangChain Hub UI in January, there were a few things. And I think you were one of the first people to talk about agents that were already in there before it got hot now. And it's obviously evolved into a much bigger framework today. Run people through what LangChain is today, how they should think about it, and all of that. [00:08:14]
Harrison: The way that we describe it or think about it internally is that LangChain is basically... I started off saying LangChain's a framework for building LLM applications, but that's really vague and not really specific. And I think part of the issue is LangChain does do a lot, so it's hard to be somewhat specific. But I think the way that we think about it internally, in terms of prioritization, what to focus on, is basically LangChain's a framework for building context-aware reasoning applications. And so that's a bit of a mouthful, but I think that speaks to a lot of the core parts of what's in LangChain. And so what concretely that means in LangChain, there's really two things. One is a set of components and modules. And these would be the prompt template abstraction, the LLM abstraction, chat model abstraction, vector store abstraction, text splitters, document loaders. And so these are combinations of things that we build and we implement, or we just have integrations with. So we don't have any language models ourselves. We don't have any vector stores ourselves, but we integrate with a lot of them. And then the text splitters, we have our own logic for that. The document loaders, we have our own logic for that. And so those are the individual modules. But then I think another big part of LangChain, and probably the part that got people using it the most, is the end-to-end chains or applications. So we have a lot of chains for getting started with question answering over your documents, chat question answering, question answering over SQL databases, agent stuff that you can plug in off the box. And that basically combines these components in a series of specific ways to do this. So if you think about a question answering app, you need a lot of different components kind of stacked. And there's a bunch of different ways to do question answering apps. So this is a bit of an overgeneralization, but basically, you know, you have some component that looks up an embedding from a vector store, and then you put that into the prompt template with the question and the context, and maybe you have the chat history as well. And then that generates an answer, and then maybe you parse that out, or you do something with the answer there. And so there's just this sequence of things that you basically stack in a particular way. And so we just provide a bunch of those assembled chains off the shelf to make it really easy to get started in a few lines of code. [00:10:09]
Alessio: And just to give people context, when you first released LangChain, OpenAI did not have a chat API. It was a completion-only API. So you had to do all the human assistant, like prompting and whatnot. So you abstracted a lot of that away. I think the most interesting thing to me is you're kind of the Switzerland of this developer land. There's a bunch of vector databases that are killing each other out there to get people to embed data in them, and you're like, I love you all. You all are great. How do you think about being an opinionated framework versus leaving a lot of choice to the user? I mean, in terms of spending time into this integration, it's like you only have 10 people on the team. Obviously that takes time. Yeah. What's that process like for you all? [00:10:50]
Harrison: I think right off the bat, having different options for language models. I mean, language models is the main one that right off the bat we knew we wanted to support a bunch of different options for. There's a lot to discuss there. People want optionality between different language models. They want to try it out. They want to maybe change to ones that are cheaper as new ones kind of emerge. They don't want to get stuck into one particular one if a better one comes out. There's some challenges there as well. Prompts don't really transfer. And so there's a lot of nuance there. But from the bat, having this optionality between the language model providers was a big important part because I think that was just something we felt really strongly about. We believe there's not just going to be one model that rules them all. There's going to be a bunch of different models that are good for a bunch of different use cases. I did not anticipate the number of vector stores that would emerge. I don't know how many we supported in the initial release. It probably wasn't as big of a focus as language models was. But I think it kind of quickly became so, especially when Postgres and Elastic and Redis started building their vector store implementations. We saw that some people might not want to use a dedicated vector store. Maybe they want to use traditional databases. I think to your point around what we're opinionated about, I think the thing that we believe most strongly is it's super early in the space and super fast moving. And so there's a lot of uncertainty about how things will shake out in terms of what role will vector databases play? How many will there be? And so I think a lot of it has always kind of been this optionality and ability to switch and not getting locked in. [00:12:19]
Swyx: There's other pieces of LangChain which maybe don't get as much attention sometimes. And the way that you explained LangChain is somewhat different from the docs. I don't know how to square this. So for example, you have at the top level in your docs, you have, we mentioned ModelIO, we mentioned Retrieval, we mentioned Chains. Then you have a concept called Agents, which I don't know if exactly matches what other people call Agents. And we also talked about Memory. And then finally there's Callbacks. Are there any of the less understood concepts in LangChain that you want to give some air to? [00:12:53]
Harrison: I mean, I think buried in ModelIO is some stuff around like few-shot example selectors that I think is really powerful. That's a workhorse. [00:13:01]
Swyx: Yeah. I think that's where I start with LangChain. [00:13:04]
Harrison: It's one of those things that you probably don't, if you're building an application, you probably don't start with it. You probably start with like a zero-shot prompt. But I think that's a really powerful one that's probably just talked about less because you don't need it right off the bat. And for those of you who don't know, that basically selects from a bunch of examples the ones that are maybe most relevant to the input at hand. So you can do some nice kind of like in-context learning there. I think that's, we've had that for a while. I don't think enough people use that, basically. Output parsers also used to be kind of important, but then function calling. There's this interesting thing where like the space is just like progressing so rapidly that a lot of things that were really important have kind of diminished a bit, to be honest. Output parsers definitely used to be an understated and underappreciated part. And I think if you're working with non-OpenAI models, they still are, but a lot of people are working with OpenAI models. But even within there, there's different things you can do with kind of like the function calling ability. Sometimes you want to have the option of having the text or the application you're building, it could return either. Sometimes you know that it wants to return in a structured format, and so you just want to take that structured format. Other times you're extracting things that are maybe a key in that structured format, and so you want to like pluck that key. And so there's just like some like annoying kind of like parsing of that to do. Agents, memory, and retrieval, we haven't talked at all. Retrieval, there's like five different subcomponents. You could also probably talk about all of those in depth. You've got the document loaders, the text splitters, the embedding models, the vector stores. Embedding models and vector stores, we don't really have, or sorry, we don't build, we integrate with those. Text splitters, I think we have like 15 or so. Like I think there's an under kind of like appreciated amount of those. [00:14:39]
Swyx: And then... Well, it's actually, honestly, it's overwhelming. Nobody knows what to choose. [00:14:43]
Harrison: Yeah, there is a lot. [00:14:44]
Swyx: Yeah. Do you have personal favorites that you want to shout out? [00:14:47]
Harrison: The one that we have in the docs is the default is like the recursive text splitter. We added a playground for text splitters the other week because, yeah, we heard a lot that like, you know, and like these affect things like the chunk overlap and the chunks, they affect things in really subtle ways. And so like I think we added a playground where people could just like choose different options. We have like, and a lot of the ideas are really similar. You split on different characters, depending on kind of like the type of text that you have marked down, you might want to split on differently than HTML. And so we added a playground where you can kind of like choose between those. I don't know if those are like underappreciated though, because I think a lot of people talk about text splitting as being a hard part, and it is a really important part of creating these retrieval applications. But I think we have a lot of really cool retrieval algorithms as well. So like self query is maybe one of my favorite things in LangChain, which is basically this idea of when you have a user question, the typical kind of like thing to do is you embed that question and then find the document that's most similar to that question. But oftentimes questions have things that just, you don't really want to look up semantically, they have some other meaning. So like in the example that I use, the example in the docs is like movies about aliens in the year 1980. 1980, I guess there's some semantic meaning for that, but it's a very particular thing that you care about. And so what the self query retriever does is it splits out the metadata filter and most vector stores support like a metadata filter. So it splits out this metadata filter, and then it splits out the semantic bit. And that's actually like kind of tricky to do because there's a lot of different filters that you can have like greater than, less than, equal to, you can have and things if you have multiple filters. So we have like a pretty complicated like prompt that does all that. That might be one of my favorite things in LangChain, period. Like I think that's, yeah, I think that's really cool. [00:16:26]
Alessio: How do you think about speed of development versus support of existing things? So we mentioned retrieval, like you got, or, you know, text splitting, you got like different options for all of them. As you get building LangChain, how do you decide which ones are not going to keep supporting, you know, which ones are going to leave behind? I think right now, as you said, the space moves so quickly that like you don't even know who's using what. What's that like for you? [00:16:50]
Harrison: Yeah. I mean, we have, you know, we don't really have telemetry on what people are using in terms of what parts of LangChain, the telemetry we have is like, you know, anecdotal stuff when people ask or have issues with things. A lot of it also is like, I think we definitely prioritize kind of like keeping up with the stuff that comes out. I think we added function calling, like the day it came out or the day after it came out, we added chat model support, like the day after it came out or something like that. That's probably, I think I'm really proud of how the team has kind of like kept up with that because this space is like exhausting sometimes. And so that's probably, that's a big focus of ours. The support, I think we've like, to be honest, we've had to get kind of creative with how we do that. Cause we have like, I think, I don't know how many open issues we have, but we have like 3000, somewhere between 2000 and 3000, like open GitHub issues. We've experimented with a lot of startups that are doing kind of like question answering over your docs and stuff like that. And so we've got them on the website and in the discord and there's a really good one, dosu on the GitHub that's like answering issues and stuff like that. And that's actually something we want to start leaning into more heavily as a company as well as kind of like building out an AI dev rel because we're 10 people now, 10, 11 people now. And like two months ago we were like six or something like that. Right. So like, and to have like 2,500 open issues or something like that, and like 300 or 400 PRs as well. Cause like one of the amazing things is that like, and you kind of alluded to this earlier, everyone's building in the space. There's so many different like touch points. LangChain is lucky enough to kind of like be a lot of the glue that connects it. And so we get to work with a lot of awesome companies, but that's also a lot of like work to keep up with as well. And so I don't really have an amazing answer, but I think like the, I think prioritize kind of like new things that, that come out. And then we've gotten creative with some of kind of like the support functions and, and luckily there's, you know, there's a lot of awesome people working on all those support coding, question answering things that we've been able to work with. [00:18:46]
Swyx: I think there is your daily rhythm, which I've seen you, you work like a, like a beast man, like mad impressive. And then there's sometimes where you step back and do a little bit of high level, like 50,000 foot stuff. So we mentioned, we mentioned retrieval. You did a refactor in March and there's, there's other abstractions that you've sort of changed your mind on. When do you do that? When do you do like the, the step back from the day to day and go, where are we going and change the direction of the ship? [00:19:11]
Harrison: It's a good question so far. It's probably been, you know, we see three or four or five things pop up that are enough to make us think about it. And then kind of like when it reaches that level, you know, we don't have like a monthly meeting where we sit down and do like a monthly plan or something. [00:19:27]
Swyx: Maybe we should. I've thought about this. Yeah. I'd love to host that meeting. [00:19:32]
Harrison: It's really been a lot of, you know, one of the amazing things is we get to interact with so many different people. So it's been a lot of kind of like just pattern matching on what people are doing and trying to see those patterns before they punch us in the face or something like that. So for retrieval, it was the pattern of seeing like, Hey, yeah, like a lot of people are using vector sort of stuff. But there's also just like other methods and people are offering like hosted solutions and we want our abstractions to work with that as well. So we shouldn't bake in this paradigm of doing like semantic search too heavily, which sounds like basic now, but I think like, you know, to start a lot of it was people needed help doing these things. But then there was like managed things that did them, hybrid retrieval mechanisms, all of that. I think another example of this, I mean, Langsmith, which we can maybe talk about was like very kind of like, I think we worked on that for like three or four months before announcing it kind of like publicly, two months maybe before giving it to kind of like anyone in beta. But this was a lot of debugging these applications as a pain point. We hear that like just understanding what's going on is a pain point. [00:20:27]
Alessio: I mean, you two did a webinar on this, which is called Agents vs. Chains. It was fun, baby. [00:20:32]
Swyx: Thanks for having me on. [00:20:33]
Harrison: No, thanks for coming. [00:20:34]
Alessio: That was a good one. And on the website, you list like RAG, which is retrieval of bank debt generation and agents as two of the main goals of LangChain. The difference I think at the Databricks keynote, you said chains are like predetermined steps and agents is models reasoning to figure out what steps to take and what actions to take. How should people think about when to use the two and how do you transition from one to the other with LangChain? Like is it a path that you support or like do people usually re-implement from an agent to a chain or vice versa? [00:21:05]
Swyx: Yeah. [00:21:06]
Harrison: You know, I know agent is probably an overloaded term at this point, and so there's probably a lot of different definitions out there. But yeah, as you said, kind of like the way that I think about an agent is basically like in a chain, you have a sequence of steps. You do this and then you do this and then you do this and then you do this. And with an agent, there's some aspect of it where the LLM is kind of like deciding what to do and what steps to do in what order. And you know, there's probably some like gray area in the middle, but you know, don't fight me on this. And so if we think about those, like the benefits of the chains are that they're like, you can say do this and you just have like a more rigid kind of like order and the way that things are done. They have more control and they don't go off the rails and basically everything that's bad about agents in terms of being uncontrollable and expensive, you can control more finely. The benefit of agents is that I think they handle like the long tail of things that can happen really well. And so for an example of this, let's maybe think about like interacting with a SQL database. So you can have like a SQL chain and you know, the first kind of like naive approach at a SQL chain would be like, okay, you have the user question. And then you like write the SQL query, you do some rag, you pull in the relevant tables and schemas, you write a SQL query, you execute that against the SQL database. And then you like return that as the answer, or you like summarize that with an LLM and return that to the answer. And that's basically the SQL chain that we have in LangChain. But there's a lot of things that can go wrong in that process. Starting from the beginning, you may like not want to even query the SQL database at all. Maybe they're saying like, hi, or something, or they're misusing the application. Then like what happens if you have some step, like a big part of the application that people with LangChain is like the context aware part. So there's generally some part of bringing in context to the language model. So if you bring in the wrong context to the language model, so it doesn't know which tables to query, what do you do then? If you write a SQL query, it's like syntactically wrong and it can't run. And then if it can run, like what if it returns an unexpected result or something? And so basically what we do with the SQL agent is we give it access to all these different tools. So it has another tool, it can run the SQL query as another, and then it can respond to the user. But then if it kind of like, it can decide which order to do these. And so it gives it flexibility to handle all these edge cases. And there's like, obviously downsides to that as well. And so there's probably like some safeguards you want to put in place around agents in terms of like not letting them run forever, having some observability in there. But I do think there's this benefit of, you know, like, again, to the other part of what LangChain is like the reasoning part, like each of those steps individually involves some aspect of reasoning, for sure. Like you need to reason about what the SQL query is, you need to reason about what to return. But there's then there's also reasoning about the order of operations. And so I think to me, the key is kind of like giving it an appropriate amount to reason about while still keeping it within checks. And so to the point, like, I would probably recommend that most people get started with chains and then when they get to the point where they're hitting these edge cases, then they think about, okay, I'm hitting a bunch of edge cases where the SQL query is just not returning like the relevant things. Maybe I should add in some step there and let it maybe make multiple queries or something like that. Basically, like start with chain, figure out when you're hitting these edge cases, add in the reasoning step to that to handle those edge cases appropriately. That would be kind of like my recommendation, right? [00:24:09]
Swyx: If I were to rephrase it, in my words, an agent would be a reasoning node in a chain, right? Like you start with a chain, then you just add a reasoning node, now it's an agent. [00:24:17]
Harrison: Yeah, the architecture for your application doesn't have to be just a chain or just an agent. It can be an agent that calls chains, it can be a chain that has an agent in different parts of them. And this is another part as well. Like the chains in LangChain are largely intended as kind of like a way to get started and take you some amount of the way. But for your specific use case, in order to kind of like eke out the most performance, you're probably going to want to do some customization at the very basic level, like probably around the prompt or something like that. And so one of the things that we've focused on recently is like making it easier to customize these bits of existing architectures. But you probably also want to customize your architectures as well. [00:24:52]
Swyx: You mentioned a bit of prompt engineering for self-ask and then for this stuff. There's a bunch of, I just talked to a prompt engineering company today, PromptOps or LLMOps. Do you have any advice or thoughts on that field in general? Like are you going to compete with them? Do you have internal tooling that you've built? [00:25:08]
Harrison: A lot of what we do is like where we see kind of like a lot of the pain points being like we can talk about LangSmith and that was a big motivation for that. And like, I don't know, would you categorize LangSmith as PromptOps? [00:25:18]
Swyx: I don't know. It's whatever you want it to be. Do you want to call it? [00:25:22]
Harrison: I don't know either. Like I think like there's... [00:25:24]
Swyx: I think about it as like a prompt registry and you store them and you A-B test them and you do that. LangSmith, I feel like doesn't quite go there yet. Yeah. It's obviously the next step. [00:25:34]
Harrison: Yeah, we'll probably go. And yeah, we'll do more of that because I think that's definitely part of the application of a chain or agent is you start with a default one, then you improve it over time. And like, I think a lot of the main new thing that we're dealing with here is like language models. And the main new way to control language models is prompts. And so like a lot of the chains and agents are powered by this combination of like prompt language model and then some output parser or something doing something with the output. And so like, yeah, we want to make that core thing as good as possible. And so we'll do stuff all around that for sure. [00:26:05]
Swyx: Awesome. We might as well go into LangSmith because we're bringing it up so much. So you announced LangSmith I think last month. What are your visions for it? Is this the future of LangChain and the company? [00:26:16]
Harrison: It's definitely part of the future. So LangSmith is basically a control center for kind of like your LLM application. So the main features that it kind of has is like debugging, logging, monitoring, and then like testing and evaluation. And so debugging, logging, monitoring, basically you set three environment variables and it kind of like logs all the runs that are happening in your LangChain chains or agents. And it logs kind of like the inputs and outputs at each step. And so the main use case we see for this is in debugging. And that's probably the main reason that we started down this path of building it is I think like as you have these more complex things, debugging what's actually going on becomes really painful whether you're using LangChain or not. And so like adding this type of observability and debuggability was really important. Yeah. There's a debugging aspect. You can see the inputs, outputs at each step. You can then quickly enter into like a playground experience where you can fiddle around with it. The first version didn't have that playground and then we'd see people copy, go to open AI playground, paste in there. Okay. Well, that's a little annoying. And then there's kind of like the monitoring, logging experience. And we recently added some analytics on like, you know, how many requests are you getting per hour, minute, day? What's the feedback like over time? And then there's like a testing debugging, sorry, testing and evaluation component as well where basically you can create datasets and then test and evaluate these datasets. And I think importantly, all these things are tied to each other and then also into LangChain, the framework. So what I mean by that is like we've tried to make it as easy as possible to go from logs to adding a data point to a dataset. And because we think a really powerful flow is you don't really get started with a dataset. You can accumulate a dataset over time. And so being able to find points that have gotten like a thumbs up or a thumbs down from a user can be really powerful in terms of creating a good dataset. And so that's maybe like a connection between the two. And then the connection in the other way is like all the runs that you have when you test or evaluate something, they're logged in the same way. So you can debug what exactly is going on and you don't just have like a final score. You have like this nice trace and thing where you can jump in. And then we also want to do more things to hook this into a LangChain proper, the framework. So I think like some of like the managing the prompts will tie in here already. Like we talked about example selectors using datasets as a few short examples is a path that we support in a somewhat janky way right now, but we're going to like make better over time. And so there's this connection between everything. Yeah. [00:28:42]
Alessio: And you mentioned the dataset in the announcement blog post, you touched on heuristic evaluation versus LLMs evaluating LLMs. I think there's a lot of talk and confusion about this online. How should people prioritize the two, especially when they might start with like not a good set of evals or like any data at all? [00:29:01]
Harrison: I think it's really use case specific in the distinction that I draw between heuristic and LLM. LLMs, you're using an LLM to evaluate the output heuristics, you have some common heuristic that you can use. And so some of these can be like really simple. So we were doing some kind of like measuring of an extraction chain where we wanted it to output JSON. Okay. One evaluation can be, can you use JSON.loads to load it? And like, right. And that works perfectly. You don't need an LLM to do that. But then for like a lot of like the question answering, like, is this factually accurate? And you have some ground truth fact that you know it should be answering with. I think, you know, LLMs aren't perfect. And I think there's a lot of discussion around the pitfalls of using LLMs to evaluate themselves. And I'm not saying they're perfect by any means, but I do think they're, we've found them to be kind of like better than blue or any of those metrics. And the way that I also like to use those is also just like guide my eye about where to look. So like, you know, I might not trust the score of like 0.82, like exactly correct, but like I can look to see like which data points are like flagged as passing or failing. And sometimes the evaluators messing up, but it's like good to like, you know, I don't have to look at like a hundred data points. I can focus on like 10 or something like that. [00:30:10]
Alessio: And then can you create a heuristic once in Langsmith? Like what's like your connection to that? [00:30:16]
Harrison: Yeah. So right now, all the evaluation, we actually do client side. And part of this is basically due to the fact that a lot of the evaluation is really application specific. So we thought about having evaluators, you could just click off and run in a server side or something like that. But we still think it's really early on in evaluation. We still think there's, it's just really application specific. So we prioritized instead, making it easy for people to write custom evaluators and then run them client side and then upload the results so that they can manually inspect them because I think manual inspection is still a pretty big part of evaluation for better or worse. [00:30:50]
Swyx: We have this sort of components of observability. We have cost, latency, accuracy, and then planning. Is that listed in there? [00:30:57]
Alessio: Well, planning more in the terms of like, if you're an agent, how to pick the right tool and whether or not you are picking the right tool. [00:31:02]
Swyx: So when you talk to customers, how would you stack rank those needs? Are they cost sensitive? Are they latency sensitive? I imagine accuracy is pretty high up there. [00:31:13]
Harrison: I think accuracy is definitely the top that we're seeing right now. I think a lot of the applications, people are, especially the ones that we're working with, people are still struggling to get them to work at a level where they're reliable [00:31:24]
Swyx: enough. [00:31:25]
Harrison: So that's definitely the first. Then I think probably cost becomes the next one. I think a few places where we've started to see this be like one of the main things is the AI simulation that came out. [00:31:36]
Swyx: Generative agents. Yeah, exactly. [00:31:38]
Harrison: Which is really fun to run, but it costs a lot of money. And so one of our team members, Lance, did an awesome job hooking up like a local model to it. You know, it's not as perfect, but I think it helps with that. Another really big place for this, we believe, is in like extraction of structured data from unstructured data. And the reason that I think it's so important there is that usually you do extraction of some type of like pre-processing or indexing process over your documents. I mean, there's a bunch of different use cases, but one use case is for that. And generally that's over a lot of documents. And so that starts to rack up a bill kind of quickly. And I think extraction is also like a simpler task than like reasoning about which tools to call next in an agent. And so I think it's better suited for that. Yeah. [00:32:15]
Swyx: On one of the heuristics I wanted to get your thoughts on, hallucination is one of the big problems there. Do you have any recommendations on how people should reduce hallucinations? [00:32:25]
Harrison: To reduce hallucinations, we did a webinar on like evaluating RAG this past week. And I think there's this great project called RAGOS that evaluates four different things across two different spectrums. So the two different spectrums are like, is the retrieval part right? Or is the generation, or sorry, like, is it messing up in retrieval or is it messing up in generation? And so I think to fix hallucination, it probably depends on where it's messing up. If it's messing up in generation, then you're getting the right information, but it's still hallucinating. Or you're getting like partially right information and hallucinating some bits, a lot of that's prompt engineering. And so that's what we would recommend kind of like focusing on the prompt engineering part. And then if you're getting it wrong in the, if you're just not retrieving the right stuff, then there's a lot of different things that you can probably do, or you should look at on the retrieval bit. And honestly, that's where it starts to become a bit like application specific as well. Maybe there's some temporal stuff going on. Maybe you're not parsing things correctly. Yeah. [00:33:19]
Swyx: Okay. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. [00:33:35]
Harrison: Yeah. Yeah. [00:33:37]
Swyx: Yeah. [00:33:38]
Harrison: Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. [00:33:56]
Swyx: Yeah. Yeah. [00:33:58]
Harrison: Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. [00:34:04]
Swyx: Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. [00:34:17]
Harrison: Yeah. Yeah. Yeah. Yeah. Yeah. Yeah, I mean, there's probably a larger discussion around that, but openAI definitely had a huge headstart, right? And that's... Clawds not even publicly available yet, I don't think. [00:34:28]
Swyx: The API? Yeah. Oh, well, you can just basically ask any of the business reps and they'll give it to you. [00:34:33]
Harrison: You can. But it's still a different signup process. I think there's... I'm bullish that other ones will catch up especially like Anthropic and Google. The local ones are really interesting. I think we're seeing a big... [00:34:46]
Swyx: Lama Two? Yeah, we're doing the fine-tuning hackathon tomorrow. Thanks for promoting that. [00:34:50]
Harrison: No, thanks for it. I'm really excited about that stuff. I mean, that's something that like we've been, you know, because like, as I said, like the only thing we know is that the space is moving so fast and changing so rapidly. And like, local models are, have always been one of those things that people have been bullish on. And it seems like it's getting closer and closer to kind of like being viable. So I'm excited to see what we can do with some fine-tuning. [00:35:10]
Swyx: Yeah. I have to confess, I did not know that you cared. It's not like a judgment on Langchain. I was just like, you know, you write an adapter for it and you're done, right? Like how much further does it go for Langchain? In terms of like, for you, it's one of the, you know, the model IO modules and that's it. But like, you seem very personally, very passionate about it, but I don't know what the Langchain specific angle for this is, for fine-tuning local models, basically. Like you're just passionate about local models and privacy and all that, right? And open source. [00:35:41]
Harrison: Well, I think there's a few different things. Like one, like, you know, if we think about what it takes to build a really reliable, like context-aware reasoning application, there's probably a bunch of different nodes that are doing a bunch of different things. And I think it is like a really complex system. And so if you're relying on open AI for every part of that, like, I think that starts to get really expensive. Also like, probably just like not good to have that much reliability on any one thing. And so I do think that like, I'm hoping that for like, you know, specific parts at the end, you can like fine-tune a model and kind of have a more specific thing for a specific task. Also, to be clear, like, I think like, I also, at the same time, I think open AI is by far the easiest way to get started. And if I was building anything, I would absolutely start with open AI. So. [00:36:27]
Swyx: It's something I think a lot of people are wrestling with. But like, as a person building apps, why take five vendors when I can take one vendor, right? Like, as long as I trust Azure, I'm just entrusting all my data to Azure and that's it. So I'm still trying to figure out the real case for local models in production. And I don't know, but fine-tuning, I think, is a good one. That's why I guess open AI worked on fine-tuning. [00:36:49]
Harrison: I think there's also like, you know, like if there is, if there's just more options available, like prices are going to go down. So I'm happy about that. So like very selfishly, there's that aspect as well. [00:37:01]
Alessio: And in the Lancsmith announcement, I saw in the product screenshot, you have like chain, tool and LLM as like the three core atoms. Is that how people should think about observability in this space? Like first you go through the chain and then you start dig down between like the model itself and like the tool it's using? [00:37:19]
Harrison: We've added more. We've added like a retriever logging so that you can see like what query is going in and what are the documents you're getting out. Those are like the three that we started with. I definitely think probably the main ones, like basically the LLM. So the reason I think the debugging in Lancsmith and debugging in general is so needed for these LLM apps is that if you're building, like, again, let's think about like what we want people to build in with LangChain. These like context aware reasoning applications. Context aware. There's a lot of stuff in the prompt. There's like the instructions. There's any previous messages. There's any input this time. There's any documents you retrieve. And so there's a lot of like data engineering that goes into like putting it into that prompt. This sounds silly, but just like making sure the data shows up in the right format is like really important. And then for the reasoning part of it, like that's obviously also all in the prompt. And so being able to like, and there's like, you know, the state of the world right now, like if you have the instructions at the beginning or at the end can actually make like a big difference in terms of whether it forgets it or not. And so being able to kind of like. [00:38:17]
Swyx: Yeah. And it takes on that one, by the way, this is the U curve in context, right? Yeah. [00:38:21]
Harrison: I think it's real. Basically I've found long context windows really good for when I want to extract like a single piece of information about something basically. But if I want to do reasoning over perhaps multiple pieces of information that are somewhere in like the retrieved documents, I found it not to be that great. [00:38:36]
Swyx: Yeah. I have said that that piece of research is the best bull case for Lang chain and all the vector companies, because it means you should do chains. It means you should do retrieval instead of long context, right? People are trying to extend long context to like 100K, 1 million tokens, 5 million tokens. It doesn't matter. You're going to forget. You can't trust it. [00:38:54]
Harrison: I expect that it will probably get better over time as everything in this field. But I do also think there'll always be a need for kind of like vector stores and retrieval in some fashions. [00:39:03]
Alessio: How should people get started with Langsmith Cookbooks? Wanna talk maybe a bit about that? [00:39:08]
Swyx: Yeah. [00:39:08]
Harrison: Again, like I think the main thing that even I find valuable about Langsmith is just like the debugging aspect of it. And so for that, it's very simple. You can kind of like turn on three environment variables and it just logs everything. And you don't look at it 95% of the time, but that 5% you do when something goes wrong, it's quite handy to have there. And so that's probably the easiest way to get started. And we're still in a closed beta, but we're letting people off the wait list every day. And if you really need access, just DM me and we're happy to give you access there. And then yeah, there's a lot that you can do with Langsmith that we've been talking about. And so Will on our team has been leading the charge on a really great like Langsmith Cookbooks repo that covers everything from collecting feedback, whether it's thumbs up, thumbs down, or like multi-scale or comments as well, to doing evaluation, doing testing. You can also use Langsmith without Langchain. And so we've got some notebooks on that in there. But we have Python and JavaScript SDKs that aren't dependent on Langchain in any way. [00:40:01]
Swyx: And so you can use those. [00:40:01]
Harrison: And then we'll also be publishing a notebook on how to do that just with the REST APIs themselves. So yeah, definitely check out that repo. That's a great resource that Will's put together. [00:40:10]
Swyx: Yeah, awesome. So we'll zoom out a little bit from Langsmith and talk about Langchain, the company. You're also a first-time founder. Yes. And you've just hired your 10th employee, Julia, who I know from my data engineering days. You mentioned Will Nuno, I think, who maintains Langchain.js. I'm very interested in like your multi-language strategy, by the way. Ankush, your co-founder, Lance, who did AutoEval. What are you staffing up for? And maybe who are you hiring? [00:40:34]
Harrison: Yeah, so 10 employees, 12 total. We've got three more joining over the next three weeks. We've got Julia, who's awesome leading a lot of the product, go-to-market, customer success stuff. And then we've got Bri, who's also awesome leading a lot of the marketing and ops aspects. And then other than that, all engineers. We've staffed up a lot on kind of like full stack infra DevOps, kind of like as we've started going into the hosted platform. So internally, we're split about 50-50 between the open source and then the platform stuff. And yeah, we're looking to hire particularly on kind of like the things, we're actually looking to hire across most fronts, to be honest. But in particular, we probably need one or two more people on like open source, both Python and JavaScript and happy to dive into the multi-language kind of like strategy there. But again, like strong focus there on engineering, actually, as opposed to maybe like, we're not a research lab, we're not a research shop. [00:41:48]
Swyx: And then on the platform side, [00:41:49]
Harrison: like we definitely need some more people on the infra and DevOps side. So I'm using this as an opportunity to tell people that we're hiring and that you should reach out if that sounds like you. [00:41:58]
Swyx: Something like that, jobs, whatever. I don't actually know if we have an official job. [00:42:02]
Harrison: RIP, what happened to your landing page? [00:42:04]
Swyx: It used to be so based. The Berkshire Hathaway one? Yeah, so what was the story, the quick story behind that? Yeah, the quick story behind that is we needed a website [00:42:12]
Harrison: and I'm terrible at design. [00:42:14]
Swyx: And I knew that we couldn't do a good job. [00:42:15]
Harrison: So if you can't do a good job, might as well do the worst job possible. Yeah, and like lean into it. And have some fun with it, yeah. [00:42:21]
Swyx: Do you admire Warren Buffett? Yeah, I admire Warren Buffett and admire his website. And actually you can still find a link to it [00:42:26]
Harrison: from our current website if you look hard enough. So there's a little Easter egg. Before we dive into more of the open source community things, [00:42:33]
Alessio: let's dive into the language thing. How do you think about parity between the Python and JavaScript? Obviously, they're very different ecosystems. So when you're working on a LangChain, is it we need to have the same abstraction in both language or are you to the needs? The core stuff, we want to have the same abstractions [00:42:50]
Harrison: because we basically want to be able to do serialize prompts, chains, agents, all the core stuff as tightly as possible and then use that between languages. Like even, yeah, like even right now when we log things to LangChain, we have a playground experience where you can run things that runs in JavaScript because it's kind of like in the browser. But a lot of what's logged is like Python. And so we need that core equivalence for a lot of the core things. Then there's like the incredibly long tail of like integrations, more researchy things. So we want to be able to do that. Python's probably ahead on a lot of like the integrations front. There's more researchy things that we're able to include quickly because a lot of people release some of their code in Python and stuff like that. And so we can use that. And there's just more of an ecosystem around the Python project. But the core stuff will have kind of like the same abstractions and be translatable. That didn't go exactly where I was thinking. So like the LangChain of Ruby, the LangChain of C-sharp, [00:43:44]
Swyx: you know, there's demand for that. I mean, I think that's a big part of it. But you are giving up some real estate by not doing it. Yeah, it comes down to kind of like, you know, ROI and focus. And I think like we do think [00:43:58]
Harrison: there's a strong JavaScript community and we wanted to lean into that. And I think a lot of the people that we brought on early, like Nuno and Jacob have a lot of experience building JavaScript tooling in that community. And so I think that's a big part of it. And then there's also like, you know, building JavaScript tooling in that community. Will we do another language? Never say never, but like... [00:44:21]
Swyx: Python JS for now. Yeah. Awesome. [00:44:23]
Alessio: You got 83 articles, which I think might be a record for such a young company. What are like the hottest hits, the most popular ones? [00:44:32]
Harrison: I think the most popular ones are generally the ones where we do a deep dive on something. So we did something a few weeks ago around evaluating CSV question answering applications, which I think is a really interesting one because most question answering, like everyone does question answering, but it's generally over unstructured data over your documents and you do the whole rag thing. And that doesn't work amazing for structured data. And so this was something that we heard, the origin of this was basically we heard from the community, you guys should improve this. And so we're like, okay, let's improve it. And then we're like, okay, well, in order to see if we improve it, we need to like evaluate it and see how we're doing. And so we kind of like wrote up a lot of our thought process there. And I think, and a lot of people like reached out about that and thought that was interesting and we're going through similar challenges and had, we posted another one a few days after that someone wrote basically as a response, which is awesome because it had a completely different strategy. And it was a really, it was a really, that was a really good piece as well. So that was like a deep dive on something like evaluation bit. I think like we did one on retrieval a while back, which was basically like, hey, we, and this was around when we changed our abstractions, like, hey, we changed our abstractions to this. This is why we did it. This is what we see coming down the pipeline. These are like the different types of retrieval that we see. I think a lot of people read and liked that one. A lot of the blogs that we do are also highlighting cool partnerships or cool applications. But in terms of, if you go by like number of views, I think the ones that get the most views are the more like deep dive ones. [00:45:55]
Swyx: Yeah. And I also noticed that you do guest posts as well. [00:45:58]
Harrison: Actually, you know, which one, and this is a guest post that got a lot of views, the multi-on one, the multi-on agent one. When we did, we did a blog where we integrated with them and that got a ton of views. [00:46:06]
Swyx: What do you think that is? [00:46:07]
Harrison: I think it's, I mean, it's one of like the few agents that's actually available and like out in the world. [00:46:15]
Swyx: They're still behind a wait list. Still behind a wait list, [00:46:17]
Harrison: but they're very active on social media. I don't know if I'm off the wait list. [00:46:21]
Swyx: I mean, you're on their blogs. They're on your blog, so I hope they give you access at some point. But that's interesting. A lot of interest in agents. I think they just opened up an API as well. Yeah, exactly. [00:46:32]
Harrison: That was the blog that we did. I was, yeah, I was a bit surprised to see that as well, but I think there's generally a lot of interest in agents and it's also really hard to get them to work. And I think multi-on is one of the first that has that. [00:46:45]
Swyx: Yeah. So my angle to this is a lot of people want to work with you. Yes. You're bombarded. I'm sure your email is just unmanageable. How should people be good partners with you? Like I work at a company and I'm like, hey, I'd love to do something on the LangChain blog or integrate to LangChain. I know Harrison's a busy guy. Like, what do I do? [00:47:03]
Harrison: Like the stuff that gets my attention honestly is like the in-depth, really thought out stuff. Obviously I love this stuff. Like this stuff is awesome. And there's so many different, there's so much to do as well. And like the biggest thing that we have trouble with internally is like figuring out what to do. [00:47:17]
Swyx: What's noise and what's signal. [00:47:19]
Harrison: Not even that, but just like what to focus on. Like there's so many different directions we could do and we want to go in like so many because there's so many interesting things, but we can't do. So if anyone kind of like takes the time to like go deep in a particular area, I love talking to them and I love reading what they write. And I love sharing what they write on the blog. Like that to me is awesome. So I think like... [00:47:37]
Swyx: Do good stuff. Be so good they can't ignore you. It sounds basic, right? [00:47:40]
Harrison: So that's why I didn't want to say it. [00:47:42]
Swyx: No, it's great. [00:47:42]
Harrison: But I think like these deep dots, yeah, there's just so much to do and these don't do shallow stuff, I guess would be. [00:47:48]
Swyx: I think that's a good call that people need reminding. [00:47:50]
Alessio: What about the other side of open source? So on Acker News, there were a couple blog posts recently, like the problem with LangChain and LangChain is pointless, all these different things. So the TLDR of some of them were, the LangChain API is like kind of verbose and complicated versus like sometimes I can just do this in like 10 lines of code. How do you balance that in terms of allowing for the complex use cases versus making maybe the ergonomics like simpler, but then trading that off later? [00:48:21]
Harrison: There's a lot to balance and there's a lot to do. And I think like posts like that are very valuable to hear basically what people are saying. And like, we have a lot of open issues. So it's not like these things hadn't been said before, but I think like that was a good emphasis on what people are saying. And I think there was a lot of things in there. I think part of it's kind of like around and we took all of it very seriously. And yeah, I think there's a lot to dive into there. There's like the documentation piece. And so I think we did a revamp of the documentation to address that. There's also like a comment in this, I think this was around, I think the top comment on the LangChain is pointless one was like basically like orchestration is like 5% of the work. And then like the other 95% is like prompt engineering and like data engineering. And those are the hard bits. I think maybe orchestration is a little bit more than 5%, but I like agree that those are like really big pain points that get exacerbated when you have these complex chains and agents where you can't really see what's going on inside of them. And I think that's partially why we built Langsmith to help out with exactly that. We also needed to do better things like make the prompts more visible and make it allow for more customizability around that. And so we've tried to add some stuff there. In terms of balancing, there's also LangChain is pointless. I don't need a wrapper. I can just call the underlying API. I think if all you're trying to do is call the underlying API, then like, yeah, that's gonna be the cleanest and simplest thing to do. And we try to get as close to that experience as possible, but we're not optimizing for calling the API. We're optimizing for helping people build context-aware reasoning applications as easily as possible. And so there's some level of abstractions that you need to add in order to assist in that. Yeah, that's definitely a balance that's tricky to strike, but I think there's also some aspect of it. Like, I do think one of the big benefits that LangChain provides is a standard interface for language models so that you can switch between them. And this kind of gets into like an ORM debate, like are ORMs generally kind of like useful or not? And so I think in this case they are. I think there's probably a larger kind of like philosophical kind of like question about that [00:50:25]
Swyx: that people have strong opinions on. Just the prompts don't transfer like you also mentioned. Yeah, yeah, there's that, yeah. [00:50:32]
Harrison: And then between kind of like allowing for, I think one helpful thing that we did in terms of like distinguishing between basically the base interfaces and then more complex stuff is part of the separation around the docs is there's like the components piece, which has the model IO, the retrieval, the agents, the callbacks, things like that. And then there's all the use cases. And so I think like the use cases, because they are like these assembly of all these things in a particular order, they start to get more complex. And it's, you know, we try our best to kind of like make clear how you can configure things. But yeah, there's a lot of different options that you might want to configure. And so I think that split has kind of helped us internally at least. And I think externally as well, because we've heard good comments about the improved documentation. I think that's made it a little bit more clear. And then another thing, one of the things that we also released soon after, and we'd been thinking about a little bit is basically like a LangChain expression language, which allows for actual composability of pieces. So LangChain, I think, has always been very good about interchangeability. Let's ignore the prompting issues, but like you could always plug in like one LLM for another one. You could swap in one vector for another one, but the chains themselves haven't actually been super actually composable. Like we had the sequential chain, but that was a bit like clunky to use. And then we had a router chain, but that was a bit, you know, that was also a bit clunky to use. And so one of the things, and so there's a million different things to do, and we didn't prioritize that. [00:51:53]
Swyx: I think after this, [00:51:53]
Harrison: we definitely bumped it up and prioritized in priority. And luckily Nuno had been doing a lot of awesome work on it already, so it wasn't too much of a lift. But yeah, now there's this way where a lot of the chains that we've been releasing are written in this LangChain expression language where they're actually truly composable, and you can see what's going on under the hood. And it's basically, it uses kind of like the pipe kind of like terminology to coordinate things and move things around. So yeah, I mean, I think there were a lot of good points in those Hacker News things, and you know, we can't respond to everything, but we try to like look at everything and take everything seriously. [00:52:25]
Swyx: You're being very diplomatic. But so first of all, I like the expression language. I think that that is the path towards sort of language agnostic LangChain kind of, or whatever, DSL. But also like, what was just kind of plain wrong or plain offensive, or like, I don't know, people can get very vitriolic sometimes on Hacker News. [00:52:40]
Harrison: Yeah, I mean, I think the comments that I appreciated were the ones where they gave specific things. And I think the ones where they said, you know, LangChain sucks. Like, okay. Can't do much of that. [00:52:51]
Swyx: Yeah, exactly. Verifacing on my question would be like, you're not the first and you won't be the last to have that kind of very intense scrutiny. What would be your advice to other people, other maintainers of projects for going through something like this? [00:53:03]
Harrison: I would probably say, try to drill into like what is actually underlying things [00:53:08]
Swyx: as much as possible. [00:53:08]
Harrison: And if there is actual substance that's being delivered, whether you agree with it or not, like, I think that's valuable to know. And then for the other stuff, like try to maybe follow up, but maybe try not to let it get under your skin too much. [00:53:22]
Swyx: Thanks for tackling that. [00:53:24]
Alessio: And I know we're getting to the time and we'll wrap up soon, but since you're going to speak at the AI Engineers Conference, what's your advice to AI engineers, especially when to start with LangChain and when they're just experimenting with a model, [00:53:38]
Swyx: when are they, [00:53:38]
Alessio: as you mentioned, if you just want to do an API call, don't use LangChain. Yeah. [00:53:43]
Harrison: I mean, my advice would just like build as many things as possible. Like, I think it's still really early in the space. No one really knows what they're doing to some extent. Like, it's a bit weird to say, but there's so many things to like discover. So I would just say like, build as many things as possible. Cause I think like the best thing is you stumble upon a really good idea and you build something really awesome. And the worst thing that happens is you just learn a lot about a field and the technology that's going to be incredibly important and rapidly kind of like changing. [00:54:11]
Alessio: What would you build if you weren't doing LangChain? [00:54:13]
Harrison: I mean, the things that are most interesting to me are kind of like things around like long-term memory and like longer running agents. So I'd probably build, and these are things that we've been wanting to build [00:54:23]
Swyx: internally as well. [00:54:23]
Harrison: But like, I think a chatbot that like actually remembers things about you as like silly as that sounds, like people like chatbots a lot and they have their delivered limited by their context window. And so I think really diving into like a specific application of memory there. [00:54:38]
Swyx: I've been trying to build a chatbot [00:54:39]
Harrison: that remembers things about you. That would be one. And then like, I know a lot of people are doing this, but like a personal assistant for like managing like email calendar, basic stuff, which I think is, I think that's like a fantastic application for these like agent like things, because if you think about personal assistants today, you usually interact, I don't have one, but I'm told you interact with them over email. And the nice thing about that, as opposed to like chat, there's not as stringent an expectation on latency as there is on chat. And so you can do a lot of things like reflection and kind of like making sure that you're on the right track and really put more safeguards and thinking about these agents as opposed to relying on like chas and interface, like the bot we have that's on GitHub answering questions on the issues, I think probably gives better answers than the bots that we have that are on chat on the website. And I think that's not because, there's just different constraints that you have in different types of problems. And I think I would be like, I think the personal assistant one's really interesting because you remove the constraint of chat, which I think at this point in time is probably pretty limited in terms of functionality. [00:55:43]
Swyx: Yeah. I've been calling this sort of long inference. If you didn't have to care about ANC and you could take like a day, a month, a year to work on something, what could you do? And yeah, that's super interesting. [00:55:56]
Harrison: I think that's a really promising place to explore. [00:55:58]
Swyx: Yeah. Have you looked at, regarding the long conversation thing, you and I have tried it about this many times. Have you looked into what character and inflection are doing? Because they're probably working on it. [00:56:08]
Harrison: I've thought about memory a bunch. Like I think it comes down to like, it comes down to like state, like what's the state you're tracking? Like what's the data structure for that? And I think that could also maybe be a bit like application specific. But if we're talking about a generic chat bot, that's kind of generic. I don't know. Yeah, I don't know how they're thinking about that. My sense is that inflection like thinks about that a bit more than character. Like I think in Inception, sorry, inflection's whole thing is they like, the bot knows you. [00:56:33]
Swyx: It's one chat. There's no history. You just talk to it. Yeah. [00:56:37]
Harrison: So they've definitely got some state that they're tracking. I'd be really curious to know what that is. Character, I don't think has lent into it too much. I think they let you do some stuff in terms of like uploading background. And I'm not entirely sure how they use that, whether they just like put that in the prompt or do some retrieval over that. But I think they're definitely, they haven't lent into it as much as inflection, I would say. [00:56:57]
Swyx: So given like, you are one of the most interested people in this space, would this be like a second product for you? If you ever want to explore that or do you want to just partner with people and you're putting out the call for people to come to you if they have solutions for that? [00:57:10]
Harrison: If I wasn't working on LangChain, I would be building an application company, for sure, first of all. Like, I don't think, like I think like there's, which I know is very hypocritical to say. [00:57:20]
Swyx: Like you're Mr. DevTools and Infra and Observability. [00:57:24]
Harrison: Yeah, I don't know. If you're building an application company that's working on something related to long-term memory or long-term agents, I would love to chat and just geek out [00:57:31]
Swyx: about a lot of this stuff. I'll show you Smalltalk at some point. Yes. Cool. Awesome. [00:57:37]
Alessio: Yeah, let's do a lightning round. [00:57:38]
Swyx: So the first one is on acceleration. What has happened in AI that you thought would take much longer than it actually ended up taking? [00:57:45]
Harrison: The function call and ability from OpenAI, like tool usage. [00:57:48]
Swyx: Yeah. [00:57:48]
Harrison: They did that really fast, I thought. [00:57:50]
Swyx: Yeah. But it's just a question of fine-tuning, no? Yeah. It's not even like reliable. [00:57:54]
Harrison: It's not terrible. They're a pretty big organization that's serving a lot of traffic. And like, this was a, yeah, it's like, it is like just fine-tuning, but I think like you still have to like collect that data set and fine-tune it and evaluate it and then release it at scale and figure out the right API. [00:58:09]
Swyx: No shade on OpenAI. Like they're moving everyone's bar as to how quickly like a 400% organization can go. Do you think it eliminates like approaches like JSONformer and all the other approaches that people, like guardrails, you know, previous guest, eliminates your output validation thing? Yeah. [00:58:26]
Harrison: I think JSONformer and stuff like that are still really interesting for like local models, for sure. And there's like 90% of people use OpenAI or something and like my made up numbers. [00:58:37]
Swyx: No, it's probably real. [00:58:38]
Harrison: And the best way to get structured output is by using the function calling ability. So yeah, absolutely. [00:58:46]
Alessio: What do you think is the most interesting unsolved question in AI? [00:58:50]
Harrison: I'm really interested like how multimodal is going to work. Like with just what that looks like. [00:58:55]
Swyx: Have you had a look at the GPT-4 vision? No, not really. [00:58:59]
Harrison: Yeah, not beyond what they- [00:59:01]
Swyx: They're doing private betas right now. So I'm very excited. [00:59:04]
Harrison: I'm excited about that as well. Yeah, I mean, I think that's, you know, you talk about like, again, this whole space is just changing so fast, but you talk about something that could like really change how, because like, you know, a lot of lang chain is kind of like a data orchestration tool in some sense. And so if you had a whole new type of data in there. [00:59:20]
Swyx: So maybe we do this thought exercise, right? Tomorrow, OpenAI releases the GPT-4 vision API. What does lang chain do? [00:59:25]
Harrison: Immediately we add support for it in like the wrapper. So however you interact, like honestly, this is another like fun thing. Everyone's API now looks like OpenAI's. [00:59:35]
Swyx: Yeah, which is great. [00:59:36]
Harrison: Which you have to do, yeah. So like our wrapper looks similar to OpenAI. So I don't think it will be that difficult to include support for it at the basic model level. And so we do that. And now that we've released the expression language bit, like a lot of the core chains, we have examples of rewriting them just in this expression language. So like for retrieval, if we're now talking about like, okay, you can do like retrieval question answering over for multimodal things, we'd probably have to figure out how those are getting stored and what's being done with them. But then from there, that should be, yeah, so probably looking to like, yeah, how are people kind of like storing and consuming this type of information? But then that step should be pretty easy to plug into the kind of like chain. [01:00:17]
Swyx: Multimodal stores? Yeah, I don't know. I always wonder what that would actually look like because a lot of multimodality in LLMs is really just an LLM, a text LLM calling a different model. And that's just no different than any API call, essentially unchanged. [01:00:32]
Harrison: I think it's probably something that you don't know until you let like a million people play around with it. [01:00:37]
Swyx: Then there'll be new LangChain for multimodal. What's one message you want everyone to remember today? [01:00:43]
Harrison: I would probably say just like build. I think it's a fantastic time to be building. [01:00:47]
Swyx: All right, just build. Yeah. [01:00:49]
Alessio: Thank you Harrison for coming on. [01:00:51]
Swyx: Thanks so much. [01:00:51]
Harrison: Thank you guys for having me. [01:00:52]
Swyx: It's a lot of fun. [01:00:53]
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Podcast: Latent Space: The AI Engineer Podcast — Practitioners talking LLMs, CodeGen, Agents, Multimodality, AI UX, GPU Infra and all things Software 3.0 (LS 33 · TOP 5% what is this?)
Episode: The End of Finetuning — with Jeremy Howard of Fast.ai
Pub date: 2023-10-19
Thanks to the over 17,000 people who have joined the first AI Engineer Summit! A full recap is coming. Last call to fill out the State of AI Engineering survey! See our Community page for upcoming meetups in SF, Paris and NYC.
This episode had good interest on Twitter and was discussed on the Vanishing Gradients podcast.
Fast.ai’s “Practical Deep Learning” courses been watched by over >6,000,000 people, and the fastai library has over 25,000 stars on Github. Jeremy Howard, one of the creators of Fast, is now one of the most prominent and respected voices in the machine learning industry; but that wasn’t always the case.
Being non-consensus and right
In 2018, Jeremy and Sebastian Ruder published a paper on ULMFiT (Universal Language Model Fine-tuning), a 3-step transfer learning technique for NLP tasks:
The paper demonstrated that pre-trained language models could be fine-tuned on a specific task with a relatively small amount of data to achieve state-of-the-art results. They trained a 24M parameters model on WikiText-103 which was beat most benchmarks.
While the paper had great results, the methods behind weren’t taken seriously by the community:
“Everybody hated fine tuning. Everybody hated transfer learning. I literally did tours trying to get people to start doing transfer learning and nobody was interested, particularly after GPT showed such good results with zero shot and few shot learning […] which I was convinced was not the right direction, but who's going to listen to me, cause as you said, I don't have a PhD, not at a university… I don't have a big set of computers to fine tune huge transformer models.”
Five years later, fine-tuning is at the center of most major discussion topics in AI (we covered some like fine tuning vs RAG and small models fine tuning), and we might have gotten here earlier if Jeremy had OpenAI-level access to compute and distribution. At heart, Jeremy has always been “GPU poor”:
“I've always been somebody who does not want to build stuff on lots of big computers because most people don't have lots of big computers and I hate creating stuff that most people can't use.”
This story is a good reminder of how some of the best ideas are hiding in plain sight; we recently covered RWKV and will continue to highlight the most interesting research that isn’t being done in the large labs.
Replacing fine-tuning with continued pre-training
Even though fine-tuning is now mainstream, we still have a lot to learn. The issue of “catastrophic forgetting” and potential solutions have been brought up in many papers: at the fine-tuning stage, the model can forget tasks it previously knew how to solve in favor of new ones.
The other issue is apparent memorization of the dataset even after a single epoch, which Jeremy covered Can LLMs learn from a single example? but we still don’t have the answer to.
Despite being the creator of ULMFiT, Jeremy still professes that there are a lot of open questions on finetuning:
“So I still don't know how to fine tune language models properly and I haven't found anybody who feels like they do.”
He now advocates for "continued pre-training" - maintaining a diversity of data throughout the training process rather than separate pre-training and fine-tuning stages. Mixing instructional data, exercises, code, and other modalities while gradually curating higher quality data can avoid catastrophic forgetting and lead to more robust capabilities (something we covered in Datasets 101).
“Even though I originally created three-step approach that everybody now does, my view is it's actually wrong and we shouldn't use it… the right way to do this is to fine-tune language models, is to actually throw away the idea of fine-tuning. There's no such thing. There's only continued pre-training.
And pre-training is something where from the very start, you try to include all the kinds of data that you care about, all the kinds of problems that you care about, instructions, exercises, code, general purpose document completion, whatever. And then as you train, you gradually curate that, you know, you gradually make that higher and higher quality and more and more specific to the kinds of tasks you want it to do. But you never throw away any data….
So yeah, that's now my view, is I think ULMFiT is the wrong approach. And that's why we're seeing a lot of these so-called alignment tax… I think it's actually because people are training them wrong.
An example of this phenomena is CodeLlama, a LLaMA2 model finetuned on 500B tokens of code: while the model is much better at code, it’s worse on generic tasks that LLaMA2 knew how to solve well before the fine-tuning.
In the episode we also dive into all the places where open source model development and research is happening (academia vs Discords - tracked on our Communities list and on our survey), and how Jeremy recommends getting the most out of these diffuse, pseudonymous communities (similar to the Eleuther AI Mafia).
Show Notes
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Jeremy’s Background
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FastMail
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Optimal Decisions
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Kaggle
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Enlitic
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fast.ai
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Rachel Thomas
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Practical Deep Learning
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fastai for PyTorch
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nbdev
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fastec2 (the underrated library we describe)
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Can LLMs learn from a single example?
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the Kaggle LLM Science Exam competition, which “challenges participants to answer difficult science-based questions written by a Large Language Model”.
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Sebastian Ruder
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Alec Radford
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Sylvain Gugger
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Stephen Merity
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Chris Lattner
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Modular.ai / Mojo
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Jono Whittaker
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Zeiler and Fergus paper
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ULM Fit
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DAWNBench
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Phi-1
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Code Llama
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AlexNet
Timestamps
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[00:00:00] Intros and Jeremy’s background
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[00:05:28] Creating ULM Fit - a breakthrough in NLP using transfer learning
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[00:06:32] The rise of GPT and the appeal of few-shot learning over fine-tuning
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[00:10:00] Starting Fast.ai to distribute AI capabilities beyond elite academics
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[00:14:30] How modern LMs like ChatGPT still follow the ULM Fit 3-step approach
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[00:17:23] Meeting with Chris Lattner on Swift for TensorFlow at Google
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[00:20:00] Continued pre-training as a fine-tuning alternative
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[00:22:16] Fast.ai and looking for impact vs profit maximization
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[00:26:39] Using Fast.ai to create an "army" of AI experts to improve their domains
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[00:29:32] Fast.ai's 3 focus areas - research, software, and courses
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[00:38:42] Fine-tuning memorization and training curve "clunks" before each epoch
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[00:46:47] Poor training and fine-tuning practices may be causing alignment failures
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[00:48:38] Academia vs Discords
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[00:53:41] Jeremy's high hopes for Chris Lattner's Mojo and its potential
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[01:05:00] Adding capabilities like SQL generation through quick fine-tuning
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[01:10:12] Rethinking Fast.ai courses for the AI-assisted coding era
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[01:14:53] Rapid model development has created major technical debt
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[01:17:08] Lightning Round
AI Summary (beta)
This is the first episode we’re trying this. Here’s an overview of the main topics before you dive in the transcript.
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Jeremy's background and philosophies on AI
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Studied philosophy and cognitive science in college
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Focused on ethics and thinking about AI even 30 years ago
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Believes AI should be accessible to more people, not just elite academics/programmers
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Created fast.ai to make deep learning more accessible
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Development of transfer learning and ULMFit
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Idea of transfer learning critical for making deep learning accessible
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ULMFit pioneered transfer learning for NLP
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Proposed training general language models on large corpora then fine-tuning - this became standard practice
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Faced skepticism that this approach would work from NLP community
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Showed state-of-the-art results on text classification soon after trying it
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Current open questions around fine-tuning LLMs
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Models appear to memorize training data extremely quickly (after 1 epoch)
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This may hurt training dynamics and cause catastrophic forgetting
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Unclear how best to fine-tune models to incorporate new information/capabilities
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Need more research on model training dynamics and ideal data mixing
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Exciting new developments
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Mojo and new programming languages like Swift could enable faster model innovation
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Still lots of room for improvements in computer vision-like innovations in transformers
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Small models with fine-tuning may be surprisingly capable for many real-world tasks
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Prompting strategies enable models like GPT-3 to achieve new skills like playing chess at superhuman levels
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LLMs are like computer vision in 2013 - on the cusp of huge new breakthroughs in capabilities
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Access to AI research
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Many key convos happen in private Discord channels and forums
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Becoming part of these communities can provide great learning opportunities
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Being willing to do real work, not just talk about ideas, is key to gaining access
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The future of practical AI
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Coding becoming more accessible to non-programmers through AI assistance
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Pre-requisite programming experience for learning AI may no longer be needed
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Huge open questions remain about how to best train, fine-tune, and prompt LLMs
Transcript
Alessio: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, partner and CTO at Residence at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol AI. [00:00:21]
Swyx: Hey, and today we have in the remote studio, Jeremy Howard all the way from Australia. Good morning. [00:00:27]
Jeremy: The remote studio, also known as my house. Good morning. Nice to see you. [00:00:32]
Swyx: Nice to see you too. I'm actually very used to seeing you in your mask as a message to people, but today we're mostly audio. But thank you for doing the very important public service of COVID awareness. It was a pleasure. [00:00:46]
Jeremy: It was all very annoying and frustrating and tedious, but somebody had to do it. [00:00:52]
Swyx: Somebody had to do it, especially somebody with your profile. I think it really drives home the message. So we tend to introduce people for them and then ask people to fill in the blanks on the personal side. Something I did not know about you was that you graduated with a BA in philosophy from the University of Melbourne. I assumed you had a PhD. [00:01:14]
Jeremy: No, I mean, I barely got through my BA because I was working 80 to 100 hour weeks at McKinsey and Company from 19 years old onwards. So I actually didn't attend any lectures in second and third year university. [00:01:35]
Swyx: Well, I guess you didn't need it or you're very sort of self-driven and self-motivated. [00:01:39]
Jeremy: I took two weeks off before each exam period when I was working at McKinsey. And then, I mean, I can't believe I got away with this in hindsight, I would go to all my professors and say, oh, I was meant to be in your class this semester and I didn't quite turn up. Were there any assignments I was meant to have done, whatever. I can't believe all of them let me basically have it. They basically always would say like, okay, well, if you can have this written by tomorrow, I'll accept it. So yeah, stressful way to get through university, but. [00:02:12]
Swyx: Well, it shows that, I guess, you min-maxed the opportunities. That definitely was a precursor. [00:02:18]
Jeremy: I mean, funnily, like in as much as I, you know, in philosophy, the things I found interesting and focused on in the little bit of time I did spend on it was ethics and cognitive science. And it's kind of really amazing that it's now come back around and those are actually genuinely useful things to know about, which I never thought would happen. [00:02:38]
Swyx: A lot of, yeah, a lot of relevant conversations there. So you were a consultant for a while and then in the magical month of June 1989, you founded both Optimal Decisions and Fastmeal, which I also briefly used. So thank you for that. [00:02:53]
Jeremy: Oh, good for you. Yeah. Cause I had read the statistics, which is that like 90% or something of small businesses fail. So I thought if I start two businesses, I have a higher chance. In hindsight, I was thinking of it as some kind of stochastic thing I didn't have control over, but it's a bit odd, but anyway. [00:03:10]
Swyx: And then you were president and chief scientist at Kaggle, which obviously is the sort of composition platform of machine learning. And then Enlitic, where you were working on using deep learning to improve medical diagnostics and clinical decisions. Yeah. [00:03:28]
Jeremy: I was actually the first company to use deep learning in medicine, so I kind of founded the field. [00:03:33]
Swyx: And even now that's still like a pretty early phase. And I actually heard you on your new podcast with Tanish, where you went very, very deep into the stuff, the kind of work that he's doing, such a young prodigy at his age. [00:03:47]
Jeremy: Maybe he's too old to be called a prodigy now, ex-prodigy. No, no. [00:03:51]
Swyx: I think he still counts. And anyway, just to round out the bio, you have a lot more other credentials, obviously, but most recently you started Fast.ai, which is still, I guess, your primary identity with Rachel Thomas. So welcome. [00:04:05]
Jeremy: Yep. [00:04:06]
Swyx: Thanks to my wife. Thank you. Yeah. Doing a lot of public service there with getting people involved in AI, and I can't imagine a better way to describe it than fast, fast.ai. You teach people from nothing to stable diffusion in seven weeks or something, and that's amazing. Yeah, yeah. [00:04:22]
Jeremy: I mean, it's funny, you know, when we started that, what was that, like 2016 or something, the idea that deep learning was something that you could make more accessible was generally considered stupid. Everybody knew that deep learning was a thing that you got a math or a computer science PhD, you know, there was one of five labs that could give you the appropriate skills and that you would join, yeah, basically from one of those labs, you might be able to write some papers. So yeah, the idea that normal people could use that technology to do good work was considered kind of ridiculous when we started it. And we weren't sure if it was possible either, but we kind of felt like we had to give it a go because the alternative was we were pretty sure that deep learning was on its way to becoming, you know, the most or one of the most, you know, important technologies in human history. And if the only people that could use it were a handful of computer science PhDs, that seemed like A, a big waste and B, kind of dangerous. [00:05:28]
Swyx: Yeah. [00:05:29]
Alessio: And, you know, well, I just wanted to know one thing on your bio that at Kaggle, you were also the top rank participant in both 2010 and 2011. So sometimes you see a lot of founders running companies that are not really in touch with the problem, but you were clearly building something that you knew a lot about, which is awesome. Talking about deep learning, you created, published a paper on ULM fit, which was kind of the predecessor to multitask learning and a lot of the groundwork that then went to into Transformers. I've read back on the paper and you turned this model, AWD LSTM, which I did the math and it was like 24 to 33 million parameters, depending on what training data set you use today. That's kind of like not even small, it's like super small. What were some of the kind of like contrarian takes that you had at the time and maybe set the stage a little bit for the rest of the audience on what was kind of like the state of the art, so to speak, at the time and what people were working towards? [00:06:32]
Jeremy: Yeah, the whole thing was a contrarian take, you know. So okay, so we started Fast.ai, my wife and I, and we thought, yeah, so we're trying to think, okay, how do we make it more accessible? So when we started thinking about it, it was probably 2015 and then 2016, we started doing something about it. Why is it inaccessible? Okay, well, A, no one knows how to do it other than a few number of people. And then when we asked those few number of people, well, how do you actually get good results? They would say like, oh, it's like, you know, a box of tricks that aren't published. So you have to join one of the labs and learn the tricks. So a bunch of unpublished tricks, not much software around, but thankfully there was Theano and rappers and particularly Lasagna, the rapper, but yeah, not much software around, not much in the way of data sets, you know, very hard to get started in terms of the compute. Like how do you get that set up? So yeah, no, everything was kind of inaccessible. And you know, as we started looking into it, we had a key insight, which was like, you know what, most of the compute and data for image recognition, for example, we don't need to do it. You know, there's this thing which nobody knows about, nobody talks about called transfer learning, where you take somebody else's model, where they already figured out like how to detect edges and gradients and corners and text and whatever else, and then you can fine tune it to do the thing you want to do. And we thought that's the key. That's the key to becoming more accessible in terms of compute and data requirements. So when we started Fast.ai, we focused from day one on transfer learning. Lesson one, in fact, was transfer learning, literally lesson one, something not normally even mentioned in, I mean, there wasn't much in the way of courses, you know, the courses out there were PhD programs that had happened to have recorded their lessons and they would rarely mention it at all. We wanted to show how to do four things that seemed really useful. You know, work with vision, work with tables of data, work with kind of recommendation systems and collaborative filtering and work with text, because we felt like those four kind of modalities covered a lot of the stuff that, you know, are useful in real life. And no one was doing anything much useful with text. Everybody was talking about word2vec, you know, like king plus queen minus woman and blah, blah, blah. It was like cool experiments, but nobody's doing anything like useful with it. NLP was all like lemmatization and stop words and topic models and bigrams and SPMs. And it was really academic and not practical. But I mean, to be honest, I've been thinking about this crazy idea for nearly 30 years since I had done cognitive science at university, where we talked a lot about the CELS Chinese room experiment. This idea of like, what if there was somebody that could kind of like, knew all of the symbolic manipulations required to answer questions in Chinese, but they didn't speak Chinese and they were kind of inside a room with no other way to talk to the outside world other than taking in slips of paper with Chinese written on them and then they do all their rules and then they pass back a piece of paper with Chinese back. And this room with a person in is actually fantastically good at answering any question you give them written in Chinese. You know, do they understand Chinese? And is this, you know, something that's intelligently working with Chinese? Ever since that time, I'd say the most thought, to me, the most thoughtful and compelling philosophical response is yes. You know, intuitively it feels like no, because that's just because we can't imagine such a large kind of system. But you know, if it looks like a duck and acts like a duck, it's a duck, you know, or to all intents and purposes. And so I always kind of thought, you know, so this is basically a kind of analysis of the limits of text. And I kind of felt like, yeah, if something could ingest enough text and could use the patterns it saw to then generate text in response to text, it could appear to be intelligent, you know. And whether that means it is intelligent or not is a different discussion and not one I find very interesting. Yeah. And then when I came across neural nets when I was about 20, you know, what I learned about the universal approximation theorem and stuff, and I started thinking like, oh, I wonder if like a neural net could ever get big enough and take in enough data to be a Chinese room experiment. You know, with that background and this kind of like interest in transfer learning, you know, I'd been thinking about this thing for kind of 30 years and I thought like, oh, I wonder if we're there yet, you know, because we have a lot of text. Like I can literally download Wikipedia, which is a lot of text. And I thought, you know, how would something learn to kind of answer questions or, you know, respond to text? And I thought, well, what if we used a language model? So language models are already a thing, you know, they were not a popular or well-known thing, but they were a thing. But language models exist to this idea that you could train a model to fill in the gaps. Or actually in those days it wasn't fill in the gaps, it was finish a string. And in fact, Andrej Karpathy did his fantastic RNN demonstration from this at a similar time where he showed like you can have it ingest Shakespeare and it will generate something that looks a bit like Shakespeare. I thought, okay, so if I do this at a much bigger scale, using all of Wikipedia, what would it need to be able to do to finish a sentence in Wikipedia effectively, to do it quite accurately quite often? I thought, geez, it would actually have to know a lot about the world, you know, it'd have to know that there is a world and that there are objects and that objects relate to each other through time and cause each other to react in ways and that causes proceed effects and that, you know, when there are animals and there are people and that people can be in certain positions during certain timeframes and then you could, you know, all that together, you can then finish a sentence like this was signed into law in 2016 by US President X and it would fill in the gap, you know. So that's why I tried to create what in those days was considered a big language model trained on the entirety on Wikipedia, which is that was, you know, a bit unheard of. And my interest was not in, you know, just having a language model. My interest was in like, what latent capabilities would such a system have that would allow it to finish those kind of sentences? Because I was pretty sure, based on our work with transfer learning and vision, that I could then suck out those latent capabilities by transfer learning, you know, by fine-tuning it on a task data set or whatever. So we generated this three-step system. So step one was train a language model on a big corpus. Step two was fine-tune a language model on a more curated corpus. And step three was further fine-tune that model on a task. And of course, that's what everybody still does today, right? That's what ChatGPT is. And so the first time I tried it within hours, I had a new state-of-the-art academic result on IMDB. And I was like, holy s**t, it does work. And so you asked, to what degree was this kind of like pushing against the established wisdom? You know, every way. Like the reason it took me so long to try it was because I asked all my friends in NLP if this could work. And everybody said, no, it definitely won't work. It wasn't like, oh, maybe. Everybody was like, it definitely won't work. NLP is much more complicated than vision. Language is a much more vastly complicated domain. You know, and you've got problems like the grounding problem. We know from like philosophy and theory of mind that it's actually impossible for it to work. So yeah, so don't waste your time. [00:15:10]
Alessio: Jeremy, had people not tried because it was like too complicated to actually get the data and like set up the training? Or like, were people just lazy and kind of like, hey, this is just not going to work? [00:15:20]
Jeremy: No, everybody wasn't lazy. So like, so the person I thought at that time who, you know, there were two people I thought at that time, actually, who were the strongest at language models were Stephen Merity and Alec Radford. And at the time I didn't know Alec, but I, after we had both, after I'd released ULM Fit and he had released GPT, I organized a chat for both of us with Kate Metz in the New York Times. And Kate Metz answered, sorry, and Alec answered this question for Kate. And Kate was like, so how did, you know, GPT come about? And he said, well, I was pretty sure that pre-training on a general large corpus wouldn't work. So I hadn't tried it. And then I read ULM Fit and turns out it did work. And so I did it, you know, bigger and it worked even better. And similar with, with Stephen, you know, I asked Stephen Merity, like, why don't we just find, you know, take your AWD-ASTLM and like train it on all of Wikipedia and fine tune it? And he's kind of like, well, I don't think that's going to really lie. Like two years before I did a very popular talk at KDD, the conference where everybody in NLP was in the audience. I recognized half the faces, you know, and I told them all this, I'm sure transfer learning is the key. I'm sure ImageNet, you know, is going to be an NLP thing as well. And, you know, everybody was interested and people asked me questions afterwards and, but not just, yeah, nobody followed up because everybody knew that it didn't work. I mean, even like, so we were scooped a little bit by Dai and Lee, Kwok Lee at Google. They had, they had, I already, I didn't even realize this, which is a bit embarrassing. They had already done a large language model and fine tuned it. But again, they didn't create a general purpose, large language model on a general purpose corpus. They only ever tested a domain specific corpus. And I haven't spoken to Kwok actually about that, but I assume that the reason was the same. It probably just didn't occur to them that the general approach could work. So maybe it was that kind of 30 years of mulling over the, the cell Chinese room experiment that had convinced me that it probably would work. I don't know. Yeah. [00:17:48]
Alessio: Interesting. I just dug up Alec announcement tweet from 2018. He said, inspired by Cobe, Elmo, and Yola, I'm fit. We should have a single transformer language model can be fine tuned to a wide variety. It's interesting because, you know, today people think of AI as the leader, kind of kind of like the research lab pushing forward the field. What was that at the time? You know, like kind of like going back five years, people think of it as an overnight success, but obviously it took a while. [00:18:16]
Swyx: Yeah. Yeah. [00:18:17]
Jeremy: No, I mean, absolutely. And I'll say like, you know, it's interesting that it mentioned Elmo because in some ways that was kind of diametrically opposed to, to ULM fit. You know, there was these kind of like, so there was a lot of, there was a lot of activity at the same time as ULM fits released. So there was, um, so before it, as Brian McCann, I think at Salesforce had come out with this neat model that did a kind of multitask learning, but again, they didn't create a general fine tune language model first. There was Elmo, um, which I think was a lip, you know, actually quite a few months after the first ULM fit example, I think. Um, but yeah, there was a bit of this stuff going on. And the problem was everybody was doing, and particularly after GPT came out, then everybody wanted to focus on zero shot and few shot learning. You know, everybody hated fine tuning. Everybody hated transfer learning. And like, I literally did tours trying to get people to start doing transfer learning and people, you know, nobody was interested, particularly after GPT showed such good results with zero shot and few shot learning. And so I actually feel like we kind of went backwards for years and, and not to be honest, I mean, I'm a bit sad about this now, but I kind of got so disappointed and dissuaded by like, it felt like these bigger lab, much bigger labs, you know, like fast AI had only ever been just me and Rachel were getting all of this attention for an approach I thought was the wrong way to do it. You know, I was convinced was the wrong way to do it. And so, yeah, for years people were really focused on getting better at zero shot and few shots and it wasn't until, you know, this key idea of like, well, let's take the ULM fit approach, but for step two, rather than fine tuning on a kind of a domain corpus, let's fine tune on an instruction corpus. And then in step three, rather than fine tuning on a reasonably specific task classification, let's fine tune on a, on a RLHF task classification. And so that was really, that was really key, you know, so I was kind of like out of the NLP field for a few years there because yeah, it just felt like, I don't know, pushing uphill against this vast tide, which I was convinced was not the right direction, but who's going to listen to me, you know, cause I, as you said, I don't have a PhD, not at a university, or at least I wasn't then. I don't have a big set of computers to fine tune huge transformer models. So yeah, it was definitely difficult. It's always been hard. You know, it's always been hard. Like I've always been somebody who does not want to build stuff on lots of big computers because most people don't have lots of big computers and I hate creating stuff that most people can't use, you know, and also stuff that's created on lots of big computers has always been like much more media friendly. So like, it might seem like a recent thing, but actually throughout my 30 years in data science, the attention's always been on, you know, the big iron results. So when I first started, everybody was talking about data warehouses and it was all about Teradata and it'd be like, oh, this big bank has this huge room full of computers and they have like terabytes of data available, you know, at the press of a button. And yeah, that's always what people want to talk about, what people want to write about. And then of course, students coming out of their PhDs and stuff, that's where they want to go work because that's where they read about. And to me, it's a huge distraction, you know, because like I say, most people don't have unlimited compute and I want to help most people, not the small subset of the most well-off people. [00:22:16]
Alessio: That's awesome. And it's great to hear, you do such a great job educating that a lot of times you're not telling your own story, you know? So I love this conversation. And the other thing before we jump into Fast.AI, actually, a lot of people that I know, they run across a new architecture and whatnot, they're like, I got to start a company and raise a bunch of money and do all of this stuff. And say, you were like, I want everybody to have access to this. Why was that the case for you? Was it because you already had a successful venture in like FastMail and you were more interested in that? What was the reasoning? [00:22:52]
Jeremy: It's a really good question. So I guess the answer is yes, that's the reason why. So when I was a teenager, I thought it would be really cool to like have my own company. You know, I didn't know the word startup. I didn't know the word entrepreneur. I didn't know the word VC. And I didn't really know what any of those things were really until after we started Kaggle, to be honest. Even the way it started to what we now call startups. I just thought they were just small businesses. You know, they were just companies. So yeah, so those two companies were FastMail and Optimal Decisions. FastMail was the first kind of synchronized email provider for non-businesses. So something you can get your same email at home, on your laptop, at work, on your phone, whatever. And then Optimal Decisions invented a new approach to insurance pricing. Something called profit-optimized insurance pricing. So I saw both of those companies, you know, after 10 years. And at that point, I had achieved the thing that as a teenager I had wanted to do. You know, it took a lot longer than it should have because I spent way longer in management consulting than I should have because I got caught up in that stupid rat race. But, you know, eventually I got there and I remember my mom saying to me, you must be so proud. You know, because she remembered my dream. She's like, you've done it. And I kind of reflected and I was like, I'm not proud at all. You know, like people quite liked FastMail. You know, it's quite nice to have synchronized email. It probably would have happened anyway. Yeah, I'm certainly not proud that I've helped some insurance companies suck more money out of their customers. Yeah, no, I'm not proud. You know, it's actually, I haven't really helped the world very much. You know, maybe in the insurance case I've made it a little bit worse. I don't know. So, yeah, I was determined to not waste more years of my life doing things, working hard to do things which I could not be reasonably sure would have a lot of value. So, you know, I took some time off. I wasn't sure if I'd ever work again, actually. I didn't particularly want to, because it felt like, yeah, it felt like such a disappointment. And, but, you know, and I didn't need to. I had enough money. Like, I wasn't super rich, but I had enough money. I didn't need to work. And I certainly recognized that amongst the other people I knew who had enough money that they didn't need to work, they all worked ridiculously hard, you know, and constantly put themselves in extremely stressful situations. And I thought, I don't want to be one of those idiots who's tied to, you know, buying a bigger plane than the next guy or whatever. You know, Kaggle came along and I mainly kind of did that just because it was fun and interesting to hang out with interesting people. But, you know, with Fast.ai in particular, you know, Rachel and I had a very explicit, you know, long series of conversations over a long period of time about like, well, how can we be the most helpful to society as a whole, and particularly to those people who maybe need more help, you know? And so we definitely saw the world going in a potentially pretty dystopian direction if the world's most powerful technology was controlled by a small group of elites. So we thought, yeah, we should focus on trying to help that not happen. You know, sadly, it looks like it still is likely to happen. But I mean, I feel like we've helped make it a little bit less likely. So we've done our bit. [00:26:39]
Swyx: You've shown that it's possible. And I think your constant advocacy, your courses, your research that you publish, you know, just the other day you published a finding on, you know, learning that I think is still something that people are still talking about quite a lot. I think that that is the origin story of a lot of people who are going to be, you know, little Jeremy Howards, furthering your mission with, you know, you don't have to do everything by yourself is what I'm saying. No, definitely. Definitely. [00:27:10]
Jeremy: You know, that was a big takeaway from like, analytic was analytic. It definitely felt like we had to do everything ourselves. And I kind of, I wanted to solve medicine. I'll say, yeah, okay, solving medicine is actually quite difficult. And I can't do it on my own. And there's a lot of other things I'd like to solve, and I can't do those either. So that was definitely the other piece was like, yeah, you know, can we create an army of passionate domain experts who can change their little part of the world? And that's definitely happened. Like I find nowadays, at least half the time, probably quite a bit more that I get in contact with somebody who's done really interesting work in some domain. Most of the time I'd say, they say, yeah, I got my start with fast.ai. So it's definitely, I can see that. And I also know from talking to folks at places like Amazon and Adobe and stuff, which, you know, there's lots of alumni there. And they say, oh my God, I got here. And like half of the people are fast.ai alumni. So it's fantastic. [00:28:13]
Swyx: Yeah. [00:28:14]
Jeremy: Actually, Andre Kapathy grabbed me when I saw him at NeurIPS a few years ago. And he was like, I have to tell you, thanks for the fast.ai courses. When people come to Tesla and they need to know more about deep learning, we always send them to your course. And the OpenAI Scholars Program was doing the same thing. So it's kind of like, yeah, it's had a surprising impact, you know, that's just one of like three things we do is the course, you know. [00:28:40]
Swyx: Yes. [00:28:40]
Jeremy: And it's only ever been at most two people, either me and Rachel or me and Sylvia nowadays, it's just me. So yeah, I think it shows you don't necessarily need a huge amount of money and a huge team of people to make an impact. [00:28:56]
Swyx: Yeah. So just to reintroduce fast.ai for people who may not have dived into it much, there is the courses that you do. There is the library that is very well loved. And I kind of think of it as a nicer layer on top of PyTorch that people should start with by default and use it as the basis for a lot of your courses. And then you have like NBDev, which I don't know, is that the third one? [00:29:27]
Jeremy: Oh, so the three areas were research, software, and courses. [00:29:32]
Swyx: Oh, sorry. [00:29:32]
Jeremy: So then in software, you know, fast.ai is the main thing, but NBDev is not far behind. But then there's also things like FastCore, GHAPI, I mean, dozens of open source projects that I've created and some of them have been pretty popular and some of them are still a little bit hidden, actually. Some of them I should try to do a better job of telling people about. [00:30:01]
Swyx: What are you thinking about? Yeah, what's on the course of my way? Oh, I don't know, just like little things. [00:30:04]
Jeremy: Like, for example, for working with EC2 and AWS, I created a FastEC2 library, which I think is like way more convenient and nice to use than anything else out there. And it's literally got a whole autocomplete, dynamic autocomplete that works both on the command line and in notebooks that'll like auto-complete your instance names and everything like that. You know, just little things like that. I try to make like, when I work with some domain, I try to make it like, I want to make it as enjoyable as possible for me to do that. So I always try to kind of like, like with GHAPI, for example, I think that GitHub API is incredibly powerful, but I didn't find it good to work with because I didn't particularly like the libraries that are out there. So like GHAPI, like FastEC2, it like autocompletes both at the command line or in a notebook or whatever, like literally the entire GitHub API. The entire thing is like, I think it's like less than 100K of code because it actually, as far as I know, the only one that grabs it directly from the official open API spec that GitHub produces. And like if you're in GitHub and you just type an API, you know, autocomplete API method and hit enter, it prints out the docs with brief docs and then gives you a link to the actual documentation page. You know, GitHub Actions, I can write now in Python, which is just so much easier than writing them in TypeScript and stuff. So, you know, just little things like that. [00:31:40]
Swyx: I think that's an approach which more developers took to publish some of their work along the way. You described the third arm of FastAI as research. It's not something I see often. Obviously, you do do some research. And how do you run your research? What are your research interests? [00:31:59]
Jeremy: Yeah, so research is what I spend the vast majority of my time on. And the artifacts that come out of that are largely software and courses. You know, so to me, the main artifact shouldn't be papers because papers are things read by a small exclusive group of people. You know, to me, the main artifacts should be like something teaching people, here's how to use this insight and here's software you can use that builds it in. So I think I've only ever done three first-person papers in my life, you know, and none of those are ones I wanted to do. You know, they were all ones that, like, so one was ULM Fit, where Sebastian Ruder reached out to me after seeing the course and said, like, you have to publish this as a paper, you know. And he said, I'll write it. He said, I want to write it because if I do, I can put it on my PhD and that would be great. And it's like, okay, well, I want to help you with your PhD. And that sounds great. So like, you know, one was the masks paper, which just had to exist and nobody else was writing it. And then the third was the Fast.ai library paper, which again, somebody reached out and said, please, please write this. We will waive the fee for the journal and everything and actually help you get it through publishing and stuff. So yeah, so I don't, other than that, I've never written a first author paper. So the research is like, well, so for example, you know, Dawn Bench was a competition, which Stanford ran a few years ago. It was kind of the first big competition of like, who can train neural nets the fastest rather than the most accurate. And specifically it was who can train ImageNet the fastest. And again, this was like one of these things where it was created by necessity. So Google had just released their TPUs. And so I heard from my friends at Google that they had put together this big team to smash Dawn Bench so that they could prove to people that they had to use Google Cloud and use their TPUs and show how good their TPUs were. And we kind of thought, oh s**t, this would be a disaster if they do that, because then everybody's going to be like, oh, deep learning is not accessible. [00:34:20]
Swyx: You know, to actually be good at it, [00:34:21]
Jeremy: you have to be Google and you have to use special silicon. And so, you know, we only found out about this 10 days before the competition finished. But, you know, we basically got together an emergency bunch of our students and Rachel and I and sat for the next 10 days and just tried to crunch through and try to use all of our best ideas that had come from our research. And so particularly progressive resizing, just basically train mainly on small things, train on non-square things, you know, stuff like that. And so, yeah, we ended up winning, thank God. And so, you know, we turned it around from being like, like, oh s**t, you know, this is going to show that you have to be Google and have TPUs to being like, oh my God, even the little guy can do deep learning. So that's an example of the kind of like research artifacts we do. And yeah, so all of my research is always, how do we do more with less, you know? So how do we get better results with less data, with less compute, with less complexity, with less education, you know, stuff like that. So ULM fits obviously a good example of that. [00:35:37]
Swyx: And most recently you published, can LLMs learn from a single example? Maybe could you tell the story a little bit behind that? And maybe that goes a little bit too far into the learning of very low resource, the literature. [00:35:52]
Jeremy: Yeah, yeah. So me and my friend, Jono Whittaker, basically had been playing around with this fun Kaggle competition, which is actually still running as we speak, which is, can you create a model which can answer multiple choice questions about anything that's in Wikipedia? And the thing that makes it interesting is that your model has to run on Kaggle within nine hours. And Kaggle's very, very limited. So you've only got 14 gig RAM, only two CPUs, and a small, very old GPU. So this is cool, you know, if you can do well at this, then this is a good example of like, oh, you can do more with less. So yeah, Jono and I were playing around with fine tuning, of course, transfer learning, pre-trained language models. And we saw this, like, so we always, you know, plot our losses as we go. So here's another thing we created. Actually, Sylvain Guuger, when he worked with us, created called fast progress, which is kind of like TQEDM, but we think a lot better. So we look at our fast progress curves, and they kind of go down, down, down, down, down, down, down, a little bit, little bit, little bit. And then suddenly go clunk, and they drop. And then down, down, down, down, down a little bit, and then suddenly clunk, they drop. We're like, what the hell? These clunks are occurring at the end of each epoch. So normally in deep learning, this would be, this is, you know, I've seen this before. It's always been a bug. It's always turned out that like, oh, we accidentally forgot to turn on eval mode during the validation set. So I was actually learning then, or, oh, we accidentally were calculating moving average statistics throughout the epoch. So, you know, so it's recently moving average or whatever. And so we were using Hugging Face Trainer. So, you know, I did not give my friends at Hugging Face the benefit of the doubt. I thought, oh, they've fucked up Hugging Face Trainer, you know, idiots. Well, you'll use the Fast AI Trainer instead. So we switched over to Learner. We still saw the clunks and, you know, that's, yeah, it shouldn't really happen because semantically speaking in the epoch, isn't like, it's not a thing, you know, like nothing happens. Well, nothing's meant to happen when you go from ending one epoch to starting the next one. So there shouldn't be a clunk, you know. So I kind of asked around on the open source discords. That's like, what's going on here? And everybody was just like, oh, that's just what, that's just what these training curves look like. Those all look like that. Don't worry about it. And I was like, oh, are you all using Trainer? Yes. Oh, well, there must be some bug with Trainer. And I was like, well, we also saw it in Learner [00:38:42]
Swyx: and somebody else is like, [00:38:42]
Jeremy: no, we've got our own Trainer. We get it as well. They're just like, don't worry about it. It's just something we see. It's just normal. [00:38:48]
Swyx: I can't do that. [00:38:49]
Jeremy: I can't just be like, here's something that's like in the previous 30 years of neural networks, nobody ever saw it. And now suddenly we see it. [00:38:57]
Swyx: So don't worry about it. [00:38:59]
Jeremy: I just, I have to know why. [00:39:01]
Swyx: Can I clarify? This is, was everyone that you're talking to, were they all seeing it for the same dataset or in different datasets? [00:39:08]
Jeremy: Different datasets, different Trainers. They're just like, no, this is just, this is just what it looks like when you fine tune language models. Don't worry about it. You know, I hadn't seen it before, but I'd been kind of like, as I say, I, you know, I kept working on them for a couple of years after ULM fit. And then I kind of moved on to other things, partly out of frustration. So I hadn't been fine tuning, you know, I mean, Lama's only been out for a few months, right? But I wasn't one of those people who jumped straight into it, you know? So I was relatively new to the kind of Lama fine tuning world, where else these guys had been, you know, doing it since day one. [00:39:49]
Swyx: It was only a few months ago, [00:39:51]
Jeremy: but it's still quite a bit of time. So, so yeah, they're just like, no, this is all what we see. [00:39:56]
Swyx: Don't worry about it. [00:39:56]
Jeremy: So yeah, I, I've got a very kind of like, I don't know, I've just got this brain where I have to know why things are. And so I kind of, I ask people like, well, why, why do you think it's happening? And they'd be like, oh, it would pretty obviously, cause it's like memorize the data set. It's just like, that can't be right. It's only seen it once. Like, look at this, the loss has dropped by 0.3, 0.3, which is like, basically it knows the answer. And like, no, no, it's just, it is, it's just memorize the data set. So yeah. So look, Jono and I did not discover this and Jono and I did not come up with a hypothesis. You know, I guess we were just the ones, I guess, who had been around for long enough to recognize that like, this, this isn't how it's meant to work. And so we, we, you know, and so we went back and like, okay, let's just run some experiments, you know, cause nobody seems to have actually published anything about this. [00:40:51]
Well, not quite true.
Some people had published things, but nobody ever actually stepped back and said like, what the hell, you know, how can this be possible? Is it possible? Is this what's happening? And so, yeah, we created a bunch of experiments where we basically predicted ahead of time. It's like, okay, if this hypothesis is correct, that it's memorized in the training set, then we ought to see blah, under conditions, blah, but not under these conditions. And so we ran a bunch of experiments and all of them supported the hypothesis that it was memorizing the data set in a single thing at once. And it's a pretty big data set, you know, which in hindsight, it's not totally surprising because the theory, remember, of the ULMFiT theory was like, well, it's kind of creating all these latent capabilities to make it easier for it to predict the next token. So if it's got all this kind of latent capability, it ought to also be really good at compressing new tokens because it can immediately recognize it as like, oh, that's just a version of this. So it's not so crazy, you know, but it is, it requires us to rethink everything because like, and nobody knows like, okay, so how do we fine tune these things? Because like, it doesn't even matter. Like maybe it's fine. Like maybe it's fine that it's memorized the data set after one go and you do a second go and okay, the validation loss is terrible because it's now really overconfident. [00:42:20]
Swyx: That's fine. [00:42:22]
Jeremy: Don't, you know, don't, I keep telling people, don't track validation loss, track validation accuracy because at least that will still be useful. Just another thing that's got lost since ULMFiT, nobody tracks accuracy of language models anymore. But you know, it'll still keep learning and it does, it does keep improving. But is it worse? You know, like, is it like, now that it's kind of memorized it, it's probably getting a less strong signal, you know, I don't know. So I still don't know how to fine tune language models properly and I haven't found anybody who feels like they do, like nobody really knows whether this memorization thing is, it's probably a feature in some ways. It's probably some things that you can do usefully with it. It's probably, yeah, I have a feeling it's messing up training dynamics as well. [00:43:13]
Swyx: And does it come at the cost of catastrophic forgetting as well, right? Like, which is the other side of the coin. [00:43:18]
Jeremy: It does to some extent, like we know it does, like look at Code Llama, for example. So Code Llama was a, I think it was like a 500 billion token fine tuning of Llama 2 using code. And also pros about code that Meta did. And honestly, they kind of blew it because Code Llama is good at coding, but it's bad at everything else, you know, and it used to be good. Yeah, I was pretty sure it was like, before they released it, me and lots of people in the open source discords were like, oh my God, you know, we know this is coming, Jan Lukinsk saying it's coming. I hope they kept at least like 50% non-code data because otherwise it's going to forget everything else. And they didn't, only like 0.3% of their epochs were non-code data. So it did, it forgot everything else. So now it's good at code and it's bad at everything else. So we definitely have catastrophic forgetting. It's fixable, just somebody has to do, you know, somebody has to spend their time training a model on a good mix of data. Like, so, okay, so here's the thing. Even though I originally created three-step approach that everybody now does, my view is it's actually wrong and we shouldn't use it. [00:44:36]
Jeremy: And that's because people are using it in a way different to why I created it. You know, I created it thinking the task-specific models would be more specific. You know, it's like, oh, this is like a sentiment classifier as an example of a task, you know, but the tasks now are like a, you know, RLHF, which is basically like answer questions that make people feel happy about your answer. So that's a much more general task and it's a really cool approach. And so we see, for example, RLHF also breaks models like, you know, like GPT-4, RLHDEFT, we know from kind of the work that Microsoft did, you know, the pre, the earlier, less aligned version was better. And these are all kind of examples of catastrophic forgetting. And so to me, the right way to do this is to fine-tune language models, is to actually throw away the idea of fine-tuning. There's no such thing. There's only continued pre-training. And pre-training is something where from the very start, you try to include all the kinds of data that you care about, all the kinds of problems that you care about, instructions, exercises, code, general purpose document completion, whatever. And then as you train, you gradually curate that, you know, you gradually make that higher and higher quality and more and more specific to the kinds of tasks you want it to do. But you never throw away any data. You always keep all of the data types there in reasonably high quantities. You know, maybe the quality filter, you stop training on low quality data, because that's probably fine to forget how to write badly, maybe. So yeah, that's now my view, is I think ULM fit is the wrong approach. And that's why we're seeing a lot of these, you know, so-called alignment tacks and this view of like, oh, a model can't both code and do other things. And, you know, I think it's actually because people are training them wrong. [00:46:47]
Swyx: Yeah, well, I think you have a clear [00:46:51]
Alessio: anti-laziness approach. I think other people are not as good hearted, you know, they're like, [00:46:57]
Swyx: hey, they told me this thing works. [00:46:59]
Alessio: And if I release a model this way, people will appreciate it, I'll get promoted and I'll kind of make more money. [00:47:06]
Jeremy: Yeah, and it's not just money. It's like, this is how citations work most badly, you know, so if you want to get cited, you need to write a paper that people in your field recognize as an advancement on things that we know are good. And so we've seen this happen again and again. So like I say, like zero shot and few shot learning, everybody was writing about that. Or, you know, with image generation, everybody just was writing about GANs, you know, and I was trying to say like, no, GANs are not the right approach. You know, and I showed again through research that we demonstrated in our videos that you can do better than GANs, much faster and with much less data. And nobody cared because again, like if you want to get published, you write a GAN paper that slightly improves this part of GANs and this tiny field, you'll get published, you know. So it's, yeah, it's not set up for real innovation. It's, you know, again, it's really helpful for me, you know, I have my own research lab with nobody telling me what to do and I don't even publish. So it doesn't matter if I get citations. And so I just write what I think actually matters. I wish there was, and, you know, and actually places like OpenAI, you know, the researchers there can do that as well. It's a shame, you know, I wish there was more academic, open venues in which people can focus on like genuine innovation. [00:48:38]
Swyx: Twitter, which is unironically has become a little bit of that forum. I wanted to follow up on one thing that you mentioned, which is that you checked around the open source discords. I don't know if it's too, I don't know if it's a pusher to ask like what discords are lively or useful right now. I think that something I definitely felt like I missed out on was the early days of Luther AI, which is a very hard bit. And, you know, like what is the new Luther? And you actually shouted out the alignment lab AI discord in your blog post. And that was the first time I even knew, like I saw them on Twitter, never knew they had a discord, never knew that there was actually substantive discussions going on in there and that you were an active member of it. Okay, yeah. [00:49:23]
Jeremy: And then even then, if you do know about that and you go there, it'll look like it's totally dead. And that's because unfortunately, nearly all the discords, nearly all of the conversation happens in private channels. You know, and that's, I guess. [00:49:35]
Swyx: How does someone get into that world? Because it's obviously very, very instructive, right? [00:49:42]
Jeremy: You could just come to the first AI discord, which I'll be honest with you, it's less bustling than some of the others, but it's not terrible. And so like, at least, to be fair, one of Emma's bustling channels is private. [00:49:57]
Swyx: I guess. [00:49:59]
Jeremy: So I'm just thinking. [00:50:01]
Swyx: It's just the nature of quality discussion, right? Yeah, I guess when I think about it, [00:50:05]
Jeremy: I didn't have any private discussions on our discord for years, but there was a lot of people who came in with like, oh, I just had this amazing idea for AGI. If you just thought about like, if you imagine that AI is a brain, then we, you know, this just, I don't want to talk about it. You know, I don't want to like, you don't want to be dismissive or whatever. And it's like, oh, well, that's an interesting comment, but maybe you should like, try training some models first to see if that aligns with your intuition. Like, oh, but how could I possibly learn? It's like, well, we have a course, just actually spend time learning. Like, you know, anyway. And there's like, okay, I know the people who always have good answers there. And so I created a private channel and put them all in it. And I got to admit, that's where I post more often because there's much less, you know, flight of fancy views about how we could solve AGI, blah, blah, blah. So there is a bit of that. But having said that, like, I think the bar is pretty low. Like if you join a Discord and you can hit the like participants or community or whatever button, you can see who's in it. And then you'll see at the top, who the admins or moderators or people in the dev role are. And just DM one of them and say like, oh, here's my GitHub. Well, here's some blog posts I wrote. You know, I'm interested in talking about this, you know, can I join the private channels? And I've never heard of anybody saying no. I will say, you know, Alutha's all pretty open. So you can do the Alutha Discord still. You know, one problem with the Alutha Discord is it's been going on for so long that it's like, it's very inside baseball. It's quite hard to get started. Yeah. Carpa AI looks, I think it's all open. That's just less stability. That's more accessible. [00:52:03]
Swyx: Yeah. [00:52:04]
Jeremy: There's also just recently, now it's research that does like the Hermes models and data set just opened. They've got some private channels, but it's pretty open, I think. You mentioned Alignment Lab, that one it's all the interesting stuff is on private channels. So just ask. If you know me, ask me, cause I've got admin on that one. There's also, yeah, OS Skunkworks, OS Skunkworks AI is a good Discord, which I think it's open. So yeah, they're all pretty good. [00:52:40]
Swyx: I don't want you to leak any, you know, Discords that don't want any publicity, but this is all helpful. [00:52:46]
Jeremy: We all want people, like we all want people. [00:52:49]
Swyx: We just want people who like, [00:52:51]
Jeremy: want to build stuff, rather than people who, and like, it's fine to not know anything as well, but if you don't know anything, but you want to tell everybody else what to do and how to do it, that's annoying. If you don't know anything and want to be told like, here's a really small kind of task that as somebody who doesn't know anything is going to take you a really long time to do, but it would still be helpful. Then, and then you go and do it. That would be great. The truth is, yeah, [00:53:19]
Swyx: like, I don't know, [00:53:20]
Jeremy: maybe 5% of people who come in with great enthusiasm and saying that they want to learn and they'll do anything. [00:53:25]
Swyx: And then somebody says like, [00:53:25]
Jeremy: okay, here's some work you can do. Almost nobody does that work. So if you're somebody who actually does the work and follows up, you will massively stand out. That's an extreme rarity. And everybody will then want to help you do more work. [00:53:41]
Swyx: So yeah. [00:53:41]
Jeremy: So just, yeah, just do work and people will want to support you. [00:53:47]
Alessio: Our Discord used to be referral only for a long time. We didn't have a public invite and then we opened it and they're kind of like channel gating. Yeah. A lot of people just want to do, I remember it used to be like, you know, a forum moderator. [00:54:00]
Swyx: It's like people just want to do [00:54:01]
Alessio: like drive-by posting, [00:54:03]
Swyx: you know, and like, [00:54:03]
Alessio: they don't want to help the community. They just want to get their question answered. [00:54:07]
Jeremy: I mean, the funny thing is our forum community does not have any of that garbage. You know, there's something specific about the low latency thing where people like expect an instant answer. And yeah, we're all somehow in a forum thread where they know it's like there forever. People are a bit more thoughtful, but then the forums are less active than they used to be because Discord has got more popular, you know? So it's all a bit of a compromise, you know, running a healthy community is, yeah, it's always a bit of a challenge. All right, we got so many more things [00:54:47]
Alessio: we want to dive in, but I don't want to keep you here for hours. [00:54:50]
Swyx: This is not the Lex Friedman podcast [00:54:52]
Alessio: we always like to say. One topic I would love to maybe chat a bit about is Mojo, modular, you know, CrystalLiner, not many of you on the podcast. So we want to spend a little time there. You recently did a hacker's guide to language models and you ran through everything from quantized model to like smaller models, larger models, and all of that. But obviously modular is taking its own approach. Yeah, what got you excited? I know you and Chris have been talking about this for like years and a lot of the ideas you had, so. [00:55:23]
Jeremy: Yeah, yeah, yeah, yeah, no, absolutely. So I met Chris, I think it was at the first TensorFlow Dev Summit. And I don't think he had even like, I'm not sure if he'd even officially started his employment with Google at that point. So I don't know, you know, certainly nothing had been mentioned. So I, you know, I admired him from afar with LLVM and Swift and whatever. And so I saw him walk into the courtyard at Google. It's just like, oh st, man, that's Chris Latner. I wonder if he would lower his standards enough to talk to me. Well, worth a try. So I caught up my courage because like nobody was talking to him. He looked a bit lost and I wandered over and it's like, oh, you're Chris Latner, right? It's like, what are you doing here? What are you doing here? And I was like, yeah, yeah, yeah. It's like, oh, I'm Jeremy Howard. It's like, oh, do you do some of this AI stuff? And I was like, yeah, yeah, I like this AI stuff. Are you doing AI stuff? It's like, well, I'm thinking about starting to do some AI stuff. Yeah, I think it's going to be cool. And it's like, wow. So like, I spent the next half hour just basically brain dumping all the ways in which AI was stupid to him. And he listened patiently. And I thought he probably wasn't even remember or care or whatever. But yeah, then I kind of like, I guess I re-caught up with him a few months later. And it's like, I've been thinking about everything you said in that conversation. And he like narrated back his response to every part of it, projects he was planning to do. And it's just like, oh, this dude follows up. Holy st. And I was like, wow, okay. And he was like, yeah, so we're going to create this new thing called Swift for TensorFlow. And it's going to be like, it's going to be a compiler with auto differentiation built in. And blah, blah, blah. And I was like, why would that help? [00:57:10]
Swyx: You know, why would you? [00:57:10]
Jeremy: And he was like, okay, with a compiler during the forward pass, you don't have to worry about saving context, you know, because a lot will be optimized in the backward. But I was like, oh my God. Because I didn't really know much about compilers. You know, I spent enough to kind of like, understand the ideas, but it hadn't occurred to me that a compiler basically solves a lot of the problems we have as end users. I was like, wow, that's amazing. Okay, you do know, right, that nobody's going to use this unless it's like usable. It's like, yeah, I know, right. So I was thinking you should create like a fast AI for this. So, okay, but I don't even know Swift. And he was like, well, why don't you start learning it? And if you have any questions, ask me. It's just like, holy s**t. Like, not only has Chris Latner lowered his standards enough to talk to me, but he's offering me personal tutoring on the programming language that he made. So I was just like, I'm not going to let him down. So I spent like the next two months, like just nerding out on Swift. And it was just before Christmas that I kind of like started writing down what I'd learned. And so I wrote a couple of blog posts on like, okay, this is like my attempt to do numeric programming in Swift. And these are all the challenges I had. And these are some of the issues I had with like making things properly performant. And here are some libraries I wrote. And I sent it to Chris and was like, I hope he's not too disappointed with me, you know, because that would be the worst. It's like, you know, and I was also like, I was like, I hope he doesn't dislike the fact that I, you know, didn't love everything. [00:58:46]
Jeremy: And yeah, he was like, oh, thanks for sending me that. Let's get on a call and talk about it. And we spoke and he was like, this is amazing. I can't believe that you made this. This is exactly what Swift needs. And he was like, and so like somebody set up like a new Swift, what they call them, the equivalent of a pep, you know, kind of RFC thing of like, oh, you know, let's look at how we can implement Jeremy's ideas and the language. And so it's like, oh, wow. And so, yeah, you know, and then we ended up like literally teaching some lessons together about Swift for TensorFlow. And we built a fast AI kind of equivalent with him and his team. It was so much fun. Then in the end, you know, Google didn't follow through, which is fair enough, like asking everybody to learn a new programming language is going to be tough. But like, it was very obvious, very, very obvious at that time that TensorFlow 2 is going to be a failure, you know, and so it's felt like, okay, I, you know, well, you know, what are you going to do? Like, you can't focus on TensorFlow 2 because it's not going to, like, it's not working. It's never going to work. You know, nobody at Google's using it. Internally. So, you know, in the end, Chris left, you know, Swift for TensorFlow got archived. [01:00:13]
Swyx: There was no backup plan. [01:00:15]
Jeremy: So it kind of felt like Google was kind of screwed, you know, and Chris went and did something else. But we kept talking and I was like, look, Chris, you know, you've got to be your own boss, man. It's like, you know, you've got the ideas, you know, like only you've got the ideas, you know, and if your ideas are implemented, we'd all be so much better off because like Python's the best of a whole bunch of s**t, you know, like I would, it's amazing, but it's awful, you know, compared to what it could be. And anyway, so eventually a few years later, he called me up and he was like, Jeremy, I've taken your advice. I've started a company. And I was like, oh my God. It's like, we've got to create a new language. We're going to create a new infrastructure. It's going to build, it's going to have all the stuff we've talked about. And it's like, oh wow. So that's what Mojo is. And so Mojo is like, you know, building on all the stuff that Chris has figured out over, I mean, really from when he did his PhD thesis, which developed LLVM onwards, you know, in Swift and MLIR, you know, the TensorFlow runtime engine, which is very good. You know, that was something that he built and has lasted. So yeah, I'm pumped about that. I mean, it's very speculative. Creating a whole new language is tough. I mean, Chris has done it before and he's created a whole C++ compiler amongst other things. Looking pretty hopeful. I mean, I hope it works because, you know, [01:01:53]
Alessio: You told them to quit his job. [01:01:55]
Swyx: So I mean, in the meantime, I will say, you know, [01:02:00]
Jeremy: Google now does have a backup plan, you know, they have Jax, which was never a strategy. It was just a bunch of people who also recognized TensorFlow 2 as s**t and they just decided to build something else. And for years, my friends in that team were like, don't tell anybody about us because we don't want to be anything but a research project. So now these poor guys, suddenly they're the great white hope for Google's future. And so Jax is, you know, also not terrible, but it's still written in Python. Like it would be cool if we had all the benefits of Jax, but in a language that was designed for those kinds of purposes. So, you know, fingers crossed that, yeah, that Mojo turns out great. [01:02:45]
Swyx: Yeah. [01:02:47]
Alessio: Any other thoughts on when, where people should be spending their time? So that's more the kind of language framework level. Then you have the, you know, GGML, some of these other like quantization focused kind of model level things. Then you got the hardware people. It's like a whole other bucket. Yeah. What are some of the exciting stuff that you're excited about? [01:03:08]
Jeremy: Well, you won't be surprised to hear me say this, but I think fine tuning transfer learning is still a hugely underappreciated area. So today's zero shot, few shot learning equivalent is retrieval augmented generation, you know, RAC, which is like, just like few shot learning is a thing. Like it's a real thing. It's a useful thing. It's not a thing anybody would want to ignore. Why are people not spending at least as much effort on fine tuning? You know, cause you know, RAG is like such a inefficient hack really, [01:03:45]
Swyx: isn't it? [01:03:45]
Jeremy: It's like, you know, segment up my data in some somewhat arbitrary way, embed it, ask questions about that, you know, hope that my embedding, you know, model embeds questions in the same bedding space as the paragraphs, which obviously is not going to, if your question is like, if I've got a whole bunch of archive papers embeddings, and I asked like, what are all the ways in which we can make inference more efficient? Like the only paragraphs it'll find is like if there's a review paper, here's a list of ways to make, you know, inference more efficient. Doesn't have any of the specifics. No, it's not going to be like, oh, here's one way, here's one way, here's a different way in different papers, [01:04:33]
Swyx: you know? Yeah. [01:04:35]
Jeremy: If you fine tune a model, then all of that information is getting directly incorporated into the weights of your model in a much more efficient and nuanced way. And then you can use RAG on top of that. So I think that that's one area that's definitely like underappreciated. And also the kind of like the confluence or like, okay, how do you combine RAG and fine tuning, for example. [01:05:00]
Swyx: Something that I think a lot of people are uncertain about, and I don't expect you to know either, is that whether or not you can fine tune new information in, and I think that that is the focus of some of your open questions. And of course you can, right? [01:05:17]
Jeremy: Like, obviously you can, because there's no such thing as fine, there's no such thing as fine tuning. There's only continued pre-training. So fine tuning is pre-training, like they're literally the same thing. So the knowledge got in there in the first place through pre-training. So how could like continuing to pre-train not put more knowledge in? Like it's the same thing. The problem is just we're really bad at it because everybody's doing it dumb ways. So, you know, it's a good question. And it's not just new knowledge, but like new capabilities. You know, I think like in my Packers Guide to LLM, into Packers Guide to LLM's talk, I show a simple, I mean, it's a funny, that's a simple example, because it doesn't sound it, but like taking a pre-trained based model and getting it to generate SQL. And it took 15 minutes to train on a single GPU. You know, I think that might surprise people that that capability is at your fingertips. And, you know, because it was already there, it was just latent in the base model. Really pushing the boundaries of what you can do with small models, I think is a really interesting question. Like what can you do with a, like, I mean, there isn't much in the way of good small models. A really underappreciated one is a BTLM 3B, which is a like kind of 7B quality 3B model. There's not much at the 1 to 2B range sadly, there are some code ones, but like the fact that there are some really good code ones in that 1 to 2B range shows you that that's a great size for doing complex tasks well. [01:06:56]
Swyx: There was PHY 1 recently, which has been the subject of a little bit of discussion about whether to train on benchmarks. [01:07:04]
Jeremy: PHY 1.5 as well. So that's not a good model yet. [01:07:09]
Swyx: Why not? [01:07:11]
Jeremy: It's good at doing, so PHY 1 in particular is good at doing a very specific thing, which is creating very small Python snippets. [01:07:19]
Swyx: The thing, okay, [01:07:21]
Jeremy: so like PHY 1.5 has never read Wikipedia, for example, so it doesn't know who Tom Cruise is, you know, it doesn't know who anybody is, it doesn't know about any movies, it doesn't really know anything about anything, like, because it's never read anything, you know, it was trained on a nearly entirely synthetic data set, which is designed for it to learn reasoning, and so it was a research project, and a really good one, and it definitely shows us a powerful direction in terms of what you can do with synthetic data, and wow, gosh, even these tiny models can get pretty good reasoning skills, pretty good math skills, pretty good coding skills, [01:08:04]
Jeremy: but I don't know if it's a model you could necessarily build on. Some people have tried to do some fine tunes of it, and again, they're like surprisingly good in some ways for a 1.5b model, but not sure you'd find it useful for anything. [01:08:24]
Swyx: I think that's the struggle of pitching small models, because small is great, you know, you don't need a lot of resources to run them, but the performance evaluation is always so iffy, it's always just like, yeah, it works on some things, and we don't trust it for others. [01:08:41]
Jeremy: Yeah, so that's why we're back to fine tuning. So Microsoft did create a 5.1.5 web, but they didn't release it, unfortunately. I would say a 5.1.5 web with fine tuning for your task, you know, might quite, you know, might solve a lot of tasks that people have in their kind of day-to-day lives. You know, particularly in kind of an enterprise setting, I think there's a lot of like repetitive kind of processing that has to be done. It's a useful thing for coders to know about, because I think quite often you can like replace some thousands and thousands of lines of complex buggy code, maybe with a fine tune, you know. [01:09:24]
Swyx: Got it. Yeah. [01:09:27]
Alessio: And Jeremy, before we let you go, I think one question on top of a lot of people's minds. So you've done practical deep learning for coders in 2018, 19, 21, 22. I feel like the more time goes by, the more the GPUs get concentrated. If you're somebody who's interested in deep learning today and you don't want to go join OpenAI, you don't want to join Anthropic, what's like the best use of their time? Should they focus on, yeah, small model development? Should they focus on fine tuning math and all of that? Should they just like focus on making Ragnar a hack and coming up with a better solution? Yeah, what's a practical deep learning for coders 2024 kind of look like? [01:10:10]
Jeremy: Yeah. [01:10:11]
Swyx: I mean, good question. [01:10:12]
Jeremy: I'm trying to figure that out for myself. You know, like what should I teach? Because I definitely feel like things have changed a bit. You know, one of the ways in which things have changed is that coding is much more accessible now. So if you look at a lot of the folks in the kind of open source LLM community, they're folks who really hadn't coded before a year ago. And they're using these models to help them build stuff they couldn't build before, which is just fantastic, you know? So one thing I kind of think is like, okay, well, we need a lot more material to help these people use this newfound skill they have because they don't really know what they're doing, you know, and they don't claim to, but they're doing it anyway. And I think that's fantastic, you know? So like, are there things we could do to help people, [01:10:58]
Swyx: you know, bridge this gap? [01:11:00]
Jeremy: Because previously, you know, I know folks who were, you know, doing manual jobs a year ago, and now they're training language models thanks to the help of Codex and Copilot and whatever. So, you know, yeah, what does it look like to like really grab this opportunity? You know, maybe Fast.ai's goals can be dramatically expanded now to being like, let's make coding more accessible, you know, kind of AI-oriented coding more accessible. If so, our course should probably look very different, you know, and we'd have to throw away that like, oh, you have to have at least a year of full-time programming, you know, as a prerequisite. Yeah, what would happen if we got rid of that? So that's kind of one thought that's in my head. You know, as to what should other people do? Honestly, I don't think anybody has any idea, like, the more I look at it, what's going on. I know I don't, you know, like, we don't really know how to do anything very well. Clearly OpenAI do, like, they seem to be quite good at some things, or they're talking to folks at, or who have recently left OpenAI. [01:12:17]
Swyx: Even there, it's clear there's a lot of stuff [01:12:19]
Jeremy: they haven't really figured out, and they're just kind of like using recipes that they've noticed have been okay, so, yeah, we don't really know how to train these models well, we don't know how to fine-tune them well, we don't know how to do React well, we don't know what they can do, we don't know what they can't do, we don't know how big a model you need to solve different kinds of problems, we don't know what kind of problems they can't do, we don't know what good prompting strategies are for particular problems, you know. Like, somebody sent me a message the other day saying they've written something that is a prompting strategy for GPT-4, for GPT-4, they've written, like, 6,000 lines of Python code, and it's to help it play chess. And then they've said they've had it play against other chess engines, including the best Stockfish engines, and it's got an ELO of 3,400, [01:13:11]
Swyx: which would make it close to [01:13:13]
Jeremy: the best chess engine in existence. And I think this is a good example of, like, people were saying, like, GPT-4 can't play chess. I mean, I was sure that was wrong. I mean, obviously, it can play chess. But the difference between, like, with no prompting strategy, it can't even make legal moves, with good prompting strategies, it might be just about the best chess engine in the world, far better than any human player. So, yeah, I mean, we don't really know what the capabilities are yet. So I feel like it's all blue sky at this point. It feels like computer vision in 2013 to me, which was, like, in 2013, computer vision was, like, OK, OK. [01:13:51]
Swyx: We just had the AlexNet. [01:13:52]
Jeremy: We've had AlexNet. We've had VGGNet. It's around the time Zyler and Fergus, like, no, it's probably before that. So we hadn't yet had the Zyler and Fergus, like, oh, this is actually what's going on inside the layers. So, you know, we don't actually know what's happening inside these transformers. We don't know how to create good training dynamics. We don't really know anything much. And there's a reason for that, right? And the reason for that is language models suddenly got really useful. And so the kind of economically rational thing to do, like, this is not criticism. This is true. The economic rational thing to do is to, like, OK, like, build that as fast as possible. You know, make something work, get it out there. And that's what, you know, OpenAI in particular did and Anthropic kind of did. But there's a whole lot of technical debt everywhere. You know, nobody's really figured this stuff out because everybody's been so busy [01:14:53]
Swyx: building what we know works as quickly as possible. [01:14:57]
Jeremy: So, yeah, I think there's a huge amount of opportunity to, you know, I think we'll find things can be made to work a lot faster, a lot less memory. I got a whole bunch of ideas I want to try, you know, every time I look at something closely, like really closely, I'm always like, oh, it turns out this person actually had no idea what they're doing, you know, [01:15:21]
Swyx: which is fine. [01:15:23]
Jeremy: Like, none of us know what we're doing. We should experiment with that. As we had a trade out on the podcast [01:15:32]
Alessio: who created FlashAttention. Yeah. And I asked him, did nobody think of using SRAM before you? Like, were people just like, no. And he was like, yeah, people just didn't think of it. They didn't try. They didn't come from like a systems background. [01:15:48]
Swyx: Yeah. [01:15:48]
Jeremy: I mean, the thing about FlashAttention is, I mean, lots of people absolutely had thought of that. So had I, right? But I mean, the honest truth is, particularly before Triton, like everybody knew that tiling is the right way to solve anything. And everybody knew that attention, fused attention wasn't tiled. That was stupid. But not everybody's got his ability to like, be like, oh, well, I am confident enough in CUDA and or Triton to use that insight to write something better, you know? And this is where, like, I'm super excited about Mojo, right? And I always talk to Chris about FlashAttention because I'm like, you know, there is a thousand FlashAttentions out there for us to build. You just got to make it easy for us to build them. Like Triton definitely helps, but it's still not easy. You know, it still requires kind of really understanding the GPU architecture and writing it in that kind of very CUDA-ish way. So yeah, I think, you know, if Mojo or something equivalent can really work well, we're going to see a lot more FlashAttentions popping up. [01:17:06]
Swyx: Great, Jerry. [01:17:08]
Alessio: And before we wrap, we usually do a quick lightning round. [01:17:10]
Swyx: We're going to have three simple questions. [01:17:13]
Alessio: So the first one is around acceleration. And you've been in this field a long time. What's something that it's already here today in AI that you thought would take much longer? I don't think anything. [01:17:24]
Jeremy: So I've actually been slightly too bullish. So in my 2014 TED talk, I had a graph and I said, like, this is like the slope of human capabilities and this is the slope of AI capabilities. And I said, oh, and I put a dot saying we are here. It was just before they passed. And I looked back at the transcript the other day and I said, in five years, I think we'll, you know, we might have crossed that threshold in which computers will be better at most human tasks than most humans or most average humans. And so that might be almost true now for non-physical tasks. So I was like, took, you know, took that twice as long as I thought it might. [01:18:11]
Jeremy: Yeah, no, I wouldn't say anything surprised me too much. It's still like, definitely like, I got to admit, you know, I had a very visceral reaction using GPT-4 for the first time. Not because I found it surprising, but actually doing it, like something I was pretty sure would exist by about now, maybe a bit earlier. But actually using it definitely is different to just feeling like it's probably on its way, you know, and yeah, whatever GPT-5 looks like. I'm sure, I imagine I'll have the same visceral reaction, you know. [01:18:56]
Swyx: It's really amazing to watch develop. We also have an exploration question. So what do you think is the most interesting unsolved question in AI? [01:19:07]
Jeremy: How do language models learn? You know, what are the training dynamics? Like I want to see, there was a great paper about ResNets a few years ago that showed how, that was able to like plot a kind of projected three-dimensional loss surface for a ConvNet with and without skip connections. And you know, you could very clearly see without the skip connections, it was bumpy, and with the skip connections, it was super smooth. That's the kind of work we need. Like, so there was actually an interesting blog post that came out just today from the PyTorch team where some of them have created this like 3D matrix product visualization thing. [01:19:56]
Swyx: The MatMul Visualizer. [01:19:58]
Jeremy: Yeah, and they actually showed some nice examples of like a GPT-2 attention layer and like showed an animation and said, like, if you look at this, we can actually see a bit about what it's doing. You know, so again, it reminds me of the Zeiler and Fergus, you know, ConvNet paper that was the first one to do these reverse convolutions to show what's actually being learned in each layer in a ConvNet. Yeah, we need a lot more of this, like, what is going on inside these models? How do they actually learn? And then how can we use those insights to help them to learn better? So I think that would be one. The other exploration I'd really like to see is a much more rigorous analysis of what kind of data do they need at what level? And when do they need it? And how often? So that kind of like dataset mixing, curation, so forth. [01:20:52]
Swyx: Right. In order to get the best capabilities. Yeah. How much is Wikipedia? Yeah. [01:20:58]
Jeremy: Yeah. [01:20:59]
Swyx: Very uncertain. [01:20:59]
Jeremy: Fine-tune what, you know, what kind of mix do you need for it to keep its capabilities? And what are the kind of underlying capabilities that it most needs to keep? And if it loses those, it would lose all these other ones. And what data do you need to keep those? And, you know, other things we can do to change the loss function, to help it to not forget to do things, stuff like that. [01:21:20]
Swyx: Awesome. [01:21:21]
Alessio: And yeah, before wrapping, what's one message, one idea you want everyone to remember and think about? [01:21:27]
Jeremy: You know, I guess the main thing I want everybody to remember is that, you know, there's a lot of people in the world. And they have a lot of, you know, diverse experiences and capabilities. And they all matter. And now that we have a, you know, newly powerful technology in our lives, we could think of that one of two ways. One would be, gee, that's really scary. What would happen if all of these people in the world had access to this technology? Some of them might be bad people. Let's make sure they can't have it. Or one might be, wow, of all those people in the world, I bet a lot of them could really improve the lives of a lot of humanity if they had this tool. This has always been the case, you know, from the invention of writing, to the invention of the printing press, to the, you know, development of education. And it's been a constant battle between people who think that the distributed power is unsafe and it should be held on to by an elite few. And people who think that humanity on net, you know, is a marvelous species, particularly when part of a society and a civilization. And we should do everything we can to enable more of them to contribute. This is a really big conversation right now. And, you know, I want to see more and more people showing up and showing what, you know, what the great unwashed masses out there can actually achieve. You know, that actually, you know, regular people are going to do a lot of really valuable work and actually help us be, you know, more safe and also flourishing in our lives and providing a future for our children to flourish in. You know, if we lock things down to the people that we think, you know, the elites that we think can be trusted to run it for us, yeah, I think all bets are off about where that leaves us as a society, you know. [01:24:00]
Alessio: Yep. Now that's an important message. And yeah, that's why we've been promoting a lot of open source developers, open source communities, I think, letting the builders build and explore. That's always a good idea. Thank you so much for coming on, Jeremy. This was great. [01:24:20]
Jeremy: Thank you for having me. [01:24:22]
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Podcast: Syntax - Tasty Web Development Treats (LS 61 · TOP 0.1% what is this?)
Episode: 678: The 2023 State of CSS Survey Part 2 × CSS Frameworks × Tooling × Browser Usage
Pub date: 2023-10-11
In this episode of Syntax, it’s part 2 of Wes and Scott’s reactions to the 2023 State of CSS survey including CSS frameworks, tooling, browser usage, SVG and CSS, and the CSS Awards.
Show Notes * 00:10 Welcome * Reacting to State of CSS Survey — Syntax Podcast 672 * State of CSS 2023 * 01:15 Syntax Brought to you by Sentry * 01:29 CSS Frameworks * Bootstrap · The most popular HTML, CSS, and JS library in the world. * Open Props: sub-atomic styles * Lightning CSS * 10:57 How happy are you with CSS frameworks? * 17:21 Other tools * CSS Analytics - Project Wallace * 19:34 Top utilities in use * 24:48 Browser usage * 29:01 CSS usage * 33:25 Browser incompatibilities * 36:42 SVG and CSS * 44:28 Resources for learning CSS * Kevin Powell | CSS Evangelist * Fireship - Learn to Code Faster * LeveUp Tutorials * 46:55 Awards * Panda CSS - Build modern websites using build time and type-safe CSS-in-JS * 50:48 SIIIIICK ××× PIIIICKS ×××
××× SIIIIICK ××× PIIIICKS ××× * Scott: A Timeline of the 1970s Heavyweight Boxing Division (Boxing Documentary) / Full Boxing Timelines * Wes: NEIKO 10181A Step Drill Bit Set
Shameless Plugs * Scott: Sentry * Wes: Wes Bos Tutorials
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Podcast: Syntax - Tasty Web Development Treats (LS 61 · TOP 0.1% what is this?)
Episode: 679: Creator of Swift, Tesla Autopilot & Tensorflow. New AI language Mojo with Chris Lattner
Pub date: 2023-10-13
In this supper club episode of Syntax, Wes and Scott talk with Chris Lattner about Mojo, a new programming language for AI developers. Should developers learn Python? Where does Mojo run? What is Chris excited about in AI’s future?
Show Notes * 00:31 Welcome * 01:05 Introducing Chris Lattner * Chris Lattner’s Homepage * Chris Lattner on Wikipedia * Chris Lattner on GitHub * Chris Lattner on Twitter * Modular (@Modular_AI) / X * Modular: AI development starts here * Swift.org - Welcome to Swift.org * 03:50 What’s the history behind the hardware? * 08:10 What’s the difference between a compiled language vs an interpreted language? * 12:13 Is Mojo a programming language? * Mojo 🔥: Programming language for all of AI * 15:12 Are Python libraries compatible with Mojo? * 15:26 Why did you choose Python? * 16:49 Why is AI so Python focused? * 19:19 Should web developers learn Python? * 21:40 Where does Mojo run? * 25:05 How did you use the flame emoji for the Mojo file extension? * 29:05 How does machine learning actually work? * 37:36 Will Mojo be open source in some way? * 39:16 How do you start developing a new programming language? * 43:14 What is the future of developer jobs? * 45:30 What are you excited about with AI in the future? * 47:24 Supper Club questions * Welcome to a World of OCaml * 52:59 SIIIIICK ××× PIIIICKS ×××
××× SIIIIICK ××× PIIIICKS ××× * Exercise
Shameless Plugs * Mojo 🔥: Programming language for all of AI
Tweet us your tasty treats * Scott’s Instagram * LevelUpTutorials Instagram * Wes’ Instagram * Wes’ Twitter * Wes’ Facebook * Scott’s Twitter * Make sure to include @SyntaxFM in your tweets * Wes Bos on Bluesky * Scott on Bluesky * Syntax on Bluesky
The podcast and artwork embedded on this page are from Wes Bos & Scott Tolinski - Full Stack JavaScript Web Developers, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Syntax - Tasty Web Development Treats (LS 61 · TOP 0.1% what is this?)
Episode: 681: What's New in AI for Web Developers
Pub date: 2023-10-18
In this episode of Syntax, Wes and Scott talk through recent developments in AI and how they might be useful for developers, whether AI is still worthy of the hype, and whether developer jobs are at risk from AI.
Show Notes * 00:10 Welcome * 03:10 Syntax Brought to you by Sentry * 03:49 v0.dev * v0 by Vercel * 09:28 Anthropic and Claude * Claude * Syntax Listener Survey * 18:02 Facebook’s Meta AI * AI at Meta * 18:48 Cloudflare AI * Large language model (LLM) * Speech to text * Translation * Sentiment Analysis * Image classification * Embedding * 27:24 AI Hardware announced * Rewind * 29:39 Cloudflare Hugging face * Hugging Face – The AI community building the future. * StarCoder: A State-of-the-Art LLM for Code * Vectorize: a vector database for shipping AI-powered applications * 36:28 OpenAI Function calling * Function calling and other API updates * 38:55 GPT-4V * GPT-4V(ision) system card * 42:36 GitHub CoPilot * 44:03 Are we still on the AI hype train? * 48:27 Are our jobs at risk as developers? * 52:24 Spotify DJ AI * Spotify Debuts a New AI DJ * 53:29 ChatGPT plugins * ChatGPT plugins * 55:19 SIIIIICK ××× PIIIICKS ×××
××× SIIIIICK ××× PIIIICKS ××× * Scott: Coding App for Kids | codeSpark Academy * Wes: Peter Santenello, Roblox
Shameless Plugs * Scott: Sentry * Wes: Wes Bos Tutorials
Tweet us your tasty treats * Scott’s Instagram * LevelUpTutorials Instagram * Wes’ Instagram * Wes’ Twitter * Wes’ Facebook * Scott’s Twitter * Make sure to include @SyntaxFM in your tweets * Wes Bos on Bluesky * Scott on Bluesky * Syntax on Bluesky
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Podcast: Off Menu with Ed Gamble and James Acaster (LS 77 · TOP 0.01% what is this?)
Episode: Ep 193: Arlo Parks
Pub date: 2023-05-24
Mercury Prize-winning musician Arlo Parks gets an in-depth education in the musical genre ‘food rock’ in this week’s episode.
Arlo Parks’ new album ‘Soft Machine’ is released on Transgressive Records on 26 May. Buy and stream it here.
Arlo Park is on tour this autumn. Go to arloparksofficial.com for dates and tickets.
Follow Arlo on Twitter @arloparks and Instagram @arlo.parks
Recorded and edited by Ben Williams for Plosive.
Artwork by Paul Gilbey (photography and design) and Amy Browne (illustrations).
Follow Off Menu on Twitter and Instagram: @offmenuofficial.
And go to our website www.offmenupodcast.co.uk for a list of restaurants recommended on the show.
Watch Ed and James's YouTube series 'Just Puddings'. Watch here.
Hosted on Acast. See acast.com/privacy for more information.
The podcast and artwork embedded on this page are from Plosive, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Syntax - Tasty Web Development Treats (LS 61 · TOP 0.1% what is this?)
Episode: Hacking the Tonal - Proxying, Intercepting + Debugging Traffic?
Pub date: 2023-09-18
In this Hasty Treat, Scott and Wes talk about how you can intercept and debug traffic going out from your computer or other internet connected devices in your home, or your garage!
Show Notes * 00:25 Welcome * 01:55 Syntax Brought to you by Sentry * 02:17 Scott’s story of wanting to intercept data * Tonal * 06:36 Other examples * 08:38 Different types of traffic * 14:52 TCP vs UDP * 16:07 Why would you want to run a proxy? * 24:20 Applications to use * Charles Web Debugging Proxy • HTTP Monitor / HTTP Proxy / HTTPS & SSL Proxy / Reverse Proxy * Proxyman · Native, Modern Web Debugging Proxy · Inspect network traffic from Mac, iOS, Android devices with ease * Intercept, debug & mock HTTP with HTTP Toolkit * mitmproxy - an interactive HTTPS proxy * Wireshark · Go Deep * Little Snitch * Capturing Modes - Fiddler Everywhere * Hacksore on Twitter * How I Hacked my Car :: Programming With Style
Tweet us your tasty treats * Scott’s Instagram * LevelUpTutorials Instagram * Wes’ Instagram * Wes’ Twitter * Wes’ Facebook * Scott’s Twitter * Make sure to include @SyntaxFM in your tweets * Wes Bos on Bluesky * Scott on Bluesky * Syntax on Bluesky
The podcast and artwork embedded on this page are from Wes Bos & Scott Tolinski - Full Stack JavaScript Web Developers, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Syntax - Tasty Web Development Treats (LS 61 · TOP 0.1% what is this?)
Episode: The Svelte + SvelteKit Show
Pub date: 2023-09-20
In this episode of Syntax, Wes reviews his experience building with Svelte and SvelteKit including templating, data fetching, actions, managing state, request handlers, and more.
Show Notes * 00:10 Welcome * 01:12 Syntax Brought to you by Sentry * 02:11 What is Svelte vs SvelteKit * From React To SvelteKit — Syntax Podcast 390 * Hasty Treat - Wes & Scott Look At Svelte 3 — Syntax Podcast 173 * Svelte • Cybernetically enhanced web apps * SvelteKit • Web development, streamlined * 05:59 Templating in Svelte * 18:20 Data fetching in SvelteKit * 25:23 Actions * 28:58 State * 32:41 Binding values * 36:18 Hooks * 37:25 Request handlers * Special elements • Docs • Svelte * website/src/actions/anchor.ts at v2 · syntaxfm/website * website/src/actions/click_outside.ts at v2 · syntaxfm/website * 39:23 Svelte Actions * 42:26 Popover API * 45:33 Routing * 47:22 Layouts * 50:08 Styling * 57:09 SIIIIICK ××× PIIIICKS ×××
××× SIIIIICK ××× PIIIICKS ××× * Scott: Aqara Smart Lock U100 * Wes: Headphones Replacement Ear Pads,Compatible for Bose Quietcomfort QC15 QC25 QC35 35 ii-(Black Floral)
Shameless Plugs * Scott: Sentry * Wes: Wes Bos Tutorials
Tweet us your tasty treats * Scott’s Instagram * LevelUpTutorials Instagram * Wes’ Instagram * Wes’ Twitter * Wes’ Facebook * Scott’s Twitter * Make sure to include @SyntaxFM in your tweets * Wes Bos on Bluesky * Scott on Bluesky * Syntax on Bluesky
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Podcast: Syntax - Tasty Web Development Treats (LS 61 · TOP 0.1% what is this?)
Episode: 674: A11y Treats - Heading Design
Pub date: 2023-10-02
In this Hasty Treat, Scott and Wes talk about headings on your website, why you should care, how to structure your headings, and what tooling is there for testing your website?
Show Notes * 00:26 Welcome * 01:21 Syntax Brought to you by Sentry * 01:41 Why do we care about Headings? * How-to: Accessible heading structure - The A11Y Project * 03:12 Heading design provides an outline for your website * 08:45 Using H1 classes? * 10:28 Is the logo an H1? * 13:03 Giving an ARIA level * 17:14 Can headings be visually hidden? * 21:00 Benefits of good heading design * 22:27 Tooling * Heading outlines - ADG * HTML Standard * Polypane, The browser for ambitious web developers * HeadingsMap - Chrome Web Store * HeadingsMap – Get this Extension for 🦊 Firefox (en-US)
Tweet us your tasty treats * Scott’s Instagram * LevelUpTutorials Instagram * Wes’ Instagram * Wes’ Twitter * Wes’ Facebook * Scott’s Twitter * Make sure to include @SyntaxFM in your tweets * Wes Bos on Bluesky * Scott on Bluesky * Syntax on Bluesky
The podcast and artwork embedded on this page are from Wes Bos & Scott Tolinski - Full Stack JavaScript Web Developers, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Syntax - Tasty Web Development Treats (LS 61 · TOP 0.1% what is this?)
Episode: Fundamentals - Web Workers and Service Workers
Pub date: 2022-08-24
In this episode of Syntax, Wes and Scott talk through the fundamentals of web workers and service workers - examples, when you should use them, how to debug, local dev, and more.
LogRocket - Sponsor LogRocket lets you replay what users do on your site, helping you reproduce bugs and fix issues faster. It’s an exception tracker, a session re-player and a performance monitor. Get 14 days free at logrocket.com/syntax.
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Freshbooks - Sponsor Get a 30 day free trial of Freshbooks at freshbooks.com/syntax
Show Notes * 00:11 Welcome * 04:34 What are threads? * 06:12 Concurrent vs parallel * 09:22 Green screen web cam example * 13:02 Examples of what you could use web workers for * Party Town * Deno * 19:52 Sponsor: LogRocket * 20:42 Post Message API and Web Workers * 25:57 What about WASM? * 27:28 Offline sync * 28:14 What are service workers? * 31:20 How do you caching sites for offline use? * 32:39 Web worker vs service worker * 34:12 Sponsor: Sanity * 35:40 What is the lifecycle of a service worker * 38:18 Possible issues with Service Workers * 42:46 Debugging service workers * Svelte Kit Service workers * Workbox * 43:04 Testing and local development * Service Workers notes from Wes’ Workshop * 46:45 Sponsor: Freshbooks * 49:59 SIIIIICK ××× PIIIICKS ×××
××× SIIIIICK ××× PIIIICKS ××× * Scott: Setex Gecko Grip 1mm Anti Slip Eyeglass Nose Pads * Wes: Samsung Frame TV
Shameless Plugs * Scott: LevelUp Tutorials * Wes: Wes Bos Tutorials
Tweet us your tasty treats * Scott’s Instagram * LevelUpTutorials Instagram * Wes’ Instagram * Wes’ Twitter * Wes’ Facebook * Scott’s Twitter * Make sure to include @SyntaxFM in your tweets
The podcast and artwork embedded on this page are from Wes Bos & Scott Tolinski - Full Stack JavaScript Web Developers, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Flip (LS 36 · TOP 2.5% what is this?)
Episode: Olugbenga 'GB' Agboola: Hard-Earned Lessons Building Flutterwave
Pub date: 2023-09-22
Today's guest is Olugbenga Agboola, better known as the one and only GB - the Co-founder and CEO of Flutterwave.
It's been a trying last year or so for Flutterwave with issues of fraud, allegations of impropriety inside the company, regulatory hurdles, and the general challenges of scaling a fintech in a tough operating environment.
Yet through it all, Flutterwave has "technology reach" in 34 countries, they've continued to ship new products beyond their core payments technology, including their rebranded remittance product Send App, and there are rumors swirling about the near-term timeline of their planned IPO.
In this episode, we'll hear from GB about many of his recent lessons, his perspectives on product and expansion strategy, and we'll ask many of the questions we've been wanting to hear from him about, including the big one about Flutterwave's IPO.
00:00 - Intro
03:13 - When is Flutterwave going public?
04:56 - What about the allegations?
09:24 - Sharing more for the benefit of the ecosystem
11:17 - Growth and expansion
16:13 - Did Flutterwave grow too fast?
21:09 - Fundraising and the African growth story
27:11 - GB's angel investing activities
30:29 - Lessons from GB's banking and big tech background
32:08 - Why did GB start Flutterwave in the first place?
33:49 - Are payments still broken?
35:55 - The vision for the future
38:02 - Final words of wisdom
Follow us on Twitter:
https://twitter.com/theflipafrica
https://twitter.com/just_norm
https://twitter.com/techprod_arch
The podcast and artwork embedded on this page are from The Flip Media, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Now Then Talks (LS 24 · TOP 10% what is this?)
Episode: #4: In Development | Director, Dumas Haddad
Pub date: 2022-06-20
In this conversation with Director Dumas Haddad, we discuss the value of making your own work, developing your own material and discrimination within the industry.
The podcast and artwork embedded on this page are from Ozzie Pullin & Craig Bingham, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Now Then Talks (LS 24 · TOP 10% what is this?)
Episode: #3: Reinventing Your Image | Director, Tom Harrison
Pub date: 2022-05-23
In this conversation, we discuss the impact of social media, reinventing your image and being a signed director.
The podcast and artwork embedded on this page are from Ozzie Pullin & Craig Bingham, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Food Chain (LS 54 · TOP 0.5% what is this?)
Episode: Is this the end of the British caff?
Pub date: 2023-04-12
The British "caff" - slang for café, and home of the breakfast fry up, or "full english" - is under threat. Many have closed, struggling to compete with changing tastes and the success of chains.
Many of Britain’s historic caffs opened in the 1940s and 50s, run by Italian migrants. Some of these original caffs are still trading, run by second and third generation Italian families.
In this programme Ruth Alexander hears stories of the famous caffs that have closed for good, and goes in search of caffs still going. She’s joined by actor Michael Simkins, who has relied on hearty caff fare during a 40-year career in the theatres of London’s West End, and meets actor and director Mark Gatiss, who is finding it increasingly hard to find a good cup of tea in the capital.
Ruth visits cafes that have been operating for decades – Bar Bruno in Soho, and Dino’s Café in east London, to learn exactly what their customers love so much about the traditional British caff.
Restaurant sector consultant James Hacon describes the changes seen in the hospitality industry in the last twenty years, and why caffs now face such stiff competition.
If you would like to get in touch with the programme, email - thefoodchain@bbc.co.uk.
Presented by Ruth Alexander.
Produced by Beatrice Pickup.
(Image: Ernie Fiori proprietor of Dino’s Café at New Spitalfields Market, East London, holding up his tea pot. Credit: BBC)
The podcast and artwork embedded on this page are from BBC World Service, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Food Programme (LS 58 · TOP 0.5% what is this?)
Episode: Conversations in cafes: all hail the greasy spoon
Pub date: 2023-04-30
Traditional cafes, greasy spoon cafes - have been a fixture of our highstreets for at least a century, providing sustenance for those looking for something cheap and cheerful.
But for a long time, they have been in decline for a number of reasons, tough competition from chains, our changing tastes and work patterns. From the early 2000s people have been calling curtains for the cafe, but, with inflation, the cost of energy and a crisis in hospitality staffing, things are looking as bad as ever.
In three meals in three different locations across the country Leyla Kazim celebrates the greasy spoon.
She start with breakfast with Guardian columnist, author and fry up expert Felicity Cloake in Bournville Cafe, Birmingham. In her book "Red Sauce Brown Sauce" Felicity explores why the fry up is so important to the British psyche by traveling the country.
For lunch, she chats to her dad who owned caffs when she was growing up in Kaz's Kitchen in Woowhich. They talk about how owning a cafe has changed over time.
She’s in Liverpool for dinner meeting Isaac Rangaswami who runs the caffs_not_cafes instagram page in Chinese caff San's Cafe. Isaac celebrates classic cafes and inexpensive restaurants, mostly in London.
There is also thoughts on the possible decline of tradespeople eating in cafes from Nick Knowles and some familiar voices tell us their all time favourite places to get a fry up:
Krishnan Guru-Murthy, Angela Hui, William Sitwell, Paula Mcintyre and Henry Jeffreys
Presenter: Leyla Kazim Producer: Sam Grist
The podcast and artwork embedded on this page are from BBC Radio 4, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Full English
Episode: On caffs, not cafes
Pub date: 2023-08-29
What is a caff when it's not a cafe? Where did the caff come from and who will mourn the greasy spoon if, as we hear, they're disappearing?
Joining Lewis Bassett is the author and Guardian columnist Felicity Cloake and Isaac Rangaswami, writer and the man behind the Instagram page Caffs Not Cafes.
Felicity's book is Red Sauce Brown Sauce. Her writing for the Guardian can be found here. Isaac has written about caffs for the Guardian and Vittles.
Mixing and sound design is from Forest DLG.
Follow the Full English on Twitter, Instagram and TikTok.
Get extra content and support the show on Patreon
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The podcast and artwork embedded on this page are from Lewis Bassett, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: London Review Bookshop Podcast (LS 48 · TOP 1% what is this?)
Episode: M. John Harrison & Jennifer Hodgson: Wish I Was Here
Pub date: 2023-08-23
M. John Harrison has produced one of the greatest bodies of fiction of any living British author, encompassing space opera, speculative fiction, fantasy, magical and literary realism. Wish I Was Here is his first work of memoir – an ‘anti-memoir’ – written in his mid-seventies with aphoristic daring and trademark originality and style, fresh after winning the Goldsmiths Prize in 2020 for The Sunken Land Begins to Rise Again. Harrison was joined in conversation with writer and critic Jennifer Hodgson.
Find more events at the Bookshop: lrb.me/eventspod
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The podcast and artwork embedded on this page are from London Review Bookshop, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Syntax - Tasty Web Development Treats (LS 61 · TOP 0.1% what is this?)
Episode: Troubleshooting
Pub date: 2021-11-24
In this episode of Syntax, Scott and Wes talk about ways they troubleshoot issues with their code.
LogRocket - Sponsor LogRocket lets you replay what users do on your site, helping you reproduce bugs and fix issues faster. It's an exception tracker, a session re-player and a performance monitor. Get 14 days free at logrocket.com/syntax.
Freshbooks - Sponsor Get a 30 day free trial of Freshbooks at freshbooks.com/syntax and put SYNTAX in the "How did you hear about us?" section.
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Show Notes * 01:13 Furniture shortages * 02:29 Managing stuff * 03:05 Basic troubleshooting skills are missing * 06:09 Sanity check * CodePen * Codesandbox * 08:05 Isolate the issue * 08:57 Commenting out code is free * 12:17 Replicate the issue * 15:07 Svelte and VS Code error * Svelte * VS Code * 17:02 Wes' course upgrades * Parcel 2 * Patch package * 18:07 Sponsor: Logrocket * 19:15 Rollback * 20:30 Reading the error message * Syntax 47 - How to Get Better at Debugging * Syntax 152 - Debugging Tools * 23:59 Crack open the node modules directory * 26:06 Sponsor: Freshbooks * 27:29 Write step by step comments to the code * 29:01 Consider outside sources * 30:56 Using the right tools for the job * 33:19 Rubber ducking it * 34:16 Wes' Big Mouth Bass story * 37:20 Scott's blown away by his leaf blower * 39:56 Copy paste a message into Google * Twitter - What are your tips for troubleshooting code or a system that doesn't work? * 41:33 Logs and metrics * 42:36 CI CD issues and Error Handler * 43:41 Using a step debugger * 44:24 Explain what's happening to someone else * 45:31 Read the documentation * 47:05 Take a break, have a cuppa * 48:42 Sponsor: Mux * 50:57 SIIIIICK PIIICKS * 57:05 Shamless plugs
Links * @jimbomoso - Do you know of any resources for developing/improving code trouble shooting?
××× SIIIIICK ××× PIIIICKS ××× * Scott: Forehead * Wes: EGO EXINNO 240W/120W Chargers
Shameless Plugs * Scott: Astro Course - Sign up for the year and save 25%! * Wes: All Courses - Use the coupon code 'Syntax' for $10 off!
Tweet us your tasty treats * Scott's Instagram * LevelUpTutorials Instagram * Wes' Instagram * Wes' Twitter * Wes' Facebook * Scott's Twitter * Make sure to include @SyntaxFM in your tweets
The podcast and artwork embedded on this page are from Wes Bos & Scott Tolinski - Full Stack JavaScript Web Developers, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Syntax - Tasty Web Development Treats (LS 61 · TOP 0.1% what is this?)
Episode: Supper Club × How Descript Built A Next Gen Video Editor In The Browser With Andrew Lisowski
Pub date: 2023-08-18
In this supper club episode of Syntax, Wes and Scott talk with Andrew Lisowski about working on Descript, web streams vs local storage, using state machines, writing CSS with Radix, monorepos, and more.
Show Notes * 00:35 Welcome * 01:07 What is Descript? * Descript | All-in-one video & podcast editing, easy as a doc. * Work — Sandwich * 02:21 Who is Andrew Lisowski? * Andrew Lisowski (@HipsterSmoothie) / X * hipstersmoothie.com * Descript (@DescriptApp) / X * devtools.fm * 04:51 How does Descript interact with the webcam? * 08:52 Web streams vs local first * Web Streams Explained — Syntax Podcast 587 * 10:06 How are you exporting video? * GitHub - Yahweasel/libav.js: This is a compilation of the libraries associated with handling audio and video in ffmpeg—libavformat, libavcodec, libavfilter, libavutil, libswresample, and libswscale—for emscripten, and thus the web. * Riverside.fm - Record Podcasts And Videos From Anywhere * 14:40 How does Descript deal with recording fails? * 17:17 How does Descript design and build the UI? * 19:37 What did you like about state machines? * XState - JavaScript State Machines and Statecharts * 24:12 How are you writing your CSS with Radix? * Themes – Radix UI * Home | Open UI * 30:30 How does the marketing site’s tech stack compare? * 31:44 Playwright vs Cypress * Fast and reliable end-to-end testing for modern web apps | Playwright * JavaScript Component Testing and E2E Testing Framework | Cypress * 36:26 What tech do you use for monorepos? * 37:01 What’s your build tool? * Workspaces | Yarn - Package Manager * Turbo * webpack * 40:18 Moving to the web means moving things to the backend * 41:37 Descript focuses AI tools on helping creators * Eye Contact: AI Video Effect | Descript * 50:50 Supper Club questions * Topre Switch Mechanical Keyboards * REALFORCE | Premium Keyboard, PBT, Capacitive Key Switch * Iosevka * Github Dark High Contrast - Visual Studio Marketplace * 56:21 SIIIIICK ××× PIIIICKS ×××
××× SIIIIICK ××× PIIIICKS ××× * Lexical * shadcn/ui
Shameless Plugs * devtools.fm
Tweet us your tasty treats * Scott’s Instagram * LevelUpTutorials Instagram * Wes’ Instagram * Wes’ Twitter * Wes’ Facebook * Scott’s Twitter * Make sure to include @SyntaxFM in your tweets * Wes Bos on Bluesky * Scott on Bluesky * Syntax on Bluesky
The podcast and artwork embedded on this page are from Wes Bos & Scott Tolinski - Full Stack JavaScript Web Developers, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Syntax - Tasty Web Development Treats (LS 61 · TOP 0.1% what is this?)
Episode: Rust for JavaScript Developers - Node vs Rust Concepts
Pub date: 2023-07-31
In this Hasty Treat, Scott and Wes talk about Rust, and how the concepts might translate for JavaScript developers looking to try writing in Rust.
Show Notes * 00:26 Welcome * 01:08 Wes’ big beer bottle and Red Green * RedGreenTV on YouTube * 05:03 Thrift store finds * 06:19 Rust in JavaScript * TOML: Tom’s Obvious Minimal Language * 11:07 Documentation * Docs.rs * Practice.rs * 16:46 Memory safety * 17:43 What about promises in Rust? * 19:24 Error handling in Rust * 27:39 What’s with the double colon?
Tweet us your tasty treats * Scott’s Instagram * LevelUpTutorials Instagram * Wes’ Instagram * Wes’ Twitter * Wes’ Facebook * Scott’s Twitter * Make sure to include @SyntaxFM in your tweets * Wes Bos on Bluesky * Scott on Bluesky * Syntax on Bluesky
The podcast and artwork embedded on this page are from Wes Bos & Scott Tolinski - Full Stack JavaScript Web Developers, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Syntax - Tasty Web Development Treats (LS 61 · TOP 0.1% what is this?)
Episode: JS Fundamentals - Decorators
Pub date: 2023-08-14
In this Hasty Treat, Scott and Wes talk about whether decorators are finally here, what the uses cases are for decorators, how to define a decorator, and what auto accessor is.
Show Notes * 00:25 Welcome * 01:00 Are decorators finally here? * TC39 proposal * How this compares to other versions of decorators * 06:47 What are use cases for decorators? * 10:55 How do you define a decorator? * 14:20 Auto Accessor
on classes
@loggged class C {} on fields
class C { @logged x = 1; } Auto Accessor
class C { accessor x = 1; } sugar for below
class C { #x = 1; // # means private get x() { return this.#x; } set x(val) { this.#x = val; } } Can be decorated and decorator can return new get and set and init functions
function logged(value, { kind, name }) { if (kind === "accessor") { let { get, set } = value; return { get() { console.log(getting ${name}); return get.call(this); }, set(val) { console.log(setting ${name} to ${val}); return set.call(this, val); }, init(initialValue) { console.log(initializing ${name} with value ${initialValue}); return initialValue; } }; } // ... } Tweet us your tasty treats * Scott’s Instagram
* LevelUpTutorials Instagram
* Wes’ Instagram
* Wes’ Twitter
* Wes’ Facebook
* Scott’s Twitter
* Make sure to include @SyntaxFM in your tweets
* Wes Bos on Bluesky
* Scott on Bluesky
* Syntax on Bluesky
The podcast and artwork embedded on this page are from Wes Bos & Scott Tolinski - Full Stack JavaScript Web Developers, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Syntax - Tasty Web Development Treats (LS 61 · TOP 0.1% what is this?)
Episode: “Serverless” Databases
Pub date: 2022-12-19
In this episode of Syntax, Wes and Scott talk about your options for database when you’re working with serverless.
Prismic - Sponsor Prismic is a Headless CMS that makes it easy to build website pages as a set of components. Break pages into sections of components using React, Vue, or whatever you like. Make corresponding Slices in Prismic. Start building pages dynamically in minutes. Get started at prismic.io/syntax.
LogRocket - Sponsor LogRocket lets you replay what users do on your site, helping you reproduce bugs and fix issues faster. It’s an exception tracker, a session re-player and a performance monitor. Get 14 days free at logrocket.com/syntax.
Show Notes * 00:25 Welcome * 00:51 Sponsor: LogRocket * 01:44 Sponsor: Prismic * 03:17 Why Serverless and related databases? * 07:45 Deploying on Deno * Deno * 08:44 Using a database with serverless functions * Syntax 542 - Serverless Limitations * 11:53 Using purpose built databases * Planetscale * Supabase * Cloudflare D1 * Cloudflare Key Value Store * AWS DynamoDB * AWS Auroa * FaunaDB * Neon * Railway * MongoDB Serverless * Redis * Cassandra * 15:01 The results of the test * 17:35 Solutions
Tweet us your tasty treats * Scott’s Instagram * LevelUpTutorials Instagram * Wes’ Instagram * Wes’ Twitter * Wes’ Facebook * Scott’s Twitter * Make sure to include @SyntaxFM in your tweets
The podcast and artwork embedded on this page are from Wes Bos & Scott Tolinski - Full Stack JavaScript Web Developers, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Syntax - Tasty Web Development Treats (LS 61 · TOP 0.1% what is this?)
Episode: Fundamentals × What Makes a Website Slow?
Pub date: 2023-03-08
In this episode of Syntax, Wes and Scott talk through all the reasons your website might be slow, and how you can troubleshoot a slow website such as issues on the server, large assets, caching, CSS, JavaScript, latency, and more.
Sentry - Sponsor If you want to know what’s happening with your code, track errors and monitor performance with Sentry. Sentry’s Application Monitoring platform helps developers see performance issues, fix errors faster, and optimize their code health. Cut your time on error resolution from hours to minutes. It works with any language and integrates with dozens of other services. Syntax listeners new to Sentry can get two months for free by visiting Sentry.io and using the coupon code TASTYTREAT during sign up.
Show Notes * 00:11 Welcome * Glove 80 keyboard * Raycast * 03:06 Sponsor: Sentry * 05:15 What makes a website slow? * Uses.tech * 06:29 Server Generation Times * 13:33 Large payloads * Redis * Gzip * Brotli compression * Cloudflare * Cloudinary * 18:13 Assets being too large * 23:01 Caching assets * 28:25 CDN * 30:35 Caching 101 * 37:04 Render blocking requests * 40:01 CSS * 42:25 JavaScript * 44:51 Latency * 49:17 Flash of dark mode or unsigned out * 55:00 Data uris * Content-visibility * vite-plugin-singlefile * Pool in your URL * 58:11 SIIIIICK ××× PIIIICKS ×××
××× SIIIIICK ××× PIIIICKS ××× * Scott: 3Blue1Brown * Wes: Suavecito Firme Clay Pomade
Shameless Plugs * Scott: LevelUp Tutorials * Wes: Wes Bos Tutorials
Tweet us your tasty treats * Scott’s Instagram * LevelUpTutorials Instagram * Wes’ Instagram * Wes’ Twitter * Wes’ Facebook * Scott’s Twitter * Make sure to include @SyntaxFM in your tweets
The podcast and artwork embedded on this page are from Wes Bos & Scott Tolinski - Full Stack JavaScript Web Developers, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Syntax - Tasty Web Development Treats (LS 61 · TOP 0.1% what is this?)
Episode: Node in the Browser × WebContainers + NodeBox
Pub date: 2023-03-06
In this Hasty Treat, Scott and Wes talk about two new services that allow you to run Node in the browser, WebContainers + NodeBox. Why Node in the browser? How does it work? And what are the differences and limitations of the services?
Sentry - Sponsor If you want to know what’s happening with your code, track errors and monitor performance with Sentry. Sentry’s Application Monitoring platform helps developers see performance issues, fix errors faster, and optimize their code health. Cut your time on error resolution from hours to minutes. It works with any language and integrates with dozens of other services. Syntax listeners new to Sentry can get two months for free by visiting Sentry.io and using the coupon code TASTYTREAT during sign up.
Show Notes * 00:25 Welcome * 01:14 Sponsor: Sentry * 02:45 Webcontainers and NodeBox * Introducing WebContainers * Code Sandbox * CodePen * Replit * GitHub Codespaces * 06:42 Why Node.js in the browser? * 11:08 How does it work? * 13:10 Clientside APIs * 14:27 Using iFrame to proxy messages * 17:39 Are these open source? * 19:22 Differences between the two services * 21:10 Wes to Figma, Scott to Penpot * Figma * Penpot * 24:51 Limitations
Tweet us your tasty treats * Scott’s Instagram * LevelUpTutorials Instagram * Wes’ Instagram * Wes’ Twitter * Wes’ Facebook * Scott’s Twitter * Make sure to include @SyntaxFM in your tweets
The podcast and artwork embedded on this page are from Wes Bos & Scott Tolinski - Full Stack JavaScript Web Developers, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Syntax - Tasty Web Development Treats (LS 61 · TOP 0.1% what is this?)
Episode: Logging
Pub date: 2023-02-20
In this Hasty Treat, Scott and Wes talk about the reasons why you should log errors, how it’s not just for debugging, where to save logs, and apps and packages to help with logging.
Sentry - Sponsor If you want to know what’s happening with your code, track errors and monitor performance with Sentry. Sentry’s Application Monitoring platform helps developers see performance issues, fix errors faster, and optimize their code health. Cut your time on error resolution from hours to minutes. It works with any language and integrates with dozens of other services. Syntax listeners new to Sentry can get two months for free by visiting Sentry.io and using the coupon code TASTYTREAT during sign up.
Show Notes * 00:25 Welcome * 01:37 Sponsor: Sentry * 02:16 What is logging? Why log? * 04:59 Logging isn’t just for debugging * 08:22 What do we log? * 13:34 What not to log * 14:58 Development, staging, and production * 17:36 Logging bots * 19:33 Where to put logs * 20:59 How to log * Log Tail * Paper Trail * Sematext Logs * DataDog * Winston * Pino
Tweet us your tasty treats * Scott’s Instagram * LevelUpTutorials Instagram * Wes’ Instagram * Wes’ Twitter * Wes’ Facebook * Scott’s Twitter * Make sure to include @SyntaxFM in your tweets
The podcast and artwork embedded on this page are from Wes Bos & Scott Tolinski - Full Stack JavaScript Web Developers, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Education Research Reading Room (LS 46 · TOP 1% what is this?)
Episode: ERRR #079. Daniel Willingham on How to Study
Pub date: 2023-06-01
Ollie Lovell · ERRR079. Daniel Willingham on How to Study This episode we’re speaking with Daniel Willingham. Dan is Professor of Psychology at the University of Virginia, where he has taught since 1992. He writes the popular “Ask the Cognitive Scientist” column for American Educator magazine, and is the author of several books including Why Don't Students Like School? and most recently Outsmart Your Brain: Why Learning Is Hard and How You Can Make it Easy, the topic of today’s podcast! His writing on education has appeared in nineteen languages, and iIn 2017 he was appointed by President Obama to serve as a Member of the National Board for Education Sciences.
Dan has been on the podcast before, back in episode 25 on the topic of ‘When can we trust the experts?’ which is an episode that I absolutely loved. Dan’s Why Don’t Students Like School is also the first book that introduced me to the simple model of memory – the environment, working memory, and long-term memory – that has provided the bedrock for my whole understanding of what it takes to learn, and therefore to teach.
And in his recent book, Outsmart your Brain, Dan has done it again! He’s produced yet another incredibly valuable and practical guide. This time, that guide is for anyone who wants to learn how to be a better learner. I have a feeling that you’re going to love this episode, and it’s even one that you might like to share with your students too, because the advice is directed at learners even more than it is at teachers!
To get a summary of this episode, sign up at www.patreon.com/errr
The ERRR podcast can also be listened to on Spotify, apple podcasts, and all other podcasting apps.
Links/resources mentioned in the show
- Dan's suggested books
- Memory and Brain by Larry Squire <-Perhaps the most influential book on Dan, but he wouldn't necessarily recommend this today because it's quite out of date now
- Ollie's Books, Cognitive Load Theory in Action and Tools for Teachers
- To get a summary of this episode, sign up at www.patreon.com/errr
This episode of the ERRR Podcast is brought to you by John Catt Educational. Use this link, along with the code provided within the podcast to get 30% off all books from John Catt Educational! https://www.johncattbookshop.com/books/errr
This episode of the ERRR podcast is brought to you by Catalyst. Catalyst transforms students' lives through learning by developing excellent teachers and Leaders through evidence-based professional learning programs. Find out more at https://catalyst.cg.catholic.edu.au/
Listen to all past episodes of the ERRR podcast here.
The post ERRR #079. Daniel Willingham on How to Study appeared first on Ollie Lovell.
The podcast and artwork embedded on this page are from Ollie Lovell: Secondary school teacher and lover of learning. Passionate about all things eduction. @ollie_lovell, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Education Research Reading Room (LS 46 · TOP 1% what is this?)
Episode: ERRR #035. Andy Matuschak and George Zonnios on Spaced Repetition Software
Pub date: 2019-10-31
The ERRR podcast can also be listened to on Spotify, apple podcasts, and all other podcasting apps.
Today we’re speaking with two guests, Andy Matschak and George Zonnios about the topic of spaced repetition software. This podcast starts with a brief summary of what spaced repetition software actually is, then we jump into a detailed discussion with Andy and George about how SRS utilises maths, tech and science to facilitate durable learning. Andy, George, and Ollie share the ins and outs of their own personal experiences using spaced repetition software, as well as their exploration of its use in the classroom and more broadly.
Read Ollie's early reflections on using Dendro: If you like spaced repetition software, you're going to love incremental reading!
If you’d like to support the Education Research Reading Room podcast, please check out the ERRR Patreon page to explore this option. Any donation, even $1 per month, is greatly appreciated.
Andy Matuschak is a software engineer, designer, and researcher. He’s spent time working for Apple, Khan Adademny, and is now engaged in several projects and initiatives, one of which, Quantum Country, we discuss in this episode. Andy’s article, ‘why books don’t work’ asserts that the way that we engage with traditional media such as books and lectures often doesn’t lead to durable memories, and this article is the stimulus for our discussion today.
George Zonnios is a schoolteacher, software designer and passionate advocate for bringing spaced repetition to mainstream learning. During his teaching career he's been highly focussed upon utilising effective learning strategies in the classroom, and this has resulted in him designing and building two spaced repetition software platforms, Vulcan Tutor, and Dendro.
Links/resources mentioned in the show
- Ollie's reflections on Dendro
- If you like spaced repetition software, you're going to love incremental reading!
- From Andy
- Quantum Country
- Andy and Michael's recent article How can we develop transformative tools for thought?
- Gwern's writing on Spaced Repetition (If you're so good, why aren't you rich?)
- Curiosities from the history of tech
- Vannevar Bush's As We May Think, in which during 1945 he predicted the worldwide web, wikis, databases, digital cameras.
- Douglas Engelbart
- Alan Kay's ‘A personal computer for children of all ages‘, 1972 – Went to Churchill in 1970, saw the first LCDs, drew an iPad on the plane flight home!
- More modern tech
- Bret Victor – One of Andy's biggest influences
- Michael Nielsen – Andy's colleague/collaborator/cofounder on Quantum Country
- From George
- Dendro
- George's Blog
- 20 rules for formulating knowledge (guide to creating flashcards)
- Piotr Wozniak
- Daniel Willingham's book Why Don't Students Like School?
- Book: Make it Stick, includes spaced repetition and interleaving
- See the work of Robert and Elizabeth Bjork, including the Craig Barton podcast with them.
Listen to all past episodes of the ERRR podcast here.
The post ERRR #035. Andy Matuschak and George Zonnios on Spaced Repetition Software appeared first on Ollie Lovell.
The podcast and artwork embedded on this page are from Ollie Lovell: Secondary school teacher and lover of learning. Passionate about all things eduction. @ollie_lovell, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Dwarkesh Podcast (Lunar Society formerly) (LS 41 · TOP 1.5% what is this?)
Episode: Nat Friedman - Reading Ancient Scrolls, Open Source, & AI
Pub date: 2023-03-22
It is said that the two greatest problems of history are: how to account for the rise of Rome, and how to account for her fall. If so, then the volcanic ashes spewed by Mount Vesuvius in 79 AD - which entomb the cities of Pompeii and Herculaneum in South Italy - hold history’s greatest prize. For beneath those ashes lies the only salvageable library from the classical world.
Nat Friedman was the CEO of Github form 2018 to 2021. Before that, he started and sold two companies - Ximian and Xamarin. He is also the founder of AI Grant and California YIMBY.
And most recently, he has created and funded the Vesuvius Challenge - a million dollar prize for reading an unopened Herculaneum scroll for the very first time. If we can decipher these scrolls, we may be able to recover lost gospels, forgotten epics, and even missing works of Aristotle.
We also discuss the future of open source and AI, running Github and building Copilot, and why EMH is a lie.
Watch on YouTube. Listen on Apple Podcasts, Spotify, or any other podcast platform. Read the full transcript here. Follow me on Twitter for updates on future episodes.
Timestamps
(0:00:00) - Vesuvius Challenge
(0:30:00) - Finding points of leverage
(0:37:39) - Open Source in AI
(0:40:32) - Github Acquisition
(0:50:18) - Copilot origin Story
(1:11:47) - Nat.org
(1:32:56) - Questions from Twitter
This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.dwarkeshpatel.com
The podcast and artwork embedded on this page are from Dwarkesh Patel, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Dwarkesh Podcast (Lunar Society formerly) (LS 41 · TOP 1.5% what is this?)
Episode: Ilya Sutskever (OpenAI Chief Scientist) - Building AGI, Alignment, Future Models, Spies, Microsoft, Taiwan, & Enlightenment
Pub date: 2023-03-27
I went over to the OpenAI offices in San Fransisco to ask the Chief Scientist and cofounder of OpenAI, Ilya Sutskever, about:
-
time to AGI
-
leaks and spies
-
what's after generative models
-
post AGI futures
-
working with Microsoft and competing with Google
-
difficulty of aligning superhuman AI
Watch on YouTube. Listen on Apple Podcasts, Spotify, or any other podcast platform. Read the full transcript here. Follow me on Twitter for updates on future episodes.
Timestamps
(00:00) - Time to AGI
(05:57) - What’s after generative models?
(10:57) - Data, models, and research
(15:27) - Alignment
(20:53) - Post AGI Future
(26:56) - New ideas are overrated
(36:22) - Is progress inevitable?
(41:27) - Future Breakthroughs
This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.dwarkeshpatel.com
The podcast and artwork embedded on this page are from Dwarkesh Patel, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Dwarkesh Podcast (Lunar Society formerly) (LS 41 · TOP 1.5% what is this?)
Episode: Eliezer Yudkowsky - Why AI Will Kill Us, Aligning LLMs, Nature of Intelligence, SciFi, & Rationality
Pub date: 2023-04-06
For 4 hours, I tried to come up reasons for why AI might not kill us all, and Eliezer Yudkowsky explained why I was wrong.
We also discuss his call to halt AI, why LLMs make alignment harder, what it would take to save humanity, his millions of words of sci-fi, and much more.
If you want to get to the crux of the conversation, fast forward to 2:35:00 through 3:43:54. Here we go through and debate the main reasons I still think doom is unlikely.
Watch on YouTube. Listen on Apple Podcasts, Spotify, or any other podcast platform. Read the full transcript here. Follow me on Twitter for updates on future episodes.
Timestamps
(0:00:00) - TIME article
(0:09:06) - Are humans aligned?
(0:37:35) - Large language models
(1:07:15) - Can AIs help with alignment?
(1:30:17) - Society’s response to AI
(1:44:42) - Predictions (or lack thereof)
(1:56:55) - Being Eliezer
(2:13:06) - Othogonality
(2:35:00) - Could alignment be easier than we think?
(3:02:15) - What will AIs want?
(3:43:54) - Writing fiction & whether rationality helps you win
This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.dwarkeshpatel.com
The podcast and artwork embedded on this page are from Dwarkesh Patel, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Dwarkesh Podcast (Lunar Society formerly) (LS 41 · TOP 1.5% what is this?)
Episode: Andy Matuschak - Self-Teaching, Spaced Repetition, & Why Books Don’t Work
Pub date: 2023-07-12
A few weeks ago, I sat beside Andy Matuschak to record how he reads a textbook.
Even though my own job is to learn things, I was shocked with how much more intense, painstaking, and effective his learning process was.
So I asked if we could record a conversation about how he learns and a bunch of other topics:
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How he identifies and interrogates his confusion (much harder than it seems, and requires an extremely effortful and slow pace)
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Why memorization is essential to understanding and decision-making
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How come some people (like Tyler Cowen) can integrate so much information without an explicit note taking or spaced repetition system.
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How LLMs and video games will change education
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How independent researchers and writers can make money
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The balance of freedom and discipline in education
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Why we produce fewer von Neumann-like prodigies nowadays
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How multi-trillion dollar companies like Apple (where he was previously responsible for bedrock iOS features) manage to coordinate millions of different considerations (from the cost of different components to the needs of users, etc) into new products designed by 10s of 1000s of people.
Watch on YouTube. Listen on Apple Podcasts, Spotify, or any other podcast platform. Read the full transcript here. Follow me on Twitter for updates on future episodes.
To see Andy’s process in action, check out the video where we record him studying a quantum physics textbook, talking aloud about his thought process, and using his memory system prototype to internalize the material.
You can check out his website and personal notes, and follow him on Twitter.
Cometeer
Visit cometeer.com/lunar for $20 off your first order on the best coffee of your life!
If you want to sponsor an episode, contact me at dwarkesh.sanjay.patel@gmail.com.
Timestamps
(00:02:32) - Skillful reading
(00:04:10) - Do people care about understanding?
(00:08:32) - Structuring effective self-teaching
(00:18:17) - Memory and forgetting
(00:34:50) - Andy’s memory practice
(00:41:47) - Intellectual stamina
(00:46:07) - New media for learning (video, games, streaming)
(01:00:31) - Schools are designed for the median student
(01:06:52) - Is learning inherently miserable?
(01:13:37) - How Andy would structure his kids’ education
(01:31:40) - The usefulness of hypertext
(01:43:02) - How computer tools enable iteration
(01:52:24) - Monetizing public work
(02:10:16) - Spaced repetition
(02:11:56) - Andy’s personal website and notes
(02:14:24) - Working at Apple
(02:21:05) - Spaced repetition 2
This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.dwarkeshpatel.com
The podcast and artwork embedded on this page are from Dwarkesh Patel, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Dwarkesh Podcast (Lunar Society formerly) (LS 41 · TOP 1.5% what is this?)
Episode: Dario Amodei (Anthropic CEO) - $10 Billion Models, OpenAI, Scaling, & AGI in 2 years
Pub date: 2023-08-08
Here is my conversation with Dario Amodei, CEO of Anthropic.
Dario is hilarious and has fascinating takes on what these models are doing, why they scale so well, and what it will take to align them.
I’m running an experiment on this episode.
I’m not doing an ad.
Instead, I’m just going to ask you to pay for whatever value you feel you personally got out of this conversation.
Pay here: https://bit.ly/3ONINtp
Watch on YouTube. Listen on Apple Podcasts, Spotify, or any other podcast platform. Read the full transcript here. Follow me on Twitter for updates on future episodes.
Timestamps
(00:02:03) - Scaling
(00:16:49) - Language
(00:24:01) - Economic Usefulness
(00:39:08) - Bioterrorism
(00:44:38) - Cybersecurity
(00:48:22) - Alignment & mechanistic interpretability
(00:58:46) - Does alignment research require scale?
(01:06:33) - Misuse vs misalignment
(01:10:09) - What if AI goes well?
(01:12:08) - China
(01:16:14) - How to think about alignment
(01:30:21) - Manhattan Project
(01:32:34) - Is modern security good enough?
(01:37:12) - Inefficiencies in training
(01:46:56) - Anthropic’s Long Term Benefit Trust
(01:52:21) - Is Claude conscious?
(01:57:17) - Keeping a low profile
This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.dwarkeshpatel.com
The podcast and artwork embedded on this page are from Dwarkesh Patel, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Real Python Podcast (LS 46 · TOP 1% what is this?)
Episode: Starting With FastAPI and Examining Python's Import System
Pub date: 2021-08-06
Have you heard of FastAPI? An application programming interface is vital to make your software accessible to users across the internet. FastAPI is an excellent option for quickly creating a web API that implements best practices. This week on the show, David Amos is back, and he’s brought another batch of PyCoder’s Weekly articles and projects.
We share an introduction to FastAPI written by the framework’s author, Sebastián Ramírez. The goal behind the article is to get you started creating production-ready APIs.
David covers an article about the Python import system and how it remains a mystery for many Python developers. We share some additional Real Python resources on the import system and statements.
We cover several other articles and projects from the Python community including, a buffet of specialized data types with Python’s collections module, maps with Django using GeoDjango, PostGIS, and Leaflet, moving SciPy to the Meson build system, what’s new in Python 3.11, a community-maintained Python framework for creating mathematical animations, and easily make PDFs with pdfme.
Course Spotlight: Python Modules and Packages: An Introduction
In this course, you’ll explore Python modules and Python packages, two mechanisms that facilitate modular programming. See how to write and import modules so you can optimize the structure of your own programs and make them more maintainable.
Topics:
- 00:00:00 – Introduction
- 00:02:18 – Python’s collections: A Buffet of Specialized Data Types
- 00:08:07 – Maps With Django: GeoDjango, PostGIS, and Leaflet
- 00:12:12 – Moving SciPy to the Meson Build System
- 00:18:16 – Sponsor: Sentry
- 00:19:18 – What’s New In Python 3.11
- 00:24:32 – Behind the Scenes: How the Python Import System Works
- 00:31:34 – Video Course Spotlight
- 00:32:40 – Using FastAPI to Build Python Web APIs
- 00:38:42 – manim: A Community-Maintained Python Framework for Creating Mathematical Animations
- 00:41:56 – pdfme: Make PDFs Easily
- 00:44:42 – Thanks and goodbye
Show Links:
Python’s collections: A Buffet of Specialized Data Types – Python has a number of useful data types beyond the built-in lists, tuples, dicts, and sets. In this tutorial, you’ll learn all about the series of specialized container data types in the collections module from the Python standard library. Learning the collections module is a great way to level up your Python programming knowledge!
Maps With Django: GeoDjango, PostGIS, and Leaflet – This quickstart guide shows you how to create a web map using Django’s GeoDjango module. Data for the map is stored in a PostgreSQL database using the PostGIS extension, and Leaflet, a lightweight JavaScript library for interactive maps, is used on the front-end. You’ll not only learn how to set up the Django application and display the map but also add markers to the map and automatically center the map on the application user’s location.
Moving SciPy to the Meson Build System – In accordance with PEP 632, distutils will be deprecated in Python 3.10 and in Python 3.12 it will be removed. This posed a big problem for SciPy, since it’s build system depends on NumPy’s distutils module — an extension of Python’s built-in distutils. The SciPy maintainers set out to find a new build system and settled on Meson, which solves a number of build issues and even scores a 4x speed-up on build times!
What’s New In Python 3.11 – Python 3.10 is still in beta, but work on Python 3.11 has already begun. Big changes include some major improvements to tracebacks as well as a new cube root function in the math module.
Behind the Scenes: How the Python Import System Works – Importing a Python module is probably one of the most used language features. But Python’s import system remains a mystery to many Python developers, even folks with years of experience. This in-depth article explores how the import system works from the top down. You’ll learn everything from the difference is between absolute and relative imports to how Python searches for modules and packages and resolves naming conflicts.
Using FastAPI to Build Python Web APIs – In this guide, written by FastAPI creator Sebastián Ramírez, you’ll learn the main concepts of FastAPI and how to use it to quickly create web APIs that implement best practices by default. By the end of it, you will be able to start creating production-ready web APIs.
Projects:
- manim: A Community-Maintained Python Framework for Creating Mathematical Animations
- pdfme: Make PDFs Easily
Additional Links:
- Common Python Data Structures (Guide): Real Python
- OrderedDict vs dict in Python: The Right Tool for the Job: Real Python Article
- Python’s Counter: The Pythonic Way to Count Objects: Real Python Article
- Make a Location-Based Web App With Django and GeoDjango: Real Python Article
- Make a Location-Based Web App With Django and GeoDjango: Real Python Course
- Python import: Advanced Techniques and Tips: Real Python Article
- Absolute vs Relative Imports in Python: Real Python Article
- Python Modules and Packages: An Introduction - Real Python Course
Support the podcast & join our community of Pythonistas
The podcast and artwork embedded on this page are from Real Python, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Real Python Podcast (LS 46 · TOP 1% what is this?)
Episode: Consuming APIs With Python and Building Microservices With gRPC
Pub date: 2021-03-05
Have you wanted to get your Python code to consume data from web-based APIs? Maybe you’ve dabbled with the requests package, but you don’t know what steps to take next. This week on the show, David Amos is back, and he’s brought another batch of PyCoder’s Weekly articles and projects.
We discuss an article titled, “Python’s APIs: A Winning Combo for Reading Public Data”. David shares another Real Python article about creating microservices using Google Remote Procedure Calls (gRPC).
We also cover several other articles and projects from the Python community including, making a difficult data analysis question easy with pandas, efficiently cleaning text with pandas, the tricky bits of Python concurrency, building rich terminal dashboards, making better assertions for Python tests, and building and managing real-life data science projects with metaflow.
Course Spotlight: Making HTTP Requests With Python
The “requests” library is the de facto standard for making HTTP requests in Python. It abstracts the complexities of making requests behind a beautiful, simple API so that you can focus on interacting with services and consuming data in your application. This course shows you how to work effectively with “requests”, from start to finish.
Topics:
- 00:00:00 – Introduction
- 00:01:46 – Python Microservices With gRPC
- 00:07:49 – Python’s APIs: A Winning Combo for Reading Public Data
- 00:15:07 – Making a Difficult Data Analysis Question Easy With Pandas
- 00:21:07 – Efficiently Cleaning Text With Pandas
- 00:34:20 – Video Course Spotlight
- 00:35:27 – Python Concurrency: The Tricky Bits
- 00:41:49 – Building Rich Terminal Dashboards
- 00:45:08 – python-precisely: Better Assertions for Python Tests
- 00:48:45 – metaflow: Build and Manage Real-Life Data Science Projects With Ease
- 00:52:35 – Thanks and goodbye
Show Links:
Python Microservices With gRPC – Learn how to build a robust and developer-friendly Python microservices infrastructure using gRPC and Kubernetes. You’ll also explore advanced topics such as interceptors and integration testing.
Python’s APIs: A Winning Combo for Reading Public Data – Learn what APIs are and how to consume them using Python. You’ll also learn some core concepts for working with APIs, such as status codes, HTTP methods, using the requests library, and much more.
Making a Difficult Data Analysis Question Easy With Pandas – A great strategy to use when faced with a tricky data analysis problem is to reshape the dataset into a format that turns it into an easy problem. In this article, you’ll look at an example involving a simple calculation and extensive reshaping in pandas.
Efficiently Cleaning Text With Pandas – In this article, you’ll see some examples of cleaning text fields in a large data file and learn several strategies for efficiently cleaning unstructured text fields using Python and pandas.
Python Concurrency: The Tricky Bits – An exploration of threads, processes, and coroutines in Python, with interesting examples that illuminate the differences between each.
Building Rich Terminal Dashboards – Learn how to use the Rich CLI library’s new terminal dashboard feature.
Projects:
- python-precisely: Better Assertions for Python Tests
- metaflow: Build and Manage Real-Life Data Science Projects With Ease
Additional Links:
- API design: Understanding gRPC, OpenAPI and REST and when to use them
- Data Cleaning IS Analysis, Not Grunt Work
- How to Lie with Statistics: Wikipedia Article
Support the podcast & join our community of Pythonistas
The podcast and artwork embedded on this page are from Real Python, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Real Python Podcast (LS 46 · TOP 1% what is this?)
Episode: Python REST APIs and The Well-Grounded Python Developer
Pub date: 2020-04-24
Are you interested in building REST APIs with Flask and SQLAlchemy? This week we have Doug Farrell on the show. We talk about his four-part Real Python article series on Python REST APIs.
We discuss the various Python tools and libraries used in the series. Doug also shares his practices for continuous learning. Doug has worked in process control, embedded systems, and has a long background in software development.
He’s currently a developer at ShutterFly, and discusses developing tools for his internal customers. He also teaches Python to kids at a STEM school near where he lives.
Doug is writing a book for Manning Publications, “The Well-Grounded Python Developer”. The book is currently available in an early access state. And as always please check out all the additional resources and tools that Doug discusses, they are all gathered for you in the show notes.
Topics:
- 00:00:00 – Introduction
- 00:01:30 – Doug’s programming background
- 00:06:16 – Building a Polargraph
- 00:08:51 – When did you get into Python?
- 00:10:43 – Working at Shutterfly
- 00:13:45 – How does Python help at Shutterfly?
- 00:16:21 – Difficulties for a self-taught developer
- 00:18:58 – How do you keep honing your skills?
- 00:20:32 – Writing articles
- 00:22:04 – Python REST APIs With Flask, Connexion, and SQLAlchemy Series
- 00:27:54 – Picking tools for REST APIs
- 00:36:27 – The Well-Ground Python Developer Book
- 00:39:27 – What topic are you most interested in covering?
- 00:42:35 – How has working with hardware helped you become a better programmer?
- 00:45:36 – Something you thought you knew about Python, but were wrong about?
- 00:46:25 – What’s a good tool to use for profiling?
- 00:47:34 – Getting up to speed on data science
- 00:50:45 – What are you excited about in the world of Python?
- 00:53:26 – Contact info, thank you and sign off
Show links:
- Python REST APIs With Flask, Connexion, and SQLAlchemy
- Python REST APIs With Flask, Connexion, and SQLAlchemy – Part 2
- Python REST APIs With Flask, Connexion, and SQLAlchemy – Part 3
- Python REST APIs With Flask, Connexion, and SQLAlchemy – Part 4
- API Integration in Python – Part 1
- Flask Tutorials - Real Python
- SQLAlchemy - The Python SQL Toolkit and Object Relational Mapper
- marshmallow: simplified object serialization
- Swagger - API Development for Everyone
- Connexion - Swagger/OpenAPI First framework for Python
- Serialization - Wikipedia article
- Working With JSON Data in Python - Real Python Article
- Ajax - Wikipedia article
- What’s a polargraph
- Polargraph (vertical plotter / drawing machine) written in Go
- The Python Profilers - docs.python.org
- Python Timer Functions: Three Ways to Monitor Your Code - Real Python
- Doug’s personal website
- The Well-Grounded Python Developer - Early Access Book
- Doug’s Linked-In Profile
Support the podcast & join our community of Pythonistas
The podcast and artwork embedded on this page are from Real Python, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Real Python Podcast (LS 46 · TOP 1% what is this?)
Episode: Lessons Learned From Four Years Programming With Python
Pub date: 2023-03-24
What are the core lessons you’ve learned along your Python development journey? What are key takeaways you would share with new users of the language? This week on the show, Duarte Oliveira e Carmo is here to discuss his recent talk, “Four Years of Python.”
Duarte works at the crossroads of machine learning, data science, and software engineering. He began using Python in his graduate studies and never looked back. In 2021, he wrote a blog post about some of the valuable lessons he’s learned. Then he decided the lessons and concepts in the post might make a good conference talk.
We cover the steps in his process of crafting the presentation, practicing it at a smaller conference, and finally presenting it at PyCon Italia last year. We also dig into the four major themes of the talk. Along the way, we share a collection of resources to help you continue learning on your Python journey.
Course Spotlight: Building a URL Shortener With FastAPI and Python
In this video course, you’ll build an app to create and manage shortened URLs. Your Python URL shortener can receive a full target URL and return a shortened URL. You’ll also use the automatically created documentation of FastAPI to try out your API endpoints.
Topics:
- 00:00:00 – Introduction
- 00:02:38 – Four years of Python
- 00:04:18 – Why did you create a blog?
- 00:06:19 – A singular vs wide focus for the blog
- 00:09:19 – Pitching the talk to conferences
- 00:13:02 – Resources for preparing your talk
- 00:16:03 – What was your programming and Python background?
- 00:19:00 – Sponsor: InfluxData
- 00:19:47 – Reading is better than Googling
- 00:26:23 – What are some of your favorite docs?
- 00:28:48 – Thoughts on GPT and Copilot
- 00:31:45 – Keep it stupid simple
- 00:36:07 – What’s extensible code?
- 00:38:29 – Video Course Spotlight
- 00:39:54 – Learning testing techniques & testing data science code
- 00:46:05 – Continuous learning
- 00:51:46 – What do you use for RSS?
- 00:53:06 – Resources for machine learning
- 00:57:20 – What are you excited about in the world of Python?
- 00:58:57 – What do you want to learn next?
- 01:00:55 – How can people follow the work you do?
- 01:01:20 – Thanks and goodbye
Show Links:
- Four years of Python - Duarte O.Carmo
- Four years of Python - Duarte Carmo - YouTube
- Practices of the Python Pro
- Pelican 4.8.0
- “One for Them, One for Me” - Blank Check Movies From Famous Directors
- PyData
- NumFOCUS: A Nonprofit Supporting Open Code for Better Science
- Proposing a Talk - PyCon US 2023
- pandas documentation - pandas 1.5.3 documentation
- scikit-learn 1.2.2 - User guide - documentation
- FastAPI - Tutorial - User Guide
- Using FastAPI to Build Python Web APIs - Real Python
- Python 3.11.2 Documentation
- Kindle Highlights Newsletter
- Reeder 5
- Welcome to Feedly
- Normconf: The Normcore Tech Conference
- Tech Blog - ★❤✰ Vicki Boykis ★❤✰
- Sebastian Raschka - Blog
- Blog of a data person. - koaning.io
- Machine Learning Design Patterns - Book
- The Practical AI Podcast - Changelog
- tidytuesday: Official repo for the #tidytuesday project
- PyCon.DE & PyData Berlin, 2023 - PyConDE & PyData Berlin 2023
- PyCon Italia - 2023
- ruff - PyPI
- Effective Python › The Book: Second Edition
- Episode #3: Effective Python and Python at Google Scale - The Real Python Podcast
- Duarte O.Carmo
- Talks - Duarte O.Carmo
- Duarte O.Carmo - LinkedIn
Level up your Python skills with our expert-led courses:
- Splitting Datasets With scikit-learn and train_test_split()
- Python REST APIs With FastAPI
- Building a URL Shortener With FastAPI and Python
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The podcast and artwork embedded on this page are from Real Python, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Real Python Podcast (LS 46 · TOP 1% what is this?)
Episode: Building Python REST APIs With Flask & Structuring Pull Requests
Pub date: 2022-11-25
How do you build a REST API using the Flask web framework? How can you quickly add endpoints while automatically generating documentation? This week on the show, Real Python author Philipp Acsany is here to discuss his tutorial series “Python REST APIs With Flask, Connexion, and SQLAlchemy.” Christopher Trudeau is also here with another batch of PyCoder’s Weekly articles and projects.
Philipp talks about updating a set of tutorials to use current libraries and best practices. The series takes you through building the base Flask project, defining endpoints, creating documentation, adding a persistent database, and implementing models with SQLAlchemy.
Christopher shares an article about contributing to an existing internal or open-source project by properly preparing pull requests. The article is titled “Ten Tasty Ingredients for a Delicious Pull Request”.
We share several other articles and projects from the Python community, including more suspicious PyPI packages using new tactics, method chaining in pandas, tools to find syntax errors without stopping, a library for searching text in videos using optical character recognition (OCR), a project for visualizing CPython’s specializing adaptive interpreter, and a library for building CLI applications based on type hints.
Course Spotlight: The Pandas DataFrame: Working With Data Efficiently
In this course, you’ll get started with pandas DataFrames, which are powerful and widely used two-dimensional data structures. You’ll learn how to perform basic operations with data, handle missing values, work with time-series data, and visualize data from a pandas DataFrame.
Topics:
- 00:00:00 – Introduction
- 00:03:09 – Philipp’s background
- 00:05:37 – Python REST APIs With Flask, Connexion, and SQLAlchemy
- 00:14:35 – Ten Tasty Ingredients for a Delicious Pull Request
- 00:24:25 – Sponsor: InfluxDB
- 00:25:13 – Method Chaining in Pandas: Bad Form or a Recipe for Success?
- 00:31:35 – More Suspicious PyPI Packages
- 00:35:48 – Video Course Spotlight
- 00:37:01 – What Tools Find Syntax Errors Without Stopping?
- 00:47:29 – Perform OCR upon entire videos
- 00:49:49 – Visualize CPython 3.11’s Specializing, Adaptive Interpreter
- 00:54:08 – Typer, build great CLIs
- 00:56:29 – Thanks and goodbye
Show Links:
- About Philipp Acsany – Real Python
- Python REST APIs With Flask, Connexion, and SQLAlchemy – Part 1
- Python REST APIs With Flask, Connexion, and SQLAlchemy – Part 2
- Python REST APIs With Flask, Connexion, and SQLAlchemy – Part 3
- Ten Tasty Ingredients for a Delicious Pull Request – LB is a core team member of the open-source project Wagtail and, as such, has a lot of experience dealing with community contributions. This article talks about how to be a good contributor, whether for your next open-source software (OSS) PR or within your own organization.
- Method Chaining in Pandas: Bad Form or a Recipe for Success? – Python trainer Matt Harrison has been creating a bit of a stir. Some of his pandas examples have elicited criticism from different folks in the Twitterverse. Dave Amos interviews Matt to discuss the pros and cons of his approach.
- More Suspicious PyPI Packages – Researchers at Phylum have come across over a dozen new malicious uploads to PyPI. Many of them are copied and pasted versions of legitimate packages that have been renamed and had malicious code inserted. This detailed article shows some of the tactics used by the bad actors.
Discussion:
- What Tools Find Syntax Errors Without Stopping?
Projects:
- videocr: Perform OCR upon entire videos to look for credentials or similar
- Visualize CPython 3.11’s Specializing, Adaptive Interpreter
- Typer, build great CLIs
Additional Links:
- What Percentage Of Websites Use WordPress In 2022?
- “Here’s a recipe to clean up the Ames housing dataset.” Matt Harrison - Twitter
- Idiomatic Pandas - Matt Harrison | Conf42 Python 2021 - YouTube
- Episode #103: Becoming More Effective at Manipulating Data With Pandas – The Real Python Podcast
- Getting started - Polars - User Guide
- py_compile — Compile Python source files — Python 3.11.0 documentation
- Build a Command-Line To-Do App With Python and Typer – Real Python
Support the podcast & join our community of Pythonistas
The podcast and artwork embedded on this page are from Real Python, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Real Python Podcast (LS 46 · TOP 1% what is this?)
Episode: Building a Hash Table in Python and Thoughtful REST API Design
Pub date: 2022-04-01
Do you understand how a hash table works? What if you could learn about building one while practicing test-driven development? What are best practices when designing a REST API? This week on the show, Christopher Trudeau is here, and he’s brought another batch of PyCoder’s Weekly articles and projects.
We talk about the recent Real Python article “Build a Hash Table in Python With TDD.” The tutorial shows how to implement a hash table prototype from scratch in Python. It also provides a hands-on crash course in test-driven development.
Christopher shares an article on designing REST APIs and provides some of his own best practices. We cover authentication implementation, good naming conventions, versioned APIs, and ways to specify dates.
We cover several other articles and projects from the Python community, including a news roundup, a PEP on removing dead batteries from the standard library, a comparison of the Python list vs tuple, a guide to writing user-friendly CLIs in Python, just enough Cython to be useful, a cross-platform TUI and ASCII animation package, and code for running black on Python code blocks in documentation files.
Course Spotlight: Command Line Interfaces in Python
Command-line arguments are the key to converting your programs into useful and enticing tools that are ready to be used in the terminal of your operating system. In this course, you’ll learn their origins, standards, and basics, and how to implement them in your program.
Topics:
- 00:00:00 – Introduction
- 00:02:23 – PEP 594: Removing Dead Batteries From the Standard Library
- 00:05:33 – Python 3.10.3, 3.9.11, 3.8.13, and 3.7.13 Now Available
- 00:08:37 – EuroPython 2022: Ticket Sales Open
- 00:09:34 – Python list vs tuple Comparison
- 00:12:19 – How to Write User-Friendly CLIs in Python
- 00:20:14 – Sponsor: Anvil
- 00:20:55 – Build a Hash Table in Python With TDD
- 00:26:11 – Just Enough Cython to Be Useful
- 00:36:21 – Video Course Spotlight
- 00:37:45 – How to Design Better REST APIs
- 00:47:10 – blacken-docs: Run black on Python Code Blocks within Documentation Files
- 00:49:09 – asciimatics: Cross Platform TUI and ASCII Animation Package
- 00:52:03 – Thanks and goodbye
News:
- PEP 594: Removing Dead Batteries From the Standard Library
- Python 3.10.3, 3.9.11, 3.8.13, and 3.7.13 Now Available
- EuroPython 2022: Ticket Sales Open
Topic Links:
- Python list vs tuple Comparison – Learn how
listandtupleare similar and how they’re different, including storage and speed differences and how to choose between them. - How to Write User-Friendly CLIs in Python – Learn how to write user-friendly command-line interface applications and an overview of several of the popular CLI libraries:
argparse,Click,Typer,Docopt, andFire. - Build a Hash Table in Python With TDD – In this step-by-step tutorial, you’ll implement the classic hash table data structure using Python. Along the way, you’ll learn how to cope with various challenges such as hash code collisions while practicing test-driven development (TDD).
- Just Enough Cython to Be Useful – Cython is a superset of Python designed to give C-like performance. Ever wanted to learn the basics? This article shows you how to get started.
- How to Design Better REST APIs – Fifteen language-agnostic tips on REST API design, including good naming conventions, ways to specify dates, versioned APIs, authentication keys, pagination, and when to use which HTTP methods.
Projects:
- blacken-docs: Run black on Python Code Blocks in Documentation Files
- asciimatics: Cross Platform TUI and ASCII Animation Package
Additional Links:
- Command Line Interface Guidelines - An Open-Source Guide
- How to Build Command Line Interfaces in Python With argparse – Real Python
- Command Line Interfaces in Python – Real Python
- Language Basics — Cython 3.0.0a10 documentation
- Building HTTP APIs With Django REST Framework – Real Python
- Python Timer Functions: Three Ways to Monitor Your Code – Real Python
Support the podcast & join our community of Pythonistas
The podcast and artwork embedded on this page are from Real Python, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Real Python Podcast (LS 46 · TOP 1% what is this?)
Episode: Detecting Deforestation With Python & Using GraphQL With Django and Vue
Pub date: 2021-06-11
Are you looking for an in-depth data science project to practice your skills on? Perhaps you would like to add new tools to your Python web development projects instead? This week on the show, David Amos is back, and he’s brought another batch of PyCoder’s Weekly articles and projects.
David shares an article about how to go about detecting deforestation from satellite images. He covers how a data science team built a machine learning (ML) solution to do just that, using FastAI for the modeling and Streamlit to create a dashboard.
We also discuss a Real Python article about building a blog using Django, Vue.js, and GraphQL. GraphQL is a great tool to enhance your API to make it more flexible. The step by step project walks you through turning your Django blog data models into a GraphQL API.
We cover several other articles and projects from the Python community including, the tools and tech used to run a one-woman hardware company, visualizing data in Python using plt.scatter(), why the sad face when using Black, how to iterate over dataframe rows (and should you?), pipx is now a PyPA member project, and real-time lossless audio compression in Python with pyFLAC.
Spotlight: Explore Your Dataset With Pandas
In this step-by-step course, you’ll learn how to start exploring a dataset with Pandas and Python. You’ll learn how to access specific rows and columns to answer questions about your data. You’ll also see how to handle missing values and prepare to visualize your dataset in a Jupyter Notebook.
Topics:
- 00:00:00 – Introduction
- 00:02:11 – Build a Blog Using Django, Vue, and GraphQL
- 00:10:06 – Detecting Deforestation From Satellite Images
- 00:16:35 – Sponsor: Digital Ocean’s App Platform
- 00:17:11 – The Tools and Tech I Use to Run a One-Woman Hardware Company
- 00:29:13 – Visualizing Data in Python Using plt.scatter()
- 00:34:24 – Why the Sad Face?
- 00:40:20 – Video Course Spotlight
- 00:41:26 – How to Iterate Over DataFrame Rows (And Should You?)
- 00:48:31 – pyFLAC: Real-Time Lossless Audio Compression in Python
- 00:53:47 – pipx: Install and Run Python Applications in Isolated Environments
- 00:59:59 – Thanks and goodbye
Show Links:
Build a Blog Using Django, Vue, and GraphQL – In this step-by-step project, you’ll build a blog from the ground up. You’ll turn your Django blog data models into a GraphQL API and consume it in a Vue application for users to read. You’ll end up with an admin site and a user-facing site you can continue to refine for your own use.
Detecting Deforestation From Satellite Images – How would you go about detecting deforestation — a contributor to climate change — from satellite images? In this article, you’ll learn how one team built a machine learning (ML) solution to do just that, using FastAI for the modeling and Streamlit to create a dashboard. The article discusses methodology and results, and is a great read about building an ML solution. The project code is available on GitHub.
The Tools and Tech I Use to Run a One-Woman Hardware Company – Winterbloom makes open-source, boutique synthesizers. There’s a lot that goes into running a hardware company. Someone has to design the hardware, code the firmware, write the documentation, not to mention administrate the company. Winterbloom does all of this with just one engineer — Stargirl Flowers. Learn what tools and tech Stargirl uses to run her company, and how Python fits into the big picture in more ways than one.
Visualizing Data in Python Using plt.scatter() – In this tutorial, you’ll learn how to create scatter plots in Python, which are a key part of many data visualization applications. You’ll get an introduction to plt.scatter(), a versatile function in the Matplotlib module for creating scatter plots.
Why the Sad Face? – The Black autoformatter adopts some conventions that might surprise you the first time you use it. One of those conventions — the “sadface dedent” — moves closing parentheses in function signatures and other block headers to their own lines. This creates a line containing nothing but “):”, which looks like a sad face emoji. Łukasz Langa, Black’s creator, explains why Black does this.
How to Iterate Over DataFrame Rows (And Should You?) – How to iterate over pandas DataFrame rows is one of the top voted questions with the pandas tag on Stack Overflow. That question is also the most copied answer with a code block on the entire site. Clearly, lots of people want to iterate over the rows in a DataFrame. But should you do this, or are there better options?
Projects
- pyFLAC: Real-Time Lossless Audio Compression in Python
- pipx: Install and Run Python Applications in Isolated Environments
Additional Links:
- Django: The Web Framework for Perfectionists With Deadlines
- Vue.js: The Progressive JavaScript Framework
- GraphQL: A query language for your API
- GraphQL? Here is what you need to know! - Syntax FM Episode
- FastAI: Simplifies training fast and accurate neural nets using modern best practices
- Streamlit: The fastest way to build and share data apps
- Winterbloom: Magical Musical Machines
- Black: The uncompromising code formatter
- pyFLAC: Real-Time Lossless Audio Compression in Python
- pipx Is Now a PyPA Member Project
Support the podcast & join our community of Pythonistas
The podcast and artwork embedded on this page are from Real Python, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Real Python Podcast (LS 46 · TOP 1% what is this?)
Episode: Targeting WebAssembly Platforms & Distilling a Minimum Viable Python
Pub date: 2023-04-28
Are you familiar with the different versions of WebAssembly? Could WASM be the “write once, run everywhere” solution that developers have searched for? Where does distributing Python applications fit in the narrative? This week on the show, we have CPython core developer Brett Cannon to discuss his recent articles about WebAssembly and MVPy.
Brett has completed his syntactic sugar series, which we discussed in a previous episode. He details the origin of the series and his process of unearthing a minimum viable version of Python. Brett shares how he updated his PyCon US talk on the subject after feedback from presenting it at PyCascades.
We also dig deep into WebAssembly, specifically WebAssembly System Interface (WASI). Brett explains the concept of a “platform target triple” and the importance of defining which system CPython is compiled for. We also discuss WebAssembly becoming a ubiquitous distribution system.
Course Spotlight: Python Basics: Building Systems With Classes
In this video course, you’ll learn how to work with classes to build complex systems in Python. By composing classes, inheriting from other classes, and overriding class behavior, you’ll harness the power of object-oriented programming (OOP).
Topics:
- 00:00:00 – Introduction
- 00:02:05 – PyCascades 2023
- 00:02:56 – Using social media for polls and checking interest
- 00:06:02 – Completing the syntactic sugar blog series
- 00:15:29 – Minimum Viable Python (MVPy) and WebAssembly
- 00:19:29 – Other teams focusing on WebAssembly
- 00:21:31 – Sponsor: Courier
- 00:22:13 – Stack of technology
- 00:26:50 – WebAssembly and its platform targets
- 00:32:35 – WASI and connecting to a runtime
- 00:38:33 – Extension modules and dynamic libraries
- 00:47:29 – Overcoming road blocks and envisioning a new WASI assignment
- 00:51:51 – Video Course Spotlight
- 00:53:26 – PEP 11 & CPython platform support for WASI
- 01:03:11 – Machine-specific runtime
- 01:04:57 – Write once, run everywhere
- 01:13:14 – Talks and summits planned for PyCon 2023
- 01:18:00 – Thanks and goodbye
Show Links:
- MVPy: Minimum Viable Python
- Python’s Syntactic Sugar - PyCon US 2023
- Episode #47: Unraveling Python’s Syntax to Its Core With Brett Cannon – The Real Python Podcast
- syntactic sugar - Tall, Snarky Canadian
- Brett Cannon (@brettcannon@fosstodon.org) - Fosstodon
- WASI - wasi.dev
- WebAssembly and its platform targets
- Introducing the Disney+ Application Development Kit (ADK) - Mike Hanley
- Compute@Edge services using WebAssembly - Fastly Developer Hub
- Experimental - Python for the Web - Visual Studio Marketplace
- PEP 11 – CPython platform support - peps.python.org
- Testing a Python project using the WASI build of CPython with
pytest - The rise of WebAssembly - InfoWorld
- Can WASM become the new Docker?
- bytecodealliance/wasmtime: A fast and secure runtime for WebAssembly
- Emscripten - Dev Documentation
- PyScript - Run Python in your HTML
Level up your Python skills with our expert-led courses:
- Inheritance and Composition: A Python OOP Guide
- Python Basics: Object-Oriented Programming
- Python Basics: Building Systems With Classes
Support the podcast & join our community of Pythonistas
The podcast and artwork embedded on this page are from Real Python, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Real Python Podcast (LS 46 · TOP 1% what is this?)
Episode: Exploring the Zen of Python & pandas Features for Finance
Pub date: 2023-06-30
What advice can you extract from the Zen of Python? How can these nineteen guiding principles help you write more idiomatic Python? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.
Christopher shares a Real Python tutorial by Bartosz Zaczyński titled “What’s the Zen of Python?” We talk about the poem’s origin and ways to access the Zen within Python. We also discuss how different sections provide contradictory advice for what makes good Python code.
We cover a recent post by previous guest Matt Harrison about using Python and pandas for finance. Matt’s article covers methods in the pandas library for aggregation, resampling, and rolling averages.
We cover several other articles and projects from the Python community, including staying up-to-date with news, solving a Legend of Zelda puzzle with Python, avoiding “simply” providing advice, displaying better stack traces, and creating files with fake data.
Course Spotlight: Speed Up Python With Concurrency
Learn what concurrency means in Python and why you might want to use it. You’ll see a simple, non-concurrent approach and then look into why you’d want threading, asyncio, or multiprocessing.
Topics:
- 00:00:00 – Introduction
- 00:01:49 – Python 3.11.4, 3.10.12, 3.9.17, 3.8.17, 3.7.17, and Python 3.12.0 beta 3 released
- 00:02:24 – Enforcement of 2FA for PyPI Began June 1st
- 00:02:54 – Faster Python 3.13 Plan
- 00:03:14 – PyPy v7.3.12 Released
- 00:03:28 – Migrate to
.readthedocs.yamlConfiguration - 00:05:14 – PyCon US 2023 and PyCascades 2023 Videos Are Up
- 00:05:37 – What’s the Zen of Python?
- 00:09:22 – Python for Finance: Pandas Resample, Groupby, and Rolling
- 00:15:24 – Python and the Legend of Zelda
- 00:17:47 – Don’t Say “Simply” Use Pyenv, Poetry or Anaconda
- 00:28:54 – Is Parallel Programming Hard?
- 00:40:54 – Video Course Spotlight
- 00:42:20 – pymg: A Better Display for Stack Traces
- 00:44:58 – faker-file: Create Files With Fake Data
- 00:49:14 – Thanks and goodbye
News:
- Python 3.11.4, 3.10.12, 3.9.17, 3.8.17, 3.7.17, and 3.12.0 Beta 2 Released
- Python Insider: Python 3.12.0 beta 3 released
- Enforcement of 2FA for PyPI Began June 1st – For those accounts that have two-factor authentication turned on for PyPI uploads, the use of 2FA is now required. Users with 2FA who were only using a password in the past will now have to perform 2FA as well. This is all part of the PyPI transition to 2FA across the board.
- Faster Python 3.13 Plan – This brief outline highlights the plan for the faster CPython project for the 3.13 release. It includes PEP 669, PEP 554, improved memory management, and more. Here’s the associated Hacker News discussion.
- PyPy v7.3.12 Released
- Migrate to
.readthedocs.yamlConfiguration – The Read the Docs site has announced the new requirement that all builds must move to using a.readthedocs.yamlconfiguration file, version 2. There are some test windows where they’ll be temporarily enforcing the change, but the final release date is September 25, 2023. Read on for details on how to migrate your project. - PyCascades 2023 Videos Are Up
- PyCon US 2023 Videos Are Up
Topic Links:
- What’s the Zen of Python? – In this tutorial, you’ll be exploring the Zen of Python, a collection of nineteen guiding principles for writing idiomatic Python. You’ll find out how they originated and whether you should follow them. Along the way, you’ll uncover several inside jokes associated with this humorous poem.
- Python for Finance: Pandas Resample, Groupby, and Rolling – When working with time series data such as financial information, the resample, grouping, and rolling features of pandas can make your life easier.
- Python and the Legend of Zelda – The Game Boy Color version of Legend of Zelda: Oracle of Ages contains a grid-based puzzle. Gaz writes about creating a brute-force program to solve the challenge using Python.
- Don’t Say “Simply” Use Pyenv, Poetry or Anaconda – This article talks about the issues that newer Python coders might encounter by adopting more complicated package management mechanisms and explains why sticking with
pipis often the better choice.
Discussion:
- Is Parallel Programming Hard?
- AsyncIO: Why I Hate It – Charles is the creator of Peewee ORM and often gets the question when will it support
asyncio? In this opinion piece, he talks about why he doesn’t likeasyncioand which alternatives he prefers.
Projects:
- pymg: A Better Display for Stack Traces
- faker-file: Create Files With Fake Data
Additional Links:
- Practicality Beats Purity: The Zen of Python’s Escape Hatch - Chris Neugebauer - YouTube
- Relieving your Python packaging pain
- Is Parallel Programming Hard, And, If So, What Can You Do About It? - Book Download
Level up your Python skills with our expert-led courses:
- Hands-On Python 3 Concurrency With the asyncio Module
- Threading in Python
- Speed Up Python With Concurrency
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Podcast: The Real Python Podcast (LS 46 · TOP 1% what is this?)
Episode: Constructing Python Library APIs & Tackling Jinja Templating
Pub date: 2023-07-14
What principles should you consider when designing a Python library? How do you construct a library API that’s understandable and easy to use? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.
We share an article about building library APIs. The piece provides advice for package structure, naming, error handling, and more. The author guides you toward Pythonic principles by comparing clunky vs elegant design examples.
Christopher discusses his recent video course on Jinja templating. The course covers creating text files with programmatic content and employing rich templates to structure the front end of Python web applications.
We cover several other articles and projects from the Python community, including several news updates, reasons why membership tests are fast for the range() function, CLI tools hidden in the Python standard library, a thread about the right way to install Python, recipes for using the Polars library, and a project for feature flags within Django.
This week’s episode is brought to you by Snyk.
Course Spotlight: Jinja Templating
With Jinja, you can build rich templates that power the front end of your web applications. But you can use Jinja without a web framework running in the background. Anytime you want to create text files with programmatic content, Jinja can help you out.
Topics:
- 00:00:00 – Introduction
- 00:02:16 – PyLadies Conference (Dec 2023) Call for Volunteers
- 00:02:32 – PSF Board Election Results
- 00:03:47 – PSF Announces New Security Developer in Residence
- 00:04:39 – Django Security Releases Issued
- 00:04:50 – Deprecation of
bdist_eggUploads to PyPI - 00:05:54 – Why Are Membership Tests So Fast for
range()in Python? - 00:11:51 – CLI Tools Hidden in the Python Standard Library
- 00:15:59 – Sponsor: Snyk
- 00:16:49 – Designing Pythonic Library APIs
- 00:28:27 – Jinja Templating
- 00:32:49 – Kill a Developer in 4 Words or Less
- 00:37:28 – Video Course Spotlight
- 00:38:51 – What is “the right way” to install Python on a new M2 MacBook?
- 00:44:11 – polars-cookbook: Recipes for Using Python’s Polars Library
- 00:46:48 – waffle: Feature Flags for Django
- 00:49:54 – Thanks and goodbye
News:
- PyLadies Conference (Dec 2023) Call for Volunteers
- PSF Board Election Results
- PSF Announces New Security Developer in Residence
- I Am the First PSF Security Developer-in-Residence – Seth was recently hired as the first security developer in residence at the PSF. His blog post talks about what his responsibilities are and how he defines success for the position.
- Deputy CPython Developer in Residence - Python Software Foundation - Career Page
- Django Security Releases Issued: 4.2.3, 4.1.10, and 3.2.20
- Deprecation of
bdist_eggUploads to PyPI – PEP 715 has been accepted and as of August 1, 2023, the.eggformat will no longer be accepted as an upload. Existing eggs on PyPI will remain in place.
Show Links:
- Why Are Membership Tests So Fast for
range()in Python? – In Python,range()is most commonly used inforloops. However, ranges have some other use cases too, as they share many properties with lists. In this tutorial, you’ll explore why it’s so fast to perform a membership test on a Python range. - CLI Tools Hidden in the Python Standard Library – There are several modules in Python that are directly callable from the command line, including the ability to gzip and pretty-print JSON. This article introduces you to what options are available and how Simon discovered them.
- Designing Pythonic Library APIs – This article summarizes principles that Ben has found useful when designing Python library APIs. Topics include structure, naming, error handling, and type annotations.
- Jinja Templating – With Jinja, you can build rich templates that power the front end of your web applications. But you can use Jinja without a web framework running in the background. Anytime you want to create text files with programmatic content, Jinja can help you out.
Discussion:
- Kill a Developer in 4 Words or Less - Twitter
- What is “the right way” to install Python on a new M2 MacBook? - Twitter
Projects:
- polars-cookbook: Recipes for Using Python’s Polars Library
- waffle: Feature Flags for Django
Additional Links:
- PSF Board of Directors Nominees - 2023 - YouTube
- pandas-cookbook: Recipes for using Python’s pandas library
Level up your Python skills with our expert-led courses:
- Get Started With Django: Build a Portfolio App
- Deploy Your Python Script on the Web With Flask
- Jinja Templating
Support the podcast & join our community of Pythonistas
The podcast and artwork embedded on this page are from Real Python, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Real Python Podcast (LS 46 · TOP 1% what is this?)
Episode: Leveraging the Features of Your Database With Postgres and Python
Pub date: 2023-07-21
Are you getting the most out of your Postgres database? What features could you leverage to improve your Python project? This week on the show, Craig Kerstiens from Crunchy Data is here to discuss getting the most out of Postgres.
Craig shares his years of PostgreSQL expertise with advice on getting more from the platform. We talk about rich data types for describing ranges, geospatial data, and JSON.
Craig shares tools for accessing performance statistics from the command line and strategies for optimizing your terminal settings for SQL searches. He discusses Postgres extensions for customizing the database to your needs. Craig also provides multiple resources for learning more and an online tool for practicing within a playground environment.
Course Spotlight: Creating Web Maps From Your Data With Python Folium
You’ll learn how to create web maps from data using Folium. The package combines Python’s data-wrangling strengths with the data-visualization power of the JavaScript library Leaflet. In this video course, you’ll create and style a choropleth world map showing the ecological footprint per country.
Topics:
- 00:00:00 – Introduction
- 00:02:36 – What are reasons for considering Postgres?
- 00:07:41 – Timeline of recent features
- 00:11:06 – Unique data types
- 00:16:34 – Storing JSON information
- 00:20:19 – Video Course Spotlight
- 00:21:50 – Storing geographic information
- 00:25:16 – What’s the process for adding extensions?
- 00:31:33 – Advice for Python developers using Postgres
- 00:33:31 – Advice on writing SQL
- 00:38:06 – Command-line tools and customizations
- 00:48:18 – Django as an entry to Python
- 00:51:13 – Resources for learning and practicing with Postgres
- 00:53:45 – What are you excited about in the world of Python?
- 00:55:55 – What do you want to learn next?
- 00:58:34 – How can people follow your work online?
- 00:59:20 – Thanks and goodbye
Show Links:
- Craig Kerstiens - Blog
- Trusted Open Source PostgreSQL & Commercial Support for the Enterprise - Crunchy Data
- Why Postgres? - Crunchy Data
- Why Postgres - Craig Kerstiens - YouTube
- A hands on experience with complex SQL - Craig Kerstiens - YouTube
- Ingres (database) - Wikipedia
- PostgreSQL specific model fields - Django documentation
- Psqlrc - PostgreSQL wiki
- The most useful Postgres extension - pg_stat_statements
- High-compression Metrics Storage with Postgres Hyperloglog
- Postgres Playground and Tutorials - Crunchy Data
- PostgreSQL Blog - Crunchy Data
- It’s at least once a week I talk with someone that “loves Postgres” but isn’t sure why… Craig Kerstiens on Twitter:
- The Wok Book: Recipes and Techniques by J. Kenji Lopez-Alt
Level up your Python skills with our expert-led courses:
- Deploying a Flask Application Using Heroku
- SQLite and SQLAlchemy in Python: Moving Your Data Beyond Flat Files
- Creating Web Maps From Your Data With Python Folium
Support the podcast & join our community of Pythonistas
The podcast and artwork embedded on this page are from Real Python, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Real Python Podcast (LS 46 · TOP 1% what is this?)
Episode: Differentiating the Versions of Python & Unlocking IPython's Magic
Pub date: 2023-07-28
What are all the different versions of Python? You may have heard of Cython, Brython, PyPy, or others and wondered where they fit into the Python landscape. This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.
Christopher shares an article from the Bite Code blog about all the different forms that Python can take. CPython is the reference implementation of the language, which is what we usually discuss. He lists several alternative projects and the use cases.
We also discuss a recent Real Python tutorial about IPython. IPython is an interactive Python shell from the team that developed Jupyter Notebooks. It includes a set of IDE-like features and unique magic commands. The tutorial digs into using the tool to learn more about Python and explore your code.
We cover several other articles and projects from the Python community, including several news updates, the state of WASI support for CPython, how Python uses garbage collection, a discussion about the current AI echo chamber, an async Python web microframework, a stand-alone CSV editor, and a project for identifying unused dependencies to avoid a bloated virtual environment.
This week’s episode is brought to you by Scout APM.
Course Spotlight: Mazes in Python Part 1: Building and Visualizing
In this two-part video course project, you’ll build a maze solver in Python using graph algorithms from the NetworkX library. Along the way, you’ll design a binary file format for the maze, represent it in an object-oriented way, and visualize the solution using scalable vector graphics (SVG).
Topics:
- 00:00:00 – Introduction
- 00:02:19 – Python 3.12.0 Beta 4 Released
- 00:02:53 – Django in Action - Christopher’s Book
- 00:04:28 – State of WASI Support for CPython: June 2023
- 00:08:32 – What’s the Deal With CPython, PyPy, MicroPython, Jython…?
- 00:12:10 – Sponsor: Scout APM
- 00:12:57 – Unlock IPython’s Magical Toolbox for Your Coding Journey
- 00:18:33 – How Python Uses Garbage Collection
- 00:21:31 – Video Course Spotlight
- 00:23:04 – Are People in Tech Inside an AI Echo Chamber?
- 00:39:39 – quart: An Async Python Web Microframework
- 00:41:33 – Modern CSV: CSV Editor/Viewer
- 00:43:09 – creosote: Identify Unused Dependencies
- 00:45:09 – Thanks and Goodbye
News:
- Python 3.12.0 Beta 4 Released
- Django in Action - Christopher’s Book - Manning Early Access Program
Show Links:
- State of WASI Support for CPython: June 2023 – This post from Brett Cannon covers the current state of WebAssembly targets in Python.
- What’s the Deal With CPython, PyPy, MicroPython, Jython…? – This comprehensive article introduces you to all the different ways that you can Python. CPython isn’t the only choice. Learn what else is out there and why you might choose an alternative.
- Unlock IPython’s Magical Toolbox for Your Coding Journey – IPython is a powerful tool that can prove useful on your journey to mastering Python. Its friendly interface will enable you to comfortably take control of your learning. In this tutorial, you’ll cover the basic concepts of using IPython and learn how its features can make coding efficient.
- How Python Uses Garbage Collection – This article outlines how Python stores variables as references and how that relates to memory management
Discussion:
- Are People in Tech Inside an AI Echo Chamber?
- Inside the AI Factory: the humans that make tech seem human - The Verge
- Is ChatGPT getting worse over time? Study claims yes, but others aren’t sure | Ars Technica
- How Is ChatGPT’s Behavior Changing over Time?
Projects:
- quart: An Async Python Web Microframework
- Modern CSV: CSV Editor/Viewer
- creosote: Identify unused dependencies and avoid a bloated virtual environment
Additional Links:
- Episode #154: Targeting WebAssembly Platforms & Distilling a Minimum Viable Python – The Real Python Podcast
- IPython 8.14.0 documentation
Level up your Python skills with our expert-led courses:
- Python Basics: Object-Oriented Programming
- Mazes in Python Part 1: Building and Visualizing
- Mazes in Python Part 2: Storing and Solving
Support the podcast & join our community of Pythonistas
The podcast and artwork embedded on this page are from Real Python, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Syntax - Tasty Web Development Treats (LS 61 · TOP 0.1% what is this?)
Episode: Is JSDoc Better than TypeScript?
Pub date: 2023-06-07
In this episode of Syntax, Wes and Scott use the fact that Svelte is being converted from TypeScript to JavaScript with JSDoc to talk about the benefits of working that way, why they are doing it, and what you can do with JSDoc that TypeScript alone doesn’t let you do.
Show Notes * 00:10 Welcome * 01:12 Roof update * 02:15 TypeScript haters need not apply * 03:17 What is JSDoc? * 04:27 What is our history with JSDoc? * 06:37 Why is Svelte moving to JSDoc? * 08:11 Why is JSDoc better than TypeScript? * 12:31 You can type things you can’t in TypeScript * 16:37 Param, Function and returns Descriptions * 21:32 Spoiler - it’s still TypeScript * 33:23 SIIIIICK ××× PIIIICKS ××× * Get Started With TypeScript the Easy Way * TypeScript without TypeScript – JSDoc superpowers * TypeScript: Documentation - JSDoc Reference * Dev Vlog: April 2023 - TypeScript vs JSDoc, Transitions API, Dominic Gannaway joins Svelte team * Svelte repo is finally being converted from Typescript to Javascript with JSDoc * If you are on a JS project and are missing the TypeScript hinting in your editor, you can still type your code with JSDoc syntax comments and VS Code will detect and use it! * Sprinkle in a little JSDoc on top of your TypeScript when needed - helpful to adding descriptions to returned values, or marking things as deprecated * TypeScript to JSDoc
××× SIIIIICK ××× PIIIICKS ××× * Scott: Watch MerPeople | Netflix Official Site * Wes: 18V ONE+ 45W HYBRID SOLDERING STATION (TOOL ONLY) | RYOBI Tools
Shameless Plugs * Scott: Sentry * Wes: Wes Bos Tutorials
Tweet us your tasty treats * Scott’s Instagram * LevelUpTutorials Instagram * Wes’ Instagram * Wes’ Twitter * Wes’ Facebook * Scott’s Twitter * Make sure to include @SyntaxFM in your tweets * Wes Bos on Bluesky * Scott on Bluesky * Syntax on Bluesky
The podcast and artwork embedded on this page are from Wes Bos & Scott Tolinski - Full Stack JavaScript Web Developers, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Syntax - Tasty Web Development Treats (LS 61 · TOP 0.1% what is this?)
Episode: WTF is an ORM
Pub date: 2023-06-28
In this episode of Syntax, Wes and Scott talk about the benefits and potential drawbacks of using an ORM on your next project, as well as what some of the popular ORMs are.
Show Notes * 00:10 Welcome * 00:39 Dental cleanings * 03:00 What’s an ORM? * 05:51 Benefits of using an ORM * 12:54 Validation in ORM * 19:18 What about Types? * 23:44 Popular ORMs * Prisma * Sequelize * Objection.js * Knex.js * DrizzleORM - next gen TypeScript ORM * Mongoose ODM v7.3.1 * TypeORM * waterline.js * 42:41 Potential downsides to using an ORM * 45:53 Database schemas * 52:30 Hooks or events * 55:27 SIIIIICK ××× PIIIICKS ×××
××× SIIIIICK ××× PIIIICKS ××× * Scott: I Think You Should Leave with Tim Robinson * Wes: Wise, Formerly TransferWise: Online Money Transfers
Shameless Plugs * Scott: Sentry * Wes: Wes Bos Tutorials
Tweet us your tasty treats * Scott’s Instagram * LevelUpTutorials Instagram * Wes’ Instagram * Wes’ Twitter * Wes’ Facebook * Scott’s Twitter * Make sure to include @SyntaxFM in your tweets * Wes Bos on Bluesky * Scott on Bluesky * Syntax on Bluesky
The podcast and artwork embedded on this page are from Wes Bos & Scott Tolinski - Full Stack JavaScript Web Developers, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Syntax - Tasty Web Development Treats (LS 61 · TOP 0.1% what is this?)
Episode: User Feedback UI - Toasts, Flash, Validation
Pub date: 2023-07-24
In this Hasty Treat, Scott and Wes talk about toast messages for validation, errors, confirmations, and more.
Show Notes * 00:24 Welcome * 01:04 Why use these types of notifications * 03:34 Old school checking stories * 05:35 What kinds of toast messages are there? * 10:02 Why toast? * 11:38 Best practices for toast messages * 17:09 Timeouts and manual close auto close * 19:38 Multiple messages stacking on top of each other * 22:56 Using a toast library * Building a toast component * react-hot-toast - The Best React Notifications in Town * Real-time notification system for products | MagicBell * 28:29 Form validation * Form validation with HTML5 and JavaScript * 33:36 HTML inputs
Tweet us your tasty treats * Scott’s Instagram * LevelUpTutorials Instagram * Wes’ Instagram * Wes’ Twitter * Wes’ Facebook * Scott’s Twitter * Make sure to include @SyntaxFM in your tweets * Wes Bos on Bluesky * Scott on Bluesky * Syntax on Bluesky
The podcast and artwork embedded on this page are from Wes Bos & Scott Tolinski - Full Stack JavaScript Web Developers, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Syntax - Tasty Web Development Treats (LS 61 · TOP 0.1% what is this?)
Episode: Polyfills, Transpiling and Monkey Patching
Pub date: 2023-07-26
In this episode of Syntax, Wes and Scott talk about what polyfills, transpiling, and monkey patching mean, how and when to do it, and libraries that can help you out.
Show Notes * 00:10 Welcome * 01:07 Toast follow up * 02:45 What are transpiling, ponyfill, polyfill, and monkey patching * TC39 Proposals * Pretty excited about the new JavaScript non-mutating array methods. Currently in stage 3 * 11:18 Transpiling unsupported CSS * 15:11 Polyfills * Popover polyfill * 19:22 Polyfilling CSS * 21:06 HTML polyfills * 27:47 How to transpile and polyfill * Babel * TypeScript: JavaScript With Syntax For Types * CoffeeScript * Civet * cronn/jsxtransformer: Pipeline for transforming JSX files using Babel.js and Uglify.js * Svelte • Cybernetically enhanced web apps * Polyfill.io * core-js - npm * 35:46 Shiv and shims * Shim vs Shiv * 38:16 Monkey patching * 49:08 SIIIIICK ××× PIIIICKS ×××
××× SIIIIICK ××× PIIIICKS ××× * Scott: Cable sleeve * Wes: Air Purifier * AliExpress
Shameless Plugs * Scott: Sentry * Wes: Wes Bos Tutorials
Tweet us your tasty treats * Scott’s Instagram * LevelUpTutorials Instagram * Wes’ Instagram * Wes’ Twitter * Wes’ Facebook * Scott’s Twitter * Make sure to include @SyntaxFM in your tweets * Wes Bos on Bluesky * Scott on Bluesky * Syntax on Bluesky
The podcast and artwork embedded on this page are from Wes Bos & Scott Tolinski - Full Stack JavaScript Web Developers, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Syntax - Tasty Web Development Treats (LS 61 · TOP 0.1% what is this?)
Episode: Potluck × Is TypeScript Fancy Duct Tape × Back Pain × Cloud Service Rate Limits
Pub date: 2023-08-09
In this potluck episode of Syntax, Wes and Scott answer your questions about TypeScript just being fancy duct tape, dealing with back pain while coding, rate limits on cloud services, what to use for email provider, is Firebase a legit platform, and more!
Show Notes * 00:11 Welcome * 03:11 The Sunday scaries * 06:03 Is TypeSctipt just a bunch of fancy Duck Tape? * Is TypeScript saving us? * 12:29 How do you go years into programming without back pain? * Hasty Treat - Stretching For Developers with Scott — Syntax Podcast 293 * 23:51 Why don’t cloud services provide an option to shut off services when a spending limit is reached? * DigitalOcean | Cloud Hosting for Builders * Vercel: Develop. Preview. Ship. For the best frontend teams * 28:41 How do you choose a CSS library for any project? * The most advanced responsive front-end framework in the world. | Foundation * 960 Grid System * 38:26 What’s happening to Level Up Tuts? * Level Up Tutorials - Learn modern web development * Wheels - Skateboard Wheels - 60mm Cali Roll - Shark Wheel * 43:43 Not a sponsored Yeti spot * 45:16 What do you do for email hosting? * Google Workspace * TechSoup Canada * Proton Mail: Get a private, secure, and encrypted email account * Outlook * Microsoft 365 Plans * Scheduling Software Everyone Will Love · SavvyCal * Synology Photos * 50:34 Is Firebase ok to run an app long term with? * Firebase * 58:57 Am I wrong to not do productive work intensely? * 01:34 SIIIIICK ××× PIIIICKS ×××
××× SIIIIICK ××× PIIIICKS ××× * Scott: MagSafe Charger, Anker 3-in-1 Cube with MagSafe * Wes: 6amLifestyle Headphone Hanger Stand Under Desk
Shameless Plugs * Scott: Sentry * Wes: Wes Bos Tutorials
Tweet us your tasty treats * Scott’s Instagram * LevelUpTutorials Instagram * Wes’ Instagram * Wes’ Twitter * Wes’ Facebook * Scott’s Twitter * Make sure to include @SyntaxFM in your tweets * Wes Bos on Bluesky * Scott on Bluesky * Syntax on Bluesky
The podcast and artwork embedded on this page are from Wes Bos & Scott Tolinski - Full Stack JavaScript Web Developers, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Rest Is History (LS 71 · TOP 0.05% what is this?)
Episode: 354. Paris 1968: The Return of De Gaulle
Pub date: 2023-07-27
Charles de Gaulle was a war hero in the First World War, and, having refused to accept his government’s armistice with Nazi Germany, became the voice of the French Resistance during the Second World War. But how did France’s largest uprising since the Paris Commune come to happen during his presidency? Join Tom and Dominic in the second part of our tour of Paris, as they look at de Gaulle’s role in the events of May 1968, and how he eventually overcame the protests.
Read more about Tom and Dominic's trip to Paris, in partnership with Wise: https://wise.com/campaign/restishistory
The Rest Is History Live Tour 2023:
Tom and Dominic are back on tour this autumn! See them live in London, New Zealand, and Australia!
Buy your tickets here: restishistorypod.com
Twitter:
@TheRestHistory
@holland_tom
@dcsandbrook
Learn more about your ad choices. Visit podcastchoices.com/adchoices
The podcast and artwork embedded on this page are from Goalhanger Podcasts, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Cracks Podcast con Oso Trava (LS 57 · TOP 0.5% what is this?)
Episode: #233. Daniel Marcos - Cómo Vender tu Empresa, Comprar Amor con Dinero, Sectas y la Segunda Mitad de tu Vida
Pub date: 2023-06-26
Daniel Marcos @daniel.marcos.escalar es CEO y co-fundador de Growth Institute, empresa líder en capacitación en línea para ejecutivos de empresas en crecimiento. Daniel es un conferencista internacional y coach de negocios con la misión de ayudar a 1 millón de emprendedores a escalar sus compañías más rápido y con menos drama en su operación.
Por favor ayúdame y sigue Cracks Podcast en YouTube aquí.
"Tienes una vida, no puedes vivir la vida de alguien más. Haz lo que te gusta a ti." - Daniel Marcos - @capitalemprende
Comparte esta frase en Twitter
Este episodio es presentado por Julius Baer, el grupo suizo líder en Wealth Management.Este es el segundo episodio que grabó con Daniel y es que en el último año he podido conocerlo más a fondo y me quedó muy claro que había muchísimo más que platicar.
Hoy Daniel y yo hablamos de cómo vender tu empresa, de cómo hacerla atractiva para compradores, de la mentalidad que tiene que desarrollar el empresario para ese evento y de los errores más comunes que se dan al momento de una transacción.
Completa el test para crear el plan de venta de tu empresa
Este episodio es la biblia para cualquiera que esté pensando en una transición de vida o simplemente quiere materializar el valor generado por su empresa a través de los años. Qué puedes aprender hoy
- Cómo sacar el máximo valor de tu empresa
- Qué es el Freedom Point
- Cuándo es el momento perfecto para vender tu empresa
- Cómo recuperar la relación con tus hijos
Este episodio es presentado por por Julius Baer, **el grupo suizo líder en Wealth Management con presencia en América Latina.
Tomar el control de la empresa familiar es una decisión importante en la vida de cualquier persona.
En Julius Baer entienden las complejidades de la sucesión y apoyan a sus clientes y sus familias en el desarrollo de una solución que ayude a garantizar que la empresa siga teniendo éxito por muchas generaciones.
Para conectar con los expertos de Julius Baer y discutir cómo pueden ayudarte a navegar el proceso de sucesión en tu empresa, visita el sitio www.juliusbaer.com**
Youtube: https://www.youtube.com/crackspodcast
Notas del episodio en:**https://cracks.la/233
The podcast and artwork embedded on this page are from Oso Trava, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Farthouse (LS 40 · TOP 1.5% what is this?)
Episode: The Night Porter by Liliana Cavani
Pub date: 2021-08-17
The “Cinephile Cuties” are ready to work the night shift at a hotel. That’s because they’re chatting about Liliana Cavani’s The Night Porter. Patrick and Casey try to figure out the best ways to murder each other. Plus, they put this film through their proprietary Fartsy Test. And Patrick recommends a drink pairing.
If you like this show, tell a friend!
Follow Farthouse on Twitter and Instagram
Follow Patrick and Casey and on Twitter
And follow Patrick and Casey on Letterboxd
The podcast and artwork embedded on this page are from Patrick Mallon, Casey O'Brien, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Projection Booth Podcast (LS 56 · TOP 0.5% what is this?)
Episode: Episode 586: The Night Porter (1974)
Pub date: 2022-09-07
We’re looking at Liliana Cavani's The Night Porter (1974). Set in 1957 Vienna, Max Aldorfer (Dirk Bogard) is the titular night porter. When a conductor and his wife, Lucia (Charlotte Rampling) check in to Max's motel, he is thrown into a tizzy. He’s been living a quiet life and trying to stay under the radar as he’s a war criminal who has just been confronted with one of his victims... who was also his sex slave.
Professor Gaetana Marrone (author of The Gaze and the Labyrinth: The Cinema of Liliana Cavani) joins us to discuss Cavani's work while Emma Westwood and Kat Ellinger discuss the film.
This show is part of the Spreaker Prime Network, if you are interested in advertising on this podcast, contact us at https://www.spreaker.com/show/5513239/advertisement
The podcast and artwork embedded on this page are from Weirding Way Media, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Music Business Worldwide (LS 35 · TOP 3% what is this?)
Episode: Will Page on streaming pricing, music's revenue 'pie' – and why the global record industry is more local than ever
Pub date: 2023-07-04
Welcome to the Music Business Worldwide podcast supported by Voly Music**.
On this podcast, one of the industry's sharpest minds, Will Page, joins MBW founder Tim Ingham to cover a range of topics including pricing, streaming, royalties – and why the global industry is more local than ever.**
Page **is the ex-Chief Economist of both Spotify and UK collection society PRS For Music.
These days he’s a consultant and the author of the book Tarzan Economics (aka Pivot), which presents compelling principles for business owners facing uncertain and disruptive times.**
Will is also the co-author of a new paper published by the London School of Economics and Political Science that focuses on what he calls ‘Glocalisation’ of music. In other words, music has never been more global as an industry, yet when you dig into the most popular tracks in individual markets, they have a decidedly local feel.
The Music Business Worldwide Podcast is supported by Voly Music.
The podcast and artwork embedded on this page are from Music Business Worldwide, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Off Menu with Ed Gamble and James Acaster (LS 77 · TOP 0.01% what is this?)
Episode: Ep 176: Paul Mescal
Pub date: 2023-01-25
The Dream Restaurant reopens its doors for series 9, and our first guest is Oscar-nominated and BAFTA-winning actor and star of 'Normal People', Paul Mescal.
Paul Mescal stars in 'Aftersun' which is available to stream now on Mubi.
Recorded and edited by Ben Williams for Plosive.
Artwork by Paul Gilbey (photography and design) and Amy Browne (illustrations).
Follow Off Menu on Twitter and Instagram: @offmenuofficial.
And go to our website www.offmenupodcast.co.uk for a list of restaurants recommended on the show.
Watch Ed and James's YouTube series 'Just Puddings'. Watch here.
Hosted on Acast. See acast.com/privacy for more information.
The podcast and artwork embedded on this page are from Plosive, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Off Menu with Ed Gamble and James Acaster (LS 77 · TOP 0.01% what is this?)
Episode: Ep 194: Tim Minchin
Pub date: 2023-05-31
Sometimes you have to be a little bit naughty. Arena-filling comedian, Matilda maestro and co-writer of Groundhog Day the musical, Tim Minchin joins us in the Dream Restaurant.
Groundhog Day is playing at the Old Vic Theatre in London until 19 August. Buy tickets here.
Follow Tim on Twitter and Instagram @timminchin
Recorded and edited by Ben Williams for Plosive.
Artwork by Paul Gilbey (photography and design) and Amy Browne (illustrations).
Follow Off Menu on Twitter and Instagram: @offmenuofficial.
And go to our website www.offmenupodcast.co.uk for a list of restaurants recommended on the show.
Watch Ed and James's YouTube series 'Just Puddings'. Watch here.
Hosted on Acast. See acast.com/privacy for more information.
The podcast and artwork embedded on this page are from Plosive, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Odd Lots (LS 60 · TOP 0.1% what is this?)
Episode: Understanding the Real Fight Over Water in Arizona
Pub date: 2023-07-14
Arizona recently announced new constraints on housing development in the areas around Phoenix. At issue is water rights and scarcity, which have been a challenge for the US Southwest for as long as people have been living there. That being said, the region is currently in the midst of a 25-year megadrought and when you combine that with booming growth, difficult choices may have to be made. But how do water rights get divided? Who holds them? How much is water worth to the housing developers, farmers and semiconductor manufacturers that have flocked to the state? To learn more, we speak with Kathryn Sorensen, director of research at the Kyl Center for Water Policy at the Morrison Institute for Public Policy at Arizona State University. We discuss both current and past water management practices in the state.
See omnystudio.com/listener for privacy information.
The podcast and artwork embedded on this page are from Bloomberg, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Odd Lots (LS 60 · TOP 0.1% what is this?)
Episode: Josh Wolfe on Where Investors Will Make Money in AI
Pub date: 2023-07-17
We're in the midst of an AI mania of sorts. In public markets, investors are placing bets on the companies perceived as being the winners of this new wave of computing. Companies that aren't even in "tech" are touting their AI bonafides. And of course, in private markets, every venture capitalist suddenly seems to be pivoting to AI in some way or another. But who will actually win? Will it be the big incumbents? Can those incumbents be disrupted? Will it be the companies who have access to unique datasets? Or will it be whoever has the most computing power? On this episode, we speak with Josh Wolfe, co-founder of Lux Capital, who has been investing in the space for several years, long before it was trendy. He talks about where he's placing his bets and how he's thinking about identifying winners.
See omnystudio.com/listener for privacy information.
The podcast and artwork embedded on this page are from Bloomberg, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Notion Capital - enterprise tech startups (LS 28 · TOP 10% what is this?)
Episode: GTM13 - The Great Fintech Unbundling, with Parker Crockford, previous Growth Leader at Upvest & Onfido
Pub date: 2021-08-04
It’s fair to say that Parker Crockford quickly became a citizen of the world; travelling and gathering experiences that have helped shape his career. In this podcast, we explore how Fintech is developing and growing in the US and Europe, and what the breakout segments look like. As ever, regulation is always lagging but can’t be ignored in building a startup servicing these new markets. And then there’s crypto - can that ever be regulated? Talking via experience, Parker takes us through his thoughts on how to approach Fintech, the impact of Open Banking and the companies that are doing it well. A fascinating listen in these hyper growth times for Fintech.
Highlights
- Europe has stolen a march on B2C Fintech and created some giants
- Fintech is splintering but each splinter is a billion dollar opportunity
- Regulation is playing catch-up with market innovation
- Stripe found the perfect gap to fill
- Can the crypto explosion be regulated?
The podcast and artwork embedded on this page are from Notion Capital, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The NFX Podcast (LS 41 · TOP 1.5% what is this?)
Episode: Ep 10: Network Effects In Gaming (NFX Masterclass)
Pub date: 2022-12-14
We are in the first wave of web3 games, and some believe that games don't have network effects built into their development or long-term strategy. In this episode, listen to NFX partner Gigi Levy-Weiss as he dives into the world of defensibility within the gaming industry, sharing the 4 layers of network effects, the future of virtual goods, and a case study on Steam - the leading platform for video game development.
This is an audio version of episode 10 from The Network Effects Masterclass, curated for audio and listening on the go. For the full video experience, transcripts, and recommended reading, join the free Network Effects Masterclass at - NFX.com/masterclass.
The podcast and artwork embedded on this page are from NFX, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Cursed Objects (LS 32 · TOP 5% what is this?)
Episode: Jamie’s Cursed Cookin’ - ft. Jonathan Nunn and Biz
Pub date: 2023-07-10
Pukka! We’re dancing in the moonlight with the king of the English culinary world, Jamie Oliver. How did someone so cringe achieve such dizzying levels of fame and power? How did he end up being an unofficial advisor to Tonty Blair? Special guests Jonathan Nunn and Biz take us through Jamie’s Naked Chef years, the Downing Street years and the Jerk Rice years, via the extremely cursed Cookin’: Music To Cook By compilation CD. From New Labour and school dinners to the notorious Lamb Curry Song (complete with dodgy Jamaican accent), it’s a wild ride with the world’s most milquetoast indie soundtrack.
Why not join our Patreon? ONLY £4 A MONTH TO SUPPORT YOUR FAV CULTURAL HISTORIANS AND GET 20+ BONUS EPISODES AND A CURSED OBJECTS STICKER PACK!
Jonathan Nunn is a writer and co-founder of online food magazine Vittles. He edited the brilliant London Feeds Itself.
Biz is Director of Resonance FM, and has written for the New Statesman, New York Times, The Nation, and the Times Literary Supplement.
Theme music and production: Mr Beatnick
Artwork: Archie Bashford
The podcast and artwork embedded on this page are from cursedobjects, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Inside The Black Box (LS 47 · TOP 1% what is this?)
Episode: Episode 11 - Air France 4590 (The Concorde Disaster)
Pub date: 2020-02-09
Follow the podcast on Twitter HERE
On the 25th of July 2000 at 4:40pm, Air France Flight 4590 sits on the taxiway of Paris Charles De Gaulle Airport in Paris France. The passengers and crew of Flight 4590 are third in line to take off from Runway 26 Right. In front of them are two other intercontinental airliners. Taking off at this moment is a Continental Airlines McDonnell Douglas DC-10. Next in the sequence is an Air France Boeing 747. The 100 passengers and 9 crewmembers of Air France 4590 are not aboard any ordinary airliner. Today they will be flying aboard the Aerospatiale/BAC Concorde. In 2000, even 30 years after its introduction, Concorde is still seen by many as a symbol of luxury and excess. Flying at more than twice the speed of sound, the aircraft will be on the ground at its destination of John F Kennedy Airport in New York in about three and a half hours. In comparison, the DC-10 taking off in front of Air France 4590 and heading for Newark Airport, also in New York, will be less than halfway across the Atlantic when the Concorde is arriving at its gate. While the journey is quick, that speed comes at an enormous price. A return trip from London or Paris to New York costs just under $8,000 making regularly scheduled flights difficult to fill. To keep the operation of Concorde profitable, as well as operating scheduled flights, Air France also offers Concorde on a charter service. Air France 4590 is just such a charter flight. Today, this Concorde has been chartered by Peter Deilmann Cruises, a prestigious cruise operator. The passengers aboard Concorde are embarking on the first stage of a luxurious journey, travelling to New York where they will board the MS Deutschland for a 14 night cruise of the Caribbean. Apart from one Austrian, two Danes and an American, the remaining 96 passengers are German. With the three flight crew and six flight attendants, the total complement is 109.
All of those aboard have less than five minutes to live.
The podcast and artwork embedded on this page are from Black Box Podcast, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Song Exploder (LS 72 · TOP 0.05% what is this?)
Episode: Re-issue: Julien Baker - Appointments
Pub date: 2023-06-14
This week, I wanted to go back and revisit an older episode. I recorded an interview with singer and songwriter Julien Baker in 2018. It was a few months after her second album came out. Since then, she’s put out another solo album, called Little Oblivions, which was critically acclaimed. And now she’s also a member of boygenius, which is the supergroup made up of Julien, Phoebe Bridgers, and Lucy Dacus. They’ve put out an EP and then, earlier this year, they put out their first full-length. Julien is a really interesting artist, and I think her songwriting is just heartbreaking, especially the song she takes apart for her episode. Here it is:
Julien Baker is from Memphis, Tennessee. She released her second album, Turn Out The Lights, in October 2017, on Matador Records. The New York Times called her music "devastating" and Pitchfork gave the album Best New Music. In this episode, Julien tells the story of her song "Appointments," and how writing it helped her work through her thoughts around addiction, depression, and relationships. Julien also takes apart the track “Over,” which was written as part of “Appointments,” but then split off as a separate track.
For more, visit songexploder.net/julien-baker.
The podcast and artwork embedded on this page are from Hrishikesh Hirway, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Song Exploder (LS 72 · TOP 0.05% what is this?)
Episode: Re-issue: Mobb Deep - Shook Ones, Pt. II
Pub date: 2023-04-19
It’s usually hard to pin down when a genre of music starts. But people point to this one party in August 1973 in the Bronx as the moment where hip-hop was born. That makes this year the 50th anniversary of hip-hop. In honor of that, I wanted to revisit an episode looking back at one of hip-hop’s classic tracks: Shook Ones, Part II, by Mobb Deep. Here’s the episode, originally recorded in June, 2020, when I spoke to Havoc from Mobb Deep:
The rappers Prodigy and Havoc met when they were still in high school in New York. Havoc grew up in Queensbridge, the biggest public housing projects in the country, and as a teenager, Prodigy lived there for a while, too. The two of them formed Mobb Deep in 1991.
In 1995, they put out their second album, The Infamous. It was a success when it came out, but in the 25 years since then, the influence of the album has only grown. Complex named it one of the 10 best rap albums of the 90s, and Pitchfork gave the album a rare perfect score, 10 out of 10. The Washington Post called it a “masterpiece” of hardcore rap, and in Slate, it was called one of the best albums of the ‘90s, and one of the best hip-hop albums ever made.
Their biggest song from the album was “Shook Ones, Pt. II.” Havoc made the now-legendary beat that he and Prodigy rap over. To celebrate the 25th anniversary, Havoc told me the story of how the whole song came together. Prodigy passed away in 2017, from complications due to sickle-cell anemia, a debilitating disease he’d battled his entire life. But the legacy of Mobb Deep lives on.
For more, visit songexploder.net/mobb-deep.
The podcast and artwork embedded on this page are from Hrishikesh Hirway, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Lenny's Podcast: Product | Growth | Career (LS 55 · TOP 0.5% what is this?)
Episode: Building minimum lovable products, stories from WeWork and Airbnb, and thriving as a PM | Jiaona Zhang (Webflow, WeWork, Airbnb, Dropbox)
Pub date: 2023-07-02
Brought to you by Brave—An independent, global search index you can use to power your search or AI app | Miro—A collaborative visual platform where your best work comes to life | Superhuman—The fastest email experience ever made
—
Jiaona Zhang (JZ) is a product leader with a strong background in consumer products and extensive hiring and management experience. She is currently Senior VP of Product at Webflow as well as a lecturer at Stanford, where she teaches a graduate-level course on product management. Before Webflow, JZ was Head of Product for the Homes Platform at Airbnb and has also led product teams at Airbnb, WeWork, and Dropbox. In today’s episode, we discuss:
• Building a “minimum lovable product” rather than a minimum viable product
• How to create better roadmaps through storytelling
• Top lessons from Dropbox, Airbnb, WeWork, and Webflow
• The importance of setting ambitious OKRs
• JZ’s first 90 days playbook: how to succeed in a new role
• Advice for early-career PMs
—
Find the transcript at: https://www.lennyspodcast.com/building-minimum-lovable-products-stories-from-wework-and-airbnb-and-thriving-as-a-pm-jiaona-zhang-webflow-wework-airbnb-dropbox/#transcript
—
Where to find Jiaona Zhang:
• Reforge: https://www.reforge.com/experts/jiaona-zhang
• LinkedIn: https://www.linkedin.com/in/jiaona/
• Website: https://www.jiaonazhang.com/
—
Where to find Lenny:
• Newsletter: https://www.lennysnewsletter.com
• Twitter: https://twitter.com/lennysan
• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/
—
In this episode, we cover:
(00:00) JZ’s background
(04:22) Common mistakes new PMs make
(06:44) Why Airbnb Plus didn’t work out, and takeaways from that experience
(10:51) Executing big dreams step-by-step
(13:45) The right way to push back against founders
(16:54) Minimum lovable product vs. minimum viable product
(20:53) What makes a product lovable
(22:20) Advice on roadmapping and prioritization
(28:04) Tips for new PMs to accelerate their career
(29:16) JZ’s top skills and how they have evolved over her career
(31:37) Designing crisp OKRs
(36:09) Lessons from WeWork
(43:01) Winning the first 90 days at a new company
(48:34) Why trust is crucial
(51:48) High-level lessons from Dropbox, Airbnb, WeWork, and Webflow
(56:38) The one piece of advice that transformed JZ’s career
(58:39) Lightning round
—
Referenced:
• Mike Lewis on LinkedIn: https://www.linkedin.com/in/mikelewis/
• “What working at Figma taught me about customer obsession,” VP of Product Sho Kuwamoto: https://www.lennysnewsletter.com/p/what-working-at-figma-taught-me-about
• WeWork: https://www.wework.com/
• WeCrashed on AppleTV+: https://tv.apple.com/us/show/wecrashed/umc.cmc.6qw605uv2rwbzutk2p2fsgvq9
• Sprint: How to Solve Big Problems and Test New Ideas in Just Five Days: https://www.amazon.com/Sprint-Solve-Problems-Test-Ideas/dp/150112174X
• The Making of a Manager: What to Do When Everyone Looks to You: https://www.amazon.com/Making-Manager-What-Everyone-Looks/dp/0735219567
• Tress of the Emerald Sea: A Cosmere Novel: https://www.amazon.com/Tress-Emerald-Sea-Brandon-Sanderson/dp/1250899656/
• Arcane on Netflix: https://www.netflix.com/title/81435684
• Snoo: https://www.happiestbaby.com/
• Midjourney: https://www.midjourney.com/
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.
—
Lenny may be an investor in the companies discussed.
Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe
The podcast and artwork embedded on this page are from Lenny Rachitsky, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Newcomer (LS 31 · TOP 5% what is this?)
Episode: Open-Source AI: Replit's Amjad Masad & Hugging Face's Clem Delangue
Pub date: 2023-04-06
Today, we have a bonus double episode of the Newcomer podcast for you — two conversations from the Cerebral Valley AI Summit last week.
Part 1: Replit CEO Amjad Masad and Hugging Face Clément Delangue
Together, they’re a charismatic open-source alliance.
We talked about the threat posed by OpenAI’s partnership with Microsoft, the questions around Replit and Hugging Face’s business models, and where they would like to see more development in artificial intelligence.
Charles Hudson, at Precursor, wrote up a smart reflection on the Cerebral Valley event and one of his main takeaways was about open-source companies like Replit and Hugging Face. Hudson wrote:
Open-source applications will play a big role in this early phase of experimentation. One of the more refreshing and interesting things for me to hear was the different approaches that open-source companies were taking relative to their more commercially-minded peers. It wasn’t simply about business models or go to market approaches — it felt way more fundamental and philosophical about how they wanted to see AI deployed and governed. I didn’t have a full appreciation for that difference before the event, but it was one of the things that I was most struck by at the event.
The Cerebral Valley AI Summit is presented by
Samsung Next invests in the boldest and most ambitious founders.
Tell us about your company. We’d love to meet.
Part 2: Adept CEO David Luan and Greylock partner Saam Motamedi
On stage with Luan and Motamedi, a major investor in Adept, I wanted to know how Adept planned to compete with foundation models like OpenAI and Anthropic — especially now that OpenAI has introduced plugins that allow third-parties to easily connect to ChatGPT.
Adept is building an AI model that mirrors humans input into computers. It’s a different approach than the language models that are getting built by other foundation model companies.
I also asked Luan about his time at OpenAI and at Google. I particularly wanted to know if he trusted his old team at OpenAI to spearhead the AI revolution.
Find the Podcast
We’re also posting all the on-stage conversations on our YouTube channel over the next few days.
Right now, you can watch:
-
Stability CEO Emad Mostaque one-on-one with me (the first half of my last podcast).
-
Shane Orlick (President at Jasper) and Cristóbal Valenzuela (CEO of Runway) with Coatue’s Caryn Marooney.
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Benchmark’s Miles Grimshaw’s conversation with Quora CEO and OpenAI board member Adam D’Angelo and with LangChain founder Harrison Chase.
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A panel of investors (Leigh Marie Braswell at Founders Fund, Bucky Moore at Kleiner Perkins, and Amber Yang at Bloomberg Beta) moderated by me.
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Volley CEO Max Child’s talk with Lisha Li (Rosebud), Caroline Zhang (Knowtex), Chun Jiang (Monterey AI), and Medha Basu (Defog).
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General Catalyst’s Deep Nishar with me (also featured in Tuesday’s podcast episode) .
-
Volley CTO James Wilsterman’s talk with Yasmin Dunsky (Wild Moose), Emily Dorsey (Pyq), and Lydia Ding (Code Complete).
Get full access to Newcomer at www.newcomer.co/subscribe
The podcast and artwork embedded on this page are from Eric Newcomer — newcomer.co, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Génération Do It Yourself (LS 64 · TOP 0.05% what is this?)
Episode: #327 - Laurent Alexandre - Auteur - ChatGPT & IA : “Dans 6 mois, il sera trop tard pour s’y intéresser”
Pub date: 2023-06-04
Laurent Alexandre est de retour dans GDIY.
Chirurgien, énarque, cofondateur de Doctissimo, il est connu pour son franc-parler et ses prises de position clivantes.
Aujourd’hui, c’est le spécialiste et l’auteur du livre “La guerre des intelligences: Le cerveau humain face à ChatGPT” que j’accueille à mon micro.
Dans un épisode impactant - voire dérangeant - Laurent Alexandre aborde les questions suivantes :
- Qu’est-ce que l’IA, les LLM, la superintelligence et la conscience artificielle ?
- L’IA sera-t-elle meilleure que les professeurs ou les chirurgiens ?
- Que pense-t-il du rôle de l’éducation nationale vis-à-vis de l’IA ?
- Pourquoi les Français doivent-ils prendre le train en marche afin d’éviter une société coupée en deux ?
- L’émergence de l’IA va-t-elle impacter la génomique, la santé et les capacités des humains ?
- L’IA entraîne-t-elle une hausse du charlatanisme ?
Un épisode essentiel pour vous faire votre propre opinion sur l’Intelligence Artificielle et prendre (ou pas) le train en marche vers une révolution technologique et sociétale.
TIMELINE :
- 00:01:00 - Présentation de Laurent Alexandre
- 00:06:00 - Rapport avec la politique, libéralisme et “toutologie”
- 00:11:40 - L’IA, une rupture sociétale profonde ?
- 00:30:00 - L’éducation à l’ère du numérique
- 00:55:00 - ChatGPT : Comment s’y mettre ?
- 01:04:00 - La génomique
- 01:13:00 - Organiser la complémentarité humain-IA
- 01:31:00 - L’art du prompt
- 01:53:00 - IA = hausse du charlatanisme ?
- 01:58:00 - L’impact de l’IA sur les développeurs
- 02:01:00 - Quelle est ta mission Laurent ?
- 02:06:00 - Laurent Alexandre, une personne clivante
- 02:11:00 - Les traditionnelles questions de fin d’épisode
On a cité avec Laurent plusieurs anciens épisodes de GDIY :
-
165 - Laurent Alexandre - Doctissimo - La nécessité d’affirmer ses idées
-
263 - Jean-Marc Jancovici - Décroissance, nucléaire, innovation : agir sous la contrainte ou par cas de conscience ?
Avec Laurent, on a parlé de :
- L’entreprise Neuralink
- L’IA générale (AGI)
- La conscience artificielle
- La superintelligence
- Sam Altman
- Les LLM
- L’auteur Yuval Noah Harari
- myBlee Math
- The Economist
- Etude : ChatGPT fait preuve de plus d’empathie que les médecins
- LangChain
- Le film Her
- La newsletter Magma
- Le livre Les Apprentis sorciers d’Alexandra Henrion Caude
- L’article Non à l'euthanasie écologique de Laurent Alexandre
- Le livre de Laurent Alexandre : La guerre des intelligences: Le cerveau humain face à ChatGPT
- L’auteur Raphaël Doan
Laurent vous recommande de lire :
- 1984 de George Orwell
- Le Hasard et la Nécessité de Jacques Monod
La musique du générique vous plaît ? C’est à Morgan Prudhomme que je la dois ! Contactez-le sur : https://studio-module.com.
Vous souhaitez sponsoriser Génération Do It Yourself ou nous proposer un partenariat ? Contactez mon label Orso Media via ce formulaire.
Vous pouvez poser toutes vos questions à Laurent Alexandre en lui envoyant un MP sur Twitter : @dr_l_alexandre.
The podcast and artwork embedded on this page are from Matthieu Stefani | Orso Media, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Syntax - Tasty Web Development Treats (LS 61 · TOP 0.1% what is this?)
Episode: Coding with the Open AI / Chat GPT API
Pub date: 2023-03-27
In this Hasty Treat, Scott and Wes talk about what can be done with the OpenAI API, how to get started with it, pricing, tuning your model, and gotchas for getting started with the OpenAI API.
Sentry - Sponsor If you want to know what’s happening with your code, track errors and monitor performance with Sentry. Sentry’s Application Monitoring platform helps developers see performance issues, fix errors faster, and optimize their code health. Cut your time on error resolution from hours to minutes. It works with any language and integrates with dozens of other services. Syntax listeners new to Sentry can get two months for free by visiting Sentry.io and using the coupon code TASTYTREAT during sign up.
Show Notes * 00:26 Welcome * 01:17 Sponsor: Sentry * 02:39 What is the OpenAI API? * 05:11 Getting started with the API * 07:41 How run OpenAI * OpenAI API * 14:16 GPT4 update * 17:58 Tune your models * 19:46 Generating questions with ChatGPT * 24:30 Speech to text * Otter.ai - Voice Meeting Notes & Real-time Transcription * Descript | All-in-one video & podcast editing, easy as a doc. * 26:12 Related API * 27:33 LangChain * 32:12 Save your replies
Tweet us your tasty treats * Scott’s Instagram * LevelUpTutorials Instagram * Wes’ Instagram * Wes’ Twitter * Wes’ Facebook * Scott’s Twitter * Make sure to include @SyntaxFM in your tweets
The podcast and artwork embedded on this page are from Wes Bos & Scott Tolinski - Full Stack JavaScript Web Developers, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Real Python Podcast (LS 46 · TOP 1% what is this?)
Episode: Automate Processes and Distribute Python Tools With RPA and RCC
Pub date: 2023-04-07
Are you exploring automation of your repetitive business tasks with Python? How are you going to share your helpful tools with co-workers? This week on the show, Sampo Ahokas from Robocorp is here to discuss robotic process automation (RPA) and distribution of these robots.
Sampo is a co-founder and VP of engineering at Robocorp. We talk about using Robot Framework, an open-source RPA tool, to develop bots that implement your existing Python skills. Sampo shares example projects and additional resources for new users.
We discuss the typical difficulties of sharing automation tools with a team and trying to avoid the dreaded “works on my machine” problem. Sampo describes how their group worked to develop a Conda-based tool for creating shareable packages and environments.
Course Spotlight: Manipulating ZIP Files With Python
In this video course, you’ll learn how to manipulate ZIP files using Python’s zipfile module from the standard library. Through hands-on examples, you’ll learn how to read, write, compress, and extract files from your ZIP files quickly.
Topics:
- 00:00:00 – Introduction
- 00:02:25 – What is robotic process automation (RPA)?
- 00:03:55 – What do you mean by automation?
- 00:05:56 – Additional examples of RPA
- 00:07:41 – What is the RPA platform?
- 00:10:06 – What is the Robot Framework?
- 00:12:42 – Robocorp portal
- 00:14:09 – Python integration
- 00:17:06 – Sponsor: REVSYS
- 00:17:56 – Distribution with RCC
- 00:20:24 – Why does the system use conda under the hood?
- 00:24:12 – What hurdles did you face creating RCC?
- 00:27:51 – Steps for the end user
- 00:30:52 – Making the project open source
- 00:35:20 – Video Course Spotlight
- 00:36:42 – Tips for someone starting with automation
- 00:42:17 – Integration with VSCode
- 00:44:18 – Intelligent document processing (IDP)
- 00:45:36 – What are you excited about in the world of Python?
- 00:47:46 – What do you want to learn next?
- 00:48:13 – How can people follow the project online?
- 00:48:46 – Thanks and goodbye
Show Links:
- Open Source RPA - Intelligent Automation Software - Robocorp
- What is RPA? A breakdown of RPA and its benefits - Robocorp
- Robocorp Portal
- RPA Documentation, Training Courses, Certificates - Robocorp documentation
- rpaframework: Collection of open-source libraries and tools for Robotic Process Automation (RPA), designed to be used with both Robot Framework and Python
- rcc: RCC is a set of tooling that allows you to create, manage, and distribute Python-based self-contained automation packages - or ‘robots’ as we call them.
- Conda - documentation
- QuantStack
- micromamba - documentation
- Low-code RPA Development Solution | Automation Studio - Robocorp
- Bolster IDP With Robotic Process Automation - DZone
- Visual Studio Code - Code Editing. Redefined
- Welcome to LangChain - 🦜🔗 LangChain 0.0.131
- Sampo Ahokas - LinkedIn
- Robocorp (@RobocorpInc) - Twitter
- Community for Software Robot Developers
- RPA Resources, White Papers and Case Studies - Robocorp
Level up your Python skills with our expert-led courses:
- Reading and Writing Files in Python
- Testing Your Code With pytest
- Manipulating ZIP Files With Python
Support the podcast & join our community of Pythonistas
The podcast and artwork embedded on this page are from Real Python, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Talk Python To Me (LS 56 · TOP 0.5% what is this?)
Episode: #417: Test-Driven Prompt Engineering for LLMs with Promptimize
Pub date: 2023-05-30
Large language models and chat-based AIs are kind of mind blowing at the moment. Many of us are playing with them for working on code or just as a fun alternative to search. But others of us are building applications with AI at the core. And when doing that, the slightly unpredictable nature and probabilistic nature of LLMs make writing and testing Python code very tricky. Enter promptimize from Maxime Beauchemin and Preset. It's a framework for non-deterministic testing of LLMs inside our applications. Let's dive inside the AIs with Max.
Links from the show
Max on Twitter: @mistercrunch
Promptimize: github.com
Introducing Promptimize ("the blog post"): preset.io
Preset: preset.io
Apache Superset: Modern Data Exploration Platform episode: talkpython.fm
ChatGPT: chat.openai.com
LeMUR: assemblyai.com
Microsoft Security Copilot: blogs.microsoft.com
AutoGPT: github.com
Midjourney: midjourney.com
Midjourney generated pytest tips thumbnail: talkpython.fm
Midjourney generated radio astronomy thumbnail: talkpython.fm
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Podcast: Syntax - Tasty Web Development Treats (LS 61 · TOP 0.1% what is this?)
Episode: Supper Club × Astro 2.0 with Fred Schott
Pub date: 2023-02-24
In this supper club episode of Syntax, Wes and Scott talk with Fred Schott about all things Astro v2.0. What is Astro and why should you use it? How do islands work? Images, edge, AI, error overlays, hybrid rendering, and more!
Show Notes * 00:40 Welcome * 01:08 Guest introduction * FredKSchott.com * @Fredkschott on Twitter * Fred on GitHub * 02:17 What is Astro and why should someone use it? * 04:57 What can you build with Astro? * 06:11 What’s an island in content? * 09:43 How do routes work with Astro? * 12:30 How is Markdown handled in Astro? * mdxjs * 14:32 How does Astro work on the edge? * 18:15 How does Astro v2 handle data fetching? * 23:25 Integrations with Astro * 26:38 Astro AI bot? * AI Langchain * 30:40 Error overlay design * 36:10 What are some of the most important upgrades in v2? * 37:18 Hybrid rendering * 40:27 Astro’s image component * Squoosh * 44:39 What happened to snowpack? Pikapkg? * 46:48 What is the financial model for Astro? * 50:28 Supper Club questions * Obsidian
××× SIIIIICK ××× PIIIICKS ××× * Chat Langchain
Shameless Plugs * Astro * Astro Discord
Tweet us your tasty treats * Scott’s Instagram * LevelUpTutorials Instagram * Wes’ Instagram * Wes’ Twitter * Wes’ Facebook * Scott’s Twitter * Make sure to include @SyntaxFM in your tweets
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Podcast: Gradient Dissent: Exploring Machine Learning, AI, Deep Learning, Computer Vision (LS 40 · TOP 1.5% what is this?)
Episode: Enabling LLM-Powered Applications with Harrison Chase of LangChain
Pub date: 2023-06-01
On this episode, we’re joined by Harrison Chase, Co-Founder and CEO of LangChain. Harrison and his team at LangChain are on a mission to make the process of creating applications powered by LLMs as easy as possible.
We discuss:
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What LangChain is and examples of how it works.
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Why LangChain has gained so much attention.
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When LangChain started and what sparked its growth.
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Harrison’s approach to community-building around LangChain.
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Real-world use cases for LangChain.
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What parts of LangChain Harrison is proud of and which parts can be improved.
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Details around evaluating effectiveness in the ML space.
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Harrison's opinion on fine-tuning LLMs.
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The importance of detailed prompt engineering.
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Predictions for the future of LLM providers.
Resources:
Harrison Chase - https://www.linkedin.com/in/harrison-chase-961287118/
LangChain | LinkedIn - https://www.linkedin.com/company/langchain/
LangChain | Website - https://docs.langchain.com/docs/
Thanks for listening to the Gradient Dissent podcast, brought to you by Weights & Biases. If you enjoyed this episode, please leave a review to help get the word out about the show. And be sure to subscribe so you never miss another insightful conversation.
OCR #DeepLearning #AI #Modeling #ML
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Podcast: The Kicker (LS 38 · TOP 2% what is this?)
Episode: Feven Merid: On Jacaranda Nigeria Limited
Pub date: 2023-03-13
In 1982, about twenty Black journalists quit their jobs at American networks, banded together under the name Jacaranda Nigeria Limited, and flew to Nigeria, where they would work under the country’s newly elected president to revamp a state-funded journalism network. On today’s episode of the Kicker, Feven Merid, a Columbia Journalism Review staff writer, tells their story.
She explains the many unforeseen challenges Jacaranda’s journalists faced — the Nigerian government’s interference in their reporting, the lack of proper training and resources, the confusion over their racial identity — and, ultimately, how the problems they went to Nigeria to escape never really disappeared.
Read Feven's article at https://www.cjr.org/the_feature/black-american-journalists-nigeria.php.
The podcast and artwork embedded on this page are from Columbia Journalism Review, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Latent Space: Founders, Engineers, and News on Software 3.0, DevTools, Computer Vision, Data Science, AI UX (LS 26 · TOP 10% what is this?)
Episode: No Moat: Closed AI gets its Open Source wakeup call — ft. Simon Willison
Pub date: 2023-05-05
It’s now almost 6 months since Google declared Code Red, and the results — Jeff Dean’s recap of 2022 achievements and a mass exodus of the top research talent that contributed to it in January, Bard’s rushed launch in Feb, a slick video showing Google Workspace AI features and confusing doubly linked blogposts about PaLM API in March, and merging Google Brain and DeepMind in April — have not been inspiring.
Google’s internal panic is in full display now with the surfacing of a well written memo, written by software engineer Luke Sernau written in early April, revealing internal distress not seen since Steve Yegge’s infamous Google Platforms Rant. Similar to 2011, the company’s response to an external challenge has been to mobilize the entire company to go all-in on a (from the outside) vague vision.
Google’s misfortunes are well understood by now, but the last paragraph of the memo: “We have no moat, and neither does OpenAI”, was a banger of a mic drop.
Combine this with news this morning that OpenAI lost $540m last year and will need as much as $100b more funding (after the complex $10b Microsoft deal in Jan), and the memo’s assertion that both Google and OpenAI have “no moat” against the mighty open source horde have gained some credibility in the past 24 hours.
Many are criticising this memo privately:
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A CEO commented to me yesterday that Luke Sernau does not seem to work in AI related parts of Google and “software engineers don’t understand moats”.
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Emad Mostaque, himself a perma-champion of open source and open models, has repeatedly stated that “Closed models will always outperform open models” because closed models can just wrap open ones.
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Emad has also commented on the moats he does see: “Unique usage data, Unique content, Unique talent, Unique product, Unique business model”, most of which Google does have, and OpenAI less so (though it is winning on the talent front)
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Sam Altman famously said that “very few to no one is Silicon Valley has a moat - not even Facebook” (implying that moats don’t actually matter, and you should spend your time thinking about more important things)
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It is not actually clear what race the memo thinks Google and OpenAI are in vs Open Source. Neither are particularly concerned about running models locally on phones, and they are perfectly happy to let “a crazy European alpha male” run the last mile for them while they build actually monetizable cloud infrastructure.
However moats are of intense interest by everybody keen on productized AI, cropping up in every Harvey, Jasper, and general AI startup vs incumbent debate. It is also interesting to take the memo at face value and discuss the searing hot pace of AI progress in open source.
We hosted this discussion yesterday with Simon Willison, who apart from being an incredible communicator also wrote a great recap of the No Moat memo. 2,800 have now tuned in on Twitter Spaces, but we have taken the audio and cleaned it up here. Enjoy!
Timestamps
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[00:00:00] Introducing the Google Memo
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[00:02:48] Open Source > Closed?
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[00:05:51] Running Models On Device
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[00:07:52] LoRA part 1
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[00:08:42] On Moats - Size, Data
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[00:11:34] Open Source Models are Comparable on Data
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[00:13:04] Stackable LoRA
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[00:19:44] The Need for Special Purpose Optimized Models
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[00:21:12] Modular - Mojo from Chris Lattner
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[00:23:33] The Promise of Language Supersets
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[00:28:44] Google AI Strategy
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[00:29:58] Zuck Releasing LLaMA
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[00:30:42] Google Origin Confirmed
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[00:30:57] Google's existential threat
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[00:32:24] Non-Fiction AI Safety ("y-risk")
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[00:35:17] Prompt Injection
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[00:36:00] Google vs OpenAI
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[00:41:04] Personal plugs: Simon and Travis
Transcripts
[00:00:00] Introducing the Google Memo
[00:00:00] Simon Willison: So, yeah, this is a document, which Kate, which I first saw at three o'clock this morning, I think. It claims to be leaked from Google. There's good reasons to believe it is leaked from Google, and to be honest, if it's not, it doesn't actually matter because the quality of the analysis, I think stands alone.
[00:00:15] If this was just a document by some anonymous person, I'd still think it was interesting and worth discussing. And the title of the document is We Have No Moat and neither does Open ai. And the argument it makes is that while Google and OpenAI have been competing on training bigger and bigger language models, the open source community is already starting to outrun them, given only a couple of months of really like really, really serious activity.
[00:00:41] You know, Facebook lama was the thing that really kicked us off. There were open source language models like Bloom before that some G P T J, and they weren't very impressive. Like nobody was really thinking that they were. Chat. G P T equivalent Facebook Lama came out in March, I think March 15th. And was the first one that really sort of showed signs of being as capable maybe as chat G P T.
[00:01:04] My, I don't, I think all of these models, they've been, the analysis of them has tend to be a bit hyped. Like I don't think any of them are even quite up to GT 3.5 standards yet, but they're within spitting distance in some respects. So anyway, Lama came out and then, Two weeks later Stanford Alpaca came out, which was fine tuned on top of Lama and was a massive leap forward in terms of quality.
[00:01:27] And then a week after that Vicuna came out, which is to this date, the the best model I've been able to run on my own hardware. I, on my mobile phone now, like, it's astonishing how little resources you need to run these things. But anyway, the the argument that this paper made, which I found very convincing is it only took open source two months to get this far.
[00:01:47] It's now every researcher in the world is kicking it on new, new things, but it feels like they're being there. There are problems that Google has been trying to solve that the open source models are already addressing, and really how do you compete with that, like with your, it's closed ecosystem, how are you going to beat these open models with all of this innovation going on?
[00:02:04] But then the most interesting argument in there is it talks about the size of models and says that maybe large isn't a competitive advantage, maybe actually a smaller model. With lots of like different people fine tuning it and having these sort of, these LoRA l o r a stackable fine tuning innovations on top of it, maybe those can move faster.
[00:02:23] And actually having to retrain your giant model every few months from scratch is, is way less useful than having small models that you can tr you can fine tune in a couple of hours on laptop. So it's, it's fascinating. I basically, if you haven't read this thing, you should read every word of it. It's not very long.
[00:02:40] It's beautifully written. Like it's, it's, I mean, If you try and find the quotable lines in it, almost every line of it's quotable. Yeah. So, yeah, that's that, that, that's the status of this
[00:02:48] Open Source > Closed?
[00:02:48] swyx: thing. That's a wonderful summary, Simon. Yeah, there, there's so many angles we can take to this. I, I'll just observe one, one thing which if you think about the open versus closed narrative, Ima Mok, who is the CEO of Stability, has always been that open will trail behind closed, because the closed alternatives can always take.
[00:03:08] Learnings and lessons from open source. And this is the first highly credible statement that is basically saying the exact opposite, that open source is moving than, than, than closed source. And they are scared. They seem to be scared. Which is interesting,
[00:03:22] Travis Fischer: Travis. Yeah, the, the, the, a few things that, that I'll, I'll, I'll say the only thing which can keep up with the pace of AI these days is open source.
[00:03:32] I think we're, we're seeing that unfold in real time before our eyes. And. You know, I, I think the other interesting angle of this is to some degree LLMs are they, they don't really have switching costs. They are going to be, become commoditized. At least that's, that's what a lot of, a lot of people kind of think to, to what extent is it Is it a, a rate in terms of, of pricing of these things?
[00:03:55] , and they all kind of become roughly the, the, the same in, in terms of their, their underlying abilities. And, and open source is gonna, gonna be actively pushing, pushing that forward. And, and then this is kind of coming from, if it is to be believed the kind of Google or an insider type type mentality around you know, where is the actual competitive advantage?
[00:04:14] What should they be focusing on? How can they get back in into the game? When you know, when, when, when, when currently the, the, the external view of, of Google is that they're kind of spinning their wheels and they have this code red,, and it's like they're, they're playing catch up already.
[00:04:28] Like how could they use the open source community and work with them, which is gonna be really, really hard you know, from a structural perspective given Google's place in the ecosystem. But a, a lot, lot, a lot of jumping off points there.
[00:04:42] Alessio Fanelli: I was gonna say, I think the Post is really focused on how do we get the best model, but it's not focused on like, how do we build the best product around it.
[00:04:50] A lot of these models are limited by how many GPUs you can get to run them and we've seen on traditional open source, like everybody can use some of these projects like Kafka and like Alaska for free. But the reality is that not everybody can afford to run the infrastructure needed for it.
[00:05:05] So I, I think like the main takeaway that I have from this is like, A lot of the moats are probably around just getting the, the sand, so to speak, and having the GPUs to actually serve these models. Because even if the best model is open source, like running it at large scale for an end is not easy and like, it's not super convenient to get a lot, a lot of the infrastructure.
[00:05:27] And we've seen that model work in open source where you have. The opensource project, and then you have a enterprise cloud hosted version for it. I think that's gonna look really different in opensource models because just hosting a model doesn't have a lot of value. So I'm curious to hear how people end up getting rewarded to do opensource.
[00:05:46] You know, it's, we figured that out in infrastructure, but we haven't figured it out in in Alans
[00:05:51] Running Models On Device
[00:05:51] Simon Willison: yet. I mean, one thing I'll say is that the the models that you can run on your own devices are so far ahead of what I ever dreamed they would be at this point. Like Vicuna 13 b i i, I, I think is the current best available open mo model that I've played with.
[00:06:08] It's derived from Facebook Lama, so you can't use it for commercial purposes yet. But the point about MCK 13 B is it runs in the browser directly on web gpu. There's this amazing web l l M project where you literally, your browser downloaded a two gigabyte file. And it fires up a chat g D style interface and it's quite good.
[00:06:27] It can do rap battles between different animals and all of the kind of fun stuff that you'd expect to be able to do the language model running entirely in Chrome canary. It's shocking to me that that's even possible, but that kind of shows that once, once you get to inference, if you can shrink the model down and the techniques for shrinking these models, the, the first one was the the quantization.
[00:06:48] Which the Lama CPP project really sort of popularized Matt can by using four bits instead of 16 bit floating point numbers, you can shrink it down quite a lot. And then there was a paper that came out days ago suggesting that you can prune the models and ditch half the model and maintain the same level of quality.
[00:07:05] So with, with things like that, with all of these tricks coming together, it's really astonishing how much you can get done on hardware that people actually have in their pockets even.
[00:07:15] swyx: Just for completion I've been following all of your posts. Oh, sorry. Yes. I just wanna follow up, Simon. You're, you said you're running a model on your phone. Which model is it? And I don't think you've written it up.
[00:07:27] Simon Willison: Yeah, that one's vina. I did, did I write it up? I did. I've got a blog post about how it it, it, it knows who I am, sort of, but it said that I invented a, a, a pattern for living called bear or bunny pattern, which I definitely didn't, but I loved that my phone decided that I did.
[00:07:44] swyx: I will hunt for that because I'm not yet running Vic on my phone and I feel like I should and, and as like a very base thing, but I'll, okay.
[00:07:52] Stackable LoRA Modules
[00:07:52] swyx: Also, I'll follow up two things, right? Like one I'm very interesting and let's, let's talk about that a little bit more because this concept of stackable improvements to models I think is extremely interesting.
[00:08:00] Like, I would love to MPM install abilities onto my models, right? Which is really awesome. But the, the first thing thing is under-discussed is I don't get the panic. Like, honestly, like Google has the most moats. I I, I was arguing maybe like three months ago on my blog. Like Google has the most mote out of a lot of people because, hey, we have your calendar.
[00:08:21] Hey, we have your email. Hey, we have your you know, Google Docs. Like, isn't that a, a sufficient mode? Like, why are these guys panicking so much? I don't, I still don't get it. Like, Sure open source is running ahead and like, it's, it's on device and whatev, what have you, but they have so much more mode.
[00:08:36] Like, what are we talking about here? There's many dimensions to compete on.
[00:08:42] On Moats - Size, Data
[00:08:42] Travis Fischer: Yeah, there's like one of, one of the, the things that, that the author you know, mentions in, in here is when, when you start to, to, to have the feeling of what we're trailing behind, then you're, you're, you're, you're brightest researchers jump ship and go to OpenAI or go to work at, at, at academia or, or whatever.
[00:09:00] And like the talent drain. At the, the level of the, the senior AI researchers that are pushing these things ahead within Google, I think is a serious, serious concern. And my, my take on it's a good point, right? Like, like, like, like what Google has modes. They, they, they're not running outta money anytime soon.
[00:09:16] You know, I think they, they do see the level of the, the defensibility and, and the fact that they want to be, I'll chime in the, the leader around pretty much anything. Tech first. There's definitely ha ha have lost that, that, that feeling. Right? , and to what degree they can, they can with the, the open source community to, to get that back and, and help drive that.
[00:09:38] You know all of the llama subset of models with, with alpaca and Vicuna, et cetera, that all came from, from meta. Right. Like that. Yeah. Like it's not licensed in an open way where you can build a company on top of it, but is now kind of driving this family of, of models, like there's a tree of models that, that they're, they're leading.
[00:09:54] And where is Google in that, in that playbook? Like for a long time they were the one releasing those models being super open and, and now it's just they, they've seem to be trailing and there's, there's people jumping ship and to what degree can they, can they, can they. Close off those wounds and, and focus on, on where, where they, they have unique ability to, to gain momentum.
[00:10:15] I think is a core part of my takeaway from this. Yeah.
[00:10:19] Alessio Fanelli: And think another big thing in the post is, oh, as long as you have high quality data, like you don't need that much data, you can just use that. The first party data loops are probably gonna be the most important going forward if we do believe that this is true.
[00:10:32] So, Databricks. We have Mike Conover from Databricks on the podcast, and they talked about how they came up with the training set for Dolly, which they basically had Databricks employees write down very good questions and very good answers for it. Not every company as the scale to do that. And I think products like Google, they have millions of people writing Google Docs.
[00:10:54] They have millions of people using Google Sheets, then millions of people writing stuff, creating content on YouTube. The question is, if you wanna compete against these companies, maybe the model is not what you're gonna do it with because the open source kind of commoditizes it. But how do you build even better data?
[00:11:12] First party loops. And that's kind of the hardest thing for startups, right? Like even if we open up the, the models to everybody and everybody can just go on GitHub and. Or hugging face and get the waste to the best model, but get enough people to generate data for me so that I can still make it good. That's, that's what I would be worried about if I was a, a new company.
[00:11:31] How do I make that happen
[00:11:32] Simon Willison: really quickly?
[00:11:34] Open Source Models are Comparable on Data
[00:11:34] Simon Willison: I'm not convinced that the data is that big a challenge. So there's this PO project. So the problem with Facebook LAMA is that it's not available for, for commercial use. So people are now trying to train a alternative to LAMA that's entirely on openly licensed data.
[00:11:48] And that the biggest project around that is this red pajama project, which They released their training data a few weeks ago and it was 2.7 terabytes. Right? So actually tiny, right? You can buy a laptop that you can fit 2.7 terabytes on. Got it. But it was the same exact data that Facebook, the same thing that Facebook Lamb had been trained on.
[00:12:06] Cuz for your base model. You're not really trying to teach it fact about the world. You're just trying to teach it how English and other languages work, how they fit together. And then the real magic is when you fine tune on top of that. That's what Alpaca did on top of Lama and so on. And the fine tuning sets, it looks like, like tens of thousands of examples to kick one of these role models into shape.
[00:12:26] And tens of thousands of examples like Databricks spent a month and got the 2000 employees of their company to help kick in and it worked. You've got the open assistant project of crowdsourcing this stuff now as well. So it's achievable
[00:12:40] swyx: sore throat. I agree. I think it's a fa fascinating point. Actually, so I've heard through the grapevine then red pajamas model.
[00:12:47] Trained on the, the data that they release is gonna be releasing tomorrow. And it's, it's this very exciting time because the, the, there, there's a, there's a couple more models that are coming down the pike, which independently we produced. And so yeah, that we, everyone is challenging all these assumptions from, from first principles, which is fascinating.
[00:13:04] Stackable LoRA
[00:13:04] swyx: I, I did, I did wanted to, to like try to get a little bit more technical in terms of like the, the, the, the specific points race. Cuz this doc, this doc was just amazing. Can we talk about LoRA. I, I, I'll open up to Simon again if he's back.
[00:13:16] Simon Willison: I'd rather someone else take on. LoRA, I've, I, I know as much as I've read in that paper, but not much more than that.
[00:13:21] swyx: So I thought it was this kind of like an optimization technique. So LoRA stands for lower rank adaptation. But this is the first mention of LoRA as a form of stackable improvements. Where he I forget what, let, just, let me just kind of Google this. But obviously anyone's more knowledgeable please.
[00:13:39] So come on in.
[00:13:40] Alessio Fanelli: I, all of Lauren is through GTS Man, about 20 minutes on GT four, trying to figure out word. It was I study computer science, but this is not this is not my area of expertise. What I got from it is that basically instead of having to retrain the whole model you can just pick one of the ranks and you take.
[00:13:58] One of like the, the weight matrix tests and like make two smaller matrixes from it and then just two to be retrained and training the whole model. So
[00:14:08] swyx: it save a lot of Yeah. You freeze part of the thing and then you just train the smaller part like that. Exactly. That seems to be a area of a lot of fruitful research.
[00:14:15] Yeah. I think Mini GT four recently did something similar as well. And then there's, there's, there's a, there's a Spark Model people out today that also did the same thing.
[00:14:23] Simon Willison: So I've seen a lot of LoRA stable, the stable diffusion community has been using LoRA a lot. So they, in that case, they had a, I, the thing I've seen is people releasing LoRA's that are like you, you train a concept like a, a a particular person's face or something you release.
[00:14:38] And the, the LoRA version of this end up being megabytes of data, like, which is, it's. You know, it's small enough that you can just trade those around and you can effectively load multiple of those into the model. But what I haven't realized is that you can use the same trick on, on language models. That was one of the big new things for me in reading the the leaks Google paper today.
[00:14:56] Alessio Fanelli: Yeah, and I think the point to make around on the infrastructure, so what tragedy has told me is that when you're figuring out what rank you actually wanna do this fine tuning at you can have either go too low and like the model doesn't actually learn it. Or you can go too high and the model overfit those learnings.
[00:15:14] So if you have a base model that everybody agrees on, then all the subsequent like LoRA work is done around the same rank, which gives you an advantage. And the point they made in the, that, since Lama has been the base for a lot of this LoRA work like they own. The, the mind share of the community.
[00:15:32] So everything that they're building is compatible with their architecture. But if Google Opensources their own model the rank that they chose For LoRA on Lama might not work on the Google model. So all of the existing work is not portable. So
[00:15:46] Simon Willison: the impression I got is that one of the challenges with LoRA is that you train all these LoRAs on top of your model, but then if you retrain that base model as LoRA's becoming invalid, right?
[00:15:55] They're essentially, they're, they're, they're built for an exact model version. So this means that being the big company with all of the GPUs that can afford to retrain a model every three months. That's suddenly not nearly as valuable as it used to be because now maybe there's an open source model that's five years old at this point and has like multiple, multiple stacks of LoRA's trained all over the world on top of it, which can outperform your brand new model just because there's been so much more iteration on that base.
[00:16:20] swyx: I, I think it's, I think it's fascinating. It's I think Jim Fan from Envidia was recently making this argument for transformers. Like even if we do come up with a better. Architecture, then transformers, they're the sheer hundreds and millions of dollars that have been invested on top of transformers.
[00:16:34] Make it actually there is some switching costs and it's not exactly obvious that better architecture. Equals equals we should all switch immediately tomorrow. It's, it's, it's
[00:16:44] Simon Willison: kinda like the, the difficulty of launching a new programming language today Yes. Is that pipeline and JavaScript have a million packages.
[00:16:51] So no matter how good your new language is, if it can't tap into those existing package libraries, it's, it's not gonna be useful for, which is why Moji is so clever, because they did build on top of Pips. They get all of that existing infrastructure, all of that existing code working already.
[00:17:05] swyx: I mean, what, what thought you, since you co-create JAO and all that do, do we wanna take a diversion into mojo?
[00:17:10] No, no. I
[00:17:11] Travis Fischer: would, I, I'd be happy to, to, to jump in, and get Simon's take on, on Mojo. 1, 1, 1 small, small point on LoRA is I, I, I just think. If you think about at a high level, what the, the major down downsides are of these, these large language models. It's the fact that they well they're, they're, they're difficult to, to train, right?
[00:17:32] They, they tend to hallucinate and they are, have, have a static, like, like they were trained at a certain date, right? And with, with LoRA, I think it makes it a lot more amenable to Training new, new updates on top of that, that like base model on the fly where you can incorporate new, new data and in a way that is, is, is an interesting and potentially more optimal alternative than Doing the kind of in context generation cuz, cuz most of like who at perplexity AI or, or any of these, these approaches currently, it's like all based off of doing real-time searches and then injecting as much into the, the, the local context window as possible so that you, you try to ground your, your, your, your language model.
[00:18:16] Both in terms of the, the information it has access to that, that, that helps to reduce hallucinations. It can't reduce it, but helps to reduce it and then also gives it access to up-to-date information that wasn't around for that, that massive like, like pre-training step. And I think LoRA in, in, in mine really makes it more, more amenable to having.
[00:18:36] Having constantly shifting lightweight pre-training on top of it that scales better than than normal. Pre I'm sorry. Fine tune, fine tuning. Yeah, that, that was just kinda my one takeaway
[00:18:45] Simon Willison: there. I mean, for me, I've never been, I want to run models on my own hard, I don't actually care about their factual content.
[00:18:52] Like I don't need a model that's been, that's trained on the most upstate things. What I need is a model that can do the bing and bar trick, right? That can tell when it needs to run a search. And then go and run a search to get extra information and, and bring that context in. And similarly, I wanted to be able to operate tools where it can access my email or look at my notes or all of those kinds of things.
[00:19:11] And I don't think you need a very powerful model for that. Like that's one of the things where I feel like, yeah, vicuna running on my, on my laptop is probably powerful enough to drive a sort of personal research assistant, which can look things up for me and it can summarize things for my notes and it can do all of that and I don't care.
[00:19:26] But it doesn't know about the Ukraine war because the Ukraine war training cutoff, that doesn't matter. If it's got those additional capabilities, which are quite easy to build the reason everyone's going crazy building agents and tools right now is that it's a few lines of Python code, and a sort of couple of paragraphs to get it to.
[00:19:44] The Need for Special Purpose Optimized Models
[00:19:44] Simon Willison: Well, let's, let's,
[00:19:45] Travis Fischer: let's maybe dig in on that a little bit. And this, this also is, is very related to mojo. Cuz I, I do think there are use cases and domains where having the, the hyper optimized, like a version of these models running on device is, is very relevant where you can't necessarily make API calls out on the fly.
[00:20:03] and Aug do context, augmented generation. And I was, I was talking with, with a a researcher. At Lockheed Martin yesterday, literally about like, like the, the version of this that's running of, of language models running on, on fighter jets. Right? And you, you talk about like the, the, the amount of engineering, precision and optimization that has to go into, to those type of models.
[00:20:25] And the fact that, that you spend so much money, like, like training a super distilled ver version where milliseconds matter it's a life or death situation there. You know, and you couldn't even, even remotely ha ha have a use case there where you could like call out and, and have, have API calls or something.
[00:20:40] So I, I do think there's like keeping in mind the, the use cases where, where. There, there'll be use cases that I'm more excited about at, at the application level where, where, yeah, I want to to just have it be super flexible and be able to call out to APIs and have this agentic type type thing.
[00:20:56] And then there's also industries and, and use cases where, where you really need everything baked into the model.
[00:21:01] swyx: Yep. Agreed. My, my favorite piece take on this is I think DPC four as a reasoning engine, which I think came from the from Nathan at every two. Which I think, yeah, I see the hundred score over there.
[00:21:12] Modular - Mojo from Chris Lattner
[00:21:12] swyx: Simon, do you do you have a, a few seconds on
[00:21:14] Simon Willison: mojo. Sure. So Mojo is a brand new program language you just announced a few days ago. It's not actually available yet. I think there's an online demo, but to zooming it becomes an open source language we can use. It's got really some very interesting characteristics.
[00:21:29] It's a super set of Python, so anything written in Python, Python will just work, but it adds additional features on top that let you basically do very highly optimized code with written. In Python syntax, it compiles down the the main thing that's exciting about it is the pedigree that it comes from.
[00:21:47] It's a team led by Chris Latner, built L L V M and Clang, and then he designed Swift at Apple. So he's got like three, three for three on, on extraordinarily impactful high performance computing products. And he put together this team and they've basically, they're trying to go after the problem of how do you build.
[00:22:06] A language which you can do really high performance optimized work in, but where you don't have to do everything again from scratch. And that's where building on top of Python is so clever. So I wasn't like, if this thing came along, I, I didn't really pay attention to it until j Jeremy Howard, who built Fast ai put up a very detailed blog post about why he was excited about Mojo, which included a, there's a video demo in there, which everyone should watch because in that video he takes Matrix multiplication implemented in Python.
[00:22:34] And then he uses the mojo extras to 2000 x. The performance of that matrix multiplication, like he adds a few static types functions sort of struck instead of the class. And he gets 2000 times the performance out of it, which is phenomenal. Like absolutely extraordinary. So yeah, that, that got me really excited.
[00:22:52] Like the idea that we can still use Python and all of this stuff we've got in Python, but we can. Just very slightly tweak some things and get literally like thousands times upwards performance out of the things that matter. That's really exciting.
[00:23:07] swyx: Yeah, I, I, I'm curious, like, how come this wasn't thought of before?
[00:23:11] It's not like the, the, the concept of a language super set hasn't hasn't, has, has isn't, is completely new. But all, as far as I know, all the previous Python interpreter approaches, like the alternate runtime approaches are like they, they, they're more, they're more sort of, Fit conforming to standard Python, but never really tried this additional approach of augmenting the language.
[00:23:33] The Promise of Language Supersets
[00:23:33] swyx: I, I'm wondering if you have many insights there on, like, why, like why is this a, a, a breakthrough?
[00:23:38] Simon Willison: Yeah, that's a really interesting question. So, Jeremy Howard's piece talks about this thing called M L I R, which I hadn't heard of before, but this was another Chris Latner project. You know, he built L L VM as a low level virtual machine.
[00:23:53] That you could build compilers on top of. And then M L I R was this one that he initially kicked off at Google, and I think it's part of TensorFlow and things like that. But it was very much optimized for multiple cores and GPU access and all of that kind of thing. And so my reading of Jeremy Howard's article is that they've basically built Mojo on top of M L I R.
[00:24:13] So they had a huge, huge like a starting point where they'd, they, they knew this technology better than anyone else. And because they had this very, very robust high performance basis that they could build things on. I think maybe they're just the first people to try and build a high, try and combine a high level language with M L A R, with some extra things.
[00:24:34] So it feels like they're basically taking a whole bunch of ideas people have been sort of experimenting with over the last decade and bundled them all together with exactly the right team, the right level of expertise. And it looks like they've got the thing to work. But yeah, I mean, I've, I've, I'm. Very intrigued to see, especially once this is actually available and we can start using it.
[00:24:52] It, Jeremy Howard is someone I respect very deeply and he's, he's hyping this thing like crazy, right? His headline, his, and he's not the kind of person who hypes things if they're not worth hyping. He said Mojo may be the biggest programming language advanced in decades. And from anyone else, I'd kind of ignore that headline.
[00:25:09] But from him it really means something.
[00:25:11] swyx: Yes, because he doesn't hype things up randomly. Yeah, and, and, and he's a noted skeptic of Julia which is, which is also another data science hot topic. But from the TypeScript and web, web development worlds there has been a dialect of TypeScript that was specifically optimized to compile, to web assembly which I thought was like promising and then, and, and eventually never really took off.
[00:25:33] But I, I like this approach because I think more. Frameworks should, should essentially be languages and recognize that they're language superset and maybe working compilers that that work on them. And then that is the, by the way, that's the direction that React is going right now. So fun times
[00:25:50] Simon Willison: type scripts An interesting comparison actually, cuz type script is effectively a superset of Java script, right?
[00:25:54] swyx: It's, but there's no, it's purely
[00:25:57] Simon Willison: types, right? Gotcha. Right. So, so I guess mojo is the soup set python, but the emphasis is absolutely on tapping into the performance stuff. Right.
[00:26:05] swyx: Well, the just things people actually care about.
[00:26:08] Travis Fischer: Yeah. The, the one thing I've found is, is very similar to the early days of type script.
[00:26:12] There was the, the, the, the most important thing was that it's incrementally adoptable. You know, cuz people had a script code basis and, and they wanted to incrementally like add. The, the, the main value prop for TypeScript was reliability and the, the, the, the static typing. And with Mojo, Lucia being basically anyone who's a target a large enterprise user of, of Mojo or even researchers, like they're all going to be coming from a, a hardcore.
[00:26:36] Background in, in Python and, and have large existing libraries. And the the question will be for what use cases will mojo be like a, a, a really good fit for that incremental adoption where you can still tap into your, your, your massive, like python exi existing infrastructure workflows, data tooling, et cetera.
[00:26:55] And, and what does, what does that path to adoption look like?
[00:26:59] swyx: Yeah, we, we, we don't know cuz it's a wait listed language which people were complaining about. They, they, the, the mojo creators were like saying something about they had to scale up their servers. And I'm like, what language requires essential server?
[00:27:10] So it's a little bit suss, a little bit, like there's a, there's a cloud product already in place and they're waiting for it. But we'll see. We'll see. I mean, emojis should be promising in it. I, I actually want more. Programming language innovation this way. You know, I was complaining years ago that programming language innovation is all about stronger types, all fun, all about like more functional, more strong types everywhere.
[00:27:29] And, and this is, the first one is actually much more practical which I, which I really enjoy. This is why I wrote about self provisioning run types.
[00:27:36] Simon Willison: And
[00:27:37] Alessio Fanelli: I mean, this is kind of related to the post, right? Like if you stop all of a sudden we're like, the models are all the same and we can improve them.
[00:27:45] Like, where can we get the improvements? You know, it's like, Better run times, better languages, better tooling, better data collection. Yeah. So if I were a founder today, I wouldn't worry as much about the model, maybe, but I would say, okay, what can I build into my product and like, or what can I do at the engineering level that maybe it's not model optimization because everybody's working on it, but like you said, it's like, why haven't people thought of this before?
[00:28:09] It's like, it's, it's definitely super hard, but I'm sure that if you're like Google or you're like open AI or you're like, Databricks, we got smart enough people that can think about these problems, so hopefully we see more of this.
[00:28:21] swyx: You need, Alan? Okay. I promise to keep this relatively tight. I know Simon on a beautiful day.
[00:28:27] It is a very nice day in California. I wanted to go through a few more points that you have pulled out Simon and, and just give you the opportunity to, to rant and riff and, and what have you. I, I, are there any other points from going back to the sort of Google OpenAI mode documents that, that you felt like we, we should dive in on?
[00:28:44] Google AI Strategy
[00:28:44] Simon Willison: I mean, the really interesting stuff there is the strategy component, right? The this idea that that Facebook accidentally stumbled into leading this because they put out this model that everyone else is innovating on top of. And there's a very open question for me as to would Facebook relic Lama to allow for commercial usage?
[00:29:03] swyx: Is there some rumor? Is that, is that today?
[00:29:06] Simon Willison: Is there a rumor about that?
[00:29:07] swyx: That would be interesting? Yeah, I saw, I saw something about Zuck saying that he would release the, the Lama weights officially.
[00:29:13] Simon Willison: Oh my goodness. No, that I missed. That is, that's huge.
[00:29:17] swyx: Let me confirm the tweet. Let me find the tweet and then, yeah.
[00:29:19] Okay.
[00:29:20] Simon Willison: Because actually I met somebody from Facebook machine learning research a couple of weeks ago, and I, I pressed 'em on this and they said, basically they don't think it'll ever happen because if it happens, and then somebody does horrible fascist stuff with this model, all of the headlines will be Meg releases a monster into the world.
[00:29:36] So, so hi. His, the, the, the, a couple of weeks ago, his feeling was that it's just too risky for them to, to allow it to be used like that. But a couple of weeks is, is, is a couple of months in AI world. So yeah, it wouldn't be, it feels to me like strategically Facebook should be jumping right on this because this puts them at the very.
[00:29:54] The very lead of, of open source innovation around this stuff.
[00:29:58] Zuck Releasing LLaMA
[00:29:58] swyx: So I've pinned the tweet talking about Zuck and Zuck saying that meta will open up Lama. It's from the founder of Obsidian, which gives it a slight bit more credibility, but it is the only. Tweet that I can find about it. So completely unsourced,
[00:30:13] we shall see. I, I, I mean I have friends within meta, I should just go ask them. But yeah, I, I mean one interesting angle on, on the memo actually is is that and, and they were linking to this in, in, in a doc, which is apparently like. Facebook got a bunch of people to do because they, they never released it for commercial use, but a lot of people went ahead anyway and, and optimized and, and built extensions and stuff.
[00:30:34] They, they got a bunch of free work out of opensource, which is an interesting strategy.
[00:30:39] There's okay. I don't know if I.
[00:30:42] Google Origin Confirmed
[00:30:42] Simon Willison: I've got exciting piece of news. I've just heard from somebody with contacts at Google that they've heard people in Google confirm the leak. That that document wasn't even legit Google document, which I don't find surprising at all, but I'm now up to 10, outta 10 on, on whether that's, that's, that's real.
[00:30:57] Google's existential threat
[00:30:57] swyx: Excellent. Excellent. Yeah, it is fascinating. Yeah, I mean the, the strategy is, is, is really interesting. I think Google has been. Definitely sleeping on monetizing. You know, I, I, I heard someone call when Google Brain and Devrel I merged that they would, it was like goodbye to the Xerox Park of our era and it definitely feels like Google X and Google Brain would definitely Xerox parks of our, of our era, and I guess we all benefit from that.
[00:31:21] Simon Willison: So, one thing I'll say about the, the Google side of things, like the there was a question earlier, why are Google so worried about this stuff? And I think it's, it's just all about the money. You know, the, the, the engine of money at Google is Google searching Google search ads, and who uses Chachi PT on a daily basis, like me, will have noticed that their usage of Google has dropped like a stone.
[00:31:41] Because there are many, many questions that, that chat, e p t, which shows you no ads at all. Is, is, is a better source of information for than Google now. And so, yeah, I'm not, it doesn't surprise me that Google would see this as an existential threat because whether or not they can be Bard, it's actually, it's not great, but it, it exists, but it hasn't it yet either.
[00:32:00] And if I've got a Chatbook chatbot that's not showing me ads and chatbot that is showing me ads, I'm gonna pick the one that's not showing
[00:32:06] swyx: me ads. Yeah. Yeah. I, I agree. I did see a prototype of Bing with ads. Bing chat with ads. I haven't
[00:32:13] Simon Willison: seen the prototype yet. No.
[00:32:15] swyx: Yeah, yeah. Anyway, I I, it, it will come obviously, and then we will choose, we'll, we'll go out of our ways to avoid ads just like we always do.
[00:32:22] We'll need ad blockers and chat.
[00:32:23] Excellent.
[00:32:24] Non-Fiction AI Safety ("y-risk")
[00:32:24] Simon Willison: So I feel like on the safety side, the, the safety side, there are basically two areas of safety that I, I, I sort of split it into. There's the science fiction scenarios, the AI breaking out and killing all humans and creating viruses and all of that kind of thing. The sort of the terminated stuff. And then there's the the.
[00:32:40] People doing bad things with ai and that's latter one is the one that I think is much more interesting and that cuz you could u like things like romance scams, right? Romance scams already take billions of dollars from, from vulner people every year. Those are very easy to automate using existing tools.
[00:32:56] I'm pretty sure for QNA 13 b running on my laptop could spin up a pretty decent romance scam if I was evil and wanted to use it for them. So that's the kind of thing where, I get really nervous about it, like the fact that these models are out there and bad people can use these bad, do bad things.
[00:33:13] Most importantly at scale, like romance scamming, you don't need a language model to pull off one romance scam, but if you wanna pull off a thousand at once, the language model might be the, the thing that that helps you scale to that point. And yeah, in terms of the science fiction stuff and also like a model on my laptop that can.
[00:33:28] Guess what comes next in a sentence. I'm not worried that that's going to break out of my laptop and destroy the world. There. There's, I'm get slightly nervous about the huge number of people who are trying to build agis on top of this models, the baby AGI stuff and so forth, but I don't think they're gonna get anywhere.
[00:33:43] I feel like if you actually wanted a model that was, was a threat to human, a language model would be a tiny corner of what that thing. Was actually built on top of, you'd need goal setting and all sorts of other bits and pieces. So yeah, for the moment, the science fiction stuff doesn't really interest me, although it is a little bit alarming seeing more and more of the very senior figures in this industry sort of tip the hat, say we're getting a little bit nervous about this stuff now.
[00:34:08] Yeah.
[00:34:09] swyx: So that would be Jeff Iton and and I, I saw this me this morning that Jan Lacoon was like happily saying, this is fine. Being the third cheer award winner.
[00:34:20] Simon Willison: But you'll see a lot of the AI safe, the people who've been talking about AI safety for the longest are getting really angry about science fiction scenarios cuz they're like, no, the, the thing that we need to be talking about is the harm that you can cause with these models right now today, which is actually happening and the science fiction stuff kind of ends up distracting from that.
[00:34:36] swyx: I love it. You, you. Okay. So, so Uher, I don't know how to pronounce his name. Elier has a list of ways that AI will kill us post, and I think, Simon, you could write a list of ways that AI will harm us, but not kill us, right? Like the, the, the non-science fiction actual harm ways, I think, right? I haven't seen a, a actual list of like, hey, romance scams spam.
[00:34:57] I, I don't, I don't know what else, but. That could be very interesting as a Hmm. Okay. Practical. Practical like, here are the situations we need to guard against because they are more real today than that we need to. Think about Warren, about obviously you've been a big advocate of prompt injection awareness even though you can't really solve them, and I, I worked through a scenario with you, but Yeah,
[00:35:17] Prompt Injection
[00:35:17] Simon Willison: yeah.
[00:35:17] Prompt injection is a whole other side of this, which is, I mean, that if you want a risk from ai, the risk right now is everyone who's building puts a building systems that attackers can trivially subvert into stealing all of their private data, unlocking their house, all of that kind of thing. So that's another very real risk that we have today.
[00:35:35] swyx: I think in all our personal bios we should edit in prompt injections already, like in on my website, I wanna edit in a personal prompt injections so that if I get scraped, like I all know if someone's like reading from a script, right? That that is generated by any iBot. I've
[00:35:49] Simon Willison: seen people do that on LinkedIn already and they get, they get recruiter emails saying, Hey, I didn't read your bio properly and I'm just an AI script, but would you like a job?
[00:35:57] Yeah. It's fascinating.
[00:36:00] Google vs OpenAI
[00:36:00] swyx: Okay. Alright, so topic. I, I, I think, I think this this, this mote is is a peak under the curtain of the, the internal panic within Google. I think it is very val, very validated. I'm not so sure they should care so much about small models or, or like on device models.
[00:36:17] But the other stuff is interesting. There is a comment at the end that you had by about as for opening open is themselves, open air, doesn't matter. So this is a Google document talking about Google's position in the market and what Google should be doing. But they had a comment here about open eye.
[00:36:31] They also say open eye had no mode, which is a interesting and brave comment given that open eye is the leader in, in a lot of these
[00:36:38] Simon Willison: innovations. Well, one thing I will say is that I think we might have identified who within Google wrote this document. Now there's a version of it floating around with a name.
[00:36:48] And I look them up on LinkedIn. They're heavily involved in the AI corner of Google. So my guess is that at Google done this one, I've worked for companies. I'll put out a memo, I'll write up a Google doc and I'll email, email it around, and it's nowhere near the official position of the company or of the executive team.
[00:37:04] It's somebody's opinion. And so I think it's more likely that this particular document is somebody who works for Google and has an opinion and distributed it internally and then it, and then it got leaked. I dunno if it's necessarily. Represents Google's sort of institutional thinking about this? I think it probably should.
[00:37:19] Again, this is such a well-written document. It's so well argued that if I was an executive at Google and I read that, I would, I would be thinking pretty hard about it. But yeah, I don't think we should see it as, as sort of the official secret internal position of the company. Yeah. First
[00:37:34] swyx: of all, I might promote that person.
[00:37:35] Cuz he's clearly more,
[00:37:36] Simon Willison: oh, definitely. He's, he's, he's really, this is a, it's, I, I would hire this person about the strength of that document.
[00:37:42] swyx: But second of all, this is more about open eye. Like I'm not interested in Google's official statements about open, but I was interested like his assertion, open eye.
[00:37:50] Doesn't have a mote. That's a bold statement. I don't know. It's got the best people.
[00:37:55] Travis Fischer: Well, I, I would, I would say two things here. One, it's really interesting just at a meta, meta point that, that they even approached it this way of having this public leak. It, it, it kind of, Talks a little bit to the fact that they, they, they felt that that doing do internally, like wasn't going to get anywhere or, or maybe this speaks to, to some of the like, middle management type stuff or, or within Google.
[00:38:18] And then to the, the, the, the point about like opening and not having a moat. I think for, for large language models, it, it, it will be over, over time kind of a race to the bottom just because the switching costs are, are, are so low compared with traditional cloud and sas. And yeah, there will be differences in, in, in quality, but, but like over time, if you, you look at the limit of these things like the, I I think Sam Altman has been quoted a few times saying that the, the, the price of marginal price of intelligence will go to zero.
[00:38:47] Time and the marginal price of energy powering that intelligence will, will also hit over time. And in that world, if you're, you're providing large language models, they become commoditized. Like, yeah. What, what is, what is your mode at that point? I don't know. I think they're e extremely well positioned as a team and as a company for leading this space.
[00:39:03] I'm not that, that worried about that, but it is something from a strategic point of view to keep in mind about large language models becoming a commodity. So
[00:39:11] Simon Willison: it's quite short, so I think it's worth just reading the, in fact, that entire section, it says epilogue. What about open ai? All of this talk of open source can feel unfair given open AI's current closed policy.
[00:39:21] Why do we have to share if they won't? That's talking about Google sharing, but the fact of the matter is we are already sharing everything with them. In the form of the steady flow of poached senior researchers until we spent that tide. Secrecy is a moot point. I love that. That's so salty. And, and in the end, open eye doesn't matter.
[00:39:38] They are making the same mistakes that we are in their posture relative to open source. And their ability to maintain an edge is necessarily in question. Open source alternatives. Canned will eventually eclipse them. Unless they change their stance in this respect, at least we can make the first move. So the argument this, this paper is making is that Google should go, go like meta and, and just lean right into open sourcing it and engaging with the wider open source community much more deeply, which OpenAI have very much signaled they are not willing to do.
[00:40:06] But yeah, it's it's, it's read the whole thing. The whole thing is full of little snippets like that. It's just super fun. Yes,
[00:40:12] swyx: yes. Read the whole thing. I, I, I also appreciate that the timeline, because it set a lot of really great context for people who are out of the loop. So Yeah.
[00:40:20] Alessio Fanelli: Yeah. And the final conspiracy theory is that right before Sundar and Satya and Sam went to the White House this morning, so.
[00:40:29] swyx: Yeah. Did it happen? I haven't caught up the White House statements.
[00:40:34] Alessio Fanelli: No. That I, I just saw, I just saw the photos of them going into the, the White House. I've been, I haven't seen any post-meeting updates.
[00:40:41] swyx: I think it's a big win for philanthropic to be at that table.
[00:40:44] Alessio Fanelli: Oh yeah, for sure. And co here it's not there.
[00:40:46] I was like, hmm. Interesting. Well, anyway,
[00:40:50] swyx: yeah. They need, they need some help. Okay. Well, I, I promise to keep this relatively tight. Spaces do tend to have a, have a tendency of dragging on. But before we go, anything that you all want to plug, anything that you're working on currently maybe go around Simon are you still working on dataset?
[00:41:04] Personal plugs: Simon and Travis
[00:41:04] Simon Willison: I am, I am, I'm having a bit of a, so datasets my open source project that I've been working on. It's about helping people analyze and publish data. I'm having an existential crisis of it at the moment because I've got access to the chat g p T code, interpreter mode, and you can upload the sequel light database to that and it will do all of the things that I, on my roadmap for the next 12 months.
[00:41:24] Oh my God. So that's frustrating. So I'm basically, I'm leaning data. My interest in data and AI are, are rapidly crossing over a lot harder about the AI features that I need to build on top of dataset. Make sure it stays relevant in a chat. G p t can do most of the stuff that it does already. But yeah the thing, I'll plug my blog simon willis.net.
[00:41:43] I'm now updating it daily with stuff because AI move moved so quickly and I have a sub newsletter, which is effectively my blog, but in email form sent out a couple of times a week, which Please subscribe to that or RSS feed on my blog or, or whatever because I'm, I'm trying to keep track of all sorts of things and I'm publishing a lot at the moment.
[00:42:02] swyx: Yes. You, you are, and we love you very much for it because you, you are a very good reporter and technical deep diver into things, into all the things. Thank you, Simon. Travis are you ready to announce the, I guess you've announced it some somewhat. Yeah. Yeah.
[00:42:14] Travis Fischer: So I'm I, I just founded a company.
[00:42:16] I'm working on a framework for building reliable agents that aren't toys and focused on more constrained use cases. And you know, I I, I look at kind of agi. And these, these audigy type type projects as like jumping all the way to str to, to self-driving. And, and we, we, we kind of wanna, wanna start with some more enter and really focus on, on reliable primitives to, to start that.
[00:42:38] And that'll be an open source type script project. I'll be releasing the first version of that soon. And that's, that's it. Follow me you know, on here for, for this type of stuff, I, I, I, everything, AI
[00:42:48] swyx: and, and spa, his chat PT bot,
[00:42:50] Travis Fischer: while you still can. Oh yeah, the chat VT Twitter bot is about 125,000 followers now.
[00:42:55] It's still running. I, I'm not sure if it's your credit. Yeah. Can you say how much you spent actually, No, no. Well, I think probably totally like, like a thousand bucks or something, but I, it's, it's sponsored by OpenAI, so I haven't, I haven't actually spent any real money.
[00:43:08] swyx: What? That's
[00:43:09] awesome.
[00:43:10] Travis Fischer: Yeah. Yeah.
[00:43:11] Well, once, once I changed, originally the logo was the Chachi VUI logo and it was the green one, and then they, they hit me up and asked me to change it. So it's now it's a purple logo. And they're, they're, they're cool with that. Yeah.
[00:43:21] swyx: Yeah. Sending take down notices to people with G B T stuff apparently now.
[00:43:26] So it's, yeah, it's a little bit of a gray area. I wanna write more on, on mos. I've been actually collecting and meaning to write a piece of mos and today I saw the memo, I was like, oh, okay. Like I guess today's the day we talk about mos. So thank you all. Thanks. Thanks, Simon. Thanks Travis for, for jumping on and thanks to all the audience for engaging on this with us.
[00:43:42] We'll continue to engage on Twitter, but thanks to everyone. Cool. Thanks everyone. Bye. Alright, thanks everyone. Bye.
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Podcast: Latent Space: Founders, Engineers, and News on Software 3.0, DevTools, Computer Vision, Data Science, AI UX (LS 26 · TOP 10% what is this?)
Episode: From RLHF to RLHB: The Case for Learning from Human Behavior - with Jeffrey Wang and Joe Reeve of Amplitude
Pub date: 2023-06-08
Welcome to the almost 3k latent space explorers that joined us last month! We’re holding our first SF listener meetup with Practical AI next Monday; join us if you want to meet past guests and put faces to voices! All events are in /community.
Who among you regularly click the ubiquitous 👍 /👎 buttons in ChatGPT/Bard/etc?
Anyone? I don’t see any hands up.
OpenAI has told us how important reinforcement learning from human feedback (RLHF) is to creating the magic that is ChatGPT, but we know from our conversation with Databricks’ Mike Conover just how hard it is to get just 15,000 pieces of explicit, high quality human responses.
We are shockingly reliant on good human feedback. Andrej Karpathy’s recent keynote at Microsoft Build on the State of GPT demonstrated just how much of the training process relies on contractors to supply the millions of items of human feedback needed to make a ChatGPT-quality LLM (highlighted by us in red):
But the collection of good feedback is an incredibly messy problem. First of all, if you have contractors paid by the datapoint, they are incentivized to blast through as many as possible without much thought. So you hire more contractors and double, maybe triple, your costs. Ok, you say, lets recruit missionaries, not mercenaries. People should volunteer their data! Then you run into the same problem we and any consumer review platform run into - the vast majority of people send nothing at all, and those who do are disproportionately representing negative reactions. More subtle problems emerge when you try to capture subjective human responses - the reason that ChatGPT responses tend to be inhumanly verbose, is because humans have a well documented “longer = better” bias when classifying responses in a “laboratory setting”.
The fix for this, of course, is to get out of the lab and learn from real human behavior, not artificially constructed human feedback. You don’t see a thumbs up/down button in GitHub Copilot nor Codeium nor Codium. Instead, they work an implicit accept/reject event into the product workflow, such that you cannot help but to give feedback while you use the product. This way you hear from all your users, in their natural environments doing valuable tasks they are familiar with. The prototypal example in this is Midjourney, who unobtrusively collect 1 of 9 types of feedback from every user as part of their workflow, in exchange for much faster first draft image generations:
The best known public example of AI product telemetry is in the Copilot-Explorer writeup, which checks for the presence of generated code after 15-600 second intervals, which enables GitHub to claim that 40% of code is generated by Copilot.
This is fantastic and “obviously” the future of productized AI. Every AI application should figure out how to learn from all their real users, not some contractors in a foreign country. Most prompt engineers and prompt engineering tooling also tend to focus on pre-production prototyping, but could also benefit from A/B testing their prompts in the real world.
In short, AI may need Analytics more than Analytics needs AI.
Amplitude’s Month of AI
This is why Amplitude is going hard on AI - and why we recently spent a weekend talking to Jeffrey Wang, cofounder and chief architect at Amplitude, and Joe Reeve, head of AI, recording a live episode at the AI + Product Hackathon where 150+ hackers gathered to compete for over $22.5k in prizes from Amplitude, New Relic, LanceDB, AWS, and more.
To put things in perspective, Amplitude is a legendary YC alum with $238M of revenue in 2022 — our first guests representing the AI efforts of a public company!
We chatted about how they have been approaching AI in their product (“question to chart” BI, text field autofill, instrumenting Amplitude with Amplitude), some of the issues they’ve had with different models, and the importance of first-party data in the world of LLMs. Another topic that came out of the Q&A was this idea of almost an “AmplitudeGPT”; rather than using language to simply generate a query, you could have these models investigate reasons for why certain behavior is happening in your user base. It was a really good discussion, and hope you all enjoy listening to it!
Sections
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[00:00:47] Amplitude's founding story and pivot
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[00:03:28] Amplitude as an AI company and opportunities
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[00:07:14] Limitations and challenges with using AI models
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[00:10:56] Using Amplitude's product to build Amplitude - instrumenting AI
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[00:12:32] Existing ML models in Amplitude's product and customer use cases
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[00:15:50] “A/Z testing” and adaptable products
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[00:19:33] The future of analytics and dashboards
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[00:21:03] Optimizing for metrics in chatbots and AI products
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[00:26:22] Using general models vs. fine-tuned models
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[00:30:24] The importance of models vs. data - Amplitude's data set
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[00:39:00] Lightning Round + Q&A
Show Notes
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Amplitude
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Sonalight to Amplitude pivot announcement
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The Slack origin story
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Reverse Engineering Copilot
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Simon Willison’s blog
Transcript
Editor’s note: all timestamps are 1 minute behind because we hadn’t yet added the intro before making these. Sorry about that!
Alessio: Thank you everyone for coming. Hopefully, some of you have listened to the podcast before, if you haven't, we focus on AI research and application. So we don't focus on “AI is going to kill us all”. We don't think about virtual girlfriends. We don't think about all of these more societal things. We're focused on models: how do you build them? How do you train them? How do you use them in production? What are some of the limitations on getting these things from demos to things that millions of users use? And obviously, a lot of you are building things. Otherwise, you wouldn't be here. And some of you have been building things for a long time, and now have a new paradigm that you want to build on top of. So I'm excited to dive in here. And maybe, I mean, I'm sure most people know you, but maybe you want to do intros and give a little background. [00:00:47]
Jeffrey: Sure. Yeah, hey, everyone, met you all this morning, but I'm Jeffrey. I'm one of the co-founders and Chief Architect here at Amplitude. Been working on this product analytics thing, helping people understand user behavior data and make great product decisions and build better products for the last decade or so. And obviously, AI is a technology that we've been leveraging for a long time, but the recent trends are particularly exciting. And yeah, we have a lot of thoughts on how to apply that to our space, what we're doing in our product, and what we think the future of AI and product development and product data is. So excited to talk through some of those. [00:01:20]
Joe: Yeah, I'm Joe, Joe Reeve. I've got a background in sort of startups and tech, been professional software engineer since I was 16, quit college. And at the moment, I'm running sort of AI R&D efforts here at Amplitude. Super excited about all the new stuff, but also all the stuff that Amplitude's been doing for a long time and how we're sort of getting renewed interest and excitement and abilities to push that even further forwards. [00:01:44]
Swyx: So I think it's useful for people listening on the podcast and also some people here. Can you contextualize Amplitude as an AI company? Like what does that mean to you? What unique opportunities do you guys have? [00:02:02]
Jeffrey: Sure, yeah, happy to speak to that. So, you know, if we think about the fundamental thing that our customers of Amplitude try to do, it's they want to look at their product data and they want to figure out how do I make my product better? And the really cool thing about product data is that one, it's often like very high fidelity, right? Digital products compared to, you know, let's say physical products before them have way more information about what's going on. And so that's why product data is, you know, even a thing at all, right? You finally have that feedback loop of, hey, I built this thing. This is how people are using it. Now let me learn from that and make my product better. Now, one of the downsides of that is that the data is massive. If you look at any of the internet scale products out there, they generate enormous amounts of data. And the ability of humans to kind of sift through that data is obviously limited. At Amplitude, we try to give people as many tools, whether AI or not, in order to process that. But at the end of the day, if you could get from the data and what user behavior is happening in your product to the insights of how to make your product better without as much manual work, that's kind of the holy grail of product analytics. And so in some sense, Amplitude has always been a company on the path to AI because figuring out how to make your product better from data is ultimately an AI problem. And so we're kind of just solving all the barriers in the way, like getting data in first, building good models for short-term things. And long-term, it's always been about, hey, how can you take product data and automatically make your product better as fast as possible? [00:03:28]
Alessio: So that's the future of Amplitude. And a lot of people here probably want to start companies and whatnot. So maybe you want to give a 60 seconds of why you started Amplitude and what the story was like and maybe the first three to six months, what the challenges were. [00:03:42]
Jeffrey: Yeah, of course. It's funny that we talk about this because the start of Amplitude is actually almost more AI than the current state. And so actually my two co-founders, Spencer and Curtis, they went through YC originally with not Amplitude, but SonaLite, which was a text-by-voice company. So it was kind of before the era of Siri and those types of technologies where they wanted to build something that would read text messages to them, that's easy, but also do voice recognition so that you could send text messages, say when you're driving, without having to pull out your phone. And so they worked on it and it was really popular back when they were doing it. After they finished YC, they realized the big innovation that they needed to figure out in order to make that successful was being really good at voice recognition, which was a different problem. They're awesome software engineers, but they don't come from an ML background. And so it's like, okay, are we going to spend the next five years solving voice recognition? Not really the thing that they had in mind when they were building product. But one thing that they happened to stumble upon as they were working on that was they spent a lot of time thinking about, hey, what was hard about that product? What made users churn? What made users really love it and engage? And they built a bunch of analytics tools to help them understand that. And they were really kind of shocked that those tools didn't exist out there in the market or they were like much more primitive than they wanted. And it turns out a bunch of other people in their YC batch felt the same. And they were like, hey, that analytics thing you're building, we want that. For you to text by voice, we want your analytics product. And so they're like, okay, fine. We will pivot, natural language and voice recognition isn't really our thing. And so we'll do distributed systems and analytics instead. That's where I came in. I'm a distributed systems and analytics guy. And so I happened to get in touch with them just through some mutual friends at the time. And then, yeah, we kind of went on it. The funny thing about a lot of things in technology is that the most forward thinking companies with respect to a lot of technologies are gaming companies. And so a lot of AmpliG's early start was either gaming companies or companies with founders that came from gaming backgrounds, where in gaming people have always been very, very rigorous about product data and optimizing engagement loops and all of that. And so they look for the best tools. We went to Zynga 15 years ago. It's like, that's where product analytics originated. And so a lot of those founders of new startups who had left Zynga were like, hey, that thing that you're building, that's trying to figure out patterns and user data and use that to make better products. That is exactly what we want after leaving Zynga. And then from there, that was Amplitude.
Swyx: Yeah, I think famously other gaming companies would be like Slack, right? Mr. Butterfield tried to make a gaming company and failed and made Flickr. Then he tried to make another gaming company and failed and made Slack. And now look out to see what he does next. Discord as well. That's right. [00:06:34]
Jeffrey: Yeah, people who come from gaming backgrounds are very rigorous in their product thinking. [00:06:39]
Swyx: That's interesting. Alessio, you have a background in games? [00:06:43]
Alessio: Yeah, in playing them, not in building them. So I will not fall into an enterprise company by doing that. Let's talk about R&D today and some of the ideas that you're working through, like some of the limitations that you run through. I think the most interesting thing about hackathons is you come with an idea and then you kind of hit a wall trying to build it. And then that takes you into another path. Like what are maybe funny things that you learn in terms of like the limitations of these models or like the missing infrastructure for using them? [00:07:14]
Joe: So we've got a couple of different frames for thinking about this. There's AI that we're putting into our products and then us knowing that our customers want to put AI into their products. So there's the, how do we support our customers in their product development using AI? But how do we do that ourselves? And this is a great opportunity for us to learn the challenges our customers are gonna see. And so the first thing there is let's just start from the beginning, assume we want to add AI to our product, which maybe isn't the best place to start, but let's just assume we want to. How do we start ideating opportunities to put stuff into our product? So we sort of came up with this framework where we look at our product and we think about what are the collaboration touch points? So where are the points that a human might hand off to another human? And then think where can we replace one of those humans with the machine? So instead of thinking of some AI, amorphous AI, LLM, whatever, we're thinking actually, what if we had a robot that we were collaborating, not just a human, not just some sort of thing that spits out numbers. So collaborating. Then there's thinking of these as tools. So this is like your auto-suggest, on your mobile keyboard or spell check or something. How do you integrate this stuff as deeply into your product? So what are the friction points that users go through? Maybe they check lots of boxes. Is there a way we can pre-check those boxes we can get? So that's the feature embedding really deeply into the tool you've already got, the product you've already got. And then you step back and think, okay, what's a tool? So a tool is like ChatGPT, where you go there, it's an AI powered tool. It's not necessarily connected to your product, but it's a supplementary tool that you add. So there's a sort of ideation process there that we went through. And we sort of landed on a couple. And one of the key things that Amplitude does is help our customers, one, collect data in like a standard and sort of queryable way. And then we help them query it and get insights out of that data. So we were thinking, what's the feature there? How do we embed that? But also what's the collaboration point? And you might be a product manager asking an analyst, hey, please help me. Let's have a conversation about this. I don't know what questions to ask, but you also might just be about to go click the big create button and fill in a bunch of fields. And can we fill in a bunch of the fields for you? So we went to what to us seemed like one of the most obvious places. And we built a text box. Surprise, surprise with LLMs. We've got a text box. You can type in a question, type in anything about your data that you want to know, and then it'll spit back a chart, which is kind of neat. And we hit a bunch of problems there with LLMs hallucinating, losing context, even within the context windows, not really sort of recalling everything within the context window. So we sort of did a bunch of experimentation and realized if we split this down to seven different questions, so instead of saying, generate me a chart and a query for this one question, let's split that into lots of sub queries, like what kinds of events should I use? How should I display this? What should I call it? Rather than asking you all of that in one go. But then we had another problem where we have one query that a user makes that actually spins out seven different queries. So how do we monitor this? We can't just say one performance metric. You know, RLHF, you can't just say yes or no. Was the query response good? Because it might've failed for one of seven reasons. And maybe multiple of them failed or maybe some of them failed and then maybe they've hallucinated. And so we're getting code errors where an enum is not being matched. So we've had lots of sort of issues going all the way down there that we've had to figure out from first principles and sort of a really exciting way for us to understand what our customers are going through. [00:10:56]
Swyx: So I wanna be clear. So you've described your exploration and how you think about products. What have you released so far? I just wanna get an idea of what has been shipped. [00:11:08]
Joe: Sure. So in terms of LLM stuff, this, we call it question to chart internally. This ask a question, get a chart out. This, we've started rolling out to customers already. So last week, actually, started rolling out to our AI design partners a sign that we had signed up, which is a really exciting process. Actually, a lot of customers are just so excited to work with us and try it out and see how they can break it. So that's something we rolled out recently, which is built in LLM. It's the first piece built on LLM that we're working on. But we've also had a bunch of long-term ML, sort of traditional ML models that we've been running and products that we've been running with customers that help them predict what their users are gonna do. Because we've got this massive behavioral data set, best behavioral data set in the world. So we can train these awesome models and help our customers predict what their users are gonna do. So they can share the more relevant content or now is the right time to ask people if they want to upgrade or they want to rate your app or that sort of thing. [00:12:05]
Swyx: Yeah, there is a little bit of a contrast, conflicts, because you already had all these ML models in-house and you're spinning up a new AI team and you're like, no, let's do all of this with GPT-3. Are the existing ML researchers saying like, no, this is a complete misuse of text generation? Or are they excited about it? Is it unlocking new things? [00:12:32]
Joe: Yeah, actually, it's the combining these things. So we're able to use the traditional ML to shorten the fields, to narrow the number of things we need to pass into the LLMs. Because the LLMs can do a lot more of the reasoning, but we can make sure that the context we're providing is much more specific and generally much better by using the traditional ML models. [00:12:53]
Swyx: Yeah, okay. And then the pain points that you're experiencing are hallucination. And then also like the multi-query thing. What do you think you wish for? Or what do you think you're thinking about to solve those pain points? [00:13:06]
Joe: So right now we're instrumenting with our own product. So we're instrumenting groups of inferences and individual inferences, which means we can then create charts that show how often they fail, why they fail, how often we need to retry to get good answers.
Swyx: So amplitude using amplitude. [00:13:23]
Joe: Exactly. To build amplitude. [00:13:24]
Swyx: Yeah, exactly. [00:13:25]
Joe: Well, I mean, we're a product company. What else would we do? [00:13:29]
Swyx: That is the second part of what you're saying, right? Which is, first of all, you want AI in the amplitude products. Second, people are shipping AI products with amplitude. You wanna talk a little bit more about what you're seeing there? [00:13:39]
Joe: Yeah. I guess the key thing here is, for a lot of people is, okay, I can build the thing that calls OpenAI's API and then gives a response back. I'm nervous that I'm gonna be giving incorrect answers. I'm nervous that I don't really know how to measure whether the answers are incorrect. And I'm nervous that I'm not gonna be able to improve over time. So a lot of people we actually hear are nervous of giving thumbs up, thumbs down buttons because they're implying to their users that they're gonna be using this to improve the results. But they actually have no idea how to use that to improve the results in a meaningful way. And particularly when you've got multiple queries going off for one request, you've gotta then fine tune lots of different things in parallel. So it gets to be quite a technically complex sort of problem if you're not using great tooling that already exists for it. So that's, and then you have the extra layer of, I'm getting a bad result. I've tweaked my prompt template that I'm sending off to OpenAI. And now, has the result got better or worse? [00:14:35]
Swyx: I don't know. [00:14:36]
Joe: I don't know how to measure that. Except by thumbs up, thumbs down, which is a difficult measure in the first place. So that's where we can start saying, measuring the behavior of users once we've generated something for them. So have they gone and shared this content? Have they used this content? They actually gotten any value out of it? Not just have they pressed thumbs up. We can actually measure, are they getting value? Are they throwing it away from their behavior? But then using that through the Amplitude product, we can then tie that through to A-B tests, which is another product that Amplitude has. So then suddenly we start, and we're not doing this yet. This is sort of next on our list, is to start putting these prompts into our A-B test variants. So then we make a tweak in the UI, and it goes off, fires on the original, the control and our variant, our new variant. See, does it get fewer or more errors? Does it get fewer or more thumbs up, thumbs down? [00:15:30]
Alessio: Have you thought about, I don't know, A-Z testing, I guess? Like one of the limitations has been, well, people can only write so much copywrite to test, but now with these generative models, you can actually generate a lot of copy. And like you go to on-demand test more and more and more copy. Have you seen any maybe fun customer stories? Like can you, anything there? [00:15:50]
Jeffrey: Yeah, so actually there's a very good example of this. I don't know if I can share the actual customer, but actually from before the LLM days, where they literally generated the versions of the copy themselves, and they made their product basically adapt, you know, multi-arm bandit style of like, hey, here's all these different variations, like just go figure out the best one. At an internal hackathon, maybe two months ago, I built a prototype of what you're talking about, which is, okay, now replace the copy generation with an LLM. So just constantly generating new variations, and then multi-arm banditing to figure out which one's the best. I think that is probably the future of copywriting, where it's like, you don't actually need a whole lot of manual work anymore. It can, almost everything can happen automatically. And it's kind of the micro example in my head of this concept that we really like, which is self-improving products, where, you know, at some point, you know, someone has to say, hey, I'm gonna build a product that does this, you know, like a newsreader or something. But then, you know, after you have that, like the title of the newsreader, like the description of the sections, your navigation, all of that, in theory, you know, if you can give it some structure that the AI can play with, the LLM can manipulate all of that for you, and then use, you know, A-B testing, multi-arm bandits and all of that to kind of figure out what's best. And that generative AI kind of makes that last piece of like, what are my options possible? And that's super exciting for us. And we wanna be there, you know, to help you measure that, help you deploy that, and make that like the way people build products in the future. [00:17:14]
Alessio: I think I've talked about this on the podcast, but this idea of like just-in-time UIs, you know, like each type of user wants to interact in a different way. And like, what you're building is a way of that, right? Like, Amplitude has been really like dashboard-driven, kind of like a diagram-driven, showing the user flow. Now each user can say, hey, I don't really want the table. I just want the charts. Or like, I don't want the charts. I just want the data. What do you think about the future of like dashboards and like BI in general? But like, the analysts used to come up with like what you should be seeing. Now each user can ask their own questions. [00:17:47]
Jeffrey: Yeah, like the future of analytics, I think, is, you know, can go a few different paths. One thing that I want to, you know, counter against the whole LLM trend a little bit is I think when you get into really important and specific questions, you know, let's say you're writing like some complicated SQL or even code, you know, code and SQL are good because they're very specific, right? You can define your semantics very precisely. And that's something that I think, you know, when people start thinking about like natural language questions, they kind of take for granted. They're like, oh yeah, why doesn't it just, you know, figure out the precise semantics from my very ambiguous words? It's like, well, it's actually, in some senses it's possible, right? Because the precise semantics are not captured by your ambiguous natural language words. And so the way we think about it, at least today, you know, who knows what's going to change in the future is like natural language is a great interface to like get started. If you don't know what the underlying data looks like, if you don't know like what questions you should be asking, it is a very, very expressive way to start, get started. It's much easier than manipulating a bunch of things, much, much easier than writing SQL and all of that. But like once you kind of know what you want, it's very hard to like make it precise. It's actually easier to make SQL or code precise than it is natural language. And so that's a little bit of what we're thinking right now. So we think, you know, for sure the way that maybe many people will interface with analytics and data will turn into natural language because maybe the precision doesn't matter to them. But like at the end of the day, when you're trying to get, you're trying to sum up your revenue or something, it's like, you want to know that it's right. And you want to know the semantics that go into that. And like, that's why, you know, that's part of why data is hard. The semantics really do matter. They can make a huge difference in the output. And so there's a boundary there that I'm curious where it will push over time, but I don't think it's quite there yet. [00:19:33]
Joe: I think this is where models sort of can become more embedded as features rather than go off and do this thing, create this analysis for me and then come back, the collaborator model. Then we're saying this field, I'm not sure what should go in there. Can you make a suggestion? And then I'm going to go and refine it over time. So it's the sort of autofill, but guessing autofill, but then you still, you can tweak everything. This is one of the core design sort of principles that we've come up is yes, you've got to be able to explain what the model's doing. And as a human, I need to understand, a user I need to understand what is the model doing and why is it doing it? But I also need to be able to tweak it once it's done it. I don't want to feel like I've just said go and then I can't stop it and it's going to go off and do stuff. And that's sometimes how things like AutoGPT can feel. It's going and it's costing me OpenAI tokens and I have no idea what's going on. So yeah, I think a key thing is servicing all the individual things the model's doing and allowing users to tweak it, stop it, retry while it's going. [00:20:33]
Swyx: For me, one of the most challenging questions is something I think you guys have maybe thought about a lot which is chat. Ideally you want, like you could say naively, for example, you want to optimize time in app, but actually that's a sign of failure if the chat session is longer than it should be. Do you have any advice on, I'm sure you've dealt with this before pre AI era, but like what do you advise AI hackers to optimize for? Like what analytics should people be looking at? [00:21:03]
Jeffrey: Yeah, our general kind of philosophy as a company is to work with customers to identify north star metrics. Right, and like time in app is not good primarily because it doesn't actually correlate with your business outcomes most of the time. And to be fair, sometimes it does. Like if you're a social media app, maybe it does correlate really well and maybe it's not a bad metric then. But for a lot of other products, right, if you're trying to do the search, for example, or like time on search, like nobody wants that. It's like, yeah, what is your success rate? You know, how many, do you get them to come back and search in the future? Like that's much more interesting than the time of your session. And so, because you know, each time you can serve apps, right, that's your business. And so it's like, if you choose a metric that's well correlated with your business outcomes, then that's at least the first step to getting that right and not getting caught up in other vanity metrics that sound like they could be good to increase, but then, you know, they can sometimes lead to negative business outcomes, you know, and then you get the worst. You've optimized the wrong metric the whole time. And that's where tying in AI and product analytics makes a lot of sense. And it's really important because product analytics, these companies that are like our customers that are trying out building features that are LMs and they're not sure what to optimize for, optimize for the same thing you're already optimizing for. You're already measuring conversions. You're measuring how much value, hopefully, your customers are getting out of your product. So continue doing that and maybe find a way to tie the LLM feature to that and sort of through A-B tests and that sort of thing. And then on the chat specifically, chat is obviously for a business maybe rolling out a chat box based on LLMs. It can be really scary. And that's another sort of mental model of framing we've been thinking around is we find LLMs right now are most useful either when you come from, either when you have a narrow input space and a broad output space, because you can be very, you know exactly what format of data, what kind of data is gonna be passed in. That's probably not coming directly from a user. It's probably coming from a button click or a toggle switch or something. And then you can have a general output and you can provide templates and that sort of thing. And then the other way is broad input space, narrow output space. So that's free form text box. And you can provide a bunch of sort of clamping, framing, validation on the output to make sure that you're not spewing out, you know, poems about Hitler or whatever it is. You know, you can be really, really deliberate when you've got a small output space. Chat is large input space, large output space, which is really, really scary. If you're, as a company, you're not selling a chat product, you're selling a, you know, an analytics product with maybe a chat support bot or something. [00:23:37]
Swyx: Yeah, I think this is one of those opportunities. I always try to raise the awareness of this, that Copilot I think did a really interesting metric or North Star, which was how much code is kept or retained by the user. And for people who are Googling along, you can actually look for this blog post about reverse engineering Copilot internals. And they actually set up custom metrics around, you know, 30 seconds after a code snippet is accepted, one minute, two minute, three minute, all the way to five minutes. And you can sort of see it construct a curve of how long Copilot suggestions stick around. And from there, they can actually make statements like this, you know, evaluate the success of the products. It's pretty cool. [00:24:18]
Joe: One of the really nice things we found actually, we accidentally did this. So our chart building interface, heavily instrumented. It's a, we're Amplitude. So we instrument our product. We also, it's one of the main tools that our customers use. So it's really, really well instrumented. And so when we tied chart creation through asking a question through an LLM, and then we tied that to a chart, an output chart, we then automatically were able to tie every time someone edits any of the parameters to that generation. So then we know, we have really detailed RLHF data for, yeah, you got everything apart from the metric, right? But you got everything apart from this event that shouldn't have been there, because that's the one that got removed. So similar to the Copilot there. [00:25:00]
Alessio: And I want to make sure we open it up for questions, but like one last thing is about, everybody knows that small is beautiful. And when you think about what models to use and some of the parameters, like there's costs, there's latency, there's like accuracy. How do you think about using, you know, GPT-4 and some of those models versus using smaller ones that are fine-tuned? What are the trade-offs? [00:25:23]
Joe: Yeah, I guess right now we're very much in the, let's explore, let's try everything and just iterate as fast as possible, which is what general models are great for. We do have some smaller, not even fine-tuned, some smaller models that we've sort of borrowed from Hugging Face that we run internally for more specific tasks. And that's often sort of selecting specific values before we pass it to a general model right now, just because the general models are much easier to communicate with and they understand most of the words we use. It's not like we use a word and suddenly we get random outputs for no reason, the sort of gold magic up type thing. So they're generally less susceptible to that. So that's why we're iterating heavily on the general models. I think we absolutely have to move to some more specific models, particularly given inference on fine-tuned open AI models gets more expensive and slower the more you do it. So yeah, that's definitely a thing we're looking at and we're doing some internal stuff, but it's the next step or one of the next steps. [00:26:22]
Jeffrey: Yeah, to give a pseudo example of that, one of the hard things to help users within Amplitude is picking the right event to analyze. It's kind of your fundamental unit of analysis. And when a user comes in and let's say that's the first time they're using Amplitudes, someone else in their company has set up the product, so they don't know what the events are. Right now in Amplitude you get this massive dropdown and it's like, all right, there's a thousand things, like which one is the one I'm looking for. And sometimes the names are good and sometimes they're not. But one thing we did was, okay, yeah, feed that into open AI. Hey, tell me which event type best matches like this user's intent. That's like pretty good at that, right? So it's all language stuff, but it's a little bit slow and it's a little bit expensive to do that every time. And so we kind of fell back to, once we validated that that works, kind of fell back to a more traditional embedding-based approach. It's like, all right, compute all those embeddings. That's more work upfront because you have to go through your database of all of these things and you got to commit like that engineering work, but it's like you validate with the general model because it's just easy. It takes like an hour to figure out that it works. And then it's like, all right, can we do the same thing with embeddings? That's way faster, way cheaper and still has reasonable quality. Embeddings also have a nice quality that you can get like magnitude of things, whereas LLMs aren't great at giving you like, hey, it matches this much. It's kind of, you can ask it for an order and that's decent, but like, yeah, anything beyond that is pretty challenging. [00:27:42]
Alessio: How do you think about the importance of the model versus the data, right? There's like a lot of companies that have a lot of data, but not a lot of AI expertise or companies that are just using off the shelf model. How should companies think about how much data to collect? What data is meaningful? What isn't, any thoughts there? [00:27:59]
Jeffrey: Yeah, I think it's safe to say that both are really important, right? Like the evolution of LLMs really was a lot of model innovation. And so I don't want to downplay that. At the same time, I think the future of AI applications and doing really cool things with it will be in the data, partially because like, you know, ChatGPT has done such a huge advance, right? The LLMs model space has advanced like crazy in the last year. And so I think a lot of the untapped potential will be in data in the future. One thing that's particularly interesting to us is like we have a pretty unique data set, actually. It's a lot of first party behavior data, right? So if you're, you know, if you're Square, for example, you instrumented like the way that people interact with Square Cash and the wallet and the, you know, the checkout system. And like, those are very specific things. Like Square can't look elsewhere in the world for that stuff. And that's really interesting because, you know, to build models of user behavior, you need user behavior data. And it turns out there's not actually a lot of examples of user behavior data out there in the world. And so to Joy's point earlier about, you know, we have one of the best user behavior data sets in the world. And so if we want to build a model around that, I think it would be a super interesting one. So if you take an analogy to what ChatGPT does, it basically takes a bunch of language examples and it, you know, learns a bunch of abstract concepts, like how to, you know, prove math things or how to render in JavaScript. It's like, wow, that's very astonishing. They kind of prove, it's almost like a proof of concept to the world that if you train a sufficiently good, you know, transformer self-attention type model with a sufficiently large data set of, you know, hundreds of gigabytes of internet text, you'll learn really interesting abstract concepts. And so we want to apply that to our data set, right? Cat GPG is great because it's a proof of concept. If it didn't exist, you know, I would have told you, yeah, you can spend $10 million training this model on a data set, you'd probably not get anything interesting because we just have no idea. But because it exists, it kind of proves to the world that if you do this correctly, there is a ton of interesting value. And so that's what I think. And so, you know, amplitude is just one example of a very interesting data set that you will train something that's, you know, fundamentally very different from GPT or any LLM out there. And there's lots of other data sets out there. And I think that's where a lot of the interesting things will come once this kind of, this phase of like rapid model evolution kind of tapers out a little bit. And you'll see a lot of the more interesting applications there. [00:30:24]
Swyx: So I've never thought about this much, but you guys must do it a lot. Like what is the ethics or best practices around training on user data when they don't know they're being watched? Like, I mean, presumably they're fine with tracking and events, but like, do we tell them that we're going to train on their data? Is it okay? [00:30:50]
Joe: I guess there are a couple of things. One is PII. Doesn't go anywhere near the stuff, right? PII with strip and like, that's just a really important thing. [00:30:58]
Swyx: You still need an identifier for streams. [00:31:02]
Joe: Yeah, yeah. But in terms of training models, we don't want any of that to go in there because then you might accidentally, you know, like, hello, ChatGPT, please hallucinate me a social security number. That's dangerous. [00:31:11]
Swyx: Also PII makes it into prompts a lot. [00:31:14]
Joe: Sure, that's true. So then you have to strip that from your... So we have some experiments where we're stripping PII that is in places that shouldn't be, you know, descriptions of things. Sometimes people copy paste big long lists of email addresses into charts and things. But some of these things are actually pretty surprisingly easy to detect and strip out. So we can do that. And we have some layers that are stripping out that sort of replacing them with tokens. So the LLMs can still operate on them. But in terms of training this data, all that training is happening internally and we're not putting any sort of private data, personally identifiable information in. I don't know if there's anything you wanted to add there. Yeah, yeah. [00:31:54]
Jeffrey: We certainly think about this a lot and our customers think about a lot. Like when I think about user privacy with respect to tracking, there's kind of this big spectrum. Around the one end, it's like literally track nothing and, you know, the end of story. And like for people like that, I mean, that's cool. You know, they're not gonna use Amplitude. They may not like us very much. You know, that is what it is. And then on the other end of the spectrum is like, we're gonna track you across the entire internet and sell your data to everyone. And like, that's obviously bad. And like, there's lots of good reasons to think that's bad. First party behavioral data, I think is actually probably almost as far. Fully anonymized first party behavior data would be like kind of the minimum. It's like web server logs with no IP, no identifier, nothing. The problem is that you can't do a lot of interesting behavioral analysis without that. You can't tell if, you know, this person that came on this day was the same one that purchased later. And so like, you can't actually, it's much harder to make your product better if you don't have that. And so, you know, we're kind of set at this place where we have, you know, like pseudo anonymized first party data. And like, we don't sell the data. You don't mix data from, you know, different places on the internet through Facebook cookies or things like that. And, you know, our philosophy is like, that is actually the most important data to build a better product. It's not the most important data to advertise, which is why Facebook and Google do what they do, but it's the most important data to build a better products. And it kind of strikes the right balance between yeah, totally tracking everything that you're doing and like not having any information to make your product better. [00:33:19]
Swyx: Yeah, cool. And I think we're going to go to audience questions. So let's start warming them up soon. But I think we have some lightning round questions [00:33:29]
Joe: The audience is thinking of questions while we go. [00:33:31]
Alessio: The first one is, what's something that already happened in AI that you thought would take much longer to be here? [00:33:39]
Jeffrey: I don't know what the constraints on our lightning round, but I think maybe creativity is the best word where it's, you know, with the image generation stuff, text generation, you know, one thing that still blows my mind, I used to be a competitive like math guy and like there's this international math Olympiad problem in one of the papers and it solves it. And I'm just like, wow, I can solve this when I was spending all my life doing this thing. Like that level of creativity really blew my mind. And what's the takeaway? It's like maybe the takeaway is that creativity is not as, you know, as not as high entropy or high dimensional as we think it is, which is kind of interesting takeaway. But yeah, that one definitely surprised me. [00:34:21]
Joe: I guess there's something actually that maybe answering the inverse question that a lot of my friends were surprised happened quickly. And I was like, this is just braindead obvious. I've got a lot of friends in the AI safety space. So they're worried that in particular, X-risk, right, extinction risk, that AI is going to kill the human race. And they were like, oh no, what if an AI escapes containment and gets access to the internet? And then we get an LLM and the first thing we do is like, hey, also GPT, here's the internet. [00:34:48]
Swyx: So you thought, it's happening faster than you thought. [00:34:53]
Joe: Well, it's happening faster than, to me it makes sense, because I'm like one of the guys connecting it to the internet. And I'm like, I'm surprised that other people were surprised it was going to be so fast. [00:35:01]
Swyx: Yeah, so a bit of context, Joe and I, we've been adjacent to the EA community and they have like smoothly migrated to the X-risk community very quickly after SBF. [00:35:13]
Joe: Yeah, after SBF, yeah, that was fun. [00:35:16]
Swyx: Okay, so next question, exploration. What do you think is the most interesting unsolved question in AI? What's next? [00:35:30]
Joe: I guess like, is it going to keep getting better at the same rate? Is it going to, and that's just a super important question that's going to change. Like, depending on that answer, 50 startups are going to pivot or not pivot, right? [00:35:43]
Swyx: Which is what's next, literally. [00:35:45]
Joe: Literally, what's next? Like in a year's time, are the models similarly better than they have been so far? Or are we about to taper off or are we about to continue going linearly? [00:35:58]
Jeffrey: Yeah, I'll throw one out that is not necessarily about AI, but like, what's intelligence, right? And if you ask people 20, 30 years ago, maybe even longer now, it's like, yeah, chess. Chess is intelligence. And then chess got solved and like, ah, that's just brute force. And it's like, well, you know, creating creative images and writing, that's intelligence. Well, it's like, that's solved too. Maybe it's just, you know, if you have enough parameters, you can capture that. So like, what is intelligence? What does it mean to have an AGI? What does that actually mean? And then what the implications that are on for our understanding of humans and our brains. I've always thought that, you know, everyone is just a stochastic machine. And so, you know, is everything consistent in my mind?
Swyx: Free will and illusion. Exactly. [00:36:43]
Joe: I guess maybe like the scaling piece is like that intelligence as you scale is gets more and more expensive on the traditional stuff. But then there's something I think I saw yesterday on Hacker News. It was people actually getting a brain to play tic-tac-toe. Like by a brain, I mean, stem cells grown into brain tissue. And they were able to train it. And like that to me is very significant because suddenly the like metal computers limitations is not applied. And then now we've got all this intelligence. What is intelligence stuff on a squishy wet computer? That makes it even harder to ask and even harder to draw lines. [00:37:18]
Swyx: Yeah. Yeah. So famously, you know, language models are so much more inefficient than wet computers, as you say. And so if you can merge that, you know, the human brain runs on 30 Watts of power as it is my favorite fact. We're not anywhere close to that yet. [00:37:36]
Alessio: Before we get into Q&A, one last takeaway that you want everybody to think about. [00:37:41]
Jeffrey: Yeah, I'll do the one that we actually repeat in Inside Amplitude very often, not about AI, but I think it applies, which is it's early. It's sometimes hard to realize that when things are happening so fast, especially in the Bay Area, but like the ramifications of AI or in our case, product data and all that are gonna play out over the next many decades. And that's just, you know, we're very fortunate to be at the beginning of it. And so yeah, take advantage of it and keep reminding yourself that it's early. [00:38:15]
Joe: I guess mine would be, let humans be good at doing human things. Let machines be good at doing machine things and let machines be good at doing machine things and help humans be good at doing human things. And like, if you don't do that, then you're gonna be building something that's either not useful or it's very scary. So yeah, get machines helping humans, not the other way around. [00:38:39]
Swyx: Get machines helping humans. All right. With that, I think we're all gonna open up to questions. We're gonna toss you the mic. [00:38:45]
Audience #1: Yeah, hey, thanks for the insight into how you guys implemented your AI, you know, question asking chatbot and how have you converted into seven sub queries and then generate the data out. I've just, I got a peak my interest about how you guys exactly do it. Like Alessio asked, like, what exactly is the model that you guys are using? Are you converting it into your, what are these queries that you generate from a single English language? Is it possible to go a little deeper just from a curiosity perspective? [00:46:34]
Joe: So we have a custom query engine. So it's not SQL or anything that we're generating. We're generating a custom query output. So I guess the types of questions range. So things like chart type, are we doing a segmentation chart, a line chart or are we doing a funnel chart? You know, the number goes down over time or up over time or between a conversion between two events and there are various other types or metrics or, and then there's also the name. What should we name this chart that answers this question? So the way that's implemented in practice, you could use something like Lang chain to sort of chain these things together. But in our experience, I think Lang chain's a great tool for certain things and definitely really great for prototyping, but we found it quite restrictive. So we've ended up building sort of an internal, it's a very, very small wrapper, internal, we use TypeScript as well, framework that allows us to basically just write in code and infer within what we call a transaction, an inference transaction, which gets monitored as one, but then also all the individual inferences within it get monitored. So it's a bit like when you're writing a database transaction with most sort of, at least in the node ecosystem, the JavaScript ecosystem, where you sort of get a transaction object that you can operate on, and then you return your, or you return, you sort of commit your transaction. So we've got an interface like that, so we can just write pure TypeScript, await this response or await these responses. And then we've got a switch case. If it's a segmentation chart, go and do these with these queries. And then each of those inferences can be a different model. So we think in the future, maybe we have one query where we have some GPT-4 responses. We want some text responses. Maybe we also want to generate an image from that same query together, and then that gets bundled. So I don't know if that answers your question.
Audience #1: Yeah, I think so. Yeah, thank you. I think so. You said in future, you're going to use GPT-4. What are you using right now for? [00:48:33]
Joe: Right now, everything's GPT-3.5. We're moving around, and I think probably for some of the prompts, we'll use something like DaVinci. Some we might use GPT-4. Some we'll be using internal ones. And we also want to be able to degrade gracefully if a customer has told us they don't want us to send anything to OpenAI, then we can degrade to some internal models that maybe are some of the open source models that have been trained on smaller datasets. [00:48:57]
Audience #1: Gotcha, makes sense. Thank you. [00:48:58]
Jeffrey: Yeah, I think to add to that a little bit, the key is breaking down the problem sufficiently, because if you break down the problem enough, you can also provide it with some examples, which is super helpful, right? You know, GPT is quite good at zero shot, but within the context of our specific domain, it doesn't know what's going on. And so being able to break down the problem to, hey, select the type of chart. Don't generate me an entire chart definition. Select me the type of chart, and then select me the specific metric based on their query, and then giving it some examples. Select me the events and properties that I want to look at. By breaking it down and having very, very contextual prompts with respect to those examples, you get a lot higher quality output than trying to generate, like, you know, if you imagine generate, like, hey, generate me a whole SQL query with all, you know, here's like the schema of all my tables, now generate it entirely. It's like, it actually struggles with stuff like that, because it's just like kind of too much information and computation to come out of language. Now, maybe GPT-5 will be different, but like, that's the state of the art today. [00:49:57]
Swyx: I'll ask a follow-up to Joe. So you mentioned, you mentioned trying LangChain, but not needing it for production. Any other comments on tooling that are out there that's interesting to you? Do you use a embedding database, for example, or do you just use a regular database? [00:50:18]
Joe: Yeah, so we've actually been running embedding sort of similarity or vector search in production for multiple months, maybe even almost a year, and just like straight up Postgres, but now we're using PG Vector, which actually Jeffrey could probably speak more to about that decision and what that was like. [00:50:40]
Swyx: So this is a pretty hot take. At Amplitude scale, all you need is Postgres? [00:50:46]
Joe: We'd use many things other than Postgres. But I mean, we, this isn't rolled out for all customers and it's not necessarily getting sort of hit with a lot of traffic. And so the scale here is very different. Our usage scale is very different to our ingestion. [00:51:04]
Swyx: Yeah, yeah, yeah. [00:51:06]
Jeffrey: Just to clarify that a little bit more, we're not putting individual end user vectors or end event vectors. We're putting in taxonomies. So if I'm DoorDash, my taxonomy is add to cart, checkout, purchase, browse. That's the cardinality. And so that's actually small. It's on the order of tens of millions. And so yeah, you use stuff that in Postgres, no problem. Now, when we talk about large behavioral models or like actually embedding events, there are many, many trillions of those. And yeah, Postgres probably doesn't work there. [00:51:41]
Swyx: Yeah, actually I wanted to comment on this slightly before, which is separating taxonomies from the actual data is one way you protect your customers against prompt injection. It's something that Simon Willison has been talking about where you want to have like query for one thing, but essentially no knowledge of the actual underlying data, just the taxonomy. So it's good practice. [00:52:00]
Audience #2: Yeah, so you talked about a model which would be trained on user behavior data like amplitude GPT. It really piqued my interest and what capabilities would emerge? What do you think that you would find and what would be the first thing you would ask the model? That's a good question. [00:52:23]
Jeffrey: So we've thought about this a little bit and I think the, right, these are sequence, token prediction models. And so at the very least, I would hope for a much better, we have a predictions feature right now, which says, hey, given what a user has done over the last 90 days, do we think they're gonna belong to this cohort in the future or not? So that cohort might be people who churn, people who purchase, people who upsell, whatever the customer wants. We think it would be much better at tasks like that, right, because if it just has a very good understanding of behavioral patterns and what's gonna come next, it would be able to do that. That's exciting, but not that exciting. If I'm trying to think about like the analogies to what we see in LLMs, it's like, okay, yeah, what is the behavioral equivalent of like learning physics concepts, right? It's like, oh, I don't actually know, but it might be this understanding of patterns of sessions and how that like, for example, categorizing users in a unsupervised way seems like a very simple output for a model that understands user behavior, right? Here's all the users and if you wanna discriminate them by their ability to achieve some outcome in the future, like here's the best way to separate that group and here's why, right? Be able to explain at that level and that would be super powerful for customers, right? A lot of times what our customers do is, hey, these people came back the next day and these people didn't, why? What was different about them? And so we have a bunch of heuristics to do that, but at the end, there's something like, causal impact is like one of the holy grails of product analytics. It's like, what was the causation behind some observed difference in behavior? And I think, yeah, a large behavioral model will be much better at assessing that and be able to give you potentially interpretable ways of answering that question that are like really hard to do, really hard, really computationally intensive, really like noisy, distilling causation correlation is obviously super hard. Those are some of the examples. The other one that I am, I don't know if I'm optimistic about it, but we really interesting is, one of the things that amplitude requires today is manual instrumentation, right? You have to decide, hey, this clicking of a button, this viewing of page, these are important things. I'm naming them in this way. There's a lot of popular tools out there that kind of just record user sessions or like track DOM events automatically. There's a lot of problems with those tools because the data is incredibly noisy. It's just so noisy, right? A lot of times you just can't actually interpret it. And so it's like, oh, it's great because I don't need to do any work. But like, well, you also don't get anything out of it. It's possible that a behavioral model would be able to actually understand what's going on there by understanding your user behavior in a correctly modeled and correctly labeled sense, and then figuring out. I don't know if that's possible. I think that would make everyone's lives a lot easier if you could somehow ask behavioral questions of data without having to instrument. All of our customers would love that, but also all of them are instrumenting because they know that's definitely not possible today. [00:55:26]
Audience #2: This is really interesting. You're looking forward to the future. If you're gonna build it, it's gonna be amazing, yeah. [00:55:31]
Jeffrey: That's the goal, that's the goal. [00:55:33]
Audience #2: Awesome. [00:55:34]
Swyx: Thanks for listening. [00:56:09]
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Podcast: Latent Space: Founders, Engineers, and News on Software 3.0, DevTools, Computer Vision, Data Science, AI UX (LS 26 · TOP 10% what is this?)
Episode: Emergency Pod: OpenAI's new Functions API, 75% Price Drop, 4x Context Length (w/ Simon Willison, Riley Goodside, Roie Schwaber-Cohen, Joshua Lochner, Stefania Druga, Eric Elliott, Mayo Oshin et al)
Pub date: 2023-06-14
Full Transcript and show notes: https://www.latent.space/p/function-agents?sd=pf
Timestamps:
[00:00:00] Intro
[00:01:47] Recapping June 2023 Updates
[00:06:24] Known Issues with Long Context
[00:08:00] New Functions API
[00:10:45] Riley Goodside
[00:12:28] Simon Willison
[00:14:30] Eric Elliott
[00:16:05] Functions API and Agents
[00:18:25] Functions API vs Google Vertex JSON
[00:21:32] From English back to Code
[00:26:14] Embedding Price Drop and Pinecone Perspective
[00:30:39] Xenova and Huggingface Perspective
[00:34:23] Function Selection
[00:39:58] Designing Code Agents with Function API
[00:42:16] Models as Routers
[00:46:48] Prompt Engineering replaced by Finetuning
[00:52:15] The 2 Code x LLM Paradigms
[00:56:30] Smol Models for the future
[00:58:54] The Evolution of the GPT API
[01:03:27] Functions API Security vs Prompt Injection
[01:16:18] GPT Model Upgrades
[01:17:36] JSONformer
[01:21:03] Closing Comments - What We Want Next
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Podcast: Articles of Interest (LS 65 · TOP 0.05% what is this?)
Episode: 3. Pockets
Pub date: 2018-12-08
Womenswear is littered with fake pockets that don’t open, or shallow pockets that can hardly hold more than a paperclip. If women’s clothes have pockets at all, they are often smaller and just fit less than men’s pockets do. And when we talk about pockets, we are talking about who has access to the tools they need. Who can walk through the world comfortably and securely?
Articles of Interest is a show about what we wear, created by Avery Trufelman; a six-part series within 99% Invisible, looking at clothing.
The podcast and artwork embedded on this page are from Avery Trufelman, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Articles of Interest (LS 65 · TOP 0.05% what is this?)
Episode: 2. Plaid
Pub date: 2018-12-07
Lumberjacks wore plaid. Punks wore plaid mini skirts. The Beach Boys used to be called the Pendletones, and they wore plaid with their surfboards. Lots of different groups have adopted the pattern over the course of the 20th century, but if we want to explore how this pattern proliferated, we’ve got to go to Scotland.
Articles of Interest is a show about what we wear, created by Avery Trufelman; a six-part series from 99% Invisible, looking at clothing.
The podcast and artwork embedded on this page are from Avery Trufelman, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Business Breakdowns (LS 51 · TOP 0.5% what is this?)
Episode: Adyen: A First Principles Payment Platform - [Business Breakdowns, EP. 50]
Pub date: 2022-03-09
This is Zack Fuss and today we’re breaking down European-based payment business, Adyen. Adyen was founded in Amsterdam in 2006 by a group of payments entrepreneurs who had already built and sold a business in this space. Adyen was their chance to start afresh and build a modern solution to displace the patchwork legacy system that merchants were being forced to use.
To break down the business, I’m joined by Michael Willar, a portfolio manager at Stenham Asset Management. Our discussion covers Adyen’s single platform solution in detail, the driving force behind their track record of profitable growth, and why payments isn’t a winner take all market. Please enjoy this breakdown of Adyen.
For the full show notes, transcript, and links to the best content to learn more, check out the episode page here.
This episode is brought to you by Visible Alpha. The team at Visible Alpha built a platform to analyze consensus data and financial metrics on over 6,000 public traded companies. Rather than having to dig through models one by one, Visible Alpha extracts data from every line item across sell-side models so you can better understand expectations on metrics beyond just revenue and earnings. Try Visible Alpha for free by visiting visiblealpha.com/breakdowns
This episode is brought to you by Scribe. Scribe is the trusted transcription provider for the business and investing community. Scribe is designed to accurately transcribe messy, real-world audio and is unique in that it’s optimized for the complexities of enterprise audio, such as company and product names, currencies, accents and numbers. Visit kensho.com/breakdowns to learn more and unlock your free trial.
Business Breakdowns is a property of Colossus, LLC. For more episodes of Business Breakdowns, visit joincolossus.com/episodes.
Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here.
Follow us on Twitter: @JoinColossus | @patrick_oshag | @jspujji | @zbfuss
Show Notes
[00:02:55] - [First question] What Adyen is and what they do
[00:05:54] - General overview of how payment processing works
[00:07:29] - Flow of a transaction and how they manifest
[00:09:52] - How the business generates revenue and their revenue model
[00:11:25] - Where Adyen sits in the industry and the size of it today
[00:13:37] - The reality of processing 50-60% of their addressable market
[00:16:19] - What about their culture and founding story makes them so nimble
[00:21:18] - The competitive strengths of the business and their innovative solutions
[00:24:07] - Key revenue drivers for Adyen
[00:26:01] - What is it about Adyen’s business structure that enables them to grow so rapidly while still being profitable
[00:29:34] - Key growth drivers
[00:32:44] - What gives Adyen its competitive advantage over other payment providers
[00:35:56] - Having one platform is beneficial but why isn’t it a more popular approach?
[00:37:42] - The secret sauce behind their successful growth trajectory
[00:39:25] - The essence of Adyen’s culture and how it manifests in their day-to-day work
[00:42:04] - What Adyen plans to do with all of the cash they produce
[00:43:35] - What keeps him up at night and potential threats to the business
[00:47:54] - Is there a chance anyone could build a platform comparable to Adyen?
[00:50:06] - Key differences between Stripe and Adyen
[00:56:23] - Lessons learned from studying Adyen and what payment service builders can learn from them
The podcast and artwork embedded on this page are from Colossus | Investing & Business Podcasts, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Business Breakdowns (LS 51 · TOP 0.5% what is this?)
Episode: Ryanair: Low Cost Obsessed - [Business Breakdowns, EP. 98]
Pub date: 2023-02-22
This is Matt Reustle and today we are breaking down Europe's largest airline, Ryanair. As we do more breakdowns, we start to look for patterns of successful business models that succeed across different industries. Ryanair is another case study in low-cost shared economies of scale. To break down Ryanair, I'm joined by Holland Advisors’ founder and portfolio manager, Andrew Hollingworth.
On this episode, we talk about what makes airlines such a difficult industry for investors, how CEO Michael O'Leary has taken a truly unique approach to building this business, and how to frame cyclical versus secular dynamics in the airline market.
Now, one quick note before we transition to the episode. You'll hear Andrew and I talk about O'Leary's unique PR approach with shareholders, the union, and pretty much anyone that he deals with. If you're interested in that type of dark arts of communication and media, make sure to check out our newest show at Colossus, Making Media. It operates as an ongoing Business Breakdown of our own business, Colossus, and we spend a lot of time studying the world of communications and media more broadly. You'll find a link to that series in our show notes. Make sure to subscribe.
For the full show notes, transcript, and links to the best content to learn more, check out the episode page here.
This episode is brought to you by Tegus. Tegus, the modern research platform for leading investors. I’m a longtime user and advocate of Tegus, a company that I’ve been so consistently impressed with that last fall my firm, Positive Sum, invested $20M to support Tegus’ mission to expand its product ecosystem. Whether it’s quantitative analysis, company disclosures, management presentations, earnings calls - Tegus has tools for every step of your investment research. They even have over 4000 fully driveable financial models. Tegus’ maniacal focus on quality, as well as its depth, breadth and recency of content makes it the one-stop, end-to-end research platform for investors. Move faster, gather deep research to build conviction and surface high-quality, alpha-driving insights to find your differentiated edge with Tegus. As a listener, you can take the Tegus platform for a free test drive by visiting tegus.co/patrick.
Business Breakdowns is a property of Colossus, LLC. For more episodes of Business Breakdowns, visit joincolossus.com/episodes.
Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here.
Follow us on Twitter: @JoinColossus | @patrick_oshag | @jspujji | @zbfuss | @ReustleMatt | @domcooke
Show Notes
[00:03:12] - [First question] - Why airlines have such a bad reputation with investors
[00:04:20] - An overview of Ryanair and its size and scale today
[00:05:43] - Unique characteristics about Ryanair’s business model that distinguishes them from their competitors
[00:09:10] - What keeps customers coming back to Ryanair
[00:10:49] - What else stands out about Michael O’Leary that is key to Ryanair’s success
[00:12:16] - Michael O’Leary: Turbulent Times for the Man Who Made Ryanair
[00:14:22] - How Ryanair’s business model has evolved against cycles and opportunities
[00:19:29] - What else goes into their cheap seat cost structure
[00:23:10] - Approaching labor in light of a unionized industry and workforce
[00:28:07] - The cyclicality of margins and how theirs look compared to their competitors
[00:33:47] - Interesting data on airplane utilization and dynamic pricing
[00:36:40] - What’s contributing to the lack of growth at easyJet
[00:42:37] - The risks to Ryanair’s growth as a shareholder
[00:44:00] - Industry responses to cycles and recessionary environments
[00:46:31] - The main takeaways from Ryanair that could be applied elsewhere
The podcast and artwork embedded on this page are from Colossus | Investing & Business Podcasts, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Business Breakdowns (LS 51 · TOP 0.5% what is this?)
Episode: Wise: Moving Money Around the World - [Business Breakdowns, EP. 99]
Pub date: 2023-03-01
This is Zack Fuss, an investor at Irenic Capital, and today we’re breaking down Wise. Wise helps individuals and small businesses move money across borders. It offers significantly faster and cheaper international transfers than traditional banking routes because of its innovative closed-loop system. Twelve years after its founding, Wise serves six million customers and earned close to £1 billion in income last year. Investors currently value the business, which is listed in London, at £6 billion.
To break down Wise, I’m joined by former payments exec and now investor at Sydney-based TDM Growth Partners, James Revell. We cover the broken system of correspondent banking, which has led to slow, opaque, and expensive transfers and then explore how Wise has counter-positioned itself to take advantage of this large market. Please enjoy our breakdown of Wise.
For the full show notes, transcript, and links to the best content to learn more, check out the episode page here.
This episode is brought to you by Tegus. Tegus is the modern research platform for leading investors. I’m a longtime user and advocate of Tegus, a company that I’ve been so consistently impressed with that last fall my firm, Positive Sum, invested $20M to support Tegus’ mission to expand its product ecosystem. Whether it’s quantitative analysis, company disclosures, management presentations, earnings calls - Tegus has tools for every step of your investment research. They even have over 4000 fully driveable financial models. Tegus’ maniacal focus on quality, as well as its depth, breadth and recency of content makes it the one-stop, end-to-end research platform for investors. Move faster, gather deep research to build conviction and surface high-quality, alpha-driving insights to find your differentiated edge with Tegus. As a listener, you can take the Tegus platform for a free test drive by visiting tegus.co/patrick.
Business Breakdowns is a property of Colossus, LLC. For more episodes of Business Breakdowns, visit joincolossus.com/episodes.
Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here.
Follow us on Twitter: @JoinColossus | @patrick_oshag | @jspujji | @zbfuss | @ReustleMatt | @domcooke
Show Notes
[00:02:27] - [First question] - Overview of Wise, their key product, and core competency
[00:04:17] - The founding story of Wise and the road leading to today
[00:09:18] - Wise’s size and scale today compared to 2011
[00:11:07] - Their competitive advantages and how it informs their goals
[00:19:24] - Exploring Wise’s closed loop system and why their model can’t be copied
[00:21:28] - Unique characteristics of their business model that allows them to capture such robust margins
[00:25:32] - Overview of Wise’s unit economics and their revenue model
[00:34:49] - Interchange fees and how Project Zero guides the business
[00:36:44] - Why their lower take rate doesn’t destroy the industry
[00:38:24] - Ways Wise’s business model can’t simply be copied and replicated
[00:44:35] - Thoughts on who their true competitors are
[00:48:10] - Their customer acquisition flywheel
[00:49:49] - Float, increasing net margin, and how they contribute to durability
[00:53:55] - Key risks associated with Wise when evaluating the business
[00:58:35] - Reasons behind the decision to raise money as a direct listing in the UK
[01:01:03] - How people looking at Wise should think about margins over time
[01:03:32] - Lessons for builders and investors when studying Wise’s story
The podcast and artwork embedded on this page are from Colossus | Investing & Business Podcasts, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Business Breakdowns (LS 51 · TOP 0.5% what is this?)
Episode: MTN Group: Connecting Africa - [Business Breakdowns, EP.109]
Pub date: 2023-05-03
This is Zack Fuss, an investor at Irenic Capital, and today we’re breaking down MTN Group. MTN is the largest mobile network operator in Africa and one of the 10 largest in the world. It has over 270 million subscribers, operates in 20 different markets, and is also one of the largest FinTech’s in the continent.
To break down MTN, I’m joined by Benjamin Isaac, founder and Chief Investment Officer at Brizo Capital. We unpack their mobile money business in some detail, contrast the development of Telcos in Africa with what we’ve experienced in the US, and explore the competitive dynamics of operating in Africa. Please enjoy this breakdown of MTN.
For the full show notes, transcript, and links to the best content to learn more, check out the episode page here.
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This episode is brought to you by Tegus. Tegus is the modern research platform for leading investors. Stretch your research budget with flexible expert calls you can trust. At a fraction of the cost of traditional expert networks, Tegus customers pay only what an expert charges – with zero markups and no confusing call credits – netting an average 70% savings. Don’t want to conduct a full hour call? Tegus offers the ability to schedule 30-minutes, an offer you won’t find anywhere else. And they don’t stop there. With white-glove custom sourcing for every project and robust compliance measures, including a dedicated 50+ analyst team that vets every call transcript, Tegus ensures your privacy and protection. As the industry innovator for qualitative insights, Tegus helps you find the right experts you need at a quality and speed that can’t be matched. For a limited time, as a listener, you can trial Tegus for free by visiting tegus.co/patrick.
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Business Breakdowns is a property of Colossus, LLC. For more episodes of Business Breakdowns, visit joincolossus.com/episodes.
Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here.
Follow us on Twitter: @JoinColossus | @patrick_oshag | @jspujji | @zbfuss | @ReustleMatt | @domcooke
Show Notes
(00:02:24) - An overview of MTN Group today
(00:04:13) - Contextualizing the scale and trajectory of the business vis-à-vis
its strong African demographic
(00:05:52) - MTN Group’s unique position in the value chain
(00:10:37) - The origin and the evolution of MTN Group
(00:13:19) - The business’ current and future revenue models and how they differ domestically and internationally
(00:15:52) - Comparing ARPU in North America and Africa
(00:18:03) - His take on why the international fintech market is developing as rapidly as it is
(00:22:48) - Understanding use cases for MTN Group’s mobile money products
(00:27:57) - The low market share held by credit card companies in Africa, and the opportunity it represents for MTN Group
(00:29:07) - Regional differences, local competition, and the overall market structure
(00:30:42) - The architects, visionaries, and capital allocators behind MTN Group
(00:34:33) - What structural separation means for a business like MTN Group
(00:36:31) - Measuring the size and scale of the business
(00:38:53) - Investing in emerging markets
(00:42:59) - The importance of location in a mobile fintech company listing
(00:45:09) - Risks and challenges facing MTN Group
(00:49:53) - How African mobile and fintech markets fared during COVID
(00:51:23) - Framing the business’ current and future picture of profitability
(00:56:23) - Lessons learned in studying the story of MTN Group
The podcast and artwork embedded on this page are from Colossus | Investing & Business Podcasts, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Business Breakdowns (LS 51 · TOP 0.5% what is this?)
Episode: PayPal: A Digital Money Marketplace - [Business Breakdowns, EP.113]
Pub date: 2023-05-31
This is Dom Cooke and today we’re breaking down PayPal. PayPal has been at the forefront of digital payments since the early days of the internet. Founded by Peter Thiel, Elon Musk and others, who have since become household names, PayPal is a payments marketplace that facilitates transactions between merchants and consumers. It found product market fit as the trusted way to send money over the internet, was quickly acquired by eBay, and had its second founding moment in 2015 when it was spun off into a public company again. The platform serves 435 million consumers and merchants and processed $1.4 trillion of payments last year.
To break down the business, I’m joined by Elliot Turner, managing partner and CIO at RGA Investment Advisors. We discuss the acquisitive history behind this business, how their portfolio of brands like Braintree, Venmo, and Honey operate within the ecosystem, and why VISA threatened to go nuclear on PayPal. Please enjoy this business breakdown of PayPal.
For the full show notes, transcript, and links to the best content to learn more, check out the episode page here.
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This episode is brought to you by Tegus, the modern research platform for leading investors. Tired of running your own expert calls to get up to speed on a company? Tegus lets you ramp faster and find answers to critical questions more efficiently than any alternative method. The gold standard for research, the Tegus platform delivers unmatched access to timely, qualitative insights through the largest and most differentiated expert call transcript database. With over 55,000 transcripts spanning 22,000 public and private companies, investors can accelerate their fundamental research process by discovering highly-differentiated and reliable insights that can’t be found anywhere else in the market. As a listener, drive your next investment thesis forward with Tegus for free at tegus.co/patrick.
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Business Breakdowns is a property of Colossus, LLC. For more episodes of Business Breakdowns, visit joincolossus.com/episodes.
Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here.
Follow us on Twitter: @JoinColossus | @patrick_oshag | @jspujji | @zbfuss | @ReustleMatt | @domcooke
Show Notes
(00:02:40) - (First question) Important milestones leading to the genesis of PayPal
(00:08:18) - eBay's acquisition of PayPal and the subsequent separation
(00:12:13) - The size and scope of PayPal today
(00:15:08) - Where PayPal fits within the overall payments ecosystem
(00:18:33) - The various transaction types involved in their business economics
(00:22:03) - How PayPal protects its users against fraudulent behavior
(00:24:37) - PayPal’s business strategy of getting people comfortable with using digital money
(00:27:31) - The value that driving customer engagement has on the bottom line
(00:31:41) - How PayPal utilizes cash within its ecosystem
(00:33:15) - Why Braintree has been such a success, and who they compete with
(00:38:50) - How PayPal revenue is split into cash flow and profits
(00:42:40) - What enables PayPal to maintain such a large advantage over its competitors
(00:46:03) - Identifying PayPal’s main competitors and partners
(00:48:30) - The dynamics of PayPal's relationship with Apple
(00:50:44) - How acquisition and R&D fosters their growth and innovation
(00:55:12) - Strategic changes adopted by PayPal to recover from the COVID period
(00:56:42) - Speculation on who could replace Dan Schulman as PayPal’s CEO
(00:58:52) - His thoughts on potential growth opportunities for PayPal’s next CEO
(01:01:40) - Potential risks that PayPal may encounter in the future
(01:04:10) - Lessons learned from studying PayPal
Learn more about your ad choices. Visit megaphone.fm/adchoices
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Podcast: ACQ2: The Acquired Interviews (LS 37 · TOP 2.5% what is this?)
Episode: Generative AI Moats in B2B with Emergence Capital’s Jake Saper
Pub date: 2023-05-09
How do you build defensible business value in an era when, as AngelList CEO Avlok Kohli said on our last ACQ2 episode, the “cost of intelligence is going to zero”? Longtime friend of the show Jake Saper and his partners at Emergence Capital have been refining their thesis for this brave new world of Generative AI in B2B, and we sit down with him to discuss. We cover topics including:
- When do exactly correct answers matter, and when do they not?
- When are human-in-the-loop systems necessary?
- When do startups have an advantage vs. incumbents, and vice-versa?
- Where can companies capture value on a durable basis?
- When do you need proprietary data in order to be defensible?
Whether you’re building or investing in existing businesses from the “pre-AI” era or brand new startups that are native to GPT, this episode has plenty of takeaways you should consider. Tune in!
Links:
- Jake’s recent blog post on Generative AI and B2B
- Follow Jake on Twitter
My First Million: https://www.mfmpod.com/
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Podcast: An Hour With An Indie
Episode: #78 How to appear on BBC's Masterchef
Pub date: 2022-12-06
Nathan Booth is the Head Chef at Panoramic 34 fine dining restaurant in Liverpool and a recent contestant on BBC's Masterchef. He joined us to talk about what it takes to build a successful cheffing career and appear on programmes such as Masterchef.
An Hour With An Indie is created by Pilla - the end to end employee management software hospitality entrepreneurs. Pilla helps operators ditch outdated practices and build awesome teams through a modern employee journey.
The podcast and artwork embedded on this page are from Pilla, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: An Hour With An Indie
Episode: #79 How will the nightlife economy survive?
Pub date: 2022-12-11
Michael Kill is the CEO of the Night Time Industry Association. An organisation which supports over 10,000 businesses in the night time economy. He joined us to talk about the challenges facing the sector and what can be done to save businesses from over the next six months.
An Hour With An Indie is created by Pilla - the end to end employee management software hospitality entrepreneurs. Pilla helps operators ditch outdated practices and build awesome teams through a modern employee journey.
The podcast and artwork embedded on this page are from Pilla, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Python Podcast.__init__ (LS 45 · TOP 1% what is this?)
Episode: Fast And Educational Exploration And Analysis Of Graph Data Structures With graph-tool
Pub date: 2021-07-07
SummaryIf you are interested in a library for working with graph structures that will also help you learn more about the research and theory behind the algorithms then look no further than graph-tool. In this episode Tiago Peixoto shares his work on graph algorithms and networked data and how he has built graph-tool to help in that research. He explains how it is implemented, how it evolved from a simple command line tool to a full-fledged library, and the benefits that he has found from building a personal project in the open.
Announcements* Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science. * When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! * Your host as usual is Tobias Macey and today I’m interviewing Tiago Peixoto about graph-tool, an efficient Python module for manipulation and statistical analysis of graphs
Interview* Introductions * How did you get introduced to Python? * Can you describe what graph-tool is and the story behind it? * What are some scenarious where someone might encounter a graph oriented data set? + In what ways are those graphs typically represented? + In your experience, what is the overlap of people who are working with networked data, and the use of graph-native databases? (e.g. Neo4J, DGraph, etc.) * What kinds of analysis or manipulation might someone need to perform on a graph structure? * There are a few different tools in Python for working with networked data. How would you characterize the current ecosystem and why someone might choose graph-tool? * Can you describe how graph-tool is implemented? + How have the goals and design of the package changed or evolved since you first began working on it? * Who are your target users and what are the guiding principles that you use to inform the API design for the package? + How much knowledge of graph theory or algorithms are required to make effective use of graph-tool? * Can you talk through an example workflow of using graph-tool to load, process, and analyze a graph? * What are some of the overlooked or underutilized aspects of graph-tool that you think more people should know about? * What are some systems/applications that you have seen which would be simplified by adopting a graph model for their data? + What is your impression of the overall awareness of the benefits of graphs for simplifying aspects of data processing and analysis? * What are some cases where a graph structure adds unnecessary complexity? * What are the most interesting, innovative, or unexpected ways that you have seen graph-tool used? * What are the most interesting, unexpected, or challenging lessons that you have learned while working on graph-tool? * When is graph-tool the wrong choice? * What do you have planned for the future of graph-tool?
Keep In Touch* Website * graph-tool
Picks* Tobias + 97 Things Every Data Engineer Should Know
Closing Announcements* Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management. * Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. * If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story. * To help other people find the show please leave a review on iTunes and tell your friends and co-workers * Join the community in the new Zulip chat workspace at pythonpodcast.com/chat
Links* Central European University * NetworkX * GML * GraphML * Neo4J * DGraph + Data Engineering Podcast Episode * NetworKit * igraph * Matplotlib * C++ Templates * Boost Graph Library * OpenMP * Maximum Matching
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
The podcast and artwork embedded on this page are from Tobias Macey, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Real Python Podcast (LS 46 · TOP 1% what is this?)
Episode: Discussing Type Hints, Protocols, and Ducks in Python
Pub date: 2021-12-03
There seem to be three kinds of Python developers: those unaware of type hints or have no opinion, ones that embrace them, and others who have an allergic reaction at the mention of them. Python is famously a dynamically typed language, but there are advantages to adding type hints to your code. This week on the show, we have Luciano Ramalho to discuss his recent talk titled, “Type hints, protocols, and good sense.”
Luciano was not a fan of type hints. He’s only recently come around to their potential with the introduction of protocols in PEP 544. Python has adopted a gradual type system that is optional at all levels. We discuss the advantages, pitfalls, and recent developments around type hinting in Python.
We also talk about the second edition of Luciano’s book Fluent Python. He researched type hints in-depth for the book, which led to his recent conference talks on the subject. He also shares his experience with adding opinionated asides to the book in a fun and unique way.
Course Spotlight: Python Type Checking
In this course, you’ll look at Python type checking. Traditionally, types have been handled by the Python interpreter in a flexible but implicit way. Recent versions of Python allow you to specify explicit type hints that can be used by different tools to help you develop your code more efficiently.
Topics:
- 00:00:00 – Introduction
- 00:02:02 – Are you interested in creative uses for Python?
- 00:04:41 – Protocol: The keystone of type hints
- 00:08:14 – What is duck typing?
- 00:12:44 – Protocols declaring one method and emerging from a code base
- 00:17:04 – An example where type hint was too lax
- 00:21:20 – What if Python always had a strict type system?
- 00:33:23 – Sponsor: Cloudsmith
- 00:34:09 – Bias in companies using type hints, and projects that fail checking
- 00:40:27 – Background on personal use of type hints and added complexity
- 00:45:07 – Unsuitability of type hints for checking business rules
- 00:52:30 – Video Course Spotlight
- 00:53:46 – Fluent Python, 2nd edition
- 00:56:05 – Who is the intended developer for the book?
- 00:58:12 – Soapbox sections of the book
- 00:59:35 – What were things you were excited to update or add to the book?
- 01:05:46 – Metaprogramming portion of the book
- 01:08:17 – What are you excited about in the world of Python?
- 01:10:35 – What do you want to learn next?
- 01:18:41 – Shoutouts, plugs, and/or social connections
- 01:19:47 – Thanks and goodbye
Show Links:
- Fluent Python, 2nd Edition
- Protocol: The keystone of type hints - Luciano Ramalho | PyCon US 2021
- Type hints, protocols, and good sense: PyCon India 2021 - Speaker Deck
- Generate buzz with realtime FM audio synthesis - Łukasz Langa | PyCon US 2021
- Garoa Hacker Clube
- Processing.py - Tutorials
- PEP 544 – Protocols: Structural subtyping (static duck typing) | Python.org
- typeshed: Collection of library stubs for Python, with static types
- Python Type Checking (Guide) – Real Python
- Protocols and structural subtyping — Mypy documentation
- Dependent type - Wikipedia
- microsoft/pyright: Static type checker for Python
- Welcome to mypy documentation!
- PEP 487 – Simpler customisation of class creation | Python.org
- PEP 636 – Structural Pattern Matching: Tutorial | Python.org
- What’s New In Python 3.10 — Better error messages
- Flutter - Build apps for any screen
- Ramalho.org/wiki
- Luciano Ramalho Twitter(@ramalhoorg)
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Podcast: The Real Python Podcast (LS 46 · TOP 1% what is this?)
Episode: Exploring the New Features of Python 3.11
Pub date: 2022-11-04
Python 3.11 is here! Our regular guests, Geir Arne Hjelle and Christopher Trudeau, return to talk about the new version. Geir Arne wrote a series of preview tutorials earlier this year, and his annual piece, titled “Python 3.11: Cool New Features for You to Try,” was published on October 24. Christopher’s video course came out the next day, covering the topics from the tutorial with visual examples of Python 3.11 in action.
Geir Arne and Christopher collaborated to create code examples for the new features. We discuss better error messages, faster code execution, task and exception groups, typing features, and native TOML support.
We dive into the updates and offer advice about ways to incorporate them into your projects. We also consider when you should start running Python 3.11.
Course Spotlight: Cool New Features in Python 3.11 – Real Python
In this video course, you’ll explore what Python 3.11 brings to the table. You’ll learn how Python 3.11 is the fastest and most user-friendly version of CPython yet, and learn about improvements to the typing system and to the asynchronous features of Python.
Topics:
- 00:00:00 – Introduction
- 00:02:19 – Preview series
- 00:03:50 – Faster CPython project
- 00:07:10 – Specializing adaptive interpreter
- 00:11:24 – Other performance stuff
- 00:16:07 – Sponsor: Deepgram
- 00:16:51 – Improved tracebacks
- 00:21:49 – Exception groups and notes
- 00:27:22 – Self type and additional type hints
- 00:36:14 – Video Course Spotlight
- 00:37:27 – asyncio and task groups
- 00:41:25 – TOML and tomllib
- 00:46:21 – ISO date parsing
- 00:50:09 – Negative zeros
- 00:53:38 – Dead battery deprecations
- 00:56:04 – Advice on upgrading
- 01:01:01 – Thanks and goodbye
Show Links:
- Python 3.11: Cool New Features for You to Try – Real Python
- Cool New Features in Python 3.11 – Video Course
- faster-cpython/plan.md - GitHub
- PEP 659 – Specializing Adaptive Interpreter - peps.python.org
- Just-in-time compilation - Wikipedia
- Episode #381 Python Perf: Specializing, Adaptive Interpreter - Talk Python To Me Podcast
- Episode #339 Making Python Faster with Guido and Mark - Talk Python To Me Podcast
- “Zero cost” exception handling · Issue #84403 · python/cpython - GitHub
- Python 3.11 Preview: Task and Exception Groups – Real Python
- Faster Startup In Python 3.11 — Python 3.11.0 documentation
- Python 3.11 Preview: Even Better Error Messages – Real Python
- PEP 657 – Include Fine Grained Error Locations in Tracebacks - peps.python.org
- Episode #105: Creating Better Error Messages for Python 3.10 & 3.11 – The Real Python Podcast
- Exception Groups and except: Irit Katriel - YouTube
- PEP 673 – Self Type - peps.python.org
- PEP 646 – Variadic Generics - peps.python.org
- How Exception Groups Will Improve Error Handling in AsyncIO - Łukasz Langa | Power IT Conference - YouTube
- Neopythonic: Reasoning about asyncio.Semaphore
- PEP 680 – tomllib: Support for Parsing TOML in the Standard Library - peps.python.org
- TOML: Tom’s Obvious Minimal Language
- Python 3.11 Preview: TOML and tomllib – Real Python
- datetime — Basic date and time types — Python 3.11.0 documentation
- 13 Month Calendar
- Signed zero - Wikipedia
- PEP 594 – Removing dead batteries from the standard library - peps.python.org
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Podcast: The Real Python Podcast (LS 46 · TOP 1% what is this?)
Episode: Wrangling Business Process Models With Python and SpiffWorkflow
Pub date: 2023-02-10
Can you describe your business processes with flowcharts? What if you could define the steps in a standard notation and implement the workflows in pure Python? This week on the show, Dan Funk from Sartography is here to discuss SpiffWorkflow.
SpiffWorkflow is a Python tool for translating Business Process Model and Notation (BPMN) diagrams into a workflow engine. You can manipulate this visual chain of events to suit your team’s business requirements. Individual events in the workflow can contain blocks or scripts of Python code to be executed.
We discuss the concept of low-code software tools. Dan also talks about how SpiffWorkflow aims at getting non-developers within an organization involved in development.
Course Spotlight: Cool New Features in Python 3.11 – Real Python
In this video course, you’ll explore what Python 3.11 brings to the table. You’ll learn how Python 3.11 is the fastest and most user-friendly version of CPython yet, and learn about improvements to the typing system and to the asynchronous features of Python.
Topics:
- 00:00:00 – Introduction
- 00:02:14 – What is SpiffWorkflow?
- 00:03:12 – What is BPMN?
- 00:06:29 – What did you need to add to the project?
- 00:07:12 – What are the components of a diagram?
- 00:12:42 – Examples of workflow
- 00:13:54 – Sponsor: TelemetryHub
- 00:14:29 – What types of industries use BPMN?
- 00:18:02 – Decision Model and Notation (DMN)
- 00:19:34 – What is low-code?
- 00:27:02 – How could someone get involved?
- 00:28:02 – How do you host a workflow?
- 00:29:43 – Video Course Spotlight
- 00:31:05 – What has the project taught you as a developer?
- 00:37:29 – Empowering more members of the organization
- 00:42:07 – Project direction for the next year
- 00:42:51 – Where to start with SpiffWorkflow?
- 00:43:15 – What are you excited about in the world of Python?
- 00:45:59 – What do you want to learn next?
- 00:51:06 – Thanks and goodbye
Show Links:
- SpiffWorkflow
- Overview SpiffWorkflow 1.2.1 documentation
- SpiffWorkflow: A powerful workflow engine implemented in pure Python - GitHub
- Sartography
- Business Process Model and Notation - Wikipedia
- Decision Model and Notation™ (DMN™) | Object Management Group
- Web-based tooling for BPMN, DMN, CMMN, and Forms | bpmn.io
- Creating a Low-Code Business Process Execution Platform With Python, BPMN, and DMN - IEEE Software
- The Low Code Wall, SpiffWorkflow
- SpiffArena, SpiffWorkflow
- Install SpiffArena then build and run your first diagram - YouTube
- MindTrails - University of Virginia
- Practices of the Python Pro
- Episode #49: The Challenges of Developing Into a Python Professional – The Real Python Podcast
- PEP 678: Exceptions can be enriched with notes - Python 3.11.1 documentation
- Building a Ship in a Bottle. : 14 Steps (with Pictures) - Instructables
- Status - Private, Secure Communication
- Dan Funk - LinkedIn
- SpiffWorkflow (@SpiffWorkflow) - Twitter
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Podcast: Talk Python To Me (LS 56 · TOP 0.5% what is this?)
Episode: #411: Things I Wish Someone Had Explained To Me Sooner About Python
Pub date: 2023-04-14
What advice would you give someone just getting into Python? What did you learn over time through hard work and a few tears that would have really helped you? It's a fun game to play and we have Jason McDonald on the podcast to give us his take. Enjoy!
Links from the show
Jason C. McDonald: @codemouse92@mastodon.online
Dead Simple Python: nostarch.com
Coroutines and Tasks: docs.python.org
Duck Typing: wikipedia.org
Static Duck Typing in Python with Protocols: daan.fyi
PEP 709: peps.python.org
PEP 289: peps.python.org
Python Packaging Strategy Discussion - Part 1: discuss.python.org
Branch-detective: github.com
Hypothesis: readthedocs.io
Pydantic v2 announcement: pydantic.dev
Michael's venv alias: digitaloceanspaces.com
Watch this episode on YouTube: youtube.com
--- Stay in touch with us ---
Subscribe to us on YouTube: youtube.com
Follow Talk Python on Mastodon: talkpython
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The podcast and artwork embedded on this page are from Michael Kennedy (@mkennedy), which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: If Books Could Kill (LS 67 · TOP 0.05% what is this?)
Episode: A Bari Special Bonus Episode [TEASER]
Pub date: 2022-12-08
Our first premium episode is a deep dive into the blog of the infamously (and dubiously) "canceled" Bari Weiss.
The podcast and artwork embedded on this page are from Michael Hobbes & Peter Shamshiri, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: If Books Could Kill (LS 67 · TOP 0.05% what is this?)
Episode: The End of History
Pub date: 2023-02-09
Francis Fukuyama's "The End of History and the Last Man" changed political discourse forever. Peter and Michael peel back his muddled history and fluffy rhetoric, revealing several more layers of muddled history and fluffy rhetoric.
Support us on Patreon: https://www.patreon.com/IfBooksPod
Where to find us:
- Peter's other podcast, 5-4
- Mike's other podcast, Maintenance Phase
Sources:
- Reflections on the End of History, Five Years Later (https://www.jstor.org/stable/2505433)
- More Proof That This Really Is the End of History (https://www.theatlantic.com/ideas/archive/2022/10/francis-fukuyama-still-end-history/671761/)
- Francis Fukuyama Postpones the End of History (https://www.newyorker.com/magazine/2018/09/03/francis-fukuyama-postpones-the-end-of-history)
- Endism: why 1989 was not the 'end of history' (https://www.opendemocracy.net/en/endism/)
- The End of the End of History (https://www.bostonreview.net/articles/maximillian-alvarez-end-end-history/)
- It's Still Not the End of History (https://www.theatlantic.com/politics/archive/2014/09/its-still-not-the-end-of-history-francis-fukuyama/379394/)
- Bring back ideology: Fukuyama's 'end of history' 25 years on (https://www.theguardian.com/books/2014/mar/21/bring-back-ideology-fukuyama-end-history-25-years-on)
- Francis Fukuyama's Shrinking Idea (https://newrepublic.com/article/152668/francis-fukuyama-identity-review-collapse-theory-liberal-democracy)
The podcast and artwork embedded on this page are from Michael Hobbes & Peter Shamshiri, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: If Books Could Kill (LS 67 · TOP 0.05% what is this?)
Episode: Malcolm Gladwell's "Outliers"
Pub date: 2022-11-10
In "Outliers," Malcolm Gladwell posited that it takes 10,000 hours of practice to become an expert in something. Mike and Peter prove him wrong by mastering his dumb book over the span of about 50 minutes.
Support us on Patreon
Links:
- Malcolm Gladwell talks about responding to critics, and the perils of ‘Talking to Strangers’
- Gladwell for Dummies
- Malcolm Gladwell interview
- The Role of Deliberate Practice in the Acquisition of Expert Performance
- The Dangers of Delegating Education To Journalists
- Good intuition takes years of practice
- Blinkered
- How Malcolm Gladwell Tricks You Into Believing
- Complexity and the Ten-Thousand-Hour Rule
- Trends in International Mathematics and Science Study
The podcast and artwork embedded on this page are from Michael Hobbes & Peter Shamshiri, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: If Books Could Kill (LS 67 · TOP 0.05% what is this?)
Episode: Freakonomics
Pub date: 2022-11-02
In 2005, two men named Steven and Stephen published the quintessential airport book. In 2022, two men named Mike and Peter started a whole podcast just to make fun of it.
Thanks to Ted Joyce and Ames Grawert for helping with the research for this episode!
Support us on Patreon
Where to find us:
- Peter's other podcast, 5-4
- Mike's other podcast, Maintenance Phase
Sources:
- The 2003 NYT article
- Freakonomics: What Went Wrong?
- A Review of Freakonomics
- Dismal Science
- Freak-Freakonomics
- The Impact of Legalized Abortion on Crime: Comment
- The Abortion–Crime Link: Evidence from England and Wales
- The Impact of an Abortion Ban on Socioeconomic Outcomes of Children: Evidence from Romania
- Did Legalized Abortion Lower Crime?
- On the Choice of Control Variables in the Crime Equation
- Steven Levitt on Abortion and Crime: Old Economics in New Bottles
- The Impact Of Legalized Abortion On Crime
- The Great American Mystery Story: Why Did Crime Decline?
- Is There an iCrime Wave?
- The Great Crime Decline
- The Crime Drop in America
- 10 (Not Entirely Crazy) Theories Explaining the Great Crime Decline
- What Caused the Crime Decline?
- Freaks and Geeks: How Freakonomics is ruining the dismal science.
- Interesting Questions in Freakonomics
- Incentives And The Economic Point Of View: The Case Of Popular Economics
- Abortion and Crime in Australia
Thanks to Mindseye for our theme song!
The podcast and artwork embedded on this page are from Michael Hobbes & Peter Shamshiri, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Well There‘s Your Problem (LS 55 · TOP 0.5% what is this?)
Episode: Bonus Episode 28 PREVIEW: Lockheed F-104 Starfighter
Pub date: 2023-01-25
it's an episode about Gerard Butler's character "Plane" in the movie "Plane" (2023) (Dir. Jean-François Richet)
full episode here: https://www.patreon.com/posts/77748575
The podcast and artwork embedded on this page are from Justin Roczniak, Liam Anderson, Alice Caldwell-Kelly, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Well There‘s Your Problem (LS 55 · TOP 0.5% what is this?)
Episode: Episode 124: Berlin-Brandenburg Airport
Pub date: 2023-02-08
meine airport vas feelink very normal UND ZEN
Ben's twitter assuming the site still exists: https://twitter.com/benwritesthings
Ben's podcast: https://badgayspod.com/
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in the commercial: Local Forecast - Elevator Kevin MacLeod (incompetech.com) Licensed under Creative Commons: By Attribution 3.0 License http://creativecommons.org/licenses/by/3.0/
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Podcast: Transfert (LS 53 · TOP 0.5% what is this?)
Episode: Les eaux sombres de Copenhague
Pub date: 2023-03-16
Qui ne s'est jamais rêvé en capitaine Nemo? Parcourir les océansà bord du Nautilus, explorer les fonds marins, vivre vingt-mille lieues sous les mers, découvrir des terres inconnues et devenir, à force de péripéties, la quintessence de l'aventurier...
Arrivé à Copenhague pour faire la fête avec des amis, Thibault va rencontrer un inventeur qui erre dans les eaux danoises à bord de son sous-marin. Mais il le découvrira bien vite: ce charmant savant n'a rien à voir avec le courageux capitaine Nemo.
L'histoire de Thibault a été recueillie au micro de Capucine Rouault.
Transfert est un podcast produit et réalisé par Slate.fr.
Direction éditoriale: Christophe Carron
Direction de la production: Sarah Koskievic
Direction artistique et habillage musical: Benjamin Saeptem Hours
Production éditoriale: Sarah Koskievic et Benjamin Saeptem Hours
Dérushage: Capucine Rouault
Prise de son et réalisation: Victor Benhamou
Musique: «Days are long», Silent Partner
L’introduction a été écrite à quatre mains par Sarah Koskievic et Benjamin Saeptem Hours. Elle est lue par Aurélie Rodrigues.
Retrouvez Transfert tous les jeudis sur Slate.fr et sur votre application d'écoute. Découvrez aussi Transfert Club, l'offre premium de Transfert. Deux fois par mois, Transfert Club donne accès à du contenu exclusif, des histoires inédites et les coulisses de vos épisodes préférés. Pour vous abonner, rendez-vous sur slate.fr/transfertclub.
Pour proposer une histoire, vous pouvez nous envoyer un mail à l'adresse transfert@slate.fr.
The podcast and artwork embedded on this page are from Slate.fr, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Noises
Episode: Jeu, set et match
Pub date: 2019-03-07
A Roland-Garros, le bonheur d'une journée au soleil en amoureux devant la crème du tennis mondial.
Vous êtes placés au cœur du public. Les balles passent de gauche à droite. Les smashs retentissent. Qui pourrait venir troubler ce moment magique?
Production: Bababam.
Réalisation: Nathalie Bernas.
Ecriture: Thomas Le Petit-Corps.
Comédiens: Mathilde Bourbin dans le rôle de Justine, Antoine Berry Roger dans le rôle de Quentin, Nathalie Bernas dans le rôle de la supportrice
Ingénieurs du sons: Léopold Roy, Sébastien Decaux, Jean-Gabriel Rassat.
Enregistré au studio Kazoo.
Voir Acast.com/privacy pour les informations sur la vie privée et l'opt-out.
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The podcast and artwork embedded on this page are from Bababam, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: New Books in Sociology (LS 38 · TOP 2% what is this?)
Episode: Nick Seaver, "Computing Taste: Algorithms and the Makers of Music Recommendation" (U Chicago Press, 2022)
Pub date: 2023-02-02
The people who make music recommender systems have lofty goals: they want to broaden listeners’ horizons and help obscure musicians find audiences, taking advantage of the enormous catalogs offered by companies like Spotify, Apple Music, and Pandora. But for their critics, recommender systems seem to embody all the potential harms of algorithms: they flatten culture into numbers, they normalize ever-broadening data collection, and they profile their users for commercial ends. Drawing on years of ethnographic fieldwork, anthropologist Nick Seaver describes how the makers of music recommendation navigate these tensions: how product managers understand their relationship with the users they want to help and to capture; how scientists conceive of listening itself as a kind of data processing; and how engineers imagine the geography of the world of music as a space they care for and control.
Computing Taste: Algorithms and the Makers of Music Recommendation(U Chicago Press, 2022) rehumanizes the algorithmic systems that shape our world, drawing attention to the people who build and maintain them. In this vividly theorized book, Seaver brings the thinking of programmers into conversation with the discipline of anthropology, opening up the cultural world of computation in a wide-ranging exploration that travels from cosmology to calculation, myth to machine learning, and captivation to care.
Nick Seaver is Assistant Professor in the Department of Anthropology and the director of the Science, Technology, and Society program at Tufts University.
Mathew Gagné is Assistant Professor in the Department of Sociology and Social Anthropology at Dalhousie University.
Learn more about your ad choices. Visit megaphone.fm/adchoices
Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/sociology
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Podcast: Develomentor (LS 34 · TOP 3% what is this?)
Episode: Erik Bernhardsson - Spotify, Recommendation Algorithms & Hiring #65
Pub date: 2020-06-15
Welcome to another episode of Develomentor. Today's guest is Erik Bernhardsson.
Erik Bernhardsson is the CTO of Better. Better is on a mission to change the enormous and hopelessly broken mortgage industry. Erik runs the technology team, which consists of roughly 50 engineers.
Before Better, Erik spent six years at Spotify, mostly building the core of the music recommendation system. He started out writing recommendation algorithms and eventually built a team of 20 people to help out with this.
In his spare time, Erik enjoy building open source software, for instance Annoy (6k stars on Github) and Luigi. Erik also co-organizes the NYC Machine Learning meetup, and writes a tech blog which has a few hundred thousand visitors per year.
Earlier in his life Erik used to do a lot of algorithm competitions. He won an IOI gold medal in 2003 and won the Nordic programming competition five times.
If you are enjoying our content, click here to support us!
Episode Summary
“The most impact a CTO can have in many ways is recruiting.”
“At Spotify, for the first year, no one told me who my manager was. No one told me a single thing about what to do. I just did things I thought would be valuable to the business."
“You’re going to want to find vectors of who wants to listen to what track. Using these vectors you can do all of these recommendation operations.”
—Erik Bernhardsson
Key Milestones
- What is it about physics and math that makes them such fertile ground for preparing programmers?
- Erik helped build recommendation algorithms and systems for Spotify as a developer and a manager. How do these recommendation systems work and what impact can they have?
- Erik made the leap from a successful company to joining a startup as the CTO. How did he know the time was right for such a transition?
- What are some of the things Erik looksRange: Why Generalists Triumph in a Specialized World for when hiring people for his team?
- What are some concrete interviewing tips?
Additional Resources
Check out Erik’s website/blog – https://erikbern.com/
Develomentor Ep. 17 Jake Mannix – Self-Professed Math Nerd Physicist turned AI Engineer
Develomentor Ep. #44 Aline Lerner – Pro Chef Starts Tech Recruiting Firm, Interviewing.io
Range: Why Generalists Triumph in a Specialized World – by David Epstein
You can find more resources in the show notes
To learn more about our podcast go to https://develomentor.com/
To listen to previous episodes go to https://develomentor.com/blog/
CONNECT WITH ERIK BERNHARDSSON
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FOLLOW DEVELOMENTOR
Twitter: @develomentor
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Support the show (https://www.patreon.com/develomentor)
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Podcast: The Analytics Engineering Podcast (LS 32 · TOP 5% what is this?)
Episode: Erik Bernhardsson: The Missing Tool in the Data Team’s Toolbox
Pub date: 2021-08-26
Erik Bernhardsson spent six years at Spotify, where he contributed to the first version of the music recommendation system. After a stint as CTO at Better.com, he’s now working on building new infrastructure tooling for data teams.
In this wide-ranging conversation with Tristan & Julia, Erik dives into the nuts and bolts of Spotify’s recommendation algorithm, (paradoxically) why you should rarely need to use ML, and the fundamental infrastructure challenges that drag down the productivity of data teams.
For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com.
The Analytics Engineering Podcast is sponsored by dbt Labs.
The podcast and artwork embedded on this page are from dbt Labs, Inc., which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: This Week in Startups (LS 64 · TOP 0.05% what is this?)
Episode: Kevin Rose on his product philosophy, Reddit & Digg’s inverse journeys, Twitter’s recent innovations & more | E1185
Pub date: 2021-03-16
Check out True Ventures: https://trueventures.com
Podcast: The Business of Content (LS 35 · TOP 3% what is this?)
Episode: Can Digg return to its former glory?
Pub date: 2020-06-22
In the mid-2000s, Digg was one of the most powerful websites on the internet. Powered by its army of users, the platform would send gargantuan amounts of server-crushing traffic to any content featured on its front page. Millions of people visited it each day and it turned its founder Kevin Rose into an internet celebrity.
But you probably know what came next. A misguided redesign triggered a user revolt, and its audience abandoned it for Reddit and other platforms. Before long, it seemed destined to follow in the footsteps of Myspace and Friendster.
Its story didn’t end there. In 2012, the site sold to the startup studio Betaworks, which immediately went about trying to revive the Digg brand. In 2018, it was purchased by a company called BuySellAds.
I recently interviewed Todd Garland, Digg’s new owner and CEO. We discussed its current editorial operations, its monetization strategy, and his plans to restore Digg to its former glory.
The podcast and artwork embedded on this page are from Simon Owens, tech and media journalist, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Papers Read on AI
Episode: Large-scale Multi-Modal Pre-trained Models: A Comprehensive Survey
Pub date: 2023-03-02
With the urgent demand for generalized deep models, many pre-trained big models are proposed, such as BERT, ViT, GPT, etc. Inspired by the success of these models in single domains (like computer vision and natural language processing), the multi-modal pre-trained big models have also drawn more and more attention in recent years. In this work, we give a comprehensive survey of these models and hope this paper could provide new insights and helps fresh researchers to track the most cutting-edge works. Specifically, we firstly introduce the background of multi-modal pre-training by reviewing the conventional deep learning, pre-training works in natural language process, computer vision, and speech.2023: Xiao Wang, Guangyao Chen, Guangwu Qian, Pengcheng Gao, Xiaoyong Wei, Yaowei Wang, Yonghong Tian, Wen Gaohttps://arxiv.org/pdf/2302.10035v1.pdf
The podcast and artwork embedded on this page are from Rob, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Papers Read on AI
Episode: LLaMA: Open and Efficient Foundation Language Models
Pub date: 2023-03-10
We introduce LLaMA, a collection of foundation language models ranging from 7B to 65B parameters. We train our models on trillions of tokens, and show that it is possible to train state-of-the-art models using publicly available datasets exclusively, without resorting to proprietary and inaccessible datasets. In particular, LLaMA-13B outperforms GPT-3 (175B) on most benchmarks, and LLaMA-65B is competitive with the best models, Chinchilla-70B and PaLM-540B. We release all our models to the research community.2023: Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aur'elien Rodriguez, Armand Joulin, Edouard Grave, Guillaume LampleRanked #1 on Question Answering on PIQAhttps://arxiv.org/pdf/2302.13971v1.pdf
The podcast and artwork embedded on this page are from Rob, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Papers Read on AI
Episode: GPT-4 Technical Report
Pub date: 2023-03-20
We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs. While less capable than humans in many real-world scenarios, GPT-4 exhibits human-level performance on various professional and academic benchmarks, including passing a simulated bar exam with a score around the top 10% of test takers. GPT-4 is a Transformer-based model pre-trained to predict the next token in a document. The post-training alignment process results in improved performance on measures of factuality and adherence to desired behavior. A core component of this project was developing infrastructure and optimization methods that behave predictably across a wide range of scales. This allowed us to accurately predict some aspects of GPT-4's performance based on models trained with no more than 1/1,000th the compute of GPT-4.2023: OpenAIRanked #1 on Multi-task Language Understanding on MMLUhttps://arxiv.org/pdf/2303.08774v2.pdf
The podcast and artwork embedded on this page are from Rob, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Towards Data Science (LS 41 · TOP 1.5% what is this?)
Episode: 98. Mike Tung - Are knowledge graphs AI’s next big thing?
Pub date: 2021-10-13
As impressive as they are, language models like GPT-3 and BERT all have the same problem: they’re trained on reams of internet data to imitate human writing. And human writing is often wrong, biased, or both, which means language models are trying to emulate an imperfect target.
Language models often babble, or make up answers to questions they don’t understand. And it can make them unreliable sources of truth. Which is why there’s been increased interest in alternative ways to retrieve information from large datasets — approaches that include knowledge graphs.
Knowledge graphs encode entities like people, places and objects into nodes, which are then connected to other entities via edges, which specify the nature of the relationship between the two. For example, a knowledge graph might contain a node for Mark Zuckerberg, linked to another node for Facebook, via an edge that indicates that Zuck is Facebook’s CEO. Both of these nodes might in turn be connected to dozens, or even thousands of others, depending on the scale of the graph.
Knowledge graphs are an exciting path ahead for AI capabilities, and the world’s largest knowledge graphs are trained by a company called Diffbot, whose CEO Mike Tung joined me for this episode of the podcast to discuss where knowledge graphs can improve on more standard techniques, and why they might be a big part of the future of AI.
Intro music by:
➞ Artist: Ron Gelinas
➞ Track Title: Daybreak Chill Blend (original mix)
➞ Link to Track: https://youtu.be/d8Y2sKIgFWc
0:00 Intro
1:30 The Diffbot dynamic
3:40 Knowledge graphs
7:50 Crawling the internet
17:15 What makes this time special?
24:40 Relation to neural networks
29:30 Failure modes
33:40 Sense of competition
39:00 Knowledge graphs for discovery
45:00 Consensus to find truth
48:15 Wrap-up
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Podcast: The Real Python Podcast (LS 46 · TOP 1% what is this?)
Episode: Moving NLP Forward With Transformer Models and Attention
Pub date: 2022-08-12
What’s the big breakthrough for Natural Language Processing (NLP) that has dramatically advanced machine learning into deep learning? What makes these transformer models unique, and what defines “attention?” This week on the show, Jodie Burchell, developer advocate for data science at JetBrains, continues our talk about how machine learning (ML) models understand and generate text.
This episode is a continuation of the conversation in episode #119. Jodie builds on the concepts of bag-of-words, word2vec, and simple embedding models. We talk about the breakthrough mechanism called “attention,” which allows for parallelization in building models.
We also discuss the two major transformer models, BERT and GPT3. Jodie continues to share multiple resources to help you continue exploring modeling and NLP with Python.
Course Spotlight: Building a Neural Network & Making Predictions With Python AI
In this step-by-step course, you’ll build a neural network from scratch as an introduction to the world of artificial intelligence (AI) in Python. You’ll learn how to train your neural network and make predictions based on a given dataset.
Topics:
- 00:00:00 – Introduction
- 00:02:20 – Where we left off with word2vec…
- 00:03:35 – Example of losing context
- 00:06:50 – Working at scale and adding attention
- 00:12:34 – Multiple levels of training for the model
- 00:14:10 – Attention is the basis for transformer models
- 00:15:07 – BERT (Bidirectional Encoder Representations from Transformers)
- 00:16:29 – GPT (Generative Pre-trained Transformer)
- 00:19:08 – Video Course Spotlight
- 00:20:08 – How far have we moved forward?
- 00:20:41 – Access to GPT-2 via Hugging Face
- 00:23:56 – How to access and use these models?
- 00:30:42 – Cost of training GPT-3
- 00:35:01 – Resources to practice and learn with BERT
- 00:38:19 – GPT-3 and GitHub Copilot
- 00:44:35 – DALL-E is a transformer
- 00:46:13 – Help yourself to the show notes!
- 00:49:19 – How can people follow your work?
- 00:50:03 – Thanks and goodbye
Show Links:
- Recurrent neural network - Wikipedia
- Long short-term memory - Wikipedia
- Vanishing gradient problem - Wikipedia
- Vanishing Gradient Problem | What is Vanishing Gradient Problem?
- Attention Is All You Need | Cornell University
- Visualizing A Neural Machine Translation Model (Mechanics of Seq2seq Models With Attention) – Jay Alammar
- Standing on the Shoulders of Giant Frozen Language Models | Cornell University
-
datalift22 Embeddings paradigm shift: Model training to vector similarity search by Nava Levy - YouTube
- Transformer Neural Networks - EXPLAINED! (Attention is all you need) - YouTube
- BERT 101 - State Of The Art NLP Model Explained
- How GPT3 Works - Easily Explained with Animations - YouTube
- Write With Transformer (GPT2 Live Playground Tool) - Hugging Face
- Language Model with Alpa (GPT3 Live Playground Tool) OPT-175B
- Big Data | Music
- OpenAI API
- 🤗 (Hugging Face)Transformers Notebooks
- GitHub Copilot: Fly With Python at the Speed of Thought
- GitHub Copilot learned about the daily struggle of JavaScript developers after being trained on billions of lines of code. | Marek Sotak on Twitter
- Jodie Burchell’s Blog - Standard error
- Jodie Burchell 🇦🇺🇩🇪 (@t_redactyl) / Twitter
- JetBrains: Essential tools for software developers and teams
Support the podcast & join our community of Pythonistas
The podcast and artwork embedded on this page are from Real Python, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Practical AI: Machine Learning, Data Science (LS 45 · TOP 1% what is this?)
Episode: OpenAI's new "dangerous" GPT-2 language model
Pub date: 2019-02-25
This week we discuss GPT-2, a new transformer-based language model from OpenAI that has everyone talking. It’s capable of generating incredibly realistic text, and the AI community has lots of concerns about potential malicious applications. We help you understand GPT-2 and we discuss ethical concerns, responsible release of AI research, and resources that we have found useful in learning about language models.
Discuss on Changelog News
Changelog++ members support our work, get closer to the metal, and make the ads disappear. Join today!
Sponsors:
- Linode – Our cloud server of choice. Deploy a fast, efficient, native SSD cloud server for only $5/month. Get 4 months free using the code
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Featuring:
- Chris Benson – Twitter, GitHub, LinkedIn, Website
- Daniel Whitenack – Twitter, GitHub, Website
Show Notes:
Relevant learning resources:
- Jay Alammar “Illustrated” blog articles:
- The illustrated transformer
- The illustrated BERT, elmo, and co
- Machine Learning Explained blog:
- An In-Depth Tutorial to AllenNLP (From Basics to ELMo and BERT)
- Paper Dissected: “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding” Explained
References/notes:
- GPT-2 blog post from OpenAI
- GPT-2 Paper
- GPT-2 GitHub Repo
- GPT-2 PyTorch implementation
- Episode 22 of Practical AI about BERT
- OpenAI’s GPT-2: the model, the hype, and the controversy (towardsdatascience)
- The AI Text Generator That’s Too Dangerous to Make Public (Wired)
- Transformer paper
- Preparing for malicious uses of AI (OpenAI blog)
Something missing or broken? PRs welcome!
The podcast and artwork embedded on this page are from Changelog Media, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Data Skeptic (LS 55 · TOP 0.5% what is this?)
Episode: Talking to GPT-2
Pub date: 2019-10-31
GPT-2 is yet another in a succession of models like ELMo and BERT which adopt a similar deep learning architecture and train an unsupervised model on a massive text corpus.
As we have been covering recently, these approaches are showing tremendous promise, but how close are they to an AGI? Our guest today, Vazgen Davidyants wondered exactly that, and have conversations with a Chatbot running GPT-2. We discuss his experiences as well as some novel thoughts on artificial intelligence.
The podcast and artwork embedded on this page are from Kyle Polich, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Real Python Podcast (LS 46 · TOP 1% what is this?)
Episode: Natural Language Processing and How ML Models Understand Text
Pub date: 2022-07-29
How do you process and classify text documents in Python? What are the fundamental techniques and building blocks for Natural Language Processing (NLP)? This week on the show, Jodie Burchell, developer advocate for data science at JetBrains, talks about how machine learning (ML) models understand text.
Jodie explains how ML models require data in a structured format, which involves transforming text documents into columns and rows. She covers the most straightforward approach, called binary vectorization. We discuss the bag-of-words method and the tools of stemming, lemmatization, and count vectorization.
We jump into word embedding models next. Jodie talks about WordNet, Natural Language Toolkit (NLTK), word2vec, and Gensim. Our conversation lays a foundation for starting with text classification, implementing sentiment analysis, and building projects using these tools. Jodie also shares multiple resources to help you continue exploring NLP and modeling.
Course Spotlight: Learn Text Classification With Python and Keras
In this course, you’ll learn about Python text classification with Keras, working your way from a bag-of-words model with logistic regression to more advanced methods, such as convolutional neural networks. You’ll see how you can use pretrained word embeddings, and you’ll squeeze more performance out of your model through hyperparameter optimization.
Topics:
- 00:00:00 – Introduction
- 00:02:47 – Exploring the topic
- 00:06:00 – Perceived sentience of LaMDA
- 00:10:24 – How do we get started?
- 00:11:16 – What are classification and sentiment analysis?
- 00:13:03 – Transforming text in rows and columns
- 00:14:47 – Sponsor: Snyk
- 00:15:27 – Bag-of-words approach
- 00:19:12 – Stemming and lemmatization
- 00:22:05 – Capturing N-grams
- 00:25:34 – Count vectorization
- 00:27:14 – Stop words
- 00:28:46 – Text Frequency / Inverse Document Frequency (TFIDF) vectorization
- 00:32:28 – Potential projects for bag-of-words techniques
- 00:34:07 – Video Course Spotlight
- 00:35:20 – WordNet and NLTK package
- 00:37:27 – Word embeddings and word2vec
- 00:45:30 – Previous training and too many dimensions
- 00:50:07 – How to use word2vec and Gensim?
- 00:51:26 – What types of projects for word2vec and Gensim?
- 00:54:41 – Getting into GPT and BERT in another episode
- 00:56:11 – How to follow Jodie’s work?
- 00:57:36 – Thanks and goodbye
Show Links:
- Why Google’s “sentient” AI LaMDA is nothing like a person.
- On NYT Magazine on AI: Resist the Urge to be Impressed | Emily M. Bender | Medium
- ELIZA - Wikipedia
- eliza.py - Python 2 version by Daniel Connelly
- dabraude/Pyliza: Python3 Implementation of Eliza
- magneticpoetry.com
- Natural Language Processing With Python’s NLTK Package – Real Python
- Practical Text Classification With Python and Keras – Real Python
- Sentiment Analysis: First Steps With Python’s NLTK Library – Real Python
- NLTK: Natural Language Toolkit
- spaCy · Industrial-strength Natural Language Processing in Python
- Stemming - Wikipedia
- Lemmatization - Wikipedia
- Binary/Count Vectorization: sklearn.feature_extraction.text.CountVectorizer— scikit-learn
- TFIDF: sklearn.feature_extraction.text.TfidfVectorizer — scikit-learn
- Porter Stemmer: nltk.stem.porter module — NLTK
- Snowball Stemmer: nltk.stem.snowball module — NLTK
- WordNet Lemmatizer: nltk.stem.wordnet module — NLTK
- Lemmatizer · spaCy API Documentation
- Applying Bag of Words and Word2Vec models on Reuters-21578 Dataset Elvin Ouyang’s Blog
- UCI Machine Learning Repository: Reuters-21578 Text Categorization Collection Data Set
- The Illustrated Word2vec – Jay Alammar
- A Complete Guide to Using WordNET in NLP Applications
- Gensim: Topic modeling for humans
- Core Tutorials — gensim
- Find Open Datasets and Machine Learning Projects | Kaggle
- Engineering All Hands: Vectorise all the things! - YouTube
- PyCon Portugal 2022
- NDC Oslo 2022 | Conference for Software Developers
- Jodie Burchell’s Blog - Standard error
- Jodie Burchell 🇦🇺🇩🇪 (@t_redactyl) / Twitter
- JetBrains: Essential tools for software developers and teams
Support the podcast & join our community of Pythonistas
The podcast and artwork embedded on this page are from Real Python, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Stack Overflow Podcast (LS 39 · TOP 2% what is this?)
Episode: Let’s talk large language models
Pub date: 2023-03-17
Our recent Pulse Survey showed how technologists visiting Stack Overflow feel about emergent technologies. The consensus is clear: AI assistants will soon be everywhere, and developers aren’t sure how they feel about that. Check out the podcast here or dive into the blog.
Learn more about the emergent abilities of large language models (LLMs).
For more on the intersection of AI and academia, listen to our episode with computer science professor Emery Berger or read his essay on how academics are coping with AI that can ace exams and do everyone’s homework.
Catch up on the adventures of the worst coder in the world.
Congrats to user d1337, whose question How to assign a name to the size() column? won a Stellar Question badge.
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Podcast: The Rachman Review (LS 44 · TOP 1% what is this?)
Episode: Ukraine series: how long will the war last?
Pub date: 2023-02-16
In the second episode in our special series, Gideon talks to war historian Hein Goemans about what it would take to end the fighting in Ukraine. FT podcast survey
A year of war in Ukraine has left Europe’s armouries dry
Military briefing: Russia prepares Ukraine spring offensive
The keyboard warriors on Ukraine’s digital front line
Russia’s invasion of Ukraine in maps — latest updates
Subscribe to The Rachman Review wherever you get your podcasts - please listen, rate and subscribe.
Presented by Gideon Rachman. Produced by Fiona Symon. Sound design is by Breen Turner
Read a transcript of this episode on FT.com
Hosted on Acast. See acast.com/privacy for more information.
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Podcast: Middle East Focus (LS 39 · TOP 2% what is this?)
Episode: Can the Lira be saved?
Pub date: 2023-03-10
MEI's US-Lebanon Fellow Fadi Nicholas Nassar speaks to Beirut-based international finance professional Mike Azar on Lebanon's financial crisis. What is the state of Lebanon's banking system, and how did it become so dysfunctional? What does Azar recommend to get Lebanon's economy back on track, and can the Lira be saved?
The podcast and artwork embedded on this page are from Middle East Institute, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Revisionist History (LS 87 · TOP 0.01% what is this?)
Episode: The Lady Vanishes
Pub date: 2016-06-16
In the late 19th century, a painting titled The Roll Call, by a virtually unknown artist, took England by storm. But after that brilliant first effort, the artist all but disappeared. Why? And what does The Roll Call tell us about the fate of those first through the door?
To learn more about the topics covered in this episode, visit www.RevisionistHistory.com
Learn more about your ad-choices at https://www.iheartpodcastnetwork.comSee omnystudio.com/listener for privacy information.
The podcast and artwork embedded on this page are from Pushkin Industries, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Highways Voices
Episode: Highways Voices 16 - Glynn Barton of Transport for London
Pub date: 2021-05-19
“The one saving grace,” says Glynn Barton while talking about the Coronavirus crisis, “is that it wasn’t the other way – we didn’t all of a sudden see a boom in traffic and movement on our very busy network”. Having overseen a huge change to traffic flows in London, been affected by the sudden drop in tube and bus passenger numbers and therefore revenue, and having managed his huge team’s switch to home working (not to mention becoming a father again), it’s been a challenging year for Transport for London’s Director of Network Management.
But Barton has taken it all in his stride, as he explains to Paul Hutton and Adrian Tatum in this week’s Highways Voices podcast. And he pays tribute to the teamwork that made things happen so quickly. “We installed a cycle lane on Park Lane, for instance, from design to installation in four days, which we've never done anything like that before,” he explains. “Working with a supply chain, we're able to do things really quickly because it was in the public interest.”
Barton discusses a range of transport challenges and solutions in the capital, from Low Traffic Neighbourhoods and active travel to funding and future projects. He highlights the need to deliver low-carbon transport and the solutions TfL are using to deliver that, as well as how he considers the latest ideas and solutions, and how he then can implement them in London, and how does he take a world-leading transport network and make it even better?
You’ll also hear Paul and Adrian round up the week’s top stories and also hear who gets recognised for excellence in the industry in “Adrian’s Accolade”.
Subscribe to Highways Voices free on Apple Podcasts, Spotify, Amazon Music, Google Podcasts or Pocket Casts and never miss an episode!
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Podcast: Simon Calder's Independent Travel Podcast (LS 39 · TOP 2% what is this?)
Episode: January 16th - Leon Daniels discusses the future of surface transport
Pub date: 2023-01-16
Ten days ago I was waxing lyrical about London's 63 bus. The future of surface transport in the capital, I called it. Well, after that Leon Daniels – former managing director of Surface Transport for Transport for London – got in touch to say that many of the innovations I welcomed had, in fact, been in place for years. So I invited Leon to tell us all more.
Of course, this podcast is free, as is my weekly newsletter, which you can subscribe to here.
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The podcast and artwork embedded on this page are from The Independent, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Govlaunch Podcast (LS 32 · TOP 5% what is this?)
Episode: Transport for London's groundbreaking commercial innovation hub benefits mobility across London and beyond
Pub date: 2021-07-26
Transport for London developed a creative approach to improving their procurement processes, cutting through bureaucracy that hindered co-developing solutions with the market.
More info:
Featured government: Transport for London, UK
Episode guests: Rikesh Shah, Head of Commercial Innovation
Visit govlaunch.com for more stories and examples of local government innovation.
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Podcast: Customer Perspective: An Ipsos Podcast (LS 29 · TOP 10% what is this?)
Episode: Season 6: Episode 1 – Hear from Sybil Nicolson, Insight Executive for Transport for London
Pub date: 2023-02-10
We’re delighted to welcome Sybil Nicolson and Kristian Green to the first episode of our sixth season of Customer Perspective. Sybil is an Insights Executive for Transport for London, which many of you will know as TfL. Kristian is a Director in Ipsos’ UK Channel Performance – Mystery Shopping team, and a transport and tourism sector expert.
For those of you not familiar with TfL, this is the organisation which runs the operation of London’s public transport network and manages London's main roads – no small task given we’re talking the likes of 1.4bn tube travellers every year, 2.3 bn bus passengers – when every journey matters… that’s quite the organisational challenge!
Sybil and Kristian describe some of TfL’s research activities, including their large-scale, long-term Mystery Shopping programme: London Underground Customer Care Monitor (CCM). They explain how it remains fresh after more than 10 years, the research and logistical challenges associated with such a major initiative and how to overcome, and, most importantly, how TfL puts this to work, to drive positive change across the organisation and improvements for customers.
For more information about how to build a successful mystery shopping programme, head to Designing a ‘Smarter’ Mystery Shopping Programme | Ipsos. And do check out our latest Ipsos CX and Channel Performance thinking.
The podcast and artwork embedded on this page are from Ipsos, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Engineers Collective (LS 36 · TOP 2.5% what is this?)
Episode: Transport for London’s Michèle Dix announces her retirement
Pub date: 2021-09-29
Transport for London (TfL) Crossrail 2 managing director Michèle Dix joins NCE editor Claire Smith and head of content and engagement Rob Horgan on the latest episode of The Engineers Collective to talk about her 21 year career with the organisation as she prepares for retirement.
Michèle talks about her work on London’s congestion charging scheme, which first took her to TfL in 2000 and how she was an early pioneer of job sharing when she jointly delivered the role of director for the project with Malcolm Murray-Clark. She continued to work with Malcolm as the scheme was extended westwards and they evolved the role to deliver London’s Low Emission Zone too.
Michèle also discusses some of her favourite projects, including delivery of London’s cable car, before exploring the challenges faced by her last project – Crossrail 2 – which has now been mothballed as a result of TfL’s pandemic funding issues.
In reflecting back on her career, Michèle also puts forward advice for young engineers hoping to emulate her success and considers what advice she’d give her younger self too.
Ahead of the interview with Michèle, Claire, Rob and reporter Catherine Kennedy explore some of the latest news stories including some intriguing innovations such as the role of fungi in preventing landslides, tunnel boring machines that can 3D print tunnel linings or bore square section tunnels through rock and how graphene might change construction materials.
The podcast and artwork embedded on this page are from New Civil Engineer, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Desert Island Discs (LS 74 · TOP 0.01% what is this?)
Episode: Martin Freeman, actor
Pub date: 2019-03-31
Martin Freeman is a multi-award winning actor, best known for his roles as the lovable Tim in BBC Two’s The Office and as Dr Watson to Benedict Cumberbatch’s Sherlock Holmes. He also played Bilbo Baggins in Peter Jackson’s Hobbit trilogy, Lester Nygaard in the US drama series Fargo and Everett K Ross in the film Black Panther. Born in Hampshire in 1971, he grew up in Teddington in south-west London. The youngest of five children, he was just 10 when his father died of a heart attack. As a teenager, he played competitive squash, making the national squad, until he realised he lacked the necessary killer instinct required and switched to youth theatre. He studied at the Central School of Speech and Drama and left in his third year to work at the National Theatre, playing minor roles. He first reached a wider audience when he was cast as Tim in The Office, which was broadcast from 2001 to 2003 and became the first British sitcom to win a Golden Globe. More screen roles followed, including playing Arthur Dent in the film of The Hitchhiker's Guide to the Galaxy. In 2010 he first appeared as Dr Watson opposite Benedict Cumberbatch’s Sherlock and went on to win both a BAFTA and an Emmy as Best Supporting Actor. He has continued to work in films, TV and on stage. He appeared in Sherlock with his ex-partner Amanda Abbington. They have two children.BOOK CHOICE: Animal Farm by George OrwellLUXURY: Tea-making Facilities CASTAWAY'S FAVOURITE: Strawberry Fields Forever by The BeatlesPresenter: Lauren LaverneProducer: Cathy Drysdale
The podcast and artwork embedded on this page are from BBC Radio 4, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: PayPod: The Payments and Fintech Podcast (LS 28 · TOP 10% what is this?)
Episode: Control and Visibility Over Business Spend with Hristo Borisov of Payhawk: Ep 257
Pub date: 2022-12-22
In a tech start-up things are moving sometimes at breakneck speed. In this episode, we had the opportunity to spend some time with Hristo Borisov, CEO and Co-Founder of Payhawk, to discuss the financial systems of tomorrow and the importance of maximizing control and visibility over business spend to keep up with the fast pace of growing companies.
Check out the show notes here: https://www.soarpay.com/2023/01/payhawk
The podcast and artwork embedded on this page are from Soar Payments LLC, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Invest Like the Best with Patrick O'Shaughnessy (LS 67 · TOP 0.05% what is this?)
Episode: Eric Glyman - Reimagining Corporate Finance - [Invest Like the Best, EP. 275]
Pub date: 2022-05-03
My guest today is Eric Glyman, co-founder and CEO of Ramp. Ramp is best known for its corporate cards but it has a range of software products to help finance teams save money and time. Since its founding in 2019, the business has grown rapidly and was last valued at $8 billion. Eric and I discuss Ramp’s initial marketing wedge, how the business has dealt with such fast growth, and why they hold stablecoins on their balance sheet. Please enjoy my conversation with Eric Glyman.
For the full show notes, transcript, and links to mentioned content, check out the episode page here.
This episode is brought to you by Canalyst. Canalyst is the leading destination for public company data and analysis. If you're a professional equity investor and haven't talked to Canalyst recently, you should give them a shout. Learn more and try Canalyst for yourself at canalyst.com/Patrick.
This episode is brought to you by Lemon.io. The team at Lemon.io has built a network of Eastern European developers ready to pair with fast-growing startups. We have faced challenges hiring engineering talent for various projects - and Lemon.io offered developers for one-off projects, developers for full start to finish product development, or developers that could be add-ons to the existing team. Check out lemon.io/patrick to learn more.
Invest Like the Best is a property of Colossus, LLC. For more episodes of Invest Like the Best, visit joincolossus.com/episodes.
Past guests include Tobi Lutke, Kevin Systrom, Mike Krieger, John Collison, Kat Cole, Marc Andreessen, Matthew Ball, Bill Gurley, Anu Hariharan, Ben Thompson, and many more.
Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here.
Follow us on Twitter: @patrick_oshag | @JoinColossus
Show Notes
[00:02:41] - [First question] - What was most notably awry about the industry before Ramp
[00:04:45] - Breakdown of Visa; The business model of the Black Card compared to the business card offering of Ramp
[00:08:40] - Causes and what he attributes their early success to
[00:11:30] - Description of Ramp’s software in the beginning and the evolution of co-building it
[00:16:34] - How he’s gone about building the company and team fast enough to handle their explosive growth curve
[00:19:47] - Approaching all aspects of recruiting and acquiring such great talent
[00:21:39] - Thoughts on the biggest mistake he’s made while building Ramp
[00:24:05] - Lessons learned about marketing that this journey has taught him
[00:26:13] - Learning to manage a senior team and advice for managing rapid growth
[00:28:58] - Unique aspects of Ramp’s approach to the financing side
[00:32:56] - Why they are storing some of their balance sheet in stablecoins
[00:34:47] - What the idealized end state of Ramp looks like
[00:37:26] - How the data and information he sees indicates trends in the economy writ large
[00:39:33] - Providing secondary liquidity to employees in a world where companies stay private for longer periods of time
[00:43:03] - Aspects of company building that are still unnecessarily hard
[00:44:55] - What has him most excited about Ramp in the next 12-18 months
[00:46:42] - The kindest thing anyone has ever done for him
The podcast and artwork embedded on this page are from Colossus | Investing & Business Podcasts, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Quest Pod with Justin Kan (LS 45 · TOP 1% what is this?)
Episode: How @Eric Glyman Re-Wrote the Credit Card Playbook at Ramp
Pub date: 2021-12-21
Welcome to The Quest Pod Season 1: Episode 34 with Eric Glyman: a two-time fintech founder shares his advice for innovating in a crowded market.
Eric Glyman is the co-founder and CEO of Ramp, the only corporate card that helps companies spend less. He also previously founded Paribus to help consumers get money back on their online purchases.
Eric’s success reflects the power of customer-centric design: focus on building something that serves and created value for your market, instead of trying to extract as much money from your customers as possible. In this conversation, we talk about leaving a stable job to found a start-up, leading big teams and unlocking innovation.
If you liked this episode, check out our YouTube channel and follow us on Twitter!
A thank to our sponsors Universe and CashApp for making this podcast possible.
THE QUEST MEDIA | CONTENT MEETS SILICON VALLEY | 📺 YouTube | 🍎 iTunes | 🎧 Spotify | 🗞 Print | 📌 Bio
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Podcast: Fintech Insider Podcast by 11:FS (LS 51 · TOP 0.5% what is this?)
Episode: 640. News: Brex backs away from SMEs while the UK ramps up BNPL regulation
Pub date: 2022-06-27
Our expert hosts, David M. Brear and Sim Rai, are joined by some great guests to talk about the most notable fintech, financial services and banking news from the past week.
This week's guests include:
- Ron Shevlin, Chief Research Officer, Cornerstone Advisors
- Tayga Baltacıoğlu, CEO, Debite
- Douglas Soltys, Editor-in-Chief, BetaKit
With soundclips form:
- Antony Stephen, CEO, Barclays Partner Finance
- David Cunningham, Chief Commercial Officer, Lextego
We cover the following stories from the fintech and financial services space:
- Brex drops tens of thousands of small business customers as Silicon Valley adjusts to new reality - 5:40
- UK outlines plans to tighten ‘buy now pay later’ rules - 14:50
- Canada’s Wealthsimple becomes the latest fintech to cut staff - 31:35
- Deutsche Bank staff forced to install app that tracks messages - 44:45
- Stripe to launch new bank transfer proposition in UK and EU to take away "operational pain” - 48:40
- The phoney 'fintech revolution' is eating itself - 49:55
- Irish banks get green light for payments app - 51:34
- Internet Explorer is being hilariously serenaded after 27 long years of browsing history - 53:55
This episode is sponsored by FullCircl
You’re under pressure on multiple fronts – demanding customers, competitors making a grab for market share, regulatory scrutiny, and high cost-to-serve. So, what to do?
Whether you’re a bank or financial services company, future-ready transformation can only be achieved by implementing a holistic Customer Lifecycle Intelligence (CLI) strategy.
This new whitepaper explores how CLI will help you find the right customers, onboard them faster, and keep them for life. Check out the whitepaper here!
Fintech Insider by 11:FS is a podcast dedicated to all things fintech, banking, technology and financial services. It’s hosted by a rotation of 11:FS experts including David M. Brear, Simon Taylor, Jason Bates and Gwera Kiwana, as well as a range of brilliant guests. We cover the latest global news, bring you interviews from industry experts or take a deep dive into subject matters such as APIs, AI or digital banking.
If you enjoyed this episode, don't forget to subscribe and please leave a review
Follow us on Twitter: @fintechinsiders where you can ask the hosts questions, or email podcasts@11fs.com!
Special Guests: Antony Stephen, Dave Cunningham, Douglas Soltys, Ron Shevlin, and Tayga Baltacıoğlu.
Links:
- Brex drops tens of thousands of small business customers as Silicon Valley adjusts to new reality
- UK outlines plans to tighten ‘buy now pay later’ rules
- Canada’s Wealthsimple becomes the latest fintech to cut staff
- Deutsche Bank staff forced to install app that tracks messages
- Stripe to launch new bank transfer proposition in UK and EU to take away "operational pain”
- The phoney 'fintech revolution' is eating itself
- Irish banks get green light for payments app
- Internet Explorer is being hilariously serenaded after 27 long years of browsing history
The podcast and artwork embedded on this page are from 11:FS, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Fintech Insider Podcast by 11:FS (LS 51 · TOP 0.5% what is this?)
Episode: 698. News: How Apple Card cost Goldman Sachs billions
Pub date: 2023-01-23
Our expert hosts, Ross Gallagher and David Barton-Grimley, are joined by some great guests to talk about the most notable fintech, financial services and banking news from the past week.
This week's guests include:
- Irina Chuchkina, Chief Marketing Officer, Thunes
- Natasha Jones, Early-Stage Investor, Octopus Ventures
We cover the following stories from the fintech and financial services space:
- Apple Card has cost Goldman Sachs over $1bn in losses - 5:00
- Cashed-up banks ready to buy UK fintech firms on the cheap in 2023, says VC chief - 18:20
- Women pass 40% mark on European financial services boards but hurdles remain - 31:15
- US tech firms are replacing workers with cheaper talent in Latin America - 42:00
- Revolut is assembling a new team to address its workplace culture - 50:10
- Amazon to widely launch 'Buy with Prime', says offering improved merchant sales - 51:50
- Keep cake away from office, suggests food watchdog head - 54:25
Fintech Insider by 11:FS is a podcast dedicated to all things fintech, banking, technology and financial services. It’s hosted by a rotation of 11:FS experts including David M. Brear, Ross Gallagher, Benjamin Ensor, and Kate Moody - as well as a range of brilliant guests. We cover the latest global news, bring you interviews from industry experts or take a deep dive into subject matters such as APIs, AI or digital banking.
If you enjoyed this episode, don't forget to subscribe and please leave a review
Send us your questions for the Fintech Insider Mailbag here
Follow us on Twitter: @fintechinsiders where you can ask the hosts questions, or email podcasts@11fs.com!
Special Guests: David Barton-Grimley, Irina Chuchkina, and Natasha Jones.
Links:
- Apple Card has cost Goldman Sachs over $1 billion in losses
- Cashed-up banks ready to buy UK fintech firms on the cheap in 2023, says VC chief
- Women pass 40% mark on European financial services boards but hurdles remain
- U.S. tech firms are replacing workers with cheaper talent in Latin America
- Revolut is assembling a new team to address its workplace culture
- Amazon to widely launch 'Buy with Prime', says offering improved merchant sales
- Keep cake away from office, suggests food watchdog head
The podcast and artwork embedded on this page are from 11:FS, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Fintech Insider Podcast by 11:FS (LS 51 · TOP 0.5% what is this?)
Episode: 701. Focus: The power of Open Finance lies in open data, with Samantha Seaton, CEO of Moneyhub
Pub date: 2023-02-01
Our expert host, David Barton-Grimley, Global Strategy Director - Embedded Financial Services here at 11:FS, is joined by a fantastic guest to continue the deep-dive into the question posed by David M. Brear and Visa's Dan Roesbery in the inaugural episode of this new Fintech Insider strand: "Can Open Finance ever be truly global?"
In this second instalment of Fintech Insider Focus (catch up on the first episode here), in association with Visa, we’re taking a burning question from financial services across the globe - and really putting it under the microscope with explainers, expert panels, and in-depth interviews all to bring the global community into focus.
In this episode, we continue our journey with Samantha Seaton, CEO of Moneyhub, to explore what Open Finance allows companies to do now that wasn’t possible before, the power of open data, how can open data and finance impact people’s lives, and Moneyhub's future plans when it comes to operating globally. All of this, and so much more!This episode is sponsored by Visa
Visa’s Fintech Fast Track program is streamlining the onboarding process for fintechs – enabling them to gain access to Visa’s powerful capabilities and network. Visa and their enablement partners help fintechs launch and scale cards, virtual credentials, and disbursement programs. To learn more visit, partner.visa.com
Fintech Insider by 11:FS is a podcast dedicated to all things fintech, banking, technology and financial services. It’s hosted by a rotation of 11:FS experts including David M. Brear, Ross Gallagher, Benjamin Ensor, and Kate Moody - as well as a range of brilliant guests. We cover the latest global news, bring you interviews from industry experts or take a deep dive into subject matters such as APIs, AI or digital banking.
If you enjoyed this episode, don't forget to subscribe and please leave a review
Send us your questions for the Fintech Insider Mailbag here
Follow us on Twitter: @fintechinsiders where you can ask the hosts questions, or email podcasts@11fs.com!
Special Guests: Dan Roesbery and Samantha Seaton.
The podcast and artwork embedded on this page are from 11:FS, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Information's 411 (LS 40 · TOP 1.5% what is this?)
Episode: Behind the Fintech Frenzy
Pub date: 2021-04-02
Why are startups like Stripe, Plaid, Brex, Ramp and Fast raising so much money so quickly? Cory talks about the phenomenon with Bain Capital Ventures partner Merritt Hummer and The Information's venture capital reporters Kate Clark and Berber Jin. Plus, an interview with Henrique Dubugras, co-CEO of Brex, a corporate credit card startup, about the pandemic, fintech valuations and company's future strategy.
The podcast and artwork embedded on this page are from The Information, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: In Depth (LS 38 · TOP 2% what is this?)
Episode: Want to go totally asynchronous? Repeat founder Sidharth Kakkar on building a remote team & autonomous culture
Pub date: 2022-07-21
Today’s episode is with Sidharth Kakkar, founder and CEO of Subscript, a subscription intelligence platform that empowers B2B SaaS leaders to better understand their revenue. (Read more about the company in this Techcrunch article.)
Previously, he was the founder, CEO of Freckle, an education platform that grew to serve 10 million students and was acquired by Renaissance Learning in 2019. As a repeat founder, Sidharth picked up a ton of valuable lessons, particularly when it comes to company culture and management.
Right from the start, he knew he wanted to build Subscript to be global, distributed, and asynchronous. That’s why there are no internal company meetings. Everyone also operates autonomously, deciding what to work on for themselves.
We dive into both the philosophy behind this unique approach and the nitty gritty details of how exactly it works in practice. Here’s a preview:
- How to share company updates asynchronously every week.
- Advice on how to approach goal-setting and performance feedback, while minimizing micromanagement.
- Tips for improving transparency and documentation, plus details on Subscript’s running product/market fit journal.
- Thoughts on how to assess asynchronous communication skills when hiring.
- How this culture impacts a founder’s role and schedule.
There’s tons of food for thought in here, whether you’re a founder thinking about shaping your company culture, or a manager looking for some fresh ideas.
You can follow Sidharth on Twitter at @sikakkar. You can email us questions directly at review@firstround.com or follow us on Twitter @firstround and @brettberson.
The podcast and artwork embedded on this page are from First Round, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: In Depth (LS 38 · TOP 2% what is this?)
Episode: The 5 phases of Figma’s community-led growth — Claire Butler
Pub date: 2022-09-01
Today’s episode is with Claire Butler, Senior Director of Marketing at Figma, and one of the company’s first 10 employees.
In today’s conversation, she sketches out Figma’s five phases of community-led growth — and shares tons of advice along the way for startups who also are looking to build an organic growth engine.
In the first phase, Claire covers the biggest lessons from Figma’s years of stealth mode — and how you can start planting the seeds for a community when you don’t have a fully-formed product. She also unpacks the decision to eventually emerge from stealth, after years of quietly building.
In the second phase, Claire opens up the pages of Figma’s launch playbook — from taking over design Twitter, to marketing to folks who tend to bristle at traditional SaaS marketing.
In the third phase, she shares how Figma leveraged the community to get folks to try the product, even if they weren’t going to switch over right away to designing in Figma full-time. In this phase of community-building, Figma built out its evangelist strategy and Claire shares tons of tips for generating excitement around your nascent product.
In the final two phases, Figma needed to connect the individual users that loved the product with a larger enterprise strategy. They didn’t layer in a sales team until four years after the product launched, and didn’t add a paid product tier until another two years after that. Claire explores the ins and outs of these GTM trade-offs.
You can follow Claire on Twitter at @clairetbutler
You can email us questions directly at review@firstround.com or follow us on Twitter @ twitter.com/firstround and twitter.com/brettberson
The podcast and artwork embedded on this page are from First Round, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: In Depth (LS 38 · TOP 2% what is this?)
Episode: Airtable’s path to product-market fit — co-founder Andrew Ofstad on building horizontal products
Pub date: 2022-08-18
Todd Jackson’s filling in as host again this week. (As a reminder, he’s hosting a few product-focused episodes this season — all about finding product-market fit.)
Today, Todd chats with Andrew Ofstad, co-founder of Airtable. In our conversation, we go deep into Airtable’s early days, and how they navigated the journey of finding traction and scaling.
Here’s a preview of what Todd and Andrew cover:
- How the founders came together, their vision for the product, and what the initial prototypes looked like.
- Airtable’s alpha, beta, and launch timelines, as well as their early traction.
- The challenges of creating a horizontal product that can do many things, including identifying initial use cases and figuring out how to describe what they were building.
- How to approach pricing and competition, as well as their early go-to-market strategy.
- What the next 3 years will look like for Airtable, and how they’ve navigated scaling while staying true to their vision.
Whether you’re a founder validating your own idea, or a product leader looking for growth advice, there are tons of tactics here that go much deeper than the typical founding stories you hear.
You can follow Andrew on Twitter at @aofstad. You can email us questions directly at review@firstround.com or follow us on Twitter @firstround and @tjack.
The podcast and artwork embedded on this page are from First Round, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Knowledge Project with Shane Parrish (LS 69 · TOP 0.05% what is this?)
Episode: #77 Mike Maples: Living in the Future
Pub date: 2020-03-03
Mike Maples, a partner at the VC firm Floodgate, shares how mental models shape his decision-making process, where to find the next big idea, and how to rally people to your cause.
Go Premium: Members get early access, ad-free episodes, hand-edited transcripts, searchable transcripts, member-only episodes, and more. Sign up at: https://fs.blog/membership/
Every Sunday our newsletter shares timeless insights and ideas that you can use at work and home. Add it to your inbox: https://fs.blog/newsletter/
Follow Shane on Twitter at: https://twitter.com/ShaneAParrish
The podcast and artwork embedded on this page are from Farnam Street, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Village Global's Venture Stories (LS 47 · TOP 1% what is this?)
Episode: Balaji Srinivasan on Navigating the Idea Maze and Finding a Co-Founder
Pub date: 2020-05-23
This episode with Balaji Srinivasan (@balajis) was recorded as part of an OnDeck fellowship session.
They discuss:
-
The concept of ikigai and how it can help you plan your next career move.
-
How to think about when to leave or join a company, and when to start something of your own.
-
How Balaji generates and validates ideas.
-
Finding a co-founder and the most important issues to work out before starting a company with them.
-
The impacts of COVID-19 on different markets.
The final deadline for applications for the summer vintage of our Network Catalyst accelerator is June 5th. Learn more and apply today at www.villageglobal.vc/network-catalyst.
Thanks for listening — if you like what you hear, please review us on your favorite podcast platform.
Check us out on the web at villageglobal.vc or get in touch with us on Twitter @villageglobal.
The podcast and artwork embedded on this page are from Village Global, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Village Global's Venture Stories (LS 47 · TOP 1% what is this?)
Episode: Mike Maples on Navigating the Idea Maze and Building Breakthrough Companies
Pub date: 2020-05-24
Mike Maples (@m2jr), co-founder and partner at Floodgate, joins Erik to talk about:
-
Insight hacking as a scientific way to generate insights, including specific steps to take and how to “backcast.”
-
The types of inflections that create potentially huge companies.
-
How to systematically find those inflections.
-
Lessons from founders who have successfully created companies built on inflections.
-
Frameworks for thinking through whether a worthwhile company can emerge from a chosen inflection.
-
How to lead effectively in the earliest stages through the growth phase.
-
Why he says “you don’t want to be the best, you want to be the only.”
-
His advice on pitching.
The final deadline for applications for the summer vintage of our Network Catalyst accelerator is June 5th. Learn more and apply today at www.villageglobal.vc/network-catalyst.
Thanks for listening — if you like what you hear, please review us on your favorite podcast platform.
Check us out on the web at villageglobal.vc or get in touch with us on Twitter @villageglobal.
The podcast and artwork embedded on this page are from Village Global, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Revisionist History (LS 87 · TOP 0.01% what is this?)
Episode: Saigon, 1965
Pub date: 2016-06-23
In the early 1960s the Pentagon set up a top-secret research project in an old villa in downtown Saigon. The task? To interview captured North Vietnamese soldiers and guerrillas in order to measure the effect of relentless U.S. bombing on their morale. Yet despite a wealth of great data, even the leaders of the study couldn’t agree on what it meant.
To learn more about the topics covered in this episode, visit www.RevisionistHistory.com
Learn more about your ad-choices at https://www.iheartpodcastnetwork.comSee omnystudio.com/listener for privacy information.
The podcast and artwork embedded on this page are from Pushkin Industries, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Revisionist History (LS 87 · TOP 0.01% what is this?)
Episode: The Big Man Can't Shoot
Pub date: 2016-06-30
Wilt Chamberlain’s brilliant career was marred by one, deeply inexplicable decision: He chose a shooting technique that made him one of the worst foul shooters in basketball—even though he had tried a better alternative. Why do smart people do dumb things?
To learn more about the topics covered in this episode, visit www.RevisionistHistory.com
Learn more about your ad-choices at https://www.iheartpodcastnetwork.comSee omnystudio.com/listener for privacy information.
The podcast and artwork embedded on this page are from Pushkin Industries, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: In Depth (LS 38 · TOP 2% what is this?)
Episode: Finding product-market fit twice — Alma’s Harry Ritter on pivots and staying close to customers
Pub date: 2022-10-13
Todd Jackson’s back on the mic this week. (As a reminder, he’s guest hosting a few product-focused episodes this season — all about finding product-market fit.)
Today, Todd chats with Harry Ritter, founder of Alma, a membership-based network that helps independent mental health care providers accept insurance and build thriving private practices.
In our conversation, we go deep into Alma’s early days, and how they navigated the journey of finding traction and scaling.
As you’ll hear in the episode, the Alma team essentially had to find product-market fit twice as they went from physical, co-working office spaces pre-pandemic, to quickly building out their virtual care capabilities.
Here’s a preview of what Todd and Harry cover:
- Approaching team building as a solo founder
- Refining the idea and getting more insights from your customers through structured interviews, using the technique doctors are trained on
- Rallying your team through a pivot
- Staying competitor aware — not competitor obsessed
- The difference between building a marketplace versus a platform.
Whether you’re in the early stages of starting a company or going through a tough pivot, there are tons of helpful tactics here.
You can follow Harry on Twitter at @harryritter1. You can email us questions directly at review@firstround.com or follow us on Twitter @firstround and @tjack.
The podcast and artwork embedded on this page are from First Round, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Lenny's Podcast: Product | Growth | Career (LS 52 · TOP 0.5% what is this?)
Episode: What it takes to become a top 1% PM | Ian McAllister (Uber, Amazon, Airbnb)
Pub date: 2022-11-20
Ian McAllister is the Senior Director of Product for Vehicles at Uber. Before moving to Uber, Ian spent over a decade directing teams at Amazon, where he created and led Amazon Smile. He was also Director of Product Management at Airbnb, where I was lucky enough to have worked alongside him. In today’s episode, we discuss Ian’s famous document about the essential attributes of the top 1% of product managers. Ian outlines the most important skills to focus on for entry-level PMs and how to broaden your experience and diversify skills as you move up the ladder. He also shares what he learned working with Jeff Wilke, Jeff Bezos, and other leaders at Amazon, and goes in depth on Amazon’s working-backwards framework.
—
Find the full transcript here: https://www.lennyspodcast.com/what-it-takes-to-become-a-top-1-pm-ian-mcallister-uber-amazon-airbnb/#transcript
—
Where to find Ian McAllister:
• Newsletter: https://ianmcallister.substack.com/
• Twitter: https://twitter.com/ianmcall
• LinkedIn: https://www.linkedin.com/in/ianmcallister/
—
Where to find Lenny:
• Newsletter: https://www.lennysnewsletter.com
• Twitter: https://twitter.com/lennysan
• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/
—
Thank you to our wonderful sponsors for making this episode possible:
• Mixpanel: https://mixpanel.com/startups
• Athletic Greens: https://athleticgreens.com/lenny
• AssemblyAI: https://www.assemblyai.com/?utm_source=lennyspodcast&utm_medium=podcast&utm_campaign=nov20
—
Referenced:
• What distinguishes the top 1% of product managers from the top 10%, on Substack: https://ianmcallister.substack.com/p/what-distinguishes-the-top-1-of-product
• What distinguishes the top 1% of product managers from the top 10%, on Quora: https://www.quora.com/What-distinguishes-the-Top-1-of-product-managers-from-the-Top-10
• Amazon’s working-backwards method: https://www.productplan.com/glossary/working-backward-amazon-method/
• Jeff Wilke on Twitter: https://twitter.com/jeffawilke
• Getting Real: The Smarter, Faster, Easier Way to Build a Successful Web Application: https://www.amazon.com/Getting-Real-Smarter-Successful-Application/dp/0578012812
• Wool (Wool trilogy #1): https://www.amazon.com/Wool-Trilogy-Howey-25-Apr-2013-Paperback/dp/B011T7ACU0/
• Energy and Civilization: A History: https://www.amazon.com/Energy-Civilization-History-MIT-Press/dp/0262035774
• How I Built This podcast: https://www.npr.org/series/490248027/how-i-built-this
• EV News Daily podcast: https://www.evnewsdaily.com/
• Yellowstone on Peacock: https://www.peacocktv.com/stream-tv/yellowstone
• Everything Everywhere All at Once on Showtime: https://www.sho.com/titles/3493875/everything-everywhere-all-at-once
• Gibson Biddle’s website: https://www.gibsonbiddle.com/
• Gibson Biddle on Lenny’s Podcast: https://www.lennyspodcast.com/gibson-biddle-on-his-dhm-product-strategy-framework-gem-roadmap-prioritization-framework-5-netflix-strategy-mini-case-studies-building-a-personal-board-of-directors-and-much-more/
• Gibson Biddle’s Ask Gib newsletter: https://askgib.substack.com/
—
In this episode, we cover:
(03:54) What Ian expected from his initial post on product management
(05:30) How the post impacted Ian’s career
(07:06) How writing can help you crystallize your thoughts
(08:26) Ian’s background
(10:57) Attributes of the top 1% of PMs
(14:32) The top three skills for new PMs to perfect
(20:32) Tips on strengthening communication and prioritization
(23:06) How to level up as a PM
(26:37) What kind of impact should new PMs expect to make?
(29:36) How to broaden your view and think big
(33:06) How to earn the trust of others
(34:30) How Ian could have done more to earn trust at Airbnb
(37:27) Why people tend to stick around Amazon for a while
(39:53) What Ian learned from Bezos and Wilke
(46:38) How teams get working backwards wrong
(53:51) The two parts of working backwards and how Ian utilizes it at Uber
(58:57) Lightning round
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.
Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe
The podcast and artwork embedded on this page are from Lenny Rachitsky, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Lenny's Podcast: Product | Growth | Career (LS 52 · TOP 0.5% what is this?)
Episode: The ultimate guide to SEO | Ethan Smith (Graphite)
Pub date: 2022-12-01
Ethan Smith is the CEO of Graphite, a boutique growth agency that’s helped companies like MasterClass, Thumbtack, Robinhood, Medium, and Honey develop and execute their SEO strategies. SEO is one of the least-understood levers for growth, while also one with the biggest payoff. This episode is a true master class on all things SEO. Ethan shares a wealth of information, including when you should begin investing in SEO, how to build an SEO team, and the three main buckets of SEO. He explains the difference between topics and keywords, gives the exact heuristics and tools to help you be successful in developing and implementing your own SEO strategy, and also goes deep on how to deal with roadblocks and advocate for resources.
—
Find the full transcript here: https://www.lennyspodcast.com/the-ultimate-guide-to-seo-ethan-smith-graphite/#transcript
—
Where to find Ethan Smith:
• Twitter: https://twitter.com/ethan_l_s
• LinkedIn: https://www.linkedin.com/in/ethanls/
• Graphite: https://www.graphitehq.com/
—
Where to find Lenny:
• Newsletter: https://www.lennysnewsletter.com
• Twitter: https://twitter.com/lennysan
• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/
—
Thank you to our wonderful sponsors for making this episode possible:
• Coda: https://coda.io/lenny
• Mixpanel: https://mixpanel.com/startups
• Lemon.io: https://lemon.io/lenny
—
Referenced:
• Product-Led SEO: The Why Behind Building Your Organic Growth Strategy https://a.co/d/2wkN4dx
• Topical Authority Analysis: https://bit.ly/topical-authority-tool
• SEO Link Analysis: https://bit.ly/diagnostic-internal-links
• SEO Links API: https://bit.ly/graphite-internal-links-api
• Screaming Frog: https://www.screamingfrog.co.uk/seo-spider/
• Brandon Lee of Power: https://www.linkedin.com/in/brandonhli
• Similarweb traffic analysis: https://www.similarweb.com/
• MasterClass: https://www.masterclass.com/
• BetterUp: https://www.betterup.com/
• NerdWallet: https://www.nerdwallet.com/
• HubSpot: https://www.hubspot.com/
• Ahrefs: https://ahrefs.com/
• Semrush: https://www.semrush.com/
• Google Search Console: https://search.google.com/search-console/about
• Clearscope: https://www.clearscope.io/
• Yuriy Timen on Lenny’s Podcast: https://www.lennyspodcast.com/how-to-grow-a-subscription-business-yuriy-timen-grammarly-canva-airtable/
• Gokul Rajaram on Lenny’s Podcast: https://www.lennyspodcast.com/gokul-rajaram-on-designing-your-product-development-process-when-and-how-to-hire-your-first-pm-a-playbook-for-hiring-leaders-getting-ahead-in-you-career-how-to-get-started-angel-investing-more/
• Luc Levesque on Twitter: https://twitter.com/luclevesque
• Search Off the Record: https://podcasts.apple.com/us/podcast/search-off-the-record/id1512522198
• GPT-3: https://gpt3demo.com/apps/openai-gpt-3-playground
—
In this episode, we cover:
(03:53) Ethan’s background
(07:53) Why technical audits are the biggest myth in SEO
(10:05) When to invest in SEO
(16:09) Heuristics to determine if SEO is worth it
(18:36) The three buckets of SEO: programmatic, editorial, and technical
(23:30) The process for creating an SEO strategy
(27:00) Why you shouldn’t be too formulaic
(28:33) What is site engagement?
(29:31) Which pages need to be indexed
(31:49) Topics vs. keywords
(36:33) How to mine competitors’ sites for information
(37:41) Useful tools for developing your SEO strategy
(40:14) How long will it take to see results?
(45:16) Factors to consider when looking to hire an SEO person
(47:33) The functions of a programmatic SEO person
(49:19) How to do testing
(54:06) Editorial SEO strategy
(57:14) How to scale based on the size of the site
(59:51) Page types
(1:01:53) How to win in a topic category
(1:03:12) How to build solid hypotheses and test them
(1:06:13) How to deal with roadblocks and advocate for resources
(1:08:54) How topical and domain authority are determined
(1:16:43) The power of internal links
(1:24:32) Why AI is not usually useful for content creation
(1:28:31) Final tips for getting started with SEO
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.
Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe
The podcast and artwork embedded on this page are from Lenny Rachitsky, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Lenny's Podcast: Product | Growth | Career (LS 52 · TOP 0.5% what is this?)
Episode: Building your product strategy stack | Ravi Mehta (Tinder, Facebook, Tripadvisor, Outpace)
Pub date: 2023-01-19
Ravi was previously CPO at Tinder, Product Director at Facebook, and VP of Product at Tripadvisor. Currently, he’s co-founder and CEO of Outpace, a coaching platform designed to help people reach their professional goals. In today’s podcast, we dive deep into Ravi’s product strategy stack framework and how it was used to develop a powerful strategy at Tinder. We also cover his other popular frameworks—the frontier of understanding and exponential feedback—and how both of them can help you grow in your career. We discuss the differences between building product at a startup versus a large tech company, and how Ravi has had to shift his mindset as he’s moved away from a product leadership role into a founder role. Finally, he shares a bit about how Outpace is using AI to amplify coaches and help make them more efficient and effective.
—
Find the transcript for this episode and all past episodes at: https://www.lennyspodcast.com/episodes/. Today’s transcript will be live by 8 a.m. PT.
—
Thank you to our wonderful sponsors for supporting this podcast:
• Merge—A single API to add hundreds of integrations into your app: http://merge.dev/lenny
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—
Where to find Ravi Mehta:
• Twitter: https://twitter.com/ravi_mehta
• LinkedIn: https://www.linkedin.com/in/ravimehta/
• Website: https://www.ravi-mehta.com/
—
Where to find Lenny:
• Newsletter: https://www.lennysnewsletter.com
• Twitter: https://twitter.com/lennysan
• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/
—
Referenced:
Disclaimer: Lenny is an angel investor Ravi’s company, Outpace
• Reforge’s Product Strategy Program created by Casey Winters and Fareed Mosavat: https://www.reforge.com/programs/product-strategy
• Matt Mochary on Lenny’s Podcast: https://www.lennyspodcast.com/videos/how-to-fire-people-with-grace-work-through-fear-and-nurture-innovation-matt-mochary/
• Indie Hackers: https://www.indiehackers.com/
• Everything Marketplaces: https://www.everythingmarketplaces.com/
• The Product Strategy Stack: https://www.ravi-mehta.com/product-strategy-stack/
• Balsamiq: https://balsamiq.com/
• Set better goals with NCTs, not OKRs: https://www.reforge.com/blog/set-better-goals-with-ncts-not-okrs
• Ravi’s product manager’s competencies framework: https://www.ravi-mehta.com/product-manager-roles/
• Hooked: How to Build Habit-Forming Products: https://www.amazon.com/Hooked-How-Build-Habit-Forming-Products/dp/0241184835/
• Working Backwards: Insights, Stories, and Secrets from Inside Amazon: https://www.amazon.com/Working-Backwards-Insights-Stories-Secrets/dp/1250267595
• Ian McAllister on Lenny’s Podcast: https://www.lennyspodcast.com/videos/what-it-takes-to-become-a-top-1-pm-ian-mcallister-uber-amazon-airbnb/
• The Ezra Klein Show podcast: https://podcasts.apple.com/us/podcast/the-ezra-klein-show/id1548604447
• Ezra Klein’s AI episode: https://podcasts.apple.com/us/podcast/a-skeptical-take-on-the-a-i-revolution/id1548604447?i=1000592835492
• Andor on Disney+: https://www.disneyplus.com/series/star-wars-andor/3xsQKWG00GL5
• Airtable: https://www.airtable.com/
• Superhuman: https://superhuman.com/
• Descript: https://www.descript.com/
• Outpace: https://www.outpace.co
• Unlock Your Product Manager Potential: https://www.outpace.co/guides/unlock-your-product-manager-potential
—
In this episode, we cover:
(00:00) Ravi’s background
(04:24) Why Ravi left Tinder, and what he’s been up to recently
(08:05) Differences between working at an established tech company vs. a startup
(12:45) Why founders should network with “early-stage” folks
(14:29) Why you need to do some research and relationship-building before starting your company
(17:49) What the product strategy stack is and how to use it
(22:08) Mission vs. vision
(23:37) How Ravi developed his strategy framework at Tripadvisor
(26:43) Why PMs should understand design, UX, and UI
(28:20) Examples of the product strategy stack in action
(32:42) Why Tinder resisted adding filters
(34:10) Monetization features at Tinder and the “whales” who spend the most
(38:18) How customer feedback led to new features at Tinder
(42:28) Why goals come after roadmap in Ravi’s framework
(44:30) Tripadvisor’s strategy for increasing bookings
(47:25) How to set goals that drive outcomes
(50:24) The four buckets of the frontier of understanding
(51:38) Different methods for trying to hit goals
(53:08) Understanding why you hit or missed your goal
(54:34) The product management competencies framework
(1:02:08) The exponential feedback framework
(1:04:25) Why you should ask for feedback—and graciously accept it
(1:06:05) How to determine the right amount of leadership your team needs
(1:09:40) What selective micro-management is
(1:12:25) How Outpace uses AI to assist in coaching
(1:15:24) Lightning round
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.
Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe
The podcast and artwork embedded on this page are from Lenny Rachitsky, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Lenny's Podcast: Product | Growth | Career (LS 52 · TOP 0.5% what is this?)
Episode: How to price your product | Naomi Ionita (Menlo Ventures)
Pub date: 2023-01-12
Naomi Ionita is a venture capitalist at Menlo Ventures. She started her career in engineering in 2002, shifted to product in 2006, and has built product, growth, and monetization teams for over a decade. Her expertise is in building full-stack growth teams and cultures, launching new products, and helping existing products monetize and retain their users. Consider today’s episode a master class on monetization and pricing. We talk about common mistakes made by founders, specific experiments for how to determine pricing, and why initial growth sometimes comes at the expense of revenue. Naomi also discusses what the modern growth stack is, how AI will play a role in growth, and what she’s most excited about for the future.
—
Find the transcript for this episode and all past episodes at: https://www.lennyspodcast.com/episodes/. Today’s transcript will be live by 8 a.m. PT.
—
Thank you to our wonderful sponsors for supporting this podcast:
• Miro—A collaborative visual platform where your best work comes to life: https://miro.com/lenny
• Notion—One workspace. Every team: https://www.notion.com/lennyspod
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—
Where to find Naomi:
• Twitter: https://twitter.com/npilosof
• LinkedIn: https://www.linkedin.com/in/naomipilosofionita/
• Website: https://www.menlovc.com/naomi-pilosof-ionita
—
Where to find Lenny:
• Newsletter: https://www.lennysnewsletter.com
• Twitter: https://twitter.com/lennysan
• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/
—
Referenced:
Disclaimer: Lenny is an angel investor in a few startups mentioned in this episode: Eppo, Endgame, Pocus
• Evernote: https://evernote.com/
• Figma: https://www.figma.com/
• The Van Westendorp pricing model: https://www.forbes.com/sites/rebeccasadwick/2020/06/22/how-to-price-products/?sh=7e6077f855c7
• OpenView: https://openviewpartners.com/
• SaaS business model at Profitwell: https://www.profitwell.com/recur/all/saas-business-model
• Envoy: https://envoy.com/
• Invoice2go: https://invoice.2go.com/
• Gas: https://apps.apple.com/us/app/gas/id1641791746
• Endgame: https://www.endgame.io/
• Pocus: https://www.pocus.com/
• Optimizely: https://www.optimizely.com/
• Eppo: https://www.geteppo.com/
• Amplitude: https://amplitude.com/
• Chargebee: https://www.chargebee.com/
• Zuora: https://www.zuora.com/
• Metronome: https://metronome.com/
• Orb: https://www.withorb.com/
• Monetizing Innovation: How Smart Companies Design the Product Around the Price: https://www.amazon.com/Monetizing-Innovation-Companies-Design-Product/dp/1119240867
• Ask the Storybots on Netflix: https://www.netflix.com/title/80108159
• Madhavan Ramanujam on Lenny’s Podcast: https://www.lennyspodcast.com/videos/the-art-and-science-of-pricing-madhavan-ramanujam-monetizing-innovation-simon-kucher/
—
In this episode, we cover:
(00:00) Naomi’s background
(03:10) Why Evernote wasn’t able to leverage the kind of growth that Notion did
(05:53) What founders get wrong when it comes to monetization
(09:45) Which features to include in a freemium product
(10:47) Day one vs. day one-hundred premium features
(13:06) Matching price to value for optimal segmentation
(16:35) When pricing should be revisited
(17:20) How to determine price, and why it’s a good idea to have a cross-functional pricing team
(21:16) How to restructure pricing holistically
(24:30) How Envoy learned that they were undercharging
(27:30) The importance of experimentation
(30:21) How to balance growth with revenue
(33:23) What is the modern data stack?
(35:01) The modern growth stack
(40:36) The importance of experimentation in the growth stack
(43:10) Platforms for billing and monetization
(45:00) Why a hybrid model of pricing tends to be most used in SaaS companies
(46:53) Leveraging AI
(48:57) Lightning round
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.
Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe
The podcast and artwork embedded on this page are from Lenny Rachitsky, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Underworld Podcast (LS 54 · TOP 0.5% what is this?)
Episode: The Punjabi Indian Gangster Rapper Killed in a Gang War: Moosewala
Pub date: 2022-06-28
Shubhdeep Singh Sidhu, better known as Moosewala, was on the cusp of becoming a global superstar. Racking up billions of views on youtube, the Punjabi rapper heavily influenced by Tupac had blown up since arriving in Canada as an international student in 2016. His lyrics about gangsters, guns, and everything that went with it in rural Punjab struck a cord with listeners around the globe. But he wasn't without controversy, sometimes running afoul of the law in India, his shows erupting in violence in Canada. And rumors followed of his involvement in a growing gang war in northern India. And then he got gunned down a month ago in a professional, highly coordinated hit. In the weeks that follow, there will be facebook posts from gangsters claiming credit, big arrests, interrogations, even a shooter caught hiding out with religious pilgrims, and what begins to unravel is a complex web of a gang war ranging from Canada to India that has a man with 700 soldiers at the center of the assassination of a rap superstar turned politician on the cusp of global superstardom
The podcast and artwork embedded on this page are from The Underworld Podcast, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Syntax - Tasty Web Development Treats (LS 61 · TOP 0.1% what is this?)
Episode: Are Web Dev GUIs Going to Replace Us?
Pub date: 2020-02-12
In this episode of Syntax, Scott and Wes talk about web dev GUIs — what are they, are they going to replace developers, are they good or bad, and more!
Hasura - Sponsor Hasura is an open source real-time GraphQL engine. It connects to your databases & microservices and instantly gives you a production-ready GraphQL API. Check it out at Hasura.io.
LogRocket - Sponsor LogRocket lets you replay what users do on your site, helping you reproduce bugs and fix issues faster. It’s an exception tracker, a session re-player and a performance monitor. Get 14 days free at logrocket.com/syntax.
Show Notes 1:30 - What is “codeless”?
- The codeless movement is coming in with a force. Are they just selling something? Or is it a real concern?
- All types of jobs are being replaced by computers
- Truckers
- Cashiers
- Lawnmowers
- Doctors
- Why not web developers?
7:22 - First experience building sites with a GUI?
- Dreamweaver
10:18 - Entire website builders:
- Wix
- Squarespace
- Webflow
- Modulz
- Grid.io
- Wordpress Builders
13:17 - When are GUIs useful?
- Brochure site
- Basic e-commerce
20:26 - Is a GUI/Codeless always better?
- It depends what you’re capable of doing
25:21 - Levels of hell GUI assistance in builders
- CMS - Just modifying content and basic markup
- Access to code, drag blocks into place
- No or minimal access to code
- No modification outside of options
31:36 - Are there GUIs for making applications?
- Native Mobile
- Zapier
36:54 - Are jobs at risk?
-
Yes
- I think a lot of WordPress tinkering has already been replaced
- The guy who knows what buttons to push is at risk?
- Webmaster jobs where the roll was just occasionally updating HTML and text
-
No
-
Government
- Educational institutions
- Major corporations that can’t have their content stored via a service
39:55 - Our favorite GUIs to help development
-
Scott:
- Netlify
- Heroku
- Studio 3T
- VS Code
-
Wes:
-
Sketch CSS Export
- Digital Ocean
- Cyberduck
- Transmit
- ZSH
- VS Code
Links * Roomba’s first autonomous lawnmower * Notepad++ * Geocities * Angelfire * Sketch * Figma * Gatsby * Excel * Meteor * Recurly * Gumroad * Begin.com
××× SIIIIICK ××× PIIIICKS ××× * Scott: Baron of Botox * Wes: Owlet Smart Sock
Shameless Plugs * Scott: How To Build A GraphQL API - Sign up for the year and save 25%! * Wes: All Courses - Use the coupon code ‘Syntax’ for $10 off!
Tweet us your tasty treats! * Scott’s Instagram * LevelUpTutorials Instagram * Wes’ Instagram * Wes’ Twitter * Wes’ Facebook * Scott’s Twitter * Make sure to include @SyntaxFM in your tweets
The podcast and artwork embedded on this page are from Wes Bos & Scott Tolinski - Full Stack JavaScript Web Developers, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Today In History with The Retrospectors (LS 45 · TOP 1% what is this?)
Episode: Meet Ebenezer Scrooge
Pub date: 2022-12-19
Charles Dickens’ novella ‘A Christmas Carol’ was written in just six weeks, and published on 19th December, 1843. The timeless story, which introduced the world to Ebeneezer Scrooge, Tiny Tim, and the Ghosts of Christmas Past, Present and Future, was conceived in part to get its author out of a sticky financial situation.
Dickens’ other motive was to put into an accessible fable the political ideas that had formed the core of his proposed pamphlet, ‘An Appeal to the People of England on behalf of the Poor Man's Child’. In so doing, he re-focussed the Christmas message around charitable giving and kindness for generations.
In this episode, Arion, Rebecca and Olly explain how Dickens plundered his own back-catalogue to surface some ‘Christmas goblins’; consider whether the book-buying public truly understood the intended message of his work; and reveal why his determination to produce it in an affordable edition hit him in the pocket…
Further Reading:
• ‘A Christmas Carol: The True History Behind the Dickens Story’ (Time, 2021): https://time.com/4597964/history-charles-dickens-christmas-carol/
• ‘How did A Christmas Carol come to be?’ (BBC Culture, 2017): https://www.bbc.com/culture/article/20171215-how-did-a-christmas-carol-come-to-be
• "What day is it?" (George C. Scott - A Christmas Carol - 1984): https://www.youtube.com/watch?v=YO17UOjcovg
Victorian #Books #Christmas
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The Retrospectors are Olly Mann, Rebecca Messina & Arion McNicoll, with Matt Hill.
Theme Music: Pass The Peas. Announcer: Bob Ravelli. Graphic Design: Terry Saunders. Edit Producer: Sophie King.
Copyright: Rethink Audio / Olly Mann 2022.
Hosted on Acast. See acast.com/privacy for more information.
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Podcast: The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch (LS 57 · TOP 0.5% what is this?)
Episode: 20VC: ARK Invest's Cathie Wood on Why ARK Has Not Had More Outflows Despite Performance, How the Global Tech Equities Market Will Go From $7Trn to $210Trn in 8-10 Years, The Future for Facebook and How Elon Musk and Jack Dorsey Could Create the Biggest Di
Pub date: 2022-11-14
Cathie Wood is the CEO & CIO @ ARK Invest, focusing solely on disruptive innovation, primarily in the public equity markets. ARK has become renowned for opening up its research and becoming a ‘sharing economy’ company in the asset management space. Prior to ARK, Cathie spent twelve years at AllianceBernstein as CIO of Global Thematic Strategies where she managed over $5 billion. Cathie joined Alliance Capital from Tupelo Capital Management, a hedge fund she co-founded, which managed $800 million in global thematic strategies. Prior to Tupelo Capital, she worked for 18 years with Jennison Associates LLC as Chief Economist, Portfolio Manager and Director.
In Today's Episode with Cathie Wood We Discuss: 1.) Entry into Hedge Funds at 20:
- How did Cathie get her first role in the world of finance at the tender age of 20?
- What is Cathie running from? What is Cathie running towards?
- What are some of Cathie's biggest lessons from seeing the dot com bust at Tupelo?
- What does Cathie know now that she wishes she had known when she started investing?
2.) Why Benchmarks and Passive Investing are Bad:
- Why does Cathie believe that benchmarks and indexes have become dangerous for consumers?
- Why does Cathie not believe what everyone else does regarding inflation?
- How much of the performance of large-cap tech stocks is tied to the growth of passive investing?
- Why does Cathie think the Fed is making a huge mistake?
3.) Time to Pick Companies:
- Why does Cathie believe that Facebook is emerging as an attractive value stock?
- How does Cathie believe Elon Musk and Jack Dorsey could build the largest universal wallet?
- If Cathie were to put all her money into one of their companies, what would it be?
- Why does Cathie believe Zoom is one of the most misunderstood companies?
4.) Why Venture: Why Now:
- Why did Cathie decide to do a venture fund with ARK now?
- Why did Cathie decide to do a no-carry structure with a higher management fee? How does that align incentives with investors?
- In venture, the asset chooses the capital, how does Cathie analyze why the best founders in the world will pick and work with ARK over other amazing VCs?
- What is the single biggest risk you are underwriting when investing in ARK's venture fund?
Items Mentioned in Today's Episode: Cathie's Favourite Book: The Emperor of All Maladies: A Biography of Cancer
The podcast and artwork embedded on this page are from Harry Stebbings, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: LINUX Unplugged (LS 49 · TOP 0.5% what is this?)
Episode: 483: Chris Is Done With Raspberry Pi
Pub date: 2022-11-07
We surprise each other with three different topics, and Chris has a big update on the ODROID H3+.
Sponsored By:
- Linode Cloud Hosting: A special offer for all Linux Unplugged Podcast listeners and new Linode customers, visit linode.com/unplugged, and receive $100 towards your new account.
- Bitwarden: Bitwarden is the easiest way for businesses and individuals to store, share, and sync sensitive data.
- Tailscale: Tailscale is a Zero config VPN. It installs on any device in minutes, manages firewall rules for you, and works from anywhere. Get 20 devices for free for a personal account.
Support LINUX Unplugged
Links:
- The Steam Linux Beta Begins…
- Steam Linux Use Tips Up In October Thanks To The Steam Deck — The growth of Steam Deck adoption also now bumps AMD's CPU marketshare among Steam on Linux gamers to 56% against Intel, an increase in October alone of +4.37%.
- GitHub Issue: Geocatching details on site
- ODROID-H3+ – ODROID — Great compatibility, a brand new x86 64-bit single board computer with large memory capacity and advanced IO ports.
- ODROID-H3 Case Type 2 – ODROID
- ODROID-H3 Case Type 3 – ODROID
- plex - LinuxServer.io
- Jellyfin intro-skipper Plugin
- Tuxies Board Room
- Ubuntu Summit 2022 — An opportunity for the broader Ubuntu community to learn and speak about the amazing work and success stories happening in the ecosystem.
- How to Revert the Last Git Commit | Linode
- Snapcraft.io
- What happened to signal-desktop? - snapcraft.io — This is due to a DMCA takedown request coming from people representing Signal. Canonical is currently working with Signal to resolve this issue.
- “signal-desktop” not found · Issue #70 · snapcrafters/signal-desktop
- Install signal-desktop on Linux | Snap Store
- Godot Engine — Free and open source 2D and 3D game engine.
- Godot’s Graduation: Godot moves to a new Foundation — Software Freedom Conservancy and the Godot leadership are excited to share their decision that the Godot project has reached a level of success for which it makes sense for Godot to have its own independent foundation.
- Announcing Godot’s Graduation from SFC!
- I will try my best to explain why the upcoming @godotengine foundation can’t offer Console ports.
- Open source game development advances with Godot Engine 3.5 out now
- The next big step: Godot 4.0 reaches Beta
- Godot: Exporting for Linux
- Mastodon gained 70,000 users after Musk’s Twitter takeover. I joined them
- With Twitter in chaos, Mastodon is on fire | CNN Business
- How to securely store secrets in BitWarden CLI and load them into your ZSH shell when needed | by Zack Proser | Oct, 2022 | Gruntwork
- Intel ME-Enabled System Needed For Updating Arc Graphics GSC Firmware — Update: Intel reached out to say firmware updating will work on AMD platforms, but awaiting to hear futher details.
- Podcasting 2.0 Apps
The podcast and artwork embedded on this page are from Jupiter Broadcasting, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Treasury Update Podcast (LS 36 · TOP 2.5% what is this?)
Episode: Coffee Break Session #59: What Is FedNow?
Pub date: 2022-04-07
What is FedNow? Coffee Break Session Host Alexa Cook catches up with Strategic Treasurer’s Managing Partner, Craig Jeffery, to discuss FedNow. Their conversation covers what FedNow is and how it is impacting both treasury departments and companies. Listen in and learn a little bit about FedNow.
The podcast and artwork embedded on this page are from Strategic Treasurer, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Buzz on Bank Automation News
Episode: Core providers, not banks, should be ‘ready’ for FedNow
Pub date: 2022-06-09
The years of anticipation surrounding the development and adoption of Federal Reserve instant payment service FedNow is a case of much ado about nothing at the bank level.
Although industry experts have wondered what the upcoming adoption of FedNow will look like at financial institutions — especially community banks and credit unions —the onus will be on core providers to adapt and provide, Vinay Prabhakar, vice president of global marketing at Volante, tells Bank Automation News in this episode of “The Buzz” podcast.
“When it comes to FedNow, or indeed, any new payment type, there are few separate factors that play into readiness,” Prabhakar says. “Community banks and credit unions are often dependent on their core providers. It's not a question of them being ready FedNow. It's a question of their core provider being ready for FedNow.”
Not all core providers are equal; while some have fully embraced real-time payment (RTP) rails, others are still where they were when RTP was first launched in 2017, Prabhakar says. Larger banks that have already enabled enterprisewide payments automation have less to worry about.
“If a bank has already invested in real-time payments modernization, already connected to RTP, they've already 24/7 enabled their operation, and have familiarity of how to deal with ISO 2002 messages, then I think those banks will find it quite straightforward to go live with FedNow on Day One.
Listen as Prabhakar talks FedNow readiness at financial institutions, along with possible hesitancy from large corporates to embrace large-value RTP.
The podcast and artwork embedded on this page are from Bank Automation News, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Odd Lots (LS 59 · TOP 0.1% what is this?)
Episode: Hyun Song Shin Explains Why This Dollar Shock Is So Unique
Pub date: 2022-10-31
It's no secret that a strong US dollar causes the rest of the world pain, but the impact of this year's rally is shaping up to be a bit different than previous episodes of dollar strength. Hyun Song Shin is the Economic Adviser and Head of Research for the Bank for International Settlements, which has just published a bulletin outlining why this particular dollar cycle is so unique. Shin has also done a ton of previous academic research on this exact topic — examining what happens to global trade and business investment when the dollar hits its highs. In this conversation, we talk to him about the impact of the dollar rally, what could stop it and what policymakers around the world can do to cope.
See omnystudio.com/listener for privacy information.
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Podcast: Odd Lots (LS 59 · TOP 0.1% what is this?)
Episode: A Broken Market Is Causing Mortgage Rates to Surge
Pub date: 2022-10-27
US mortgage rates have jumped to a two-decade high, with the average 30-year home loan now running above 7%. Of course, this makes sense. The Federal Reserve is raising benchmark interest rates and that's supposed to translate into a tightening of financial conditions, which includes housing credit. But the jump in mortgage rates far exceeds the increase in benchmarks, with the difference between average mortgage rates and the yield on equivalent US Treasuries at its highest on record. So what's going on? On this episode, we speak with Guillermo Roditi Dominguez, managing director at New River Investments, about what's happening deep in the market for mortgage-backed bonds to make rates surge this much. As he describes it, a sea change is helping to keep borrowing rates extra high.
See omnystudio.com/listener for privacy information.
The podcast and artwork embedded on this page are from Bloomberg, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Real Vision Daily Briefing: Finance & Investing (LS 57 · TOP 0.5% what is this?)
Episode: The Next Big Trade - The Godfather of MMT Explains It All
Pub date: 2022-10-16
Warren Mosler, an American economist and entrepreneur, is perhaps the leading proponent of Modern Monetary Theory (MMT). But what is MMT? And how can it help us understand what’s happening at a macro level right now? Mosler explains it all to Harry Melandri on another riveting episode of The Next Big Trade.
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Podcast: Lex Fridman Podcast (LS 75 · TOP 0.01% what is this?)
Episode: #275 – Rick Rubin: Legendary Music Producer
Pub date: 2022-04-10
Rick Rubin is one of the greatest music producers of all time, working with many of the greats including Beastie Boys, Eminem, Metallica, LL Cool J, Kanye West, Slayer, Tom Petty, Johnny Cash, Dixie Chicks, Aerosmith, Adele, Danzig, Red Hot Chili Peppers, System of a Down, Jay-Z, Black Sabbath. Please support this podcast by checking out our sponsors: – Lambda: https://lambdalabs.com/lex – Theragun: https://therabody.com/lex to get 30 day trial – ROKA: https://roka.com/ and use code LEX to get 20% off your first order – Onnit: https://lexfridman.com/onnit to get up to 10% off – ExpressVPN: https://expressvpn.com/lexpod and use code LexPod to
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Podcast: Invest Like the Best with Patrick O'Shaughnessy (LS 67 · TOP 0.05% what is this?)
Episode: Matt Clifford – Investing Pre-Company - [Invest Like the Best, EP.154]
Pub date: 2020-01-14
My guest today is Matt Clifford. He’s the co-founder of Entrepreneur First, the world’s leading talent investor. They invest “pre-company” by helping the best people in cities around the world find a co-founder, develop an idea, and start a company. So far, they’ve helped 1000 people start 200 companies worth a combined $1.5B. This conversation covers their entire ecosystem and holds lessons for anyone building a business. I especially loved Matt’s ideas on the history of ambition.
Please enjoy our conversation.
For more episodes go to InvestorFieldGuide.com/podcast.
Sign up for the book club, where you’ll get a full investor curriculum and then 3-4 suggestions every month at InvestorFieldGuide.com/bookclub.
Follow Patrick on Twitter at @patrick_oshag
Show Notes
1:20 – (First Question) – An overview on talent investing
4:37 – The history of ambition
10:08 – How do they search for ambitious people
12:21 – What happens early on for these formed teams
17:43 – Assigning an idea to a talented team
20:52 – Opportunities in deep technology
27:16 – A closer look at the hardware and machinery of the deep technology changes
30:54 – The geographical focus of venture capital investments
37:16 – Problems with the way early-stage investment world works
41:22 – People who are creating value in a management company and how they manage their investments
55:12 – Advice to people creating investment companies and pricing power
1:00:31 – The power of cities
1:02:46 – Topics they cover in their newsletter; technological sovereignty as one example
1:04:11 – Experience and thoughts on China
1:06:51 – A.I. Nationalism
1:12:03 – Kindest thing anyone has done for Matt
Learn More
For more episodes go to InvestorFieldGuide.com/podcast.
Sign up for the book club, where you’ll get a full investor curriculum and then 3-4 suggestions every month at InvestorFieldGuide.com/bookclub
Follow Patrick on twitter at @patrick_oshag
The podcast and artwork embedded on this page are from Colossus | Investing & Business Podcasts, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Invest Like the Best with Patrick O'Shaughnessy (LS 67 · TOP 0.05% what is this?)
Episode: Alice Bentinck - Building a Start-Up Machine - [Invest Like the Best, EP.285]
Pub date: 2022-07-12
My guest today is Alice Bentinck, co-founder of Entrepreneur First. Entrepreneur First, or EF, invests pre-company by systematizing the way that talented individuals find co-founders, develop ideas, and scale into companies. They’re an incubator of teams and ideas on a mission to create impactful companies that, without their help, wouldn’t exist. I first spoke with Alice’s co-founder, Matt Clifford, over two years ago and have been fascinated with EF’s model of investing ever since. Please enjoy my conversation with Alice Bentinck.
For the full show notes, transcript, and links to mentioned content, check out the episode page here.
This episode is brought to you by Canalyst. Canalyst is the leading destination for public company data and analysis. If you're a professional equity investor and haven't talked to Canalyst recently, you should give them a shout. Learn more and try Canalyst for yourself at canalyst.com/Patrick.
Today's episode is brought to you by Brex. Brex is the integrated financial platform trusted by the world's most innovative entrepreneurs and fastest-growing companies. With Brex, you can move money fast for instant impact with high-limit corporate cards, payments, venture debt, and spend management software all in one place. Ready to accelerate your business? Learn more at brex.com/best.
Invest Like the Best is a property of Colossus, LLC. For more episodes of Invest Like the Best, visit joincolossus.com/episodes.
Past guests include Tobi Lutke, Kevin Systrom, Mike Krieger, John Collison, Kat Cole, Marc Andreessen, Matthew Ball, Bill Gurley, Anu Hariharan, Ben Thompson, and many more.
Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here.
Follow us on Twitter: @patrick_oshag | @JoinColossus
Show Notes
[00:02:33] - [First question] - Overview of what Entrepreneur First is today
[00:05:57] - How she identifies the people to bring into each cohort and convince them to quit their job to join EF for eight weeks
[00:10:10] - Categories of the main types of people who join the EF program
[00:12:32] - What she’s learned about negative screening at the first stage of recruits
[00:14:07] - Positive signals she looks for in early admissions
[00:17:46] - What the program itself feels like as a participant
[00:21:29] - Reasons partners tend to fail and whether or not EF advises equity splits between founders
[00:24:49] - How important the idea is that the team will be working on
[00:28:04] - Exercises she enjoys doing with the new cohorts around social norms
[00:30:38] - How the experience looks physically in each city
[00:32:57] - Categories of data collected as the cohorts unfold and making investment decisions
[00:36:46] - Ways the companies mature after EF and what kinds of investors fund the next stage of their startups
[00:40:55] - Why aren’t there ten EF style initiatives or organizations
[00:44:26] - Motivations for the change in their holding company structure
[00:46:48] - The love of product and ideas she’s playing with right now
[00:51:49] - Cities she has her eye on that EF is not a participant in today and criteria that makes a city desirable for EF
[00:54:03] - A piece of software that EF could benefit from that doesn’t exist yet
[00:55:30] - The keys to her harmonious relationship with her co-founder Matt
[00:59:01] - National and international impediments that directly impact company building
[01:01:36] - The kindest thing anyone has ever done for her
The podcast and artwork embedded on this page are from Colossus | Investing & Business Podcasts, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The NFX Podcast (LS 41 · TOP 1.5% what is this?)
Episode: Building Confidence Networks In Latin America
Pub date: 2021-11-10
NFX has been increasingly active in LatAm over the last few years, largely drawn there by an incredibly talented pool of Founders in the region. To understand the unique challenges and opportunities of LatAm, James talked with Antonia Rojas, the youngest female partner at ALLVP and a rising star in the startup ecosystem there. They co-led the Nuvocargo Seed round together.
Based in Mexico City, she has a “boots on the ground” view of Latin America that I love. Methods for building network effects there is not what one might think -- and as a result, go-to-market in Latin America demands building trust bridges and leveraging confidence networks. Understanding the psychology of LatAm consumers is a powerful unlock for new Founders entering the region.
The podcast and artwork embedded on this page are from NFX, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: In Depth (LS 38 · TOP 2% what is this?)
Episode: From product roadmapping to sprint planning: How to ship software at scale — Snir Kodesh
Pub date: 2022-09-08
Today’s episode is with Snir Kodesh, Head of Engineering at Retool, which is a development platform for building custom business tools. Before joining Retool, Snir spent six years as a Senior Director of Engineering at Lyft.
In our conversation, we cover some of the biggest differences between leading engineering teams for a consumer product versus an enterprise platform — and the things that are consistent across both orgs.
First, Snir pulls back the curtain on the software development cycle, starting with setting the product roadmap while balancing a diverse set of customer needs. He outlines who’s in the room to represent product, engineering and design, and what those meetings actually look and sound like.
Next, he dives into how engineering actually starts taking that product roadmap and making a plan of action using the “try, do, consider” framework. He makes the case for leaning on QBRs instead of OKRs, why scope creep gets a bad rap, and his advice for getting better at estimating how long a feature will actually take to complete.
Finally, we zoom out and cover his essential advice for engineering leaders — especially folks who are scaling quickly from leading a small team to a much bigger one.
You can follow Snir on Twitter at @snirkodesh
You can email us questions directly at review@firstround.com or follow us on Twitter @ twitter.com/firstround and twitter.com/brettberson
The podcast and artwork embedded on this page are from First Round, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Knowledge Project with Shane Parrish (LS 69 · TOP 0.05% what is this?)
Episode: #146 Barbara Tversky: Action Shapes Thought
Pub date: 2022-09-06
My guest today is acclaimed psychologist and longtime Stanford University professor Barbara Tversky who calls on her nearly 50 years in the field of cognitive psychology for an in-depth discussion about how our minds work.
We discuss the Nine Laws of Cognition, why action shapes thought, how the language we use changes what we think, tactics to communicate better on Zoom, why she dove into the work of Leonardo da Vinci, when to use charts and when to avoid them, the importance of perspective taking, learned knowledge vs. earned knowledge, and so much more.
--
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Follow Shane on Twitter at: https://twitter.com/ShaneAParrish
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Podcast: Comedy of the Week (LS 66 · TOP 0.05% what is this?)
Episode: Josie Long: What Next?
Pub date: 2022-06-20
Three-time Edinburgh Comedy Award nominee and self-effacing national treasure Josie Long returns to the R4 airwaves to turn her sharp but affectionate eye on the state of the nation – and world, and planet – as we begin to emerge from two years of upheaval.
‘What’s Next?’ was a slogan stencilled all over major cities by climate change campaigners during the pandemic. It’s a fair question. In this stand-up masterclass – adapted from one of the most lauded Edinburgh Fringe shows in recent years – Josie considers the responsibility we have to our children with the planet in the parlous state it is.
She has become a mother herself (the first person ever, to her knowledge, to have not one, but two babies) and, through the prism of new parenthood, there is a lot to be alarmed about - corrupt governments, melting icecaps, health-food entrepreneurs making unsubstantiated claims about dates. And yet, in among all the existential crisis of the world in 2022, Josie finds hope and humanity.
A memoir of life-altering experiences broadened out into a manifesto for the direction we take now, post-pandemic, What’s Next deals surehandedly with both the personal and the global, showcasing the talents of a comic with an unusual and much-cherished ability to straddle the playful and the profound.
She may not have all the answers to our many societal crises, but nobody poses the questions in quite such an impassioned and entertaining way.
Written and performed by Josie Long
Produced by Siren Turner and Lianne Coop
An Impatient production for BBC Radio 4
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Podcast: Lenny's Podcast: Product | Growth | Career (LS 43 · TOP 1.5% what is this?)
Episode: How to sell your ideas and rise within your company | Casey Winters, Eventbrite
Pub date: 2022-07-21
The people who rise fastest in product know how to sell their ideas to customers, and also to their coworkers. Casey Winters, the Chief Product Officer at Eventbrite (previously at Grubhub, Pinterest, and advisor to dozens of companies) shares what it takes to be successful as you rise in the ranks within product. In this episode we’ll talk about how to land presentations, how to win over executives with strategic communication, the skill sets that are most in demand in product, and new growth trends. Join us.
—
Thank you to our wonderful sponsors for making this episode possible:
• Coda: http://coda.io/lenny
• Mixpanel: https://mixpanel.com/startups
• Whimsical: https://whimsical.com/lenny
—
Where to find Casey Winters:
• Twitter: https://twitter.com/onecaseman
• LinkedIn: https://www.linkedin.com/in/caseywinters/
—
Where to find Lenny:
• Newsletter: https://www.lennysnewsletter.com
• Twitter: https://twitter.com/lennysan
• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/
—
In this episode, learn:
[00:00] What to expect in this episode with Casey Winters
[03:23] An overview of Casey’s career
[06:18] A look into the most-fulfilling and challenging roles Casey has energized
[06:50] Communicating upward
[11:18] How to derisk meetings
[13:53] Are you properly preparing for your meetings?
[19:09] Striving for perceived simplicity
[24:22] Justifying non-sexy product improvements
[27:47] Protecting what you’ve built vs continuously scaling
[31:03] The downfall of functional ops roles
[35:21] The CPO role: what it is and how to get there
[40:44] The spectrum of product people
[45:11] How to level up your skills
[47:01] New growth trends, tactics, and strategies
[50:32] Casey’s two stages of growth: kindle strategies and fire strategies
[51:51] Under appreciated growth strategies
[54:02] Where to find Casey
Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe
The podcast and artwork embedded on this page are from Lenny Rachitsky, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Lenny's Podcast: Product | Growth | Career (LS 43 · TOP 1.5% what is this?)
Episode: How to create a winning product strategy | Melissa Perri
Pub date: 2022-07-28
Every company wants to develop a winning strategy—but what are signs your strategy isn’t working, and how do you change course? Melissa Perri has worked trained PMs and product leaders at nearly all the Fortune 100 companies, and in this conversation shares how to reset a struggling strategy, align your team, and build winning strategy. Join us.
—
Thank you to our wonderful sponsors for making this episode possible:
• Amplitude: https://amplitude.com/
• RevenueCat: https://www.revenuecat.com/
• Makelog: https://www.makelog.com/lenny
—
Where to find Melissa:
• Website: https://melissaperri.com/
• Twitter: https://twitter.com/lissijean
• LinkedIn: https://www.linkedin.com/in/melissajeanperri/
—
Where to find Lenny:
• Newsletter: https://www.lennysnewsletter.com
• Twitter: https://twitter.com/lennysan
• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/
—
Referenced:
• Melissa’s Book: https://melissaperri.com/book
—
In this episode, we cover:
[00:00] What to expect with guest Melissa Perri
[02:57] Melissa’s incredibly vast experience working with product manager’s
[04:20] Melissa’s current focus: training and education of PM’s
[05:59] The most common problems that product teams face
[09:48] When to hire your first CPO
[14:27] What to do before hiring a CPO
[16:16] When to bring an interim CPO consultant like Melissa
[21:26] Signs your team doesn’t have a strategy
[22:59] Identifying your vision, strategy and intentions as a company
[27:48] Signs you’re doing a bad job as a PM
[30:30] The process of defining strategic visions
[33:28] How to hone your craft as a PM
[43:55] Melissa’s Book — Escaping the Build Trap: How Effective Product Management Creates Real Value
[48:43] How to avoid burnout
[52:19] Where to find Melissa
Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe
The podcast and artwork embedded on this page are from Lenny Rachitsky, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: In Depth (LS 38 · TOP 2.5% what is this?)
Episode: Building a highly-technical enterprise product? Essential advice for product leaders — Nate Stewart of Cockroach Labs
Pub date: 2022-05-19
Today’s episode is with Nate Stewart, CPO of Cockroach Labs, the creator of database product CockroachDB.
In today’s conversation, we cover his essential advice for building a highly-technical product. He sketches out how the Cockroach team decided on the specific use case for its database product. Nate explains the steps the team took to reach conviction on their go-forward plan — which meant saying no to a lot of customers who didn’t align with the product roadmap. Nate dives into the tactical ways to avoid taking on too many customer commitments, which he calls tech debt for product teams.
Next, Nate dives into his advice for approaching design partnerships, especially when handling more conservative enterprise clients. He explains the different types of design partners, and why you should have all of those represented in the early days of your startup.
Finally, we wrap up with his advice for other product leaders, including how to create a rock-solid partnership with a CEO as the first head of product, and how he solicits honest feedback across the executive team.
You can follow Nate on Twitter at @Nate_Stewart
You can email us questions directly at review@firstround.com or follow us on Twitter @ twitter.com/firstround and twitter.com/brettberson
The podcast and artwork embedded on this page are from First Round, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: In Depth (LS 38 · TOP 2.5% what is this?)
Episode: From PM to VP of Product: Jiaona Zhang’s career advice from Webflow, Airbnb & Dropbox
Pub date: 2022-06-23
For our 60th episode, we’re doing things a little bit differently — with a new guest host! Welcome to Todd Jackson, who’s filling in for Brett Berson this week.
Todd is also a Partner at First Round, and the episodes he hosts will mostly focus on product, given his previous product roles, from the VP of Product & Design at Dropbox and Director of Product Management at Twitter, to being a PM at Facebook and Google, leading Newsfeed and Gmail. He was also a founder — his startup Cover was backed by First Round in 2013 and later acquired by Twitter. (For more on Todd and his advice for company building, check out his article in The First Round Review from a couple years ago.)
Today, Todd chats with Jiaona Zhang, the VP of Product at Webflow. (She goes by JZ though, so you’ll hear that throughout their conversation.) You might remember her popular Review article, Don’t Serve Burnt Pizza (And Other Lessons in Building Minimum Lovable Products)
Before joining Webflow, JZ was the Senior Director of Product Management at WeWork, a Product Lead at Airbnb, and a PM at Dropbox and at Pocket Gems, a mobile gaming company. JZ also teaches product at Stanford and mentors a lot of rising product leaders, so she’s the perfect person to talk to about building a career in product.
As the framework for the entire conversation, we start with why she doesn’t think of it as a career ladder, but rather as three distinct phases: contributing as a PM, managing PMs, and then leading the function. Here’s a preview of what Todd and JZ cover:
- The PM role. Advice on breaking into the function, what you should look for when you’re a candidate interviewing for PM roles, and the mistakes that are easy to make early on.
- The managing phase, including how to think more strategically as you get more senior, archetypes to look for when hiring, and her advice for first-time managers.
- The executive phase. JZ talks about thinking of your org as a product, and she shares super tactical pointers for working with your CEO, your peers on the exec team, and the board.
Whether you’re trying to break into product, grow in your career, or you’re a founder looking for hiring advice, there’s tons in this conversation for you.
You can follow JZ on Twitter at @jiaonazhang. You can email us questions directly at review@firstround.com or follow us on Twitter @firstround and @tjack.
The podcast and artwork embedded on this page are from First Round, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Rachman Review (LS 45 · TOP 1% what is this?)
Episode: Wirecard: the investigation that brought down a German tech giant
Pub date: 2022-06-30
It took years of digging and a lucky break to uncover the fraud at the heart of Wirecard. Gideon talks to Dan McCrum about the strange netherworld of financial speculators, private detectives, bumbling accountants and outright criminals that he encountered along the way.
Clips: Bloomberg, Money Talks
Why we trust fraudsters
Less work for EY auditors? What about more accountability
Wirecard middleman pleads guilty to hacking
Inside Wirecard
Subscribe to The Rachman Review wherever you get your podcasts - please listen, rate and subscribe.
Presented by Gideon Rachman. Produced by Fiona Symon. Sound design is by Breen
Read a transcript of this episode on FT.com
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Podcast: Middle East Focus (LS 41 · TOP 2% what is this?)
Episode: Tunisia's Economy
Pub date: 2022-07-07
Intissar Fakir is joined by Marwa Haddar and Fadil Aliriza to discuss the economic issues Tunisia is facing, international financial institutions' role in the crisis, and the government's actions, or lack thereof, to help the country.
The podcast and artwork embedded on this page are from Middle East Institute, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Normal Gossip (LS 56 · TOP 0.5% what is this?)
Episode: S1 Ep7: No One Tells Josh Gondelman the Good Gossip
Pub date: 2022-02-16
This week, comedian Josh Gondelman loves the gossip story we told him so much he compared it to O. Henry and Poe.
Follow Josh on Twitter @joshgondelman.
Episode transcript here.
Follow the show on Instagram @normalgossip, and if you have gossip, email us at normalgossip@defector.com or leave us a voicemail at 26-79-GOSSIP.
Normal Gossip is hosted by Kelsey McKinney (@mckinneykelsey) and produced by Alex Sujong Laughlin (@alexlaughs).
Subscribe to Defector Media and get your first month for 99 cents at defector.com/normalgossip.
The podcast and artwork embedded on this page are from Normal Gossip, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: In Depth (LS 38 · TOP 2.5% what is this?)
Episode: The Product Strategy Playbook that Powered Growth at Tinder & TripAdvisor — Ravi Mehta
Pub date: 2022-01-20
Today’s episode is with Ravi Mehta, who is formerly the Chief Product Officer at Tinder, and taught product strategy as an Executive in Residence at Reforge.
In today’s conversation, we dive exceptionally deep into product strategy, starting with what Ravi sees as the most common disconnect between product strategy and what product teams actually work on day-to-day. In the bulk of our discussion, we walk through the core tenants of what he calls the product strategy stack, which includes the company mission, company strategy, product strategy, product roadmap, and product goals.
Next, he unpacks his alternative approach to OKRs, called NCTs. He makes the case that outlining narratives, commitments, and tasks sidesteps some of the most common headaches when it comes to OKRs, and gives suggestions for implementing NCTs within your own product teams.
Strategy is often misunderstood and has come to mean all sorts of different things. What struck me about Ravi is how clearly he’s able to articulate these amorphous ideas like “mission” or “vision.” He’s also got plenty of examples from his own career at TripAdvisor and Tinder, plus his work as an advisor for other fast-growing startups.
You can follow Ravi on Twitter at @ravi_mehta.
You can email us questions directly at review@firstround.com or follow us on Twitter @ twitter.com/firstround and twitter.com/brettberson
The podcast and artwork embedded on this page are from First Round, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: In Depth (LS 38 · TOP 2.5% what is this?)
Episode: The startup playbook for expanding internationally — Advice from Faire CEO Max Rhodes
Pub date: 2022-02-10
Today’s episode is with Max Rhodes, the co-founder and CEO of Faire, an online wholesale marketplace that connects independent retailers and brands.
Prior to starting Faire in 2017, Max spent several years at Square, where he was a founding member of Square Capital, the first product manager on Square Cash, and a Director of Consumer Product for Caviar.
In today’s conversation, we dive deep into how startups can get international expansion right. After launching in the U.K. and Netherlands in March 2021, Faire company expanded into countries like France, Germany, Italy and the Nordic region. They’re now in 15 markets, with over 700 employees in 10 offices around the world.
After sharing the company’s origin story and initial strategy, Max offers a helpful analogy that helped him decide when to go international, and details some lessons he learned from other companies like DoorDash and Airbnb.
Next, Max takes us through the nuts and bolts of how the Faire team approached their first international launch, from staffing and operations, to how they thought about local competitors. Max also walks us through the operating cadence and strategic planning process that powered Faire’s international growth. We also talk about the human side of scaling internationally, and the growing pains that come along with it.
To help mitigate the effects, Max shares how he’s implemented the concepts from the First Round Review article on “Giving away your Legos.” Read the article here: https://review.firstround.com/give-away-your-legos-and-other-commandments-for-scaling-startups
You can follow Max on Twitter at @MaxRhodesOK. You can email us questions directly at review@firstround.com or follow us on Twitter @firstround and @brettberson.
The podcast and artwork embedded on this page are from First Round, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: In Depth (LS 38 · TOP 2.5% what is this?)
Episode: Never done sales before? Meka Asonye shares GTM playbooks from Stripe, Mixpanel, and backing founders at First Round
Pub date: 2022-03-03
Today’s episode is with Meka Asonye, a Partner at First Round Capital. This week marks the one year anniversary since he joined, making the transition from seasoned GTM leader to full-time early-stage investor.
Prior to First Round, Meka served as the VP of Sales & Services at Mixpanel, where he ran the more than 100-person global revenue team and owned the customer lifecycle from first website visit to renewal. Meka also spent four years at Stripe as it scaled from 250 to 2000 people and matured its sales org. When he first joined in 2016, he served as one of the payments company’s early account executives, leading their first attempts to go upmarket and land enterprise logos. For the next three years, he headed up Stripe’s Startup/SMB business.
In today’s conversation, Meka starts by digging into his playbook for founder-led sales, from what a great first customer conversation looks like, to how to self-diagnose what went wrong. He also shares advice for founders making their first hire, including the leveling mistake that’s easy to make, and what to ask in the interview and in reference calls. He also offers thoughts on comp and the leading indicators to look for after onboarding.
We then dig into structuring early pilots, from what makes for a good design partner, to how to make sure your ICP is well defined enough. We also cover helpful tactics for customer success, which Meka finds is often the most overlooked aspect of go-to-market. Throughout the conversation, we also touch on how Meka’s experiences have translated into his first year as a VC. We end on his advice for startup folks looking to transition into venture.
To read more of Meka’s go-to-market advice for founders, check out his article in the First Round Review: https://review.firstround.com/this-gtm-leader-turned-investor-crowdsources-early-lessons-from-stripe-figma-and-more
You can follow Meka on Twitter at @BigMekaStyle. You can email us questions directly at review@firstround.com or follow us on Twitter @firstround and @brettberson.
The podcast and artwork embedded on this page are from First Round, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Barron's Live (LS 41 · TOP 1.5% what is this?)
Episode: How the UK's Most Valuable Tech Startup Revolut Plans to Crack America
Pub date: 2021-08-26
Revolut's US CEO Ron Oliveira talks to Emily Nicolle at Financial News about the SoftBank-backed fintech firm's plans to reinvent the way Americans bank, after being valued at $33bn earlier this year.
The podcast and artwork embedded on this page are from Barron's Live, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Twenty Minute VC: Venture Capital | Startup Funding | The Pitch (LS 57 · TOP 0.5% what is this?)
Episode: 20VC: Revolut Founder Nikolay Storonsky on His Leadership Style and Relationship To Ambition, His Biggest Lessons From Scaling Revolut Globally; What Worked, What Did Not & Why Speed of Product Shipment is The Most Important Thing
Pub date: 2021-04-12
Nikolay Storonsky is the Founder & CEO @ Revolut, one of the world's largest and fastest-growing fintechs offering everything from personal to business banking, providing a better way to manage your money. To date, Nikolay has raised over $905M with Revolut from Ribbit, Index, DST, Balderton and Bond Capital to name a few. Nikolay has scaled Revolut to over 2,000 employees across 4 continents. Before changing the world of neo-banking, Nikolay spent 8 years as a derivatives trader at both Lehman Brothers and Credit Suisse in London.
In Today’s Episode with Nikolay Storonsky You Will Learn: 1.) How Nikolay made his way into the world of startups from derivatives trading and how that led to his changing the world of fintech with Revolut?
2.) How would Nikolay describe his style of leadership today? How did his time in banking impact his operating style? What elements has Nikolay found the hardest to scale into as a leader? How does Nikolay assess his relationship to ambition? What drives him today? How does Nikolay deal with self-doubt and vulnerability in leadership?
3.) Why does Nikolay feel the most important thing in a company is the speed of product shipment? From a product perspective, how does Nikolay determine what to do next vs what to do later? What does that prioritisation process look like? Has it changed over time?
4.) How does Nikolay think about gepgraphic expansion today? Given Monzo's challenges in the US, why did Revolut decide the US remained a good strategy? What does it take to launch and scale a new country? How does Nikolay think about the relationship between growth and profitability? What companies does Nikolay admire most for their international scaling?
Item’s Mentioned In Today’s Episode with Nikolay Storonsky Nikolay’s Favourite Book: Principles by Ray Dalio (PDF)
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Podcast: Fintech Insider Podcast by 11:FS (LS 53 · TOP 0.5% what is this?)
Episode: 617. Insights: How banks paved the way for a Singapore fintech revolution
Pub date: 2022-04-08
Our expert host, Benjamin Ensor, is joined by some great guests to break down how fintech currently operates in Singapore.
In the decades after gaining independence in 1965, Singapore rapidly developed from a low-income country to a high-income country.
With this, came a push for better financial services and the island nation has established itself as one of Asia and the world’s key financial hubs.
So today we’re talking about how things look in 2022, what challenges there are still to overcome, and what’s next for Singapore's ecosystem?
This week's guests include:
- Jonas Thürig, Head, F10 Singapore
- Dayana Yermolayeva, CEO & Co-Founder, JiPay
This episode is sponsored by Austrade
Everyone wants to stay ahead of the fintech curve, and a great way to start is by powering your investment portfolio with Australia's best and brightest fintechs. From buy-now-pay-later, to open banking, from embedded finance, to global payments - it won't be long before the next Clearpay, Cover Genius or Airwallex hits the international stage. Learn more today, visit
www.shinewithaustralia.com.au/fintech
Fintech Insider by 11:FS is a podcast dedicated to all things fintech, banking, technology and financial services. It’s hosted by a rotation of 11:FS experts including David M. Brear, Simon Taylor, Jason Bates and Gwera Kiwana, as well as a range of brilliant guests. We cover the latest global news, bring you interviews from industry experts or take a deep dive into subject matters such as APIs, AI or digital banking.
If you enjoyed this episode, don't forget to subscribe and please leave a review
Follow us on Twitter: @fintechinsiders where you can ask the hosts questions, or email podcasts@11fs.com!
Special Guests: Dayana Yermolayeva and Jonas Thürig.
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Podcast: Lex Fridman Podcast (LS 75 · TOP 0.05% what is this?)
Episode: #267 – Mark Zuckerberg: Meta, Facebook, Instagram, and the Metaverse
Pub date: 2022-02-26
Mark Zuckerberg is CEO of Meta, formerly Facebook.
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OUTLINE:
Here’s the timestamps for the episode. On some podcast players you should be able to click the timestamp to jump to that time.
(00:00) – Introduction
(11:29) – Metaverse
(31:06) – Identity in Metaverse
(43:15) – Security
(47:40) – Social Dilemma
(1:09:46) – Instagram whistleblower
(1:14:31) – Social media and mental health
(1:19:56) – Censorship
(1:37:05) – Translation
(1:44:40) – Advice for young people
(1:50:28) – Daughters
(1:53:16) – Mortality
(1:57:49) – Question for God
(2:00:55) – Meaning of life
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Podcast: How to Fix Democracy (LS 33 · TOP 5% what is this?)
Episode: Gideon Rachman
Pub date: 2020-06-05
Individual choice | Gideon Rachman is the chief foreign affairs columnist for the Financial Times. He was previously at the Economist for fifteen years, during which time he was a foreign correspondent in Brussels, Washington, and Bangkok, and editor of the business and Asia sections. In this interview with Andrew Keen, Rachman discusses how globalization has deepened conflict between capitalism and democracy. Individual choice is at the core of both capitalism and democracy, and so the two work better together than it may seem today, when anti-democratic, anti-globalist politics are emerging in many democratic, free market countries.
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Podcast: The Rachman Review (LS 44 · TOP 1.5% what is this?)
Episode: French presidential election too close to call
Pub date: 2022-04-07
Far-right leader Marine Le Pen has put in an unexpectedly strong showing and looks set to go head to head with Emmanuel Macron in the second round of France’s presidential election. Gideon talks to the FT’s Anne-Sylvaine Chassany and Bruno Cautrès of Sciences Po about the issues French voters care about and what happens next.
Clips: Reuters, HuffPost, France inter
www.ft.com/rachman-review
https://play.acast.com/s/therachmanreview
Want to read more?
French election polls: the race for the presidency
Rightwing presidential candidates’ immigration ‘obsession’ belies reality of modern France
Emmanuel Macron warns he could lose French election to the far right
France votes: Macron’s frontrunner status conceals deep rifts in society
Subscribe to The Rachman Review wherever you get your podcasts - please listen, rate and subscribe.
Presented by Gideon Rachman. Produced by Fiona Symon. Sound design by Jasiu Sigsworth
Read a transcript of this episode on FT.com
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Podcast: Music Man
Episode: S3 E27 • Aretha Franklin’s This Girl’s In Love With you
Pub date: 2021-05-24
This episode I go into one of the hidden gems of Aretha Franklin called This Girl’s In Love With you. I talk about how Aretha kills it on all the songs, some songs being covers of other musicians music, then I get into a random tangent on covers so hope you enjoy this episode!!!!!
Support this podcast: https://anchor.fm/austin-castro/support
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Podcast: Broken Record with Rick Rubin, Malcolm Gladwell, Bruce Headlam and Justin Richmond (LS 69 · TOP 0.05% what is this?)
Episode: Sam Sanders on Janet Jackson's Legacy
Pub date: 2022-01-11
Today we're sharing an episode from our friend Sam Sanders over at NPR’s It's Been a Minute podcast. This year marks the 35th anniversary of Janet Jackson's classic album, Control. That album was her first real commercial hit and, looking back, helped redefine all of pop music. It also helped establish two star producers in Jimmy Jam and Terry Lewis. In the late 80s and 90s, Janet was one of the biggest stars of our time—right up there with her brother Michael and Madonna. And yet, Jackson isn't always given her due.
In this episode, host Sam Sanders explores why that is, how she made the album Control, and the incident that lasted all but a second, that changed the course of her career.
Subscribe to Broken Record’s YouTube channel to hear all of our interviews: https://www.youtube.com/brokenrecordpodcast and follow us on Twitter @BrokenRecord
You can also check out past episodes here: https://brokenrecordpodcast.com
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Podcast: Broken Record with Rick Rubin, Malcolm Gladwell, Bruce Headlam and Justin Richmond (LS 69 · TOP 0.05% what is this?)
Episode: John Frusciante
Pub date: 2022-04-01
Today is the start of a month-long run of episodes in celebration of the Red Hot Chili Peppers’ newest album, Unlimited Love. We kick things off with Rick Rubin in conversation with the Chili Peppers' guitarist, John Frusciante. If you’re a diehard fan you know why the release of their new album is so exciting. Unlimited Love is their first record with John in 16 years.
In this episode, you’ll hear John tell Rick about his deep musical exploration as a young guitar player. He also talks about how he fell in love with the Chili Peppers as a teenager, and what it was like joining the band he’d become such a big fan of at just 18 years-old. And in the end, John and Rick are joined by a very special guest.
Part Two of this conversation continues on the next episode . . .
Subscribe to Broken Record’s YouTube channel to hear all of our interviews: https://www.youtube.com/brokenrecordpodcast and follow us on Twitter @BrokenRecord
You can also check out past episodes here: https://brokenrecordpodcast.com
Hear a playlist of all of our favorite Red Hot Chili Peppers songs HERE.
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Podcast: Broken Record with Rick Rubin, Malcolm Gladwell, Bruce Headlam and Justin Richmond (LS 69 · TOP 0.05% what is this?)
Episode: John Frusciante and Anthony Kiedis
Pub date: 2022-04-05
We’re continuing our run of episodes celebrating the release of the Red Hot Chili Peppers’ new album, Unlimited Love, produced by Rick Rubin. We left off our episode last week with Anthony Kiedis popping in to join Rick’s conversation with Chili Peppers' guitarist John Frusciante. Today we have part two of Rick’s conversation with John and the band’s iconic frontman.
On this episode we'll hear Anthony talk about how some of the new song lyrics came together, the lengths he went to commute to the studio in Hawaii where he was recording vocals with Rick. And both Anthony and John give their accounts of John’s thirdreturn to the band.
Make sure to check out Rick's interview next week with the almighty Flea.
Subscribe to Broken Record’s YouTube channel to hear all of our interviews: https://www.youtube.com/brokenrecordpodcast and follow us on Twitter @BrokenRecord
You can also check out past episodes here: https://brokenrecordpodcast.com
Hear a playlist of all of our favorite Red Hot Chili Peppers songs HERE.
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Podcast: Syntax - Tasty Web Development Treats (LS 62 · TOP 0.1% what is this?)
Episode: Potluck - Handling Auth × Are Web Dev Real Developers? × Handling Git Conflicts × Converting PNG to Box-Shadow × Bad Docs vs No Docs × Making Shopify Headless
Pub date: 2022-03-16
In this potluck episode of Syntax, Wes and Scott answer your questions about handling auth, are web dev real developers, handling Git conflicts, converting PNG to Box-Shadow, bad docs vs no docs, making Shopify headless, and more.
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Show Notes * 00:10 Welcome * 01:32 Fool’s winter * 03:25 How do you handle authentication in an app? * 09:42 Responding to people who don’t think developers are real developers? * 12:21 How do you handle git conflicts in package-lock.json and/or yarn.lock files? * 14:42 I built a small project which converts .png images to CSS box-shadow values. * Img to Box Shadow * 16:37 January 19th, 2038 at 03:14:08 is the end of epoch * Office Space * 20:31 Sponsor: Sentry * 22:44 Should I begin to add PropTypes to my packages and projects? * 25:59 What’s worse: bad documentation or no documentation? * 27:37 How do you find the motivation or discipline to follow through in side projects? * 29:48 I need to take an existing Shopify site and make it headless - what should I use? * 37:55 Sponsor: Sanity * 39:18 You have a ?token= query param and some token value for it. Could you explain a bit more on what is that for? * 44:05 Have you thought about selling Syntax.fm shirts? * 46:05 Can I migrate my Express routes to Next.js’ API and get the same httpOnly cookies workflow? * 52:03 Sponsor: Freshbooks * 52:52 Sick Picks
××× SIIIIICK ××× PIIIICKS ××× * Scott: Okeeffe’s Lip Balm * Wes: Okeef’s Working Hands
Level Up Course Drop - https://youtu.be/LATf_lVYoMQ?t=829 Shameless Plugs * Scott: Level Up Course Drop * Wes: Wes Bos Course player update
Tweet us your tasty treats * Scott’s Instagram * LevelUpTutorials Instagram * Wes’ Instagram * Wes’ Twitter * Wes’ Facebook * Scott’s Twitter * Make sure to include @SyntaxFM in your tweets
The podcast and artwork embedded on this page are from Wes Bos & Scott Tolinski - Full Stack JavaScript Web Developers, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Syntax - Tasty Web Development Treats (LS 62 · TOP 0.1% what is this?)
Episode: Part 2 of Wes and Scott React to the State of JS
Pub date: 2022-03-09
In this second part episode of Syntax, Wes and Scott continue talking about the 2021 State of JavaScript survey: mobile and desktop libraries, testing, monorepo, runtimes, flavors of JavaScript, and more!
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Sanity - Sponsor Sanity.io is a real-time headless CMS with a fully customizable Content Studio built in React. Get a Sanity powered site up and running in minutes at sanity.io/create. Get an awesome supercharged free developer plan on sanity.io/syntax.
Freshbooks - Sponsor Get a 30 day free trial of Freshbooks at freshbooks.com/syntax
Show Notes * 00:10 Welcome * 01:20 Scott’s new sound panels * 03:32 Instacart * 2021 State of JS Survey * Tauri * 07:46 Mobile and Desktop libraries * 13:50 Testing * Vitest * Playwright * Cypress * 19:48 Sponsor: Sentry * 21:26 Monorepo tooling * 27:00 Sponsor: Sanity.io * 28:18 JavaScript Runtimes * 30:51 JavaScript Flavors * 32:32 Non JavaScript Languages * 39:38 Utilities * Syntax 401: Monorepo * pnpm * Turborepo * 40:19 Resources * Syntax.fm 403: JavaScript in 2022 - New, Coming and Proposed Features * 43:18 Opinions * 47:21 Features missing from JavaScript * 49:30 Awards * 52:58 Sponsor: Freshbooks * 53:38 SIIIIICK ××× PIIIICKS * 56:41 Shameless Plugs
××× SIIIIICK ××× PIIIICKS ××× * Scott: StoryPal * Wes: Heartbeat Hot Sauce * Matty Matheson on Hot Ones * Gordon Ramsay on Hot Ones
Shameless Plugs * Scott: LevelUp Tutorials * Wes: Wes Bos Tutorials
Tweet us your tasty treats * Scott’s Instagram * LevelUpTutorials Instagram * Wes’ Instagram * Wes’ Twitter * Wes’ Facebook * Scott’s Twitter * Make sure to include @SyntaxFM in your tweets
The podcast and artwork embedded on this page are from Wes Bos & Scott Tolinski - Full Stack JavaScript Web Developers, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Syntax - Tasty Web Development Treats (LS 62 · TOP 0.1% what is this?)
Episode: Wes and Scott React to the State of JS
Pub date: 2022-03-02
In this episode of Syntax, Wes and Scott take a look at the 2021 State of JS survey that was recently published, including demographics, salary, browser APIs, overall happiness, and more!
Linode - Sponsor Whether you’re working on a personal project or managing enterprise infrastructure, you deserve simple, affordable, and accessible cloud computing solutions that allow you to take your project to the next level. Simplify your cloud infrastructure with Linode’s Linux virtual machines and develop, deploy, and scale your modern applications faster and easier. Get started on Linode today with a $100 in free credit for listeners of Syntax. You can find all the details at linode.com/syntax. Linode has 11 global data centers and provides 24/7/365 human support with no tiers or hand-offs regardless of your plan size. In addition to shared and dedicated compute instances, you can use your $100 in credit on S3-compatible object storage, Managed Kubernetes, and more. Visit linode.com/syntax and click on the “Create Free Account” button to get started.
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Show Notes * 2021 State of JS Survey * 01:51 Winter birthday fun * 05:38 The State of JS survey * 07:37 Demographics * 09:18 Salary range * 09:58 Language features * 14:05 Browser APIs * 17:36 Custom elements and Shadow DOM * 18:18 Page visibility API * 19:28 File system API * 19:58 Web share API * 22:15 Sponsor: Linode * 23:12 Libraries * 27:30 Stimulus * 29:21 Trends * 31:32 Happiness with the state of front end frameworks * 32:28 Sponsor: LogRocket * 34:04 Backend Frameworks * 38:16 Backend tool satisfaction * 44:35 Happiness of build tools * 46:32 Sponsor: Freshbooks * 48:27 The best podcast in web development * 49:05 Sick Picks
××× SIIIIICK ××× PIIIICKS ××× * Scott: Inventing Anna * Wes:Everlane Crewneck Sweater
Shameless Plugs * Scott: LevelUp Course Drop Party * Wes: Wes Bos Tutorials
Tweet us your tasty treats * Scott’s Instagram * LevelUpTutorials Instagram * Wes’ Instagram * Wes’ Twitter * Wes’ Facebook * Scott’s Twitter * Make sure to include @SyntaxFM in your tweets
The podcast and artwork embedded on this page are from Wes Bos & Scott Tolinski - Full Stack JavaScript Web Developers, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Syntax - Tasty Web Development Treats (LS 62 · TOP 0.1% what is this?)
Episode: Hasty Treat WTF × SSR vs JamStack vs Serverless?
Pub date: 2022-03-14
In this Hasty Treat, Scott and Wes talk about the differences between SSR, JamStack, and Serverless.
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Show Notes * 00:21 Welcome * 01:20 Sponsor: LogRocket * 02:26 Sponsor: Retool * 03:49 What exactly is server side rendering vs. tech like Jamstack and serverless? * Cloudinary * Mux * 12:15 Why use one or the other? * Svelte Kit * Syntax.fm * 16:55 Where does Serverless fit into this? * 19:12 What’s the ideal scenario?
Tweet us your tasty treats * Scott’s Instagram * LevelUpTutorials Instagram * Wes’ Instagram * Wes’ Twitter * Wes’ Facebook * Scott’s Twitter * Make sure to include @SyntaxFM in your tweets
The podcast and artwork embedded on this page are from Wes Bos & Scott Tolinski - Full Stack JavaScript Web Developers, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Syntax - Tasty Web Development Treats (LS 62 · TOP 0.1% what is this?)
Episode: SSL Certs, Approvals and Cloudflare
Pub date: 2022-03-21
In this Hasty Treat, Scott and Wes talk about getting SSL certificates set up between your hosting, Cloudflare, and other web apps you may use.
Prismic - Sponsor Prismic is a Headless CMS that makes it easy to build website pages as a set of components. Break pages into sections of components using React, Vue, or whatever you like. Make corresponding Slices in Prismic. Start building pages dynamically in minutes. Get started at prismic.io/syntax.
LogRocket - Sponsor LogRocket lets you replay what users do on your site, helping you reproduce bugs and fix issues faster. It’s an exception tracker, a session re-player and a performance monitor. Get 14 days free at logrocket.com/syntax.
Show Notes * 00:21 Welcome * 01:13 Sponsor: LogRocket * 02:06 Sponsor: Prismic * 03:23 Wes’ story of SSL * Render * 05:43 How LetsEncrypt works * LetsEncrypt * 08:32 What is Cloudflare? * Cloudflare * 10:33 The problem Wes ran into * 12:27 Support is tricky * 13:54 What is Cloudapp? * Cloudapp * Vercel * 15:34 Two SSL Certs are needed * 16:41 First solution * 17:36 Second solution * 22:36 What about A Records?
Tweet us your tasty treats * Scott’s Instagram * LevelUpTutorials Instagram * Wes’ Instagram * Wes’ Twitter * Wes’ Facebook * Scott’s Twitter * Make sure to include @SyntaxFM in your tweets
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Podcast: Another Podcast (LS 39 · TOP 2% what is this?)
Episode: Talking about crypto
Pub date: 2022-03-01
Crypto is so big and yet so unclear that we can’t even agree what to call it. What does ‘web3’ mean, what might it mean, how do we ignore the noise, and what questions might matter?
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Podcast: LSE: Public lectures and events (LS 55 · TOP 0.5% what is this?)
Episode: In Conversation with Nadia Calviño Santamaría
Pub date: 2022-02-17
Contributor(s): Nadia Calviño Santamaría, Professor Iain Begg | Nadia Calviño Santamaría discusses issues related to the current economic recovery, with a particular focus on the policy lessons from the pandemic and the way ahead.
The podcast and artwork embedded on this page are from London School of Economics and Political Science, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: LSE: Public lectures and events (LS 55 · TOP 0.5% what is this?)
Episode: SHORTCAST | Systemic Risk in Interconnected Financial Markets
Pub date: 2022-02-22
Contributor(s): Professor Luitgard Veraart | Domino effects of losses can bring down entire financial systems with severe knock-on effects on the real economy. This talk considers insights from mathematics to model loss cascades and apply them to recent financial stress events. We live in an interconnected world. As the COVID-19 pandemic has demonstrated, interconnections affect both our lives and our livelihoods. In this talk, Luitgard Veraart will show how we can use mathematical models to quantify and manage risk arising from interconnections in financial markets. A particular focus will be on systemic risk and financial stability. Examples provided from the 2007-2009 financial crisis and the economic effects of the COVID-19 pandemic will illustrate how mathematical models can inform the debate on mitigating systemic risk. Meet our speaker and chair Luitgard Veraart is a Professor in the Department of Mathematics at LSE. She joined LSE in 2010 after holding positions in the USA and in Germany. She is a co-winner of the 2019 Adams Prize awarded by the University of Cambridge for her research in the Mathematics of Networks. Jan van den Heuvel is Professor of Mathematics and Head of the Department of Mathematics at LSE. More about this event The Department of Mathematics (@LSEMaths) is internationally recognised for its teaching and research in the fields of discrete mathematics, game theory, financial mathematics and operations research.
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Podcast: LSE: Public lectures and events (LS 55 · TOP 0.5% what is this?)
Episode: COVID by Numbers: making sense of the pandemic with data
Pub date: 2022-03-14
Contributor(s): Dr Anthony Masters, Professor Sir David Spiegelhalter | Anthony Masters is Statistical Ambassador for the Royal Statistical Society. David Spiegelhalter is Chair of the Winton Centre for Risk and Evidence Communication, Centre for Mathematical Sciences, University of Cambridge. They are the authors of COVID by Numbers: making sense of the pandemic with data. Qiwei Yao is Professor in the Department of Statistics at LSE.
The podcast and artwork embedded on this page are from London School of Economics and Political Science, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Inside Round
Episode: #4: Ayo Omojola on problem selection, going unreasonably deep and building products in a regulated space
Pub date: 2021-10-13
Ayo Omojola is currently the SVP of Product at Carbon Health. Previously, he was a product lead at Cash App and cofounded a consumer social startup. In this episode, we dive into techniques for problem selection, the importance of going unreasonably deep in a domain, and Ayo's thoughts on building products in a regulated space.
You can find Ayo on Twitter @ay_o.
The podcast and artwork embedded on this page are from Kojo Osei, Matrix Partners, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: In Depth (LS 38 · TOP 2.5% what is this?)
Episode: Product lessons from Cash App & Carbon Health — Ayo Omojola on going “unreasonably deep”
Pub date: 2020-11-12
Our second episode is with Ayo Omojola, VP of Product at Carbon Health. Previously, he was the founding product manager on the banking team for Cash App at Square, where he co-created the Cash Card and helped build out Square’s technical banking infrastructure. He’s also a former founder of a Y Combinator-backed startup and an active angel investor, which gives him a unique lens into finding and evaluating startup ideas.
Tapping into Ayo’s experience working in the heavily regulated spaces of healthcare and financial services, we dive into how he untangles regulations to find “the opportunities where it’s easy to stop” and goes “unreasonably deep” when building early products. Ayo thinks a lot about problem selection and makes the case for putting more effort into choosing what to work on. It’s a must-listen for anyone who’s thinking about starting a company someday, or a product leader who hopes to help a new product take shape.
But even if those aren’t goals of yours, there’s still tons to learn. Ayo shares the individuals he learned the most from during his time at Square and the frameworks he picked up from them, such as on how to get better at process, setting context, and “optimizing for the outstanding.” Last but not least, we get into his management and hiring philosophy, including why he loves to hire former founders.
You can follow Ayo on Twitter at @ay_o. For reference, the leaders he gave a shout out to in the episode include Robert Andersen (the founding designer at Square), Dhanji Prasanna (who led engineering for Cash App), Jim Esposito (Operations Lead for Cash App) and Emily Chiu (who led strategic development efforts for Cash App).
You can email us questions directly at review@firstround.com or follow us on Twitter @ twitter.com/firstround and twitter.com/brettberson
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Podcast: Product Decoded (LS 33 · TOP 5% what is this?)
Episode: Todd Yellin, VP of Product at Netflix
Pub date: 2018-04-24
Todd Yellin, Vice President of Product at Netflix, shares his passion for story-telling, personalization, design, and how he works to delight more than 120M Netflix members worldwide through both “aiming high and aiming low.”
The podcast and artwork embedded on this page are from Krista Gambrel, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Azeem Azhar's Exponential View (LS 60 · TOP 0.5% what is this?)
Episode: How the Russia-Ukraine Conflict Will Change Cyberwar (with Robert Hannigan)
Pub date: 2022-03-09
Many experts expected Russia’s war with Ukraine to be accompanied by a large-scale cyberattack, but that hasn’t yet materialized. Azeem Azhar speaks to Robert Hannigan, the former director of the Government Communications Headquarters (GCHQ) – the UK’s equivalent to America’s NSA, to find out how the conflict is playing out in cyberspace and what might happen next.
@Azeem
@exponentialview
Blue Voyant
Further resources:
‘AI and the Future of Warfare’ – Exponential View Podcast, 2019
‘Cybersecurity in the Age of AI’ – Exponential View Podcast, 2019
‘AI, Warfare and Global Security’ – Exponential View Podcast, 2018
The podcast and artwork embedded on this page are from HBR Presents / Azeem Azhar, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Prime Venture Partners Podcast (LS 41 · TOP 2% what is this?)
Episode: #47 Succeeding In A Highly Competitive Market with Sameer Nigam Founder & CEO PhonePe
Pub date: 2020-11-19
Sameer Nigam Founder & CEO, PhonePe chats with Sanjay Swamy, Managing Partner Prime Venture Partners.
Listen to the podcast to learn about:
02:30 - Betting on opportunities
03:35 - Moment of Serendipity
05:00 - Smartest decision made
07:30 - Growth phases of UPI
10:30 - The most competitive sector in India
12:45 - Why PhonePe is succeeding
15:12 - Zero MDR
18:40 - Value Vs Cost
21:10 - Competition is a good thing
25:00 - New opportunities for Fin-tech entrepreneurs
30:45 - Why India needs hyperlocal apps
34:00 - PhonePe's acquisition by Flipkart
38:15 - 2020 Sameer's advice to 2010 Sameer
Read the complete transcript here
Enjoyed the podcast? Please consider leaving a review on Apple Podcasts and subscribe wherever you are listening to this.
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LinkedIn: https://www.linkedin.com/company/primevp/
This podcast is for you. Do let us know what you like about the podcast, what you don't like, the guests you'd like to have on the podcast and the topics you'd like us to cover in future episodes.
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The podcast and artwork embedded on this page are from Prime Venture Partners: Early Stage VC Fund, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Village Global's Venture Stories (LS 46 · TOP 1% what is this?)
Episode: The State of Fintech in 2022 with Sheel Mohnot
Pub date: 2022-02-15
Sheel Mohnot (@pitdesi), founder of Better Tomorrow Ventures, joins Lucas Bagno (@lucasbagnocv) of Village Global to discuss:
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Sheel’s reflections on raising a fund and the opportunities he sees in fintech today.
-
The importance of ownership in seed investing.
-
How the fintech landscape has evolved over the last year.
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Why non-fintech companies are integrating fintech into their business.
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Whether banks can be disrupted and the unique regulatory environment that influences the financial market.
-
The advice he gives to founders on which investors to work with.
-
Opportunities in fintech globally.
Thanks for listening — if you like what you hear, please review us on your favorite podcast platform.
Check us out on the web at www.villageglobal.vc or get in touch with us on Twitter @villageglobal.
Want to get updates from us? Subscribe to get a peek inside the Village. We’ll send you reading recommendations, exclusive event invites, and commentary on the latest happenings in Silicon Valley. www.villageglobal.vc/signup
The podcast and artwork embedded on this page are from Village Global, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Swyx Mixtape
Episode: The Racecar Growth Framework [Lenny Rachitsky]
Pub date: 2022-02-03
Listen to the Creator Lab: https://www.listennotes.com/podcasts/creator-lab/lenny-rachitsky-lennys-bUBLKO_NIG4/
Growth Loops are the New Funnels: https://www.reforge.com/blog/growth-loops
The Racecar Growth Gramework: https://www.reforge.com/blog/racecar-growth-framework
Transcript
today for the sake of focus we're going to start with this growth framework called the race car growth framework which i thought was really incredible um so let's just kick it off man from what i could see from the outside from reading your work there's four components to this product growth which is
- the first one was the growth engine
- the second one is called turbo boosts
- third is lubricants
- the fourth is fuel
we're going to explain what all of those are in a second um but before we even go into the details could you just share like a bit of context around what who who is this for and who is it not for
yeah because absolutely this isn't for every single company in the world and um you know you've probably applied this in many different contexts but yeah curious what what stands out to you there so maybe zooming out even further i spent a lot of my time with this newsletter looking into growth stories and how growth strategies and essentially understanding how all the most successful companies grew and i've spent a lot of time on the early days of how they got their first say a thousand users and then i've also spent a lot of time on the longer term down the road strategy of how do they grow long-term what can what needs to work for a company to continue growing and so now there's kind of these two ends of the spectrum that are coming into focus for me of how do i get your early users and then how to long term grow your business so this race car framework is focused on the long term how do you grow eventually and long term and then we can even talk about how to get your first users and then i'm slowly filling in these puzzle pieces through more and more of my research of how companies go from zero to say a million and it turns out it's it's not as mysterious as people think there's not actually that many options it's more of a question of which of the options do you choose and then how do you become the best at that or do something remarkable within that option so so we could start at the end and then we can come back to the beginning days
so the end is this race car framework and i this is uh based on work that i did with a buddy of mine dan haukenmeyer so this post is something we both put together and what we found is there's a really cool mental model of thinking about how businesses grow long-term and it turns out you can think of your company like a race car which includes these four components that you're talking about there's the engine there's uh turbo boosts lubricants and then fuel and the engine is the most important part because that's what drives your business and
it turns out there's essentially four engines you can choose from as a company and uh and these engines are self-sustaining loops that keep your business growing and there's kind of like a fuel that goes into it and then the output is growth so should we dive into those yeah yeah so
i love that so i'll repeat kind of what i heard and understood and then you can clarify if i heard it wrong so the growth the growth engine is the most important part is self-sustaining the turbo boosts from what i understand are more like these one-off events or big hero moments maybe events a super bowl ad and they can maybe make a big splash but they're not self-sustainable the way a growth engine is and we'll go into the details of what that means and then the lubricants are more about running efficiently exactly things that make everything run better exactly and then the fuel is what's actually needed to make the car run so the input that's needed that's that's awesome so maybe yeah we can start off by going into the growth engine itself so i've heard you talk about loops and engines and i have a visual of this thing going around and people talk about flywheels i found like a lot of the jargon sometimes is like overly used but in this case i think it actually is really helpful to see it visually um because i think if you think of like a growth engine in a traditional business you think of a marketing funnel which is not exactly the same thing but a lot of the time when you're getting new users and getting them to convert and become paying users and then spread and share something like in business school you might learn it like a funnel and i think what i like about this is it's not necessarily like a linear thing that just goes from top to bottom it's more something that keeps feeding itself is that accurate before we move on from there yeah that's exactly accurate and there's this uh group called reforge who was one of the first uh i guess um groups that kind of figured out that this is the thing that matters more than funnels so they kind of have this famous blog post that loops for the new funnels and in reality they're both important like funnels are a part of these loops and so they both uh are worth thinking about but when you're starting out it's a lot more important to think about the flywheel slash engine slash loop they're all kind of the same idea and it's just your point there are these things that kind of feed themselves and keep going yeah and so what we're going to do is we're going to be talking about a lot of theory but what we're going to try to do is layer on examples wherever possible so if we're talking about a funnel or a sorry not a funnel a a uh flywheel is there one that we could just explain to people yeah like airbnb's flywheel or a company that people know about
ENGINES
let me share the four engines first and then we can talk about yeah that sounds great leverage each one
so essentially the way to think about this is if you think about like all the things you can do to grow your business there's like pr there's events there's paid ads there's seo there's this like whole collection of options and what this concept tries to help you with is which ones should you focus on deeply and which are just kind of these one-off things or just micro-optimizations
so the engines are the there's only four ways your business is really going to grow long term and the four ways are
-
performance marketing and the way that loop works is you spend money to run an ad the ad drives customers the customers generate revenue and you can feed that into more ads so that's a pretty straightforward one
-
the next is virality which is what we all know and love when we think about viral growth essentially users draw new users and those users join and invite their friends and their friends join and it goes on and on and they'll give examples but maybe an example that one is like yeah snapchat or telegram whatsapp facebook things where you're kind of encouraged in by your friends and we can talk about like how to know which of these your business is most naturally suited for because it's not like choose any it's usually based on the type of product that you have one is going to fit best and there's actually this kind of growing meme of first time founders focus on product and second time founders focus on distribution and a lot of that is that's actually very true i find and knowing these engines is how you think about that is almost working backwards from i have a unique way of being really good at one of these things what product can i build to take advantage of that that's that's in it that's a mental model that i find useful so anyway let me go through the four and then we can talk in more depth
-
content is the third one and that includes seo as one and usually the the most popular one but also includes like viral videos and content people share with each other so an example of a company that grew primarily through content is like glassdoor or uh trulia or quora and reddit where you just think of like looking for a thing and then shows up in your google results and then you go find it yeah
-
and then the fourth is just sales where you hire a salesperson they generate they find customers customers generator revenue hire more sales people that's usually the most common engine for enterprise businesses b2b businesses like a force i think it's probably a good example of yeah salesforce is a classic example the king that's right
yes all right that's awesome and i i know that you've mentioned those are the four the four main ones in the blog post you also mentioned that there's some others people think about but like partnerships physical space um so like physical space could be retail stores uh like a showrooming thing that casper done for example came to mind for me um shelf placement casper is a good example where they they're almost exclusively performance marketing driven and then they started expanding into other engines like retail where like the loop of a physical location in physical placement is you pay money for a location either on a shelf or a on a block people walk by you know for those people buy stuff generate revenue and keep the business going sometimes it's just like a brand building thing where it's not meant to make money but that's i think directly yeah yeah and then the other one was partnerships which we'll just touch on briefly because even though it's not part of those those four components i think increasingly we're seeing really interesting examples of this
even in the creator world um that both of us semi-playing well you're definitely playing and i'm partially playing in now you're definitely playing it yeah yeah i'm definitely kind of in there nowadays um but yeah so i think it's an interesting one as well because a lot of people don't always think of this and maybe it's not the primary way to start but for some people it can be a huge growth driver i mean even at google when i was there and that's a really obviously huge established business channel sales was uh channel partners were a huge uh driver for us um even on a smaller scale affiliates for other companies i've worked with as well and even on a smaller scale if you're an independent creator i would think about partnerships too because um you know the way i'd see it like i did something with the hustle newsletter that wasn't like i didn't pay for anything but we both like i brought something to the table and they wanted to feature it so there was a way for me to be featured in their their newsletter where there's distribution so um i think it's probably worth mentioning those two as well but for the sake of this conversation we'll we'll start with those four
WRINKLES
an important wrinkle to think about when we talk about this stuff uh the kind of the core idea of an engine is that it continues to grow your business and sometimes partnerships do that where they continue to draw traffic sometimes it's kind of like a one-off event and it doesn't mean you shouldn't do that like other those other parts of the car are great it's just they're not going to continue driving growth and that's the core is you need to find a way that continues that growth otherwise you're just going to flatten out it again um
another really important point of this concept is it's not that you just pick one and you'll win it's to actually do well you have to become one of the best in the world at that engine in order to have a chance and the way i think about it is you kind of find the cross-section of your market and one of these engines where you could be in a huge company if you're the best at say content for travel and another business can be amazing at performance marketing for travel so as an example airbnb is incredibly successful as a virality driven travel company but booking.com very large successful business is also really big but they become really good at performance marketing and and then there's like tripadvisor which became really big through content so the way to think about this is you just need to find a way to become the best at your market in one of these engines to have a really good chance and the main thing the main issue companies have is they don't realize how they have to become incredibly good at one of these things they think they can just do all these things in some small way and have a chance and it turns out that's really really hard
The podcast and artwork embedded on this page are from Swyx, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Village Global's Venture Stories (LS 46 · TOP 1% what is this?)
Episode: SaaS Growth, Product Management, Retention, Flywheels: Behind the Paywall with Lenny Rachitsky
Pub date: 2020-11-05
Lenny Rachitsky (@lennysan), creator of Lenny’s Newsletter and a former PM at Airbnb, joins Erik on this episode to discuss:
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The best bottom-up SaaS metrics to track and why these companies are so sought-after right now.
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What separates great bottom-up strategies from the rest.
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How to think about when to hire your first PM.
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How to increase retention.
-
How to think about strategy.
-
How to increase your conversion rate.
-
How to evaluate marketplaces.
-
How to create effective flywheels.
Thanks for listening — if you like what you hear, please review us on your favorite podcast platform.
Check us out on the web at www.villageglobal.vc or get in touch with us on Twitter @villageglobal.
Want to get updates from us? Subscribe to get a peek inside the Village. We’ll send you reading recommendations, exclusive event invites, and commentary on the latest happenings in Silicon Valley. www.villageglobal.vc/signup
The podcast and artwork embedded on this page are from Village Global, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: La Story (LS 57 · TOP 0.5% what is this?)
Episode: Molière, l’entrepreneur du roi
Pub date: 2022-02-22
Episode 1 : En janvier 1622 naissait Jean-Baptiste Poquelin. On sait tout de Molière, l’homme de théâtre, mort sur scène (ou presque). Mais que sait-on de Molière, le startuppeur ? Dans « La Story », le podcast d’actualité des « Echos », Pierrick Fay et ses invités reviennent – en deux épisodes - sur le parcours économique d’un saltimbanque du Roi.
La Story est un podcast des « Echos » présenté par Pierrick Fay. Cet épisode a été enregistré en février 2022. Rédaction en chef : Clémence Lemaistre. Invités : Jules Grandin (chef du service Infographie aux « Echos ») et Christophe Schuwey (coauteurs avec Clara Dealberto de « L’Atlas Molière » aux éditions Les Arènes). Réalisation : Willy Ganne. Musique : Théo Boulenger. Identité graphique : Upian. Photo : Alamy/ABACA. Sons : Théâtre du Soleil, Molière (1978), The Office, Chantal Goya « Molière », « L’Avare » (Comédie-Française, 2009).
Voir Acast.com/privacy pour les informations sur la vie privée et l'opt-out.
The podcast and artwork embedded on this page are from Les Echos, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: La Story (LS 57 · TOP 0.5% what is this?)
Episode: La Russie de Poutine : portrait d’une « démocrature »
Pub date: 2022-03-05
Alors que la violence de l’invasion de l’Ukraine décidée par Vladimir Poutine sidère le monde, en juillet 2019, dans « La Story », le podcast d’actualité des « Echos », Pierrick Fay avait recueilli le témoignage d’un auteur et d’une chercheuse sur le régime russe qui sous le vernis démocratique mène des politiques autoritaires et réprime toute opposition.
La Story est un podcast des « Echos » présenté par Pierrick Fay. Cet épisode a été enregistré en juillet 2019 dans les locaux des « Echos » (Paris, 15e). Rédaction en chef : Clémence Lemaistre. Invités : Dmitry Glukhovsky (écrivain, auteur de « Texto » aux éditions L’Atalante) et Tatiana Jean (chercheuse à l’IFRI). Réalisation : Nicolas Jean et Mathias Arrignon. Chargée de production et d’édition : Michèle Warnet. Musique : Théo Boulenger. Identité graphique : Upian. Photo : Russian Pool/via REUTERS TV. Sons : TV5 Monde, Euronews, France 3.
Voir Acast.com/privacy pour les informations sur la vie privée et l'opt-out.
The podcast and artwork embedded on this page are from Les Echos, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Everything Everywhere Daily (LS 49 · TOP 1% what is this?)
Episode: The Red Cross
Pub date: 2021-05-07
Historically, in the aftermath of a battle, there was nothing formal in place to take care of injured or captured combatants. There was nothing formally or informally that dictated how such people should be treated.
One man in the 19th century, having seen the horror of war, decided to do something about it. It led to the creation of a movement that would go on to save millions of lives.
Learn more about the International Red Cross and Red Crescent on this episode of Everything Everywhere Daily.
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The podcast and artwork embedded on this page are from Gary Arndt, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Washington Post Live (LS 43 · TOP 1.5% what is this?)
Episode: International Committee of the Red Cross President Peter Maurer
Pub date: 2021-06-08
The President of the International Committee of the Red Cross Peter Maurer joins The Post to discuss the humanitarian crisis caused by covid-19 and what can be done to tackle global inequities.
The podcast and artwork embedded on this page are from The Washington Post, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Starting Greatness (LS 54 · TOP 0.5% what is this?)
Episode: David Sacks: Legendary Startup Product Expert
Pub date: 2021-04-19
David Sacks, known as one of the best startup product strategists and operators of the last 20 years, discusses key lessons learned from his tenure in the PayPal Mafia, where he was head of product, along with key takeaways as founding CEO of Yammer and what he learned from working directly with industry greats like Peter Thiel and Elon Musk.
The podcast and artwork embedded on this page are from Floodgate, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Starting Greatness (LS 54 · TOP 0.5% what is this?)
Episode: Matthew Prince and Michelle Zatlyn of Cloudflare: How to build a better Company as well as a better Internet
Pub date: 2021-06-28
It takes a lot of effort to build a breakthrough product. It's perhaps even rarer to design a company that endures. In this interview, Mike Maples Jr of Floodgate interviews Matthew Prince and Michelle Zatlyn of Cloudflare to highlight what we can learn about how to get a startup's foundation right, along with recruiting, hiring, and company design.
The podcast and artwork embedded on this page are from Floodgate, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Inside Intercom (LS 46 · TOP 1% what is this?)
Episode: Andrew Chen on how tech’s giants drive growth with network effects
Pub date: 2021-12-16
Software may be eating the world, but building and scaling products is still quite a challenge. So how do you get past the awkward “cold start problem” of zero users and build the networks that make your product thrive? Andrew Chen, partner at Andreessen Horowitz and author of The Cold Start Problem, joins Intercom Co-Founder and CSO Des Traynor to discuss.
You can also listen to Andrew's previous outing on Inside Intercom here: https://www.intercom.com/blog/podcasts/andrew-chen-on-growth/
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The podcast and artwork embedded on this page are from Intercom, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Tim Ferriss Show (LS 81 · TOP 0.01% what is this?)
Episode: #550: Andrew Chen — Metaverse, Metrics, and Meerkats
Pub date: 2021-11-30
Andrew Chen — Metaverse, Metrics, and Meerkats | Brought to you by 80,000 Hours free career advice for high impact and doing good in the world, Wealthfront automated investing, and Helix Sleep premium mattresses. More on all three below.
Andrew Chen (@andrewchen) is a general partner at Andreessen Horowitz, where he invests in consumer technology, including social, marketplace, entertainment, and gaming experiences. Today, Andrew serves on the boards of All Day Kitchens, Clubhouse, Envoy, Hipcamp, Maven, Reforge, Sandbox VR, Singularity 6, Sleeper, Snackpass, and Substack.
Andrew is a prolific writer and leading voice on mobile, metrics, and user growth. For the past decade, he’s covered the topic on AndrewChen.com. He is the author of The Cold Start Problem, a book exploring how new startups are launched. He is also a board member and instructor at Reforge, which offers selective, growth-focused programs for experienced professionals in marketing, product, data, and engineering.
Please enjoy!
This episode is brought to you by 80,000 Hours! You have roughly 80,000 hours in your career. That’s 40 hours a week, 50 weeks a year for 40 years. They add up and are one of your biggest opportunities, if not the biggest opportunity, to make a positive impact on the world. Some of the best strategies, best research, and best tactical advice I’ve seen and heard come from 80,000 Hours, a nonprofit co-founded by Will MacAskill, an Oxford philosopher and a popular past guest on this podcast.
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Podcast: StrictlyVC Download (LS 32 · TOP 5% what is this?)
Episode: Andrew Chen on Cold Starts, Substack and What’s Next for Clubhouse
Pub date: 2021-12-11
Connie & Alex pick their favorite tech story of the week and then talk to Andrew Chen, a General Partner at Andreessen Horowitz, about his new book, The Cold Start Problem, which tries to unlock how some of the most prominent startups in Silicon Valley have used network effects to scale.
Music:
1. "Inspired" by Kevin MacLeod (https://incompetech.filmmusic.io/song/3918-inspired)
2. "Blippy Trance" by Kevin MacLeod (https://incompetech.filmmusic.io/song/5759-blippy-trance)
3. "Dream Catcher" by Kevin MacLeod (https://incompetech.filmmusic.io/song/4650-dream-catcher)
4. "Pamgaea" by Kevin MacLeod (https://incompetech.filmmusic.io/song/4193-pamgaea)
5. "EDM Detection Mode" by Kevin MacLeod (https://incompetech.filmmusic.io/song/3687-edm-detection-mode)
License: https://filmmusic.io/standard-license
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Podcast: a16z Live (LS 33 · TOP 5% what is this?)
Episode: Scaling Marketplace Startups with the CEOs of GOAT, Cameo, Faire, StyleSeat, Neighbor, Whatnot, & more
Pub date: 2021-04-02
The CEOs of GOAT, StyleSeat, Cameo, Faire, Whatnot, and Neighbor join a16z partners Andrew Chen, Connie Chan, D'Arcy Coolican, Jeff Jordan, Katie Baynes, and Anne Lee Skates to talk about the cold start problem, 2020 metrics, growth, and the tricky business of scaling a marketplace company.
See more marketplace analysis, including a ranking of the 100 largest marketplace startups & private companies, in the Marketplace 100.
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Podcast: Intelligence Squared (LS 60 · TOP 0.5% what is this?)
Episode: Business Weekly: Scaling up success
Pub date: 2021-12-06
Andrew Chen is a specialist in growing tech businesses and for his new book, The Cold Start Problem, he has spoken to the founders of companies such as LinkedIn, Zoom, Uber, Dropbox, Tinder and Airbnb, to learn how startups can maximise their potential. Andrew has spent a career working with tech companies and tech investors, plus he's also a prolific writer with both a popular blog and newsletter. He joins economist and broadcaster Linda Yueh to discuss the new book and offer his insider's perspective on Silicon Valley success.
Learn more about your ad choices. Visit megaphone.fm/adchoices
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Podcast: Noah Kagan Presents (LS 61 · TOP 0.5% what is this?)
Episode: Solving the Cold Start Problem (w/ Andrew Chen)
Pub date: 2021-12-02
Andrew Chen is a general partner at a16z where he invests in consumer startups and marketplaces.
He is also one of my oldest best friends and played a MAJOR role in the success of my company Appsumo.
His new book, The Cold Start Problem, is dropping on December 7th.
In the book, he shares the core secrets of starting and scaling tech startups after interviewing CEOs from companies like Zoom, Twitch, and Slack
Show notes: https://okdork.com/podcast/227
Use this link to get 10% off at checkout on AppSumo: https://appsumo.com/?coupon=noah10&code=noah10
The podcast and artwork embedded on this page are from Noah Kagan, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Village Global's Venture Stories (LS 46 · TOP 1% what is this?)
Episode: How To Solve The Cold Start Problem with Andrew Chen
Pub date: 2021-12-21
Andrew Chen (@andrewchen), partner at Andreessen Horowitz and author of The Cold Start Problem, joins Erik Torenberg and Lucas Bagno on this episode to discuss:
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Why the secret to why Bay Area tech companies have been so successful is their ability to connect people in different ways.
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Stories of how different tech companies solved the cold start problem in the earliest days. For example, Tinder threw a party at USC and required people to install the app to get in.
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Why colleges are such a fertile environment for consumer tech.
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The promise of Web 3 and how it differs from previous eras of the internet.
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His requests for startups.
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Andrew's thoughts on the metaverse, the passion economy, gaming, and more.
Thanks for listening — if you like what you hear, please review us on your favorite podcast platform.
Check us out on the web at www.villageglobal.vc or get in touch with us on Twitter @villageglobal.
Want to get updates from us? Subscribe to get a peek inside the Village. We’ll send you reading recommendations, exclusive event invites, and commentary on the latest happenings in Silicon Valley. www.villageglobal.vc/signup
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Podcast: She Plays Games Podcast (LS 33 · TOP 5% what is this?)
Episode: Rosa Carbó-Mascarell (Lead Game Designer - Loveshark)
Pub date: 2022-01-31
Rosa is a Lead Game Designer at Loveshark having worked in the games industry for over 4 years. In her journey, Rosa’s been recognized for her achievements in games design as well as her work helping others to transition into games. In this episode we talk about her games design principles and how she managed to snag the amazing opportunity to work as an artist on the remake of Dear Esther with the Chinese Room.
This is episode 65 of She Plays Games.
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Podcast: Starting Greatness (LS 54 · TOP 0.5% what is this?)
Episode: Rahul Vohra of Superhuman: How to create business products people enjoy using
Pub date: 2021-09-21
Rahul Vohra of Superhuman has adopted some of the most cutting-edge approaches to making a business product people want to use, rather than have to use. In this episode, Mike Maples, Jr of FLOODGATE talks to Rahul about how any startup founder can apply the principles of game design to their products, and why this is becoming increasingly important in a world where users (rather than IT) increasingly decide which products win.
The podcast and artwork embedded on this page are from Floodgate, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Diary Of A CEO with Steven Bartlett
Episode: E106: Jimmy Carr: The Easiest Way To Live A Happier Life
Pub date: 2021-11-15
Jimmy Carr needs no introduction and is one of the biggest names in British comedy. Jimmy has hardly been off our screens for the last two decades. He has countless shows to his name, even performing in front of the queen. But what people may not know is that Jimmy has another side to him. When he was in mid-20s, Jimmy was in a boring office job, without much joy or direction in his life. But he set himself on a journey of self-improvement. Out of nowhere, he decided to quit and start again, the rest, as they say, is history. Today, Jimmy shares with us the actionable lessons in how he did it and how they are relevant to YOU. Jimmy is always looking for lessons to take out of his incredible story, and I think after listening to this, you’ll definitely see that behind Jimmy’s extraordinary story lies a very ordinary person committed to trying new things, working hard, and pursuing his passion.
Topics: * Your early years * Mental health and online connections * Fatherhood * Atheism * Leaving the corporate world for comedy * Find purpose and knowing you’re enough * Whats happiness? * Hard work * Branding * Tax avoidance - anxiety and depression * Losing your virginity at 26 * NLP - Neuro-linguistic programming * The last guests question
Jimmy’s Book: http://smarturl.it/BeforeAndLaughter
An in-depth audience Q&A from Jimmy about his book: https://www.momenthouse.com/jimmycarr
Jimmy: https://www.jimmycarr.com/ https://www.youtube.com/user/jimmycarrfans
https://twitter.com/jimmycarr
Watch the episodes on Youtube: https://www.youtube.com/StevenBartlettYT?sub_confirmation=1
THE DIARY OF A CEO LIVE TICKETS ON SALE NOW 🚀- https://g2ul0.app.link/diaryofaceolive
Sponsors:
Huel - https://uk.huel.com/
Myenergi - https://bit.ly/3oeWGnl
Update Description
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Podcast: Invest Like the Best with Patrick O'Shaughnessy (LS 67 · TOP 0.05% what is this?)
Episode: Roelof Botha - Sequoia’s Crucible Moment - [Invest Like the Best, EP. 250]
Pub date: 2021-11-05
My guest today is Roelof Botha, a partner at one of the world’s oldest and most successful venture firms, Sequoia Capital. A few days ago before I sat down with Roelof, he announced Sequoia’s boldest innovation since the firm was founded by Don Valentine in the early 1970s. Going forward, the firm will break from the traditional VC mould of fund cycles and instead restructure around a single, open-ended, permanent structure named The Sequoia Fund.
In our conversation, we first discuss the details of this change from all different angles and then dive into Roelof’s career. We talk about what’s changed over the past twenty years, his days at PayPal, what legendary investors he’s worked with have had in common, and what he’s learned from being involved in businesses like Square, YouTube, and Unity.
Please enjoy this great conversation with Roelof Botha.
For the full show notes, transcript, and links to the best content to learn more, check out the episode page here.
This episode is brought to you by Canalyst. Canalyst is the leading destination for public company data and analysis. If you've been scrambling to keep up with the deluge of IPOs and SPACs these days, Canalyst has models on Robinhood, Marqeta, Grab, and everything in between. Learn more and try Canalyst for yourself at canalyst.com/patrick.
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Invest Like the Best is a property of Colossus, LLC. For more episodes of Invest Like the Best, visit joincolossus.com/episodes.
Past guests include Tobi Lutke, Kevin Systrom, Mike Krieger, John Collison, Kat Cole, Marc Andreessen, Matthew Ball, Bill Gurley, Anu Hariharan, Ben Thompson, and many more.
Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here.
Follow us on Twitter: @patrick_oshag | @JoinColossus
Show Notes
[00:02:53] - [First question] - What led Sequoia to change their structure
[00:05:53] - Parallels between their approach and the problem Square set out to solve
[00:07:36] - The mechanics of the new fund and how it’ll affect their clients
[00:10:42] - How much discretion LPs will have when choosing to participate in sub-funds
[00:13:11] - What the future looks like and how public securities could be a dominant force
[00:15:02] - Benefits and value-unlocks that the new fund offers that weren’t available before
[00:16:55] - Comparing their structure to the current crossover funds we see emerging
[00:18:21] - What alignment looks like in this new structure for LPs
[00:22:02] - Cost of capital, interest rates, and their impacts on rates of return
[00:25:39] - Changes in the industry and founders that he’s noticed
[00:28:56] - What matters to him when meeting with young companies for the first time
[00:31:47] - The importance placed on value creation over value capture in the early days
[00:33:09] - Things that would dissuade him from partnering with a company
[00:34:18] - What the growth and leadership at Square has taught him over the years
[00:35:44] - Things he’s most excited about for payments looking forward
[00:37:34] - How often a company lowering friction with technology appeals to him
[00:38:38] - Thoughts on Unity and its role in the growing trend of the metaverse
[00:40:28] - Why the open and decentralized nature of the future is so beneficial
[00:42:05] - Lessons learned about content and internet from working with YouTube
[00:44:08] - The landscape of developers today and MongoDB's role in it
[00:45:24] - Commonalities between companies who have a successful second act
[00:48:16] - Good board members support founders during their pivotal moments
[00:49:26] - Learning to identify and hunt for crucible moments
[00:50:50] - Curiosity is the key ingredient of a great investor
[00:52:05] - What makes for a fantastic investment memo
[00:53:20] - The most memorable investment memo he’s ever read
[00:54:07] - Honing his leadership as his role has changed at Sequoia these past years
[00:55:51] - Thoughts on Sequoia’s brand and the scope of his ambition
[00:58:05] - What he’s most curious about in the world today
[00:58:46] - What technology wants most from people today
[01:01:13] - The difference between an accountant and an actuary's mindset and when each one is appropriate to inhabit
[01:02:38] - Differences between talent and genius
[01:04:12] - Closing principals about business building he finds important to consider
[01:06:17] - The kindest thing anyone has ever done for him
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Podcast: The Economist Podcasts (LS 70 · TOP 0.05% what is this?)
Episode: Insecurities in securities: why markets are sliding
Pub date: 2022-01-28
Huge swings and downward trends: markets are forward-looking, and it is clear they do not see much to look forward to in 2022. Warnings about infectious bugs resistant to antibiotics have long been around; to see the effects just look to South Asia. And our data journalists reveal another benefit of widespread veganism: huge tracts of habitable land. For full access to print, digital and audio editions of The Economist, subscribe here www.economist.com/intelligenceoffer
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Podcast: FT Weekend (LS 53 · TOP 0.5% what is this?)
Episode: A trip to our secret book vault. Plus: the best books of 2021
Pub date: 2021-12-11
This weekend, we’re going behind the scenes of the FT’s legendary Books of the Year roundup. Literary editor Frederick Studemann and deputy books editor Laura Battle take us into a secret room in the basement of the FT, where all the books sent in for review are kept behind lock and key. You’ll leave this episode with a lot on your reading list, including recommendations from editor Roula Khalaf, FT weekend editor Alec Russell, chief economics commentator Martin Wolf and more.
If you want a $1 trial or 50% off a digital subscription, go to http://ft.com/weekendpodcast
Want to say hi? Email us at ftweekendpodcast@ft.com. We’re on Twitter @ftweekendpod, and Lilah is on Instagram and Twitter @lilahrap.
We want your cultural predictions, wishes, or questions for 2022! Share them with Lilah and FT Magazine editor Matt Vella by Sunday, December 12. Open your phone’s voice memo app, get close to the mic and say your name, location and your thoughts, then email it to ftweekendpodcast@ft.com. You can write to us, too. But you’ll sound great on tape, we promise.
Links and mentions from the episode:
–Roula Khalaf recommends Empire of Pain by Patrick Radden Keefe
–Pilita Clark recommends The Hydrogen Revolution by Marco Alvira and How to Blow Up a Pipeline by Andreas Malm. Her whole climate list: https://on.ft.com/3DFcYLr
–Alec Russell recommends Sentient by Jackie Higgins and Free by Lea Ypi
–Edwin Heathcote recommends Public House: A Cultural and Social History of the London Pub. His whole architecture and design list: https://www.ft.com/content/37545da9-7142-408b-a0bb-e458079ebd53
–One of Edwin’s favorite books of the past few years is Sandfuture by Justin Beal. Here’s his review (free to read): https://www.ft.com/content/91a35024-4e41-4325-81ca-2373321ae4ff
–Fred Studemann recommends Notes from Deep Time by Helen Gordon, The Passenger by Ulrich Boschwitz and Just the Plague by Lyudmila Ulitskaya
–Laura Battle recommends Crossroads by Jonathan Franzen, Small Things Like These by Claire Keegan, and the audiobook of Harlem Shuffle by Colson Whitehead. Her whole fiction list: https://www.ft.com/content/7a881a03-2462-459e-930c-f526e4e54449
–Martin Wolf’s economics list: https://www.ft.com/content/25ca2b59-8047-4f9b-bf99-e7f7c15d8d51
–Explore the whole Books of the Year package: https://www.ft.com/booksof2021
Original music by Metaphor Music. Mixing and sound design is by Breen Turner.
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Podcast: FT Weekend (LS 53 · TOP 0.5% what is this?)
Episode: Predictions for 2022: Britney, flip phones and the metaverse
Pub date: 2021-12-18
It's our final episode of 2021, and we are marking the end of one unpredictable year and the start of another. What do you think will happen in 2022? Matt Vella, FT Weekend Magazine editor, joins Lilah to discuss listeners’ cultural predictions. A lot of them had an air of nostalgia: Will Britney make a documentary about her life? Will flip phones make a comeback? Then, our pop critic Ludovic Hunter-Tilney teaches us the art of the perfect holiday playlist.
If you want a great offer on an FT subscription specifically for listeners (and not a bad Christmas gift!) use this link: http://ft.com/weekendpodcast
Thank you for listening to the show this year. We’ll be back on January 8! What culture will you be reading, watching, listening to during the holidays? Say hi and let us know! Email us at ftweekendpodcast@ft.com. We’re on Twitter @ftweekendpod, and Lilah is on Instagram and Twitter @lilahrap.
Links and mentions from the episode:
–Two books by the late, great bell hooks: The Will to Change, and All About Love
–The FT’s Christmas roundup-(the complete guide to eating, drinking, giving and self-caring your way to a very merry holiday this year): https://www.ft.com/content/3d6c80dd-dbc3-4e0e-939f-b917aa401dfc
Here are Ludo’s reviews of his favourite albums of the year (all free to read):
–The Weather Station: Ignorance https://www.ft.com/content/57aef341-cce1-4816-9939-3c71a3fe5edf
–Nation of Language: A Way Forward https://www.ft.com/content/ed7f3da8-d033-4ca0-90c7-1b7e4b425a19
–Pharoah Sanders, Floating Points and the London Symphony Orchestra: Promises https://www.ft.com/content/c00c0655-013d-4d3b-8c7c-bf7dea47c1fc
Thank you to everyone who shared your notes, including: Andrei Berghianu from Romania, Olga Sihmane from Stockholm, Ashley Harris from Brooklyn, Lily Bland, Roger Ralph, Manish Prayaga, Helen Beedham, April from Los Angeles and so many more.
Original music by Metaphor Music. Mixing and sound design is by Breen Turner.
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The podcast and artwork embedded on this page are from Financial Times, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The NFX Podcast (LS 42 · TOP 1.5% what is this?)
Episode: The Founders' List: 7 Principles of Game Design from Design Gym
Pub date: 2021-06-29
This is The Founders' List - audio versions of essays from technology’s most important leaders, selected by the founder community.
Life is, by some measures, an infinite game, where the rules constantly change and winners turn into losers, and vice versa. This article is titled, “7 Principles of Game Design” and was written by The Design Gym - an NYC-based agency that has worked with top companies like Instagram, Netflix, Etsy, and many more.
Setting up games, with clear goals and constraints helps focus our energies and efforts and can improve and clarify outcomes and motivate us to move forward to the next clearly defined challenge and reward cycle.
Read the essay here - https://www.thedesigngym.com/seven-principles-of-game-design-and-five-innovation-games-that-work/
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Podcast: The NFX Podcast (LS 42 · TOP 1.5% what is this?)
Episode: "A Tragedy of Measurement" with Rob Goldman & James Currier
Pub date: 2021-07-09
Rob Goldman joined the growth team at Facebook in 2012 - the same year that News Feed ads launched. He became Facebook's VP of Ads and was responsible for more than 99% of their revenue until he left seven years later in 2019.
Today, Rob and NFX Partner James Currier focus on the tragedy of measurement inside these complex dynamic systems like Facebook, Twitter, or YouTube. Very few people in the world understand how to pick the measurements to be measured, measure them accurately, then display them accurately to the people in the organization so that they can run the business based on them. And then further, to communicate them to the outside world, in a way that they can understand and participate in the network that they are a part of.
Read the NFX article here - https://www.nfx.com/post/rob-goldman/
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Podcast: Acquired (LS 63 · TOP 0.1% what is this?)
Episode: Twitter (with Dick Costolo)
Pub date: 2020-10-28
A week before the 2020 US Presidential election, former Twitter CEO Dick Costolo joins us to tell the story of a company that has impacted all of our lives (political and otherwise) like none other. While it's easy to forget now, a very viable alternate history exists where it's Twitter, not Facebook, who owns Instagram, and Vine, not TikTok, that's the global platform for mobile video. We dive into it all on this episode — and of course while we had Dick, we also had to discuss his controversial recent deleted tweet.
If you want more more Acquired and the tools + resources to become the best founder, operator or investor you can be, join our LP Program for access to our LP Show, the LP community on Slack and Zoom, and our live Book Club discussions with top authors. Join here at: https://acquired.fm/lp/
New! We're codifying our own Playbook notes and takeaways from each episode, and posting them here in the show notes and on our website. You can read them below or at: https://www.acquired.fm/episodes/twitter-with-dick-costolo
Sponsors:
- Thanks to Tiny for being our presenting sponsor for all of Acquired Season 7. Tiny is building the "Berkshire Hathaway of the internet" — if you own a wonderful internet business that you want to sell, or know someone who does, you should get in touch with them. Unlike traditional buyers, they commit to quick, simple diligence, a 30-day or less process, and will leave your business to do its thing for the long term. You can learn more about Tiny here: http://tinycapital.com
- Thank you as well to Bamboo Growth and to Perkins Coie. You can learn more about them at:
- https://growwithbamboo.com
- https://www.perkinscoie.com/
Playbook Themes from this Episode:
- Sometimes early advantages matter. A lot. In a network economy industry (like social media), it's almost impossible to chase down someone with an established lead. The only way you can hope to compete is by changing the game. And even that is a long shot.
- Once Facebook — and then Instagram within Facebook — had established a meaningful active user lead over Twitter, there was no "down the middle" play Twitter could run to catch up. Twitter recognized this and attempted all sorts of orthogonal strategies: video (Vine), live (Periscope), music (Twitter Music), syndication (Moments), exclusive content (the NFL deal). In each case either Facebook was able to copy and co-opt the innovation, or the attempt simply failed.
- Sometimes reach matters. A lot. If you're operating in a network economy, your service MUST deliver a first-class experience on every platform that matters.
- Vine launched on iOS and immediately went to #1 in the App Store. But they didn't get a good Android experience out fast enough, which fractured the social graph that users could share across. Instagram was able to respond aggressively with a first-class video experience across both iOS and Android before Vine could stop the bleeding — and the rest is history.
- Network Resiliency. Some network graphs are more inherently defensible than others. How easy it is to "rehydrate" your network somewhere else should drive how closely you guard it.
- Facebook, LinkedIn and WhatsApp all have relatively low defensible networks — if you were to exfiltrate their graphs, you could recreate their value quickly. This is why all of those companies / products significantly restrict connection exporting, and also why they were able to bootstrap quickly in the early days by importing users' address books.
- By contrast, the Twitter graph is about interest, not social connections. Even if you exfiltrated all its connections, it'd be very difficult to recreate Twitter (people have tried). This dynamic made it more difficult for Twitter to scale quickly, but also has made it more resilient over time. The core Twitter product is just as robust — if not more — today than it was in 2010... the same can't be said for the blue Facebook product, which has bled out to Instagram, WhatsApp, TikTok, Snap, iMessage, etc.
- Balancing forest fires and forestry management. As a leader of a rapidly growing organization, you face two types of challenges: "forest fires" (this crazy thing just happened and we need to respond), and "forestry management" (this set of crazy things will keep happening until we figure out a solution that scales). You need a different mental state to approach each, and balancing between the two is incredibly difficult when you're see-sawing back and forth every day.
Links:
- Ashish Goel at Stanford: https://web.stanford.edu/~ashishg/twitter.html
- Dick and Adam's new firm, 01 Advisors: https://01a.com/
Carve Outs:
- Vote! https://www.vote.org
The podcast and artwork embedded on this page are from Ben Gilbert and David Rosenthal, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Acquired (LS 63 · TOP 0.1% what is this?)
Episode: 7 Powers with Hamilton Helmer
Pub date: 2021-12-08
You've heard us talk about him every episode for the past two years... but until now most of you have never actually heard us talk to him! To celebrate the LP Show going public, we've remastered our first, canonical interview with Hamilton Helmer, originally released as an LP episode in March 2020. Hamilton’s 7 Powers framework gives a deep, academic investigation to the question, “what creates an enduring company?” This episode is an absolute must-listen for anyone working or investing in tech (and beyond), and we're so excited to finally make it available to everyone.
And if you want more episodes like this, we have good news... all back catalog LP Show episodes are now free and available to anyone! You can follow our new public LP Show feed here in the podcast player of your choice. It's already chock-full of 60+ great episodes like our VC Fundamentals series, interviews with founders of top early-stage startups, and master classes on pricing, marketplaces, SaaS investing and many more topics. Happy listening and happy holidays to everyone!
Sponsors:
- 7 Powers on Amazon
Sponsors:
- Thanks to SoftBank Latin America for being our presenting sponsor for this special episode. If you are an entrepreneur, employee, other investor or simply someone who's interested in learning about the best young companies in LatAm right now, get in touch with them at: https://bit.ly/acquiredsoftbanklatam , and tell them that Ben and David sent you!
- You can find career opportunities at the portfolio companies here: https://www.latinamericafund.com/careers
- Thank you as well to Modern Treasury and to Fundrise. You can learn more about them at:
- https://bit.ly/acquiredmoderntreasury (and you can find our reverse interview with them at https://www.moderntreasury.com/acquired )
- https://bit.ly/acquiredfundrise
Note: Acquired hosts and guests may hold assets discussed in this episode. This podcast is not investment advice, and is intended for informational and entertainment purposes only. You should do your own research and make your own independent decisions when considering any financial transactions.
The podcast and artwork embedded on this page are from Ben Gilbert and David Rosenthal, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Psychology Podcast (LS 66 · TOP 0.05% what is this?)
Episode: Tim Grover || The Victory Mentality
Pub date: 2021-05-22
Today it’s great to have Tim Grover on the podcast. Tim is the CEO of Attack Athletics Inc. which he founded in 1989 and author of the international bestseller Relentless: From Good to Great to Unstoppable. World renowned for his work with Michael Jordan, Kobe Bryant, Dwayne Wade, and thousands of athletes and business professionals. He appears around the world as a keynote speaker and consultant to business leaders, athletes, and lead achievers in every field. His latest book is called Winning: The Unforgivable Race to Greatness.
Topics
[00:01:58] Tim’s childhood and upbringing
[00:09:08] Choosing to become a professional trainer
[00:11:50] From "fat kid" to competitive basketball player
[00:16:26] "The most successful are the most coachable"
[00:19:30] Victim Mentality vs. Victory Mentality
[00:22:51] The early days of Tim’s career
[00:25:46] Meeting and working with Michael Jordan
[00:30:55] Michael Jordan’s feedback about Tim
[00:33:54] Being a part of the Chicago Bulls Dynasty
[00:41:50] Meeting Kobe Bryant
[00:45:42] The phone call just before Kobe’s passing
[00:50:39] "The unforgiveable race to winning"
[00:54:48] The difference between competing and winning
[00:56:40] The importance of grit for winning
[01:01:36] How uplifting others is the ultimate win
[01:05:11] Creating personal definitions of winning
Support this podcast: https://anchor.fm/the-psychology-podcast/support
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Podcast: OPTIMIZE with Brian Johnson | More Wisdom in Less Time (LS 51 · TOP 0.5% what is this?)
Episode: PNTV: Relentless by Tim Grover (#379)
Pub date: 2021-09-17
Optimize: https://optimize.me/ (← Get Free Stuff + Free 2-Week Trial!) Optimize Coach: https://optimize.me/coach (← Join 2,000+ Optimizers from 70+ Countries!)
Here are 5 of my favorite Big Ideas from "Relentless" by Tim Grover. Hope you enjoy!
Tim Grover was Michael Jordan’s trainer and, basically, his mental toughness coach. Kobe Bryant’s as well. And Dwayne Wade’s. And... Well, a ton of other elite athletes. He’s one of the world’s top mental toughness coaches and this book is, as per the sub-title, a manual on how to go “From Good to Great to Unstoppable.” Big Ideas we explore include: relentless commitment (vs. "Meh, good enough"), Do. The. Work (eat frogs and dominate), Pressure (pressure, pressure! BRING IT ON!), the source of true confidence, greatness math (remember: effort counts twice; just ask Michael Jordan and Jerry Rice), and turning your dreams into reality (ready?).
The podcast and artwork embedded on this page are from Brian Johnson, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Acquired (LS 63 · TOP 0.1% what is this?)
Episode: Episode 18: Special—An Acquirer’s View into M&A with Taylor Barada, head of Corp Dev at Adobe
Pub date: 2016-08-22
Join the Acquired Limited Partner program! https://kimberlite.fm/acquired/ (works best on mobile) Ben & David are joined by special guest Taylor Barada, VP and Head of Corporate Development & Strategic Partnerships at Adobe, to discuss how large tech acquirers approach buying companies. This episode is full of great insights for startups & entrepreneurs who might find themselves navigating the M&A process, as well as anyone curious about the craft of dealmaking and the strategic approach of large acquirers. Topics covered include: * How conversations begin between startups and acquirers * The importance of building a relationship with acquirers over time and "investing in lines, not dots” (just like raising VC) * The often under-appreciated role of culture fit between acquirers and acquisition targets * How entrepreneurs should evaluate acquirers throughout the M&A process * Two examples of successful acquisitions Taylor completed at Yahoo in Citizen Sports and IntoNow * The M&A process at large technology acquirers, from initial conversations to LOI, due diligence and the definitive merger agreement * The relative roles of Corp Dev, business/product owners and executive sponsors in the M&A process * Common mistakes startups (and VC’s) often make in the M&A process * Different “categories” of M&A that acquirers think about, and the relative risks & opportunities of “core" acquisitions vs transformative new businesses * What percentage of deals Adobe looks at actually happen, and the importance of being willing to say no * M&A as a tool for strategy, and the different M&A cultures & approaches at different companies * Tech themes Taylor and Adobe think about as part of their M&A strategy * Evaluating the longterm success of deals and the importance of the M&A integration function
Followups: * Ben & David’s quick take on Instagram Stories!
The Carve Out * Ben: Why the Concorde failed by Vox * David: Simone Biles, the greatest gymnast of all time * Taylor: Mindset by Carol Dweck, Shoe Dog by Phil Knight, Originals: How Non-Conformists Move the World by Adam Grant
The podcast and artwork embedded on this page are from Ben Gilbert and David Rosenthal, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Broken Record with Rick Rubin, Malcolm Gladwell, Bruce Headlam and Justin Richmond (LS 69 · TOP 0.05% what is this?)
Episode: Neil Young, Part One
Pub date: 2021-12-21
When we first talked about making Broken Record, we had a short list of absolute dream guests for the podcast and Neil Young was at the top of that list. So when Neil’s new record, Barn, was announced and we were told he wanted to speak with Rick about it we were beyond excited.
On today’s episode, Rick and Neil talk about the new album, and all of the archival projects he plans on releasing in the coming year. They also reminisce about the time they spent working together on some abandoned songs in 1997 that may soon be released. And Neil’s time in a Rick James fronted band that was signed to Motown, and how Neil drove from Canada to LA in a hearse. The two talked for so long we decided to make this the first of two episodes with Neil Young.
Subscribe to Broken Record’s YouTube channel to hear all of our interviews: https://www.youtube.com/brokenrecordpodcast and follow us on Twitter @BrokenRecord
You can also check out past episodes here: https://brokenrecordpodcast.com
Check out our favorite Neil Young songs HERE.
Learn more about your ad-choices at https://www.iheartpodcastnetwork.comSee omnystudio.com/listener for privacy information.
The podcast and artwork embedded on this page are from Pushkin Industries, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Broken Record with Rick Rubin, Malcolm Gladwell, Bruce Headlam and Justin Richmond (LS 69 · TOP 0.05% what is this?)
Episode: Pino Palladino and Blake Mills
Pub date: 2021-12-30
Pino Palladino and Blake Mills are renowned session musicians who recently collaborated on their own album, Notes With Attachments. It’s an experimental, jazz-leaning project that blew Rick Rubin’s mind. Pino Palladino is a bass player who has played on records with everyone from D’Angelo to The Who to Adele. Guitarist Blake Mills co-founded the band Dawes in 2005, and he has gone on to release critically acclaimed solo albums and produce records for the Alabama Shakes, John Legend and Fiona Apple.
Three years ago, Pino and Blake started collaborating on what would become Notes With Attachments. The album features other incredible session musicians and pulls from influences as diverse as West African, Cuban, and English folk music.
Rick talks first with Pino Palladino on today’s episode about those wide array of influences, and how hearing Motown music as a young boy in Wales changed his life. Pino also walks us through his evolution to becoming one of the most in-demand session players. Later Blake Mills joins the conversation to talk about collaborating with Pino and why he feels bad for the touring musicians who have to play his bass parts on the road.
Subscribe to Broken Record’s YouTube channel to hear all of our interviews: https://www.youtube.com/brokenrecordpodcast and follow us on Twitter @BrokenRecord
You can also check out past episodes here: https://brokenrecordpodcast.com
Check out our favorite Pino Palladino and Blake Mills songs HERE.
Learn more about your ad-choices at https://www.iheartpodcastnetwork.comSee omnystudio.com/listener for privacy information.
The podcast and artwork embedded on this page are from Pushkin Industries, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Syntax - Tasty Web Development Treats (LS 62 · TOP 0.1% what is this?)
Episode: Headless CMS Break Down & Roundup
Pub date: 2020-06-03
In this episode of Syntax, Scott and Wes talk about headless content management systems — why you might want to use one, things you should take into account, and more!
Sanity - Sponsor Sanity.io is a real-time headless CMS with a fully customizable Content Studio built in React. Get a Sanity powered site up and running in minutes at sanity.io/create. Get an awesome supercharged free developer plan on sanity.io/syntax.
Freshbooks - Sponsor Get a 30 day free trial of Freshbooks at freshbooks.com/syntax and put SYNTAX in the “How did you hear about us?” section.
Show Notes 02:38 - What and why
- A headless CMS doesn’t mean you have to use a static site generator
- A headless CMS has an API:
- REST
- GraphQL
- GROQ
- A headless CMS can pipe its content into:
- Static Site on build - like Gatsby
- An app on run/refresh
- An existing website - widget
- What makes a CMS? Do you need a UI?
- What is the threshold?
- Is a hotdog a sandwich?
- Things to think about when choosing an API
- Auth - Rules + roles + levels
- How easy is it to create new data types?
- Is it code or UI?
- Can my mom use it?
- Can you create one-off content types? (e.g. settings pages)
- Custom workflows
- dRafts, revisions, roll backs
- How are images handled? Do you need another service for that?
- Pre-determined UI
- Is custom UI possible?
- Two-way relationships?
- Tags?
- Data validation?
- UI + API?
- Hosting?
- Pricing?
- Content movability?
- Can you get your data out?
- Schemas
- Users
- Revisions
- CORS or realtime API?
- APIs
- Can you insert data via the API? (or just pull)
- Node API
- Importing data
24:37 - Hosted
- Sanity
- Prismic
- Contentful
- Dato CMS
- Storyblok
- 8Base
- Agility CMS
- Seems to be making a big corporate play
36:59 - Self-Hosted
- WordPress
- WordPress REST API
- GraphQL API
- WP GraphQL
- Advanced Custom Fields for custom UI
- Most things in WordPress are custom post types and taxonomies, so data from plugins can often be surfaced
- The theme UI from plugins is often lost
- Drupal
- contentacms.org
- Craft CMS
- Ghost
- Strapi
- Keystone
- Joomla (JK!)
44:33 - API (BYOUI)
- Hasura
- Prisma
- Firebase
47:37 - Git Based
- Netlify
- Forestry
50:30 - Other
- Google Sheets
- Sheety
- Airtable
- Tina CMS
- Not a CMS
- Markdown under the hood
- Notion
Links * https://twitter.com/wesbos/status/1254772936935739393 * Pixel & Tonic * GraphCool * GraphCMS * Sapper
××× SIIIIICK ××× PIIIICKS ××× * Scott: DEWALT Random Orbit Sander * Wes: Shelf Brackets
Shameless Plugs * Scott: New course on Sapper - Sign up for the year and save 25%! * Wes: Wes’ YouTube Channel
Tweet us your tasty treats! * Scott’s Instagram * LevelUpTutorials Instagram * Wes’ Instagram * Wes’ Twitter * Wes’ Facebook * Scott’s Twitter * Make sure to include @SyntaxFM in your tweets
The podcast and artwork embedded on this page are from Wes Bos & Scott Tolinski - Full Stack JavaScript Web Developers, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Syntax - Tasty Web Development Treats (LS 62 · TOP 0.1% what is this?)
Episode: Potluck - enums, WASM, Lighthouse, Redirects
Pub date: 2021-12-15
In this episode of Syntax, Scott and Wes answer your questions on a Potluck episode of Syntax.
Sanity - Sponsor Sanity.io is a real-time headless CMS with a fully customizable Content Studio built in React. Get a Sanity powered site up and running in minutes at sanity.io/create. Get an awesome supercharged free developer plan on sanity.io/syntax.
Sentry - Sponsor If you want to know what’s happening with your code, track errors and monitor performance with Sentry. Sentry’s Application Monitoring platform helps developers see performance issues, fix errors faster, and optimize their code health. Cut your time on error resolution from hours to minutes. It works with any language and integrates with dozens of other services. Syntax listeners new to Sentry can get two months for free by visiting Sentry.io and using the coupon code TASTYTREAT during sign up.
Linode - Sponsor Whether you’re working on a personal project or managing enterprise infrastructure, you deserve simple, affordable, and accessible cloud computing solutions that allow you to take your project to the next level. Simplify your cloud infrastructure with Linode’s Linux virtual machines and develop, deploy, and scale your modern applications faster and easier. Get started on Linode today with a $100 in free credit for listeners of Syntax. You can find all the details at linode.com/syntax. Linode has 11 global data centers and provides 24/7/365 human support with no tiers or hand-offs regardless of your plan size. In addition to shared and dedicated compute instances, you can use your $100 in credit on S3-compatible object storage, Managed Kubernetes, and more. Visit linode.com/syntax and click on the “Create Free Account” button to get started.
Show Notes * 02:07 Google Chrome to start measuring user experience beyond the completion of page load * 05:47 How can I ensure that I am executing npm commands safely? * 07:58 How should I prefix booleans? * 09:46 How do I decide between using an enum vs a union type in Typescript * 13:40 What is Web Assembly? * 18:34 Sponsor: Sanity * 19:45 what happened to Scott using Linux? * PopOS * 22:44 Sponsor: Linode * 23:57 How do you batch requests in nodejs to an api? * 26:15 What are micro-frontends? * 29:55 Sponsor: Sentry * 31:16 Since Astro seems so amazing, aren’t you tempted to rebuild your site now in Astro instead of Sveltekit? * Astro * 33:04 Can you please shed some light on redirects in express/ koa? * 36:41 How do deal with ADHD? * 41:52 Should I repeat the name of the issue in the commit message or just "Resolves #$issue-number"? * 44:21 Do browsers update automatically? * 47:52 What do you do when working on a big project? * 49:55 Can you guys help to breakdown and explain jargons and differences of RPC, REST, gRPC, GraphQL? * 53:25 How to ask a question * 53:42 Sick Picks * 56:17 Shamless plugs
××× SIIIIICK ××× PIIIICKS ××× * Scott: Satechi 3-in-1 Magnetic Wireless Charging Stand * Wes: The Always Sunny podcast
Shameless Plugs * Scott: Astro Course * Wes: All Courses
Tweet us your tasty treats * Scott's Instagram * LevelUpTutorials Instagram * Wes' Instagram * Wes' Twitter * Wes' Facebook * Scott's Twitter * Make sure to include @SyntaxFM in your tweets
The podcast and artwork embedded on this page are from Wes Bos & Scott Tolinski - Full Stack JavaScript Web Developers, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: La Story (LS 57 · TOP 0.5% what is this?)
Episode: Le spectre de l’urgence alimentaire
Pub date: 2021-12-08
Les prix des produits agricoles connaissent des hausses historiques. Dans « La Story », le podcast d’actualité des « Echos », Pierrick Fay et ses invités en dévoilent les conséquences en termes de sécurité alimentaire pour les populations les plus fragiles chez qui elles se combinent à des pertes de revenus et aux aléas climatiques.
La Story est un podcast des « Echos » présenté par Pierrick Fay. Cet épisode a été enregistré en décembre 2021. Rédaction en chef : Clémence Lemaistre. Invités : Sébastien Abis (chercheur à l’Iris et directeur du club de réflexion sur l’agriculture « Le Déméter ») et Etienne Goetz (journaliste au service Marchés des « Echos »). Réalisation : Willy Ganne. Musique : Théo Boulenger. Identité graphique : Upian. Photo : Canbedone/Shutterstock. Sons : Michel Sardou « Ils ont le pétrole mais c’est tout » (1979), Manbouss « La chanson du Pain », Europe 1, Euronews.
Voir Acast.com/privacy pour les informations sur la vie privée et l'opt-out.
The podcast and artwork embedded on this page are from Les Echos, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: La Story (LS 57 · TOP 0.5% what is this?)
Episode: Mariage Frères, le thé dans tous ses états
Pub date: 2021-11-30
La magie de cette marque de thés raffinés, née dans le Marais à Paris, opère depuis près de 150 ans. Dans « La Story », le podcast d’actualité des « Echos », Pierrick Fay et ses invités déroulent l’histoire d’une « success story » qu’un duo de passionnés a hissée dans le luxe mondialisé à partir des années 1980.
La Story est un podcast des « Echos » présenté par Pierrick Fay. Cet épisode a été enregistré en novembre 2021. Rédaction en chef : Clémence Lemaistre. Invités : Nathalie Villard (journaliste pour « Les Echos Week-End ») et Etienne Goetz (journaliste au service Marchés des « Echos »). Réalisation : Willy Ganne. Musique : Théo Boulenger. Identité graphique : Upian. Photo : Manuel Braun pour Les Echos Week-End. Sons : Centre culturel de Chine à Paris, Naïma « Thé A La Menthe », Lipton, Jérôme Commandeur « Le thé », Binnie Hale « A Nice Cup of Tea », Chansons pour enfants, Compote de Prod.
Voir Acast.com/privacy pour les informations sur la vie privée et l'opt-out.
The podcast and artwork embedded on this page are from Les Echos, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Acquired (LS 63 · TOP 0.1% what is this?)
Episode: Not Boring (with Packy McCormick)
Pub date: 2021-12-03
When does a creator become a company? Who says that media companies — or venture firms — have to be organizations? How high is the ceiling on one person + the internet? Acquired has the answers and they are... Not Boring. 🙂
Big news!! All back catalog LP Show episodes are now free and available to anyone!! You can follow our new public LP Show feed here http://pod.link/acquiredlp in the podcast player of your choice. It's already chock-full of 60+ great episodes like our VC Fundamentals series, interviews with founders of top early-stage startups, and master classes on pricing, marketplaces, SaaS investing and many more topics. Happy listening and happy holidays to everyone!!
Sponsors:
- Thank you to our presenting sponsor for all of Season 9, Pilot.com! Pilot takes care of startups' bookkeeping, tax and CFO services so busy founders can focus on what matters. To paraphrase Jeff Bezos's AWS analogy: bookkeeping and tax don't make your product any better — so you should let Pilot handle them for you. Pilot is in fact backed by Bezos himself, along with other all-star investors including Sequoia, Index, and Stripe. They are truly the gold standard for startup bookkeeping, and many of the companies we work with run on them. You can get in touch with Pilot here: https://bit.ly/acquiredfmpilot , and Acquired listeners get 20% off their first 6 months! (use the link above)
- Thank you as well to PitchBook and to Nord Security. You can learn more about them at:
- https://bit.ly/acquiredpitchbook
- https://bit.ly/acquirednord
Links:
- Not Boring: https://www.notboring.co/
- Proto-Not Boring: https://www.packym.com/blog/nyc-debate-club
- Not Boring's first sponsor deck: https://projector.com/story/2cb6296d-e229-4774-839c-efbf86d7f69f?scene=7127c7a0
- Jake Singer's Not Boring Flywheel: https://theflywheel.substack.com/p/not-boring-packy-m
- Proof Ben was famous even before Acquired: https://web.archive.org/web/20150814041757/https://www.seattletimes.com/business/high-octane-leader-drives-microsoftrsquos-innovation-garage/
Carve Outs:
- Velcro swaddles (dad life...): https://www.amazon.com/s?k=velcro+swaddle
- An Engineer's Hype-Free Observations on Web3: https://www.psl.com/feed-posts/web3-engineer-take
- Rabbits: https://www.amazon.com/Rabbits-Novel-Terry-Miles-ebook/dp/B08HY29VM2
Note: Acquired hosts and guests may hold assets discussed in this episode. This podcast is not investment advice, and is intended for informational and entertainment purposes only. You should do your own research and make your own independent decisions when considering any financial transactions.
The podcast and artwork embedded on this page are from Ben Gilbert and David Rosenthal, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Acquired (LS 63 · TOP 0.1% what is this?)
Episode: Michael Mauboussin Master Class — Moats, Skill, Luck, Decision Making and a Whole Lot More
Pub date: 2021-10-05
We sit down with the one & only Michael Mauboussin to dive deep into his incredible body of work: untangling skill and luck, measuring moats, persistence of returns in venture capital, decision making and — particularly timely — expectations investing and how to think about valuations in the current 2021 market environment. (!!) Michael's work is maybe our most frequent carve out on Acquired, so we're pumped to finally have a chance to interview the man himself. Big thank you to Patrick O'Shaughnessy and Brent Beshore for introducing us all at Capital Camp this year!
If you love Acquired and want more, join our LP Community for access to over 50 LP-only episodes, monthly Zoom calls, and live access for big events like emergency pods and book club discussions with authors. We can't wait to see you there. Join here at: https://acquired.fm/lp/
Sponsors:
- Thanks to SoftBank Latin America for being our presenting sponsor for this special episode. If you are an entrepreneur, employee, other investor or simply someone who's interested in learning about the best young companies in LatAm right now, get in touch with them at: https://bit.ly/acquiredsoftbanklatam , and tell them that Ben and David sent you!
- You can get learn more about careers at their portfolio company QuintoAndar at https://carreiras.quintoandar.com.br
- Thank you as well to Modern Treasury and to Fundrise. You can learn more about them at:
- https://bit.ly/acquiredmoderntreasury (and you can find our reverse interview with them at https://www.moderntreasury.com/acquired )
- https://bit.ly/acquiredfundrise
Jobs!
- Big news — we now have a full Acquired Job Board! It's a one-stop-shop with all the very best opportunities from the amazing companies in the Acquired community, including folks like Solana, Italic, Pilot, RabbitHole, Modern Treasury, Vouch, Zapier, Levels and more. AND, if you're more casually open to opportunities, we have a form you can fill out and we'll handpick the best ones and personally send to you as they come up. Check it out at https://www.acquired.fm/jobs
Links:
- Michael's wonderful talk at Google: https://youtu.be/1JLfqBsX5Lc
- The new revised edition of Expectations Investing: https://www.amazon.com/Expectations-Investing-Reading-Returns-Heilbrunn/dp/0231203047/
- The Success Equation: https://www.amazon.com/The-Success-Equation-Untangling-Investing/dp/1422184234/
- Measuring the Moat: https://research-doc.credit-suisse.com/docView?language=ENG&format=PDF&sourceid=csplusresearchcp&document_id=1066439791&serialid=4uA2wHojCvFKzqWfwIyDvkSN1pkXRpb43LvyclLcJsk%3D&cspId=null
- Public to Private Equity: https://www.morganstanley.com/im/publication/insights/articles/articles_publictoprivateequityintheusalongtermlook_us.pdf
Note: Acquired hosts and guests may hold assets discussed in this episode. This podcast is not investment advice, and is intended for informational and entertainment purposes only. You should do your own research and make your own independent decisions when considering any financial transactions.
The podcast and artwork embedded on this page are from Ben Gilbert and David Rosenthal, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Modern Finance (LS 58 · TOP 0.5% what is this?)
Episode: Cryptopunks - The NFTs That Started It All -- Their Origin Story and Future Plans.
Pub date: 2021-04-08
NFT mania is in full swing, but where did it really start to gain momentum? CryptoPunks, 10,000 unique, algorithmically generated pixel art characters were sent to the Ethereum blockchain by Matt Hall and John Watkinson of Larva Labs in 2017, where they soon became a sensation. Kevin talks to Matt and John about the CryptoPunks origin story, their current chapter, and what we can expect from them in the future.
SHOW NOTES
Here's what CryptoPunks are, who created them, and how they're responsible for setting the current NFT craze in motion. [00:26]
Matt Hall and John Watkinson of Larva Labs take us back to the conception of CryptoPunks. What inspired them at this time? [03:08]
What made Matt and John draw the connection between centralized collectible and decentralized, on-chain collectible? Were they already involved in the world of cryptocurrency? [04:33]
How did the Ethereum blockchain become the ideal platform for their experiment? [06:41]
How is this data actually stored? [08:35]
What is a hash? [10:00]
Were there similar art projects going on at this time? [11:13]
Who came up with the name "CryptoPunks," and why was this format selected? [11:58]
Why did they decide on limiting the number of CryptoPunks to 10,000? [12:58]
Most CryptoPunks are human in appearance, but there are also a small number of zombies, apes, and aliens. How did these enter the picture, and were there any interesting ideas that got left on the cutting room floor? [14:26]
Knowing now that these creations would carry historical significance in the circles of fine art and finance, do Matt and John ever wish they'd done things differently? [16:21]
Do Matt and John have any personal favorite CryptoPunks? [18:24]
There are no duplicated CryptoPunks. How many attributes were they programmed to exhibit, and do the number of attributes contribute to (or detract from) a piece's rarity? [21:49]
How were CryptoPunks launched, and what could someone expect to pay for one circa June 2017? What helped to escalate their visibility and popularity within the crypto community? [23:30]
When did Matt and John notice CryptoPunks commanding mind-boggling prices in secondary marketplaces? [27:08]
How many CryptoPunks have gone missing? [30:09]
Have traditional museums or curators reached out to Matt and John during the current NFT craze? [32:25]
Where do CryptoPunks and NFTs go from here? Are we in a bubble, or are we just experiencing the beginning of a new, sustainable commodity? [35:00]
What are Autoglyphs, what inspired their creation, and how do they work? [39:39]
What project is next for Matt and John? Will it be as groundbreaking for the cryptosphere as CryptoPunks and Autoglyphs? [42:59]
With so many new blockchains popping up all the time, do Matt and John think Ethereum will endure in the fine arts space? [44:45]
What other NFT projects do Matt and John find interesting and impressive? [45:41]
Thoughts on projects that might be interpreted by some as flattering imitations and by others as derivative knock-offs of CryptoPunks and Autoglyphs. [48:21]
From a copyright perspective, how do Matt and John protect CryptoPunks and Autoglyphs from third parties exploiting their work? How can someone who owns an NFT defend their property? [50:05]
In spite of their good fortune thus far, Matt and John remain a two-person team. Do they anticipate scaling up? [53:15]
Parting thoughts. [55:01]
The podcast and artwork embedded on this page are from Kevin Rose, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Modern Finance (LS 58 · TOP 0.5% what is this?)
Episode: Beeple - His Story, The Future of NFTs, His Favorite Digital Artist, and More.
Pub date: 2021-04-02
Beeple (@beeple, aka Mike Winkelmann) is a graphic designer who has worked on concert visuals for Justin Bieber, One Direction, Katy Perry, Nicki Minaj, Eminem, Zedd, deadmau5, and many more. He recently made headlines when he sold a collage of his first 5,0000 "everyday" pieces of art as an NFT for $69,346,250 -- making it the third most expensive artwork by a living artist.
SHOW NOTES
- Where's Beeple based, and where's he been? He's in Charleston, SC, but he's from Wis-con-sin. [01:54]
- Why does Beeple view sports and art as similar constructs? [02:56]
- Why did Beeple choose art as a career over programming, and how did he start? [04:54]
- Would you buy O'Reilly classic cover art as NFTs? [07:18]
- How Beeple clips became ubiquitous in the VJ world (which was as surprising to him as anyone). [08:35]
- What's the difference between an artist and a designer? [13:21]
- When did Beeple really start to feel like he could make a legitimate living from his art? [14:49]
- How was Beeple introduced to the world of NFTs and cryptocurrency? [16:09]
- Where did Beeple sell his first NFT, and what were his early impressions of the format? [20:25]
- Why the "everyday" pieces are perfectly made for collecting, and how Beeple justifies their value to the collectors. [23:30]
- Imagining the possibilities: what NFT collecting might look like in the future. [29:36]
- Who were the subjects of Beeple's earliest work, and how did his process evolve over time? [36:08]
- What drives Beeple's more topical work? [37:59]
- So...the Dick-Milking Factory. What's that all about? And will it incite a future Hot Pockets vs. Beeple ruling by the highest court in the land? [40:54]
- What's next for Beeple? [43:33]
- How can an art collector of modest means pick up a piece of Beeple's work nowadays, and how does this compare to what his art was selling for just a few months ago? [44:21]
- At the end of the day, how does Beeple feel about copyright infringement? [47:42]
- Other NFT artists Beeple thinks we should keep an eye on. [49:16]
- Digital art isn't new. Only the ability to quantify and commodify it is. [50:19]
- Parting thoughts. [52:16]
- Enjoy the show? Please consider leaving a nice review here so others can learn about and enjoy it, too! [52:45]
The podcast and artwork embedded on this page are from Kevin Rose, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Foundation
Episode: Episode 35: Brian Armstrong
Pub date: 2013-12-18
Episode 35: Brian Armstrong
The podcast and artwork embedded on this page are from Kevin Rose, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Acquired (LS 63 · TOP 0.1% what is this?)
Episode: TSMC
Pub date: 2021-09-07
It's time. We dive into the unbelievable history behind the quietest technology giant of them all — and as of recording the world's 9th (!) most valuable company — the Taiwan Semiconductor Manufacturing Company. This story checks every box in the Acquired pantheon of greatness: China, America, MIT, Don Valentine, Silicon Valley, "real men" looking silly, and... moats literally built by lasers. We're not kidding. Pull up a seat and settle in for a great one!
If you love Acquired and want more, join our LP Community for access to over 50 LP-only episodes, monthly Zoom calls, and live access for big events like emergency pods and book club discussions with authors. We can't wait to see you there. Join here at: https://acquired.fm/lp/
Sponsors:
- Thank you to Pilot for being our presenting sponsor for all of Acquired Season 9! Pilot takes care of startups' bookkeeping, tax and CFO services so busy founders can focus on what matters. To paraphrase Jeff Bezos's AWS analogy: bookkeeping and tax don't make your product any better — so you should let Pilot handle them for you. Pilot is in fact backed by Bezos himself, along with other all-star investors including Sequoia, Index, and Stripe. They are truly the gold standard for startup bookkeeping, and many of the companies we work with run on them. You can get in touch with Pilot here: https://bit.ly/acquiredfmpilot , and Acquired listeners get 20% off their first 6 months! (use the link above)
- Thank you as well to PitchBook and to Nord Security. You can learn more about them at:
- https://bit.ly/acquiredpitchbook
- https://bit.ly/acquirednord
Links:
- Episode Sources: https://docs.google.com/document/d/1rQMgQDY5c3Fs5DsjB4cZHVZGdhN4kK-6HapO_r7pjyQ/edit?usp=sharing
Carve Outs:
- Ted Lasso (Season 1): https://tv.apple.com/us/show/ted-lasso/umc.cmc.vtoh0mn0xn7t3c643xqonfzy
- Greek: https://www.imdb.com/title/tt0976014/
- Who is Michael Ovitz?: https://www.amazon.com/dp/B07B2HS77M/
The podcast and artwork embedded on this page are from Ben Gilbert and David Rosenthal, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Acquired (LS 63 · TOP 0.1% what is this?)
Episode: Kevin Rose from Web 2.0 to Web3
Pub date: 2021-08-30
We sit down with the one and only Kevin Rose to talk about his journey from pioneering Web 2.0 with Digg to leading the charge on Web3 and NFT + DeFi investing as a partner at True Ventures and his new show Modern Finance. We cover it all -- TechTV, Digg's true origin story, Milk, Hodinkee, interviewing Beeple and where MoFi goes from here. This was an episode we’ve been wanting to do forever, and Kevin was truly a blast to hang out with. Tune in and then go check out everything he’s building now over at Modern Finance!
If you love Acquired and want more, join our LP Community for access to over 50 LP-only episodes, monthly Zoom calls, and live access for big events like emergency pods and book club discussions with authors. We can't wait to see you there. Join here at: https://acquired.fm/lp/
Sponsors:
- Thanks to SoftBank Latin America for being our presenting sponsor for this special episode. If you are an entrepreneur, employee, other investor or simply someone who's interested in learning about the best young companies in LatAm right now, get in touch with them at: https://bit.ly/acquiredsoftbanklatam, and tell them that Ben and David sent you!
- You can get in touch Rappi's Sebastian Mejia at sebastian@rappi.com or @mejiasebas on Twitter
- Thank you as well to Modern Treasury and to Fundrise. You can learn more about them at:
- https://bit.ly/acquiredmoderntreasury (and you can find our reverse interview with them at https://www.moderntreasury.com/acquired )
- https://bit.ly/acquiredfundrise
Links:
- Modern Finance: https://modern.finance
- Especially the Beeple episode: https://modern.finance/episode/beeple-his-story-the-future-of-nfts-his-favorite-digital-artist-and-more/
- Proof: https://www.proof.xyz
- Kevin on Twitter: https://twitter.com/kevinrose
The podcast and artwork embedded on this page are from Ben Gilbert and David Rosenthal, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Acquired (LS 63 · TOP 0.1% what is this?)
Episode: Standard Oil Part II
Pub date: 2021-10-18
We bring the epic saga of Standard Oil and John D. Rockefeller to a close (for now) with two of history's greatest second acts: Rockefeller's pioneering of modern philanthropy (and really modern life itself), and perhaps the single greatest shareholder value "unlock" of all-time in the breakup of Standard Oil. And like any great American saga, of course the good guys win in the end. The only question is... just who were the good guys??
If you love Acquired and want more, join our LP Community for access to over 50 LP-only episodes, monthly Zoom calls, and access for live events and discussions with episode guests. We can't wait to see you there. Join here at: https://acquired.fm/lp/
Sponsors:
- Thank you to Pilot for being our presenting sponsor for all of Acquired Season 9! Pilot takes care of startups' bookkeeping, tax and CFO services so busy founders can focus on what matters. To paraphrase Jeff Bezos's AWS analogy: bookkeeping and tax don't make your product any better — so you should let Pilot handle them for you. Pilot is in fact backed by Bezos himself, along with other all-star investors including Sequoia, Index, and Stripe. They are truly the gold standard for startup bookkeeping, and many of the companies we work with run on them. You can get in touch with Pilot here: https://bit.ly/acquiredfmpilot , and Acquired listeners get 20% off their first 6 months! (use the link above)
- Thank you as well to PitchBook and to Nord Security. You can learn more about them at:
- https://bit.ly/acquiredpitchbook
- https://bit.ly/acquirednord
Links:
- Titan by Ron Chernow: https://www.amazon.com/gp/product/1400077303/
- Episode sources: https://docs.google.com/document/d/1PzIts5VVgKhE70sAeHnLKcmDktTkXRgEVtuHDktfNCw/edit?usp=sharing
Carve Outs:
- The to-be-announced (as of recording) M1X MacBook Pro...
- Starfish Space: https://www.starfishspace.com/
- Peloton: https://www.onepeloton.com
Note: Acquired hosts and guests may hold assets discussed in this episode. This podcast is not investment advice, and is intended for informational and entertainment purposes only. You should do your own research and make your own independent decisions when considering any financial transactions.
The podcast and artwork embedded on this page are from Ben Gilbert and David Rosenthal, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Acquired (LS 63 · TOP 0.1% what is this?)
Episode: Standard Oil Part I
Pub date: 2021-09-22
It's time. We dive into the original American capitalist mega winner, Standard Oil, and its legendary founder John D. Rockefeller. This company and man almost defy characterization -- Elon, Bezos, Gates, Buffett... they've got nothing on old John D. Not only was Rockefeller the wealthiest person in modern human history, his company wrote the blueprint for today's corporations and everything we now know about business and capitalism. Pull up a chair and get ready to hear how this hillbilly, nobody kid from the sticks grew up to became the richest person in the world, creating a legend along the way that would become the American Dream...
If you love Acquired and want more, join our LP Community for access to over 50 LP-only episodes, monthly Zoom calls, and access for live events and discussions with episode guests. We can't wait to see you there. Join here at: https://acquired.fm/lp/
Sponsors:
- Thank you to Pilot for being our presenting sponsor for all of Acquired Season 9! Pilot takes care of startups' bookkeeping, tax and CFO services so busy founders can focus on what matters. To paraphrase Jeff Bezos's AWS analogy: bookkeeping and tax don't make your product any better -- so you should let Pilot handle them for you. Pilot is in fact backed by Bezos himself, along with other all-star investors including Sequoia, Index, and Stripe. They are truly the gold standard for startup bookkeeping, and many of the companies we work with run on them. You can get in touch with Pilot here: https://bit.ly/acquiredfmpilot, and Acquired listeners get 20% off their first 6 months! (use the link above)
- Thank you as well to PitchBook and to Nord Security. You can learn more about them at:
- https://bit.ly/acquiredpitchbook
- https://bit.ly/acquirednord
Links:
- Episode sources: https://docs.google.com/document/d/1X7oskTGkPX_rIKqZFlN49USR20Ii6C9p2SJCGzEXHLU/edit?usp=sharing
Carve Outs:
- There Will Be Blood: https://www.imdb.com/title/tt0469494/
- Deadwood: https://www.imdb.com/title/tt0348914/
- SB Nation's Secret Base Atlanta Falcons series: https://youtu.be/Lx_ORMhpmoU
Note: Acquired hosts and guests may hold assets discussed in this episode. This podcast is not investment advice, and is intended for informational and entertainment purposes only. You should do your own research and make your own independent decisions when considering any financial transactions.
The podcast and artwork embedded on this page are from Ben Gilbert and David Rosenthal, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Business Breakdowns (LS 49 · TOP 1% what is this?)
Episode: Taboola: The Open Web’s Suggestion Engine - [Business Breakdowns, EP. 27]
Pub date: 2021-09-22
Today, we are breaking down Taboola, a company you may not know but one you’ve definitely seen. When you read articles on CNBC, Bloomberg, or the Independent, Taboola powers the sidebar and banner recommendations for what you should read next.
The company works with publishers and advertisers to help readers discover what’s new and interesting. Founded in 2007, Taboola recently went public and is now the leading recommendation engine for the open web, serving over 500 million users a day.
To break down the business, host Jesse Pujji is joined by Taboola’s founder and CEO, Adam Singolda. During our conversation, we cover the ways in which Taboola’s value prop differs from Facebook and Google, unpack the advertising concepts of Yield and ex-TAC, and dive into Adam’s vision for Taboola to recommend anything, anywhere.
Please enjoy this breakdown of Taboola.
For the full show notes, transcript, and links to the best content to learn more, check out the episode page here.
This episode is brought to you by Tegus. We created Business Breakdowns to uncover the lessons and frameworks behind every business, and that's what makes Tegus our perfect launch partner. Much of the foundational prep for these episodes starts with research on the Tegus platform.
With Tegus, you can learn everything you’d want to know about a company in an on-demand digital platform. Investors share their expert calls, allowing others to instantly access more than 20,000 calls on Coinbase, Hinge Health, Farfetch, or almost any company of interest. All you have to do is log in. If you're ready to go deeper on any company and you appreciate the value of primary research, head to tegus.co/breakdowns for a free trial.
Business Breakdowns is a property of Colossus, Inc. For more episodes of Business Breakdowns, visit joincolossus.com/episodes.
Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here.
Follow us on Twitter: @JoinColossus | @patrick_oshag | @jspujji | @zbfuss
Show Notes
[00:02:48] - [First question] - What is Taboola?
[00:03:35] - The scale of the business, revenue, impressions, and payouts to date
[00:05:18] - What problem Taboola solves for advertisers and their user experience
[00:07:22] - Comparing the advertising differences between Taboola and Facebook
[00:09:15] - Why a publisher would choose Taboola and what they solve for them
[00:11:06] - Reasons why advertising through Taboola is desirable
[00:14:41] - The founding insight and early struggles of building Taboola
[00:18:42] - Important metrics when evaluating their business and what generates revenue
[00:20:32] - What they offer to both sides of the marketplace to grow their business
[00:22:26] - Defining yield and how they position their rates
[00:25:38] - Things they do to improve the value proposition for clients
[00:29:29] - What allows Taboola to grow and remain competitive
[00:30:37] - Prioritizing sales and marketing spend to ensure their long term success
[00:32:24] - Positive and negative factors in relation to scaling a business like this
[00:34:11] - Reasons why they wanted to merge with Outbrain
[00:35:16] - Their latest deal with Connexity and his thoughts on M&A for the future
[00:37:50] - The top things that would maximize their success over the next decade
[00:40:05] - Cookies, privacy, and the role they might play in years to come
[00:42:09] - The biggest threats and risks for the future of Taboola
[00:44:14] - The competitive landscape of advertising and content placement
[00:46:09] - Lessons for builders and investors when studying Taboola’s story
[00:47:47] - Where to go if you want to learn more about Taboola
The podcast and artwork embedded on this page are from Colossus, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Business Breakdowns (LS 49 · TOP 1% what is this?)
Episode: Salesforce: The Cloud & SaaS Pioneer [Business Breakdowns, EP. 29]
Pub date: 2021-10-06
Today, we are breaking down the cloud and SaaS trailblazer, Salesforce. Founded by Marc Benioff in 1999, Salesforce has grown rapidly to become the global leader in the $100 billion CRM market. The business has 150,000 customers, including 90% of the Fortune 500, and is currently valued north of $270 billion.
To break down Salesforce, Patrick O’Shaughnessy is joined by Matt Garratt, general partner at VC firm CRV and former head of Salesforce Ventures, where he led investments in companies like Snowflake, Twilio, and Zoom.
In our conversation, we discuss the attributes that make Marc Benioff special, how he pushed against convention to usher in a new era of cloud-based businesses, and ways in which he has built a world around Salesforce’s product lines. We also cover decision-making in the company, why its culture derives from the beaches of Hawaii, and how it’s transitioning from builder to buyer. Please enjoy this breakdown of Salesforce.
For the full show notes, transcript, and links to the best content to learn more, check out the episode page here.
This episode is brought to you by Quartr. With Quartr, you can access conference calls, investor presentations, transcripts, and earnings reports – straight from your pocket. Quartr is 100% free and includes companies from 12 markets including the US, the UK, Canada, India, and all the Scandanavian countries. Quartr is available for both iOS and Android, so check out the app today.
This episode is brought to you by Brex. Brex began as the first corporate card for startups and now offers a full financial stack built for scale. Get 10-20x higher credit limits, uncapped rewards, easy deposits and payments, and expense management all in one. Grow your business faster with Brex.
Business Breakdowns is a property of Colossus, Inc. For more episodes of Business Breakdowns, visit joincolossus.com/episodes.
Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here.
Follow us on Twitter: @JoinColossus | @patrick_oshag | @jspujji | @zbfuss
Show Notes
[00:03:14] - [First question] - What Salesforce is and what it does
[00:04:55] - The scale and revenue scope of the business today
[00:06:13] - Driving variables of revenue growth and their current model
[00:09:10] - The unique founding story and becoming the first SaaS company
[00:11:06] - What about Marc Benioff made him so compelling and successful
[00:14:20] - An experience in his time at Salesforce that changed and moved him
[00:15:16] - The first buyer and what they were served as a product
[00:17:07] - Overview of Salesforce as a software platform
[00:19:58] - The core database that powers their infrastructure and user experience
[00:21:26] - Transitioning from being mostly a builder to largely a buyer and acquirer
[00:23:46] - Why building trust early on is so crucial when doing something new
[00:25:45] - What is Dreamforce, and how it’s evolved over time
[00:27:24] - The connection between Hawaiian culture and Salesforce
[00:29:14] - How they continue to market and acquire customers and spend so much on marketing
[00:30:44] - Their current addressable market and plans to expand into those areas
[00:35:05] - How priorities are set, picked, and followed through on
[00:35:58] - What is V2MOM and the role it plays with the executive team
[00:38:08] - The philosophy behind Salesforce Ventures and the function it serves
[00:40:27] - Potential risks the business faces going forward
[00:44:24] - Key characteristics that separate Salesforce from other businesses out there
[00:46:47] - Lessons for investors and builders when studying Salesforce’s story
The podcast and artwork embedded on this page are from Colossus, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Reith Lectures (LS 55 · TOP 0.5% what is this?)
Episode: Can These Bones Live?
Pub date: 2017-07-04
Hilary Mantel analyses how historical fiction can make the past come to life. She says her task is to take history out of the archive and relocate it in a body. "It's the novelist's job: to put the reader in the moment, even if the moment is 500 years ago." She takes apart the practical job of "resurrection", and the process that gets historical fiction on to the page. "The historian will always wonder why you left certain things out, while the literary critic will wonder why you left them in," she says. How then does she try and get the balance right?
The lecture is recorded in front of an audience in Exeter, near Mantel's adopted home in East Devon, followed by a question and answer session. The Reith Lectures are chaired by Sue Lawley and produced by Jim Frank.
The podcast and artwork embedded on this page are from BBC Radio 4, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Reith Lectures (LS 55 · TOP 0.5% what is this?)
Episode: Silence Grips the Town
Pub date: 2017-06-27
The story of how an obsessive relationship with history killed the young Polish writer Stanislawa Przybyszewska, told by best-selling author, Hilary Mantel. The brilliant Przybyszewska wrote gargantuan plays and novels about the French Revolution, in particular about the revolutionary leader Robespierre. She lived in self-willed poverty and isolation and died unknown in 1934. But her work, so painfully achieved, did survive her. Was her sacrifice worthwhile? "She embodied the past until her body ceased to be," Dame Hilary says. "Multiple causes of death were recorded, but actually she died of Robespierre."
Over the course of these five lectures, she discusses the role that history plays in our lives. How do we view the past, she asks, and what is our relationship with the dead? The lecture is recorded before an audience in the ancient Vleeshuis in Antwerp, a city which features in Mantel's novels about Thomas Cromwell and the cosmopolitan world of the early Tudors. The lecture is followed by a question and answer session chaired by Sue Lawley.
The producer is Jim Frank.
The podcast and artwork embedded on this page are from BBC Radio 4, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Reith Lectures (LS 55 · TOP 0.5% what is this?)
Episode: The Iron Maiden
Pub date: 2017-06-20
How do we construct our pictures of the past, including both truth and myth, asks best-selling author Hilary Mantel. Where do we get our evidence? She warns of two familiar errors: either romanticising the past, or seeing it as a gory horror-show. It is tempting, but often condescending, to seek modern parallels for historical events. "Are we looking into the past, or looking into a mirror?" she asks. "Dead strangers...did not live and die so we could draw lessons from them." Above all, she says, we must all try to respect the past amid all its strangeness and complexity.
Over the course of the lecture series, Dame Hilary discusses the role that history plays in our culture. She asks how we view the past and what our relationship is with the dead.
The programme is recorded in front of an audience at Middle Temple in London, followed by a question and answer session.
The Reith Lectures are chaired by Sue Lawley and produced by Jim Frank.
The podcast and artwork embedded on this page are from BBC Radio 4, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Reith Lectures (LS 55 · TOP 0.5% what is this?)
Episode: The Day Is for the Living
Pub date: 2017-06-13
Art can bring the dead back to life, argues the best-selling novelist Hilary Mantel, starting with the story of her own great-grandmother. "We sense the dead have a vital force still," she says. "They have something to tell us, something we need to understand. Using fiction and drama, we try to gain that understanding." She describes how and why she began to write fiction about the past, and how her view of her trade has evolved. We cannot hear or see the past, she says, but "we can listen and look".
Over this series of five lectures, Dame Hilary discusses the role that history plays in our culture. How can we understand the past, she asks, and how can we convey its nature today? Above all, she believes, we must all try to respect the past amid all its strangeness and complexity.
The lecture is recorded in front of an audience at Halle St Peter's in Manchester, and is followed by a question and answer session chaired by Sue Lawley. The producer is Jim Frank.
The podcast and artwork embedded on this page are from BBC Radio 4, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Reith Lectures (LS 55 · TOP 0.5% what is this?)
Episode: Nanotechnology and Nanoscience
Pub date: 2005-04-27
This year's Reith Lecturer is the distinguished engineer, Lord Broers. He is President of the Royal Academy of Engineering and Chairman of the House of Lords Science and Technology Committee. He was a pioneer of nanotechnology and the first person to use the scanning electron microscope for the fabrication of micro-miniature structures.
In his fourth Reith Lecture, Lord Broers examines nanotechnology - the manipulation of matter at an atomic or molecular scale. He believes it has captured the public's imagination and given rise to the full range of emotions from admiration to fear. He explores the origins of nanotechnology with its roots in electronics and uses the relationship between it and nanoscience to illustrate the more general relationship between science and technology.
The podcast and artwork embedded on this page are from BBC Radio 4, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Reith Lectures (LS 55 · TOP 0.5% what is this?)
Episode: Innovation and Management
Pub date: 2005-04-20
This year's Reith Lecturer is the distinguished engineer, Lord Broers. He is President of the Royal Academy of Engineering and Chairman of the House of Lords Science and Technology Committee.
In his third Reith lecture Lord Broers argues that profound changes have taken place in the development of ideas and their translation in to the market place. This innovation revolution demands a new approach to research and product development.
Some argue that technology threatens our way of life and must be controlled through regulation, however, Lord Broers believes that this is rarely necessary. He argues that it is better to allow the market - and the customers - to decide whether technologies succeed or not.
The podcast and artwork embedded on this page are from BBC Radio 4, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Reith Lectures (LS 55 · TOP 0.5% what is this?)
Episode: Collaboration
Pub date: 2005-04-13
This year's Reith Lecturer is the distinguished engineer, Lord Broers. He is President of the Royal Academy of Engineering and Chairman of the House of Lords Science and Technology Committee.
In the second of his Reith Lectures, Lord Broers explores the origins of modern technologies and argues that global collaboration is essential for success. He argues that advancement must take in to account, social, environmental, economic, and political factors on a world level.
The podcast and artwork embedded on this page are from BBC Radio 4, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Reith Lectures (LS 55 · TOP 0.5% what is this?)
Episode: Technology will Determine the Future of the Human Race
Pub date: 2005-04-06
This year's Reith Lecturer is the distinguished engineer, Lord Broers. Alec Broers is President of the Royal Academy of Engineering and Chairman of the House of Lords Science and Technology Committee. He was a pioneer of nanotechnology and the first person to use the scanning electron microscope for the fabrication of micro-miniature structures.
Lord Broers delivers the first of his five Reith Lectures in which he sets out his belief that technology can and should hold the key to the future. He argues that man's way of life has depended on technology since the beginning of civilization - the flint stone, the control of fire, the wheel, the printing press, but are we coping with the newest cascade of technological advances that are happening now?
Lord Broers examines the social implications of the advances and argues that it has become essential that we study their social consequences. He believes that if poverty and disease are to be alleviated and the environment sustained, then technology must be harnessed on a vast and all inclusive scale.
The podcast and artwork embedded on this page are from BBC Radio 4, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Reith Lectures (LS 55 · TOP 0.5% what is this?)
Episode: A New Politics of the Common Good
Pub date: 2009-06-30
Professor Michael Sandel delivers four lectures about the prospects of a new politics of the common good. The series is presented and chaired by Sue Lawley.
Sandel makes the case for a moral and civic renewal in democratic politics. Recorded at George Washington University in Washington DC, he calls for a new politics of the common good and says that we need to think of ourselves as citizens, not just consumers.
The podcast and artwork embedded on this page are from BBC Radio 4, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Reith Lectures (LS 55 · TOP 0.5% what is this?)
Episode: Genetics and Morality
Pub date: 2009-06-23
Professor Michael Sandel delivers four lectures about the prospects of a new politics of the common good. The series is presented and chaired by Sue Lawley.
Recorded at the Centre for Life in Newcastle, Sandel considers how we should use our ever-increasing scientific knowledge. New genetic technologies hold great promise for treating and curing disease, but how far we should go in using them to manipulate muscles, moods and gender?
The podcast and artwork embedded on this page are from BBC Radio 4, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Reith Lectures (LS 55 · TOP 0.5% what is this?)
Episode: Morality in Politics
Pub date: 2009-06-16
Professor Michael Sandel delivers four lectures about the prospects of a new politics of the common good. The series is presented and chaired by Sue Lawley.
Sandel considers the role of moral argument in politics. He believes that it is often not possible for government to be neutral on moral questions and calls for a more engaged civic debate about issues such as commercial surrogacy and same-sex marriage.
The podcast and artwork embedded on this page are from BBC Radio 4, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Reith Lectures (LS 55 · TOP 0.5% what is this?)
Episode: Markets and Morals
Pub date: 2009-06-09
Michael Sandel, Harvard Professor of Government, delivers four lectures about the prospects of a new politics of the common good. The series is presented and chaired by Sue Lawley.
Sandel considers the expansion of markets and how we determine their moral limits. Should immigrants, for example, pay for citizenship? Should we pay schoolchildren for good test results, or even to read a book? He calls for a more robust public debate about such questions, as part of a 'new citizenship'.
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Podcast: The Public Philosopher (LS 46 · TOP 1% what is this?)
Episode: Global Philosopher: Should there be any limits to free speech?
Pub date: 2018-02-06
Sixty people from around the world join Professor Michael Sandel in a digital studio at Harvard to discuss free speech. Free speech is a cornerstone of democracy and freedom of expression is regarded as a fundamental human right. But even in democracies there are disputes about the limits to free speech. And most countries have laws restricting free speech, such as libel laws, or laws controlling forms of pornography. But should limits be placed on free speech? Should people be allowed to say and write whatever they like, even if it is untrue and is deeply offensive to vulnerable individuals or groups? Professor Sandel unpicks the philosophy of free speech.
Audience producer: Louise Coletta
Producer: David Edmonds
Executive Producer: Emma Rippon
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Podcast: Intelligence Squared (LS 60 · TOP 0.5% what is this?)
Episode: Debate: Michael Sandel vs Adrian Wooldridge on Meritocracy
Pub date: 2021-09-10
Meritocracy has long been an article of faith in the modern Western world. Get an education, work hard and the rewards of success will be yours, regardless of class, privilege or wealth. But recently meritocracy has come under attack, with the charge led by Michael Sandel, the Harvard philosopher whose public debates on how we define the common good have won him a global following. But not everyone agrees. Taking issue with much of Sandel’s arguments is Adrian Wooldridge, the political editor at The Economist. In this week's debate they argue whether we need more or less meritocracy in society. The host is BBC broadcaster Ritula Shah. For Michael Sandel's new bool click here: https://www.primrosehillbooks.com/product/the-tyranny-of-merit-whats-become-of-the-common-good-michael-j-sandel-pb/ For Adrian Wooldridge's new book click here: https://www.primrosehillbooks.com/product/the-aristocracy-of-talent-how-meritocracy-made-the-modern-world-adrian-wooldridge/
Support this show http://supporter.acast.com/intelligencesquared.
See acast.com/privacy for privacy and opt-out information.
Learn more about your ad choices. Visit megaphone.fm/adchoices
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Podcast: LSE: Public lectures and events (LS 55 · TOP 0.5% what is this?)
Episode: The Tyranny of Merit: what's become of the common good?
Pub date: 2020-09-09
Contributor(s): Professor Michael Sandel | Join us for this online public event with Michael Sandel who will be discussing his latest book, The Tyranny of Merit: What's Become of the Common Good? In this new book Sandel argues that to overcome the polarised politics of our time, we must rethink the attitudes toward success and failure that have accompanied globalisation and rising inequality. Sandel highlights the hubris a meritocracy generates among the winners and the harsh judgement it imposes on those left behind. He offers an alternative way of thinking about success - more attentive to the role of luck in human affairs, more conducive to an ethic of humility, and more hospitable to a politics of the common good. Michael Sandel teaches political philosophy at Harvard University. His writings—on justice, ethics, democracy, and markets--have been translated into 27 languages. His course “Justice” is the first Harvard course to be made freely available online and on television. It has been viewed by tens of millions of people around the world, including in China, where Sandel was named the “most influential foreign figure of the year.” (China Newsweek) Sandel’s books relate enduring themes of political philosophy to the most vexing moral and civic questions of our time. They include What Money Can’t Buy: The Moral Limits of Markets; Justice: What’s the Right Thing to Do?; The Case against Perfection: Ethics in the Age of Genetic Engineering; Public Philosophy: Essays on Morality in Politics; Democracy’s Discontent: America in Search of a Public Philosophy; and Liberalism and the Limits of Justice. You can order the book, The Tyranny of Merit: What's Become of the Common Good?, (UK delivery only) from our official LSE Events independent book shop, Pages of Hackney. Andrés Velasco (@AndresVelasco) is Professor of Public Policy and Dean of the School of Public Policy at the London School of Economics and Political Science. The School of Public Policy (@LSEPublicPolicy) is an international community where ideas and practice meet. Our approach creates professionals with the ability to analyse, understand and resolve the challenges of contemporary governance.
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Podcast: LSE: Public lectures and events (LS 55 · TOP 0.5% what is this?)
Episode: The Euro@30: has the common currency finally grown up?
Pub date: 2021-10-06
Contributor(s): Professor Paul de Grauwe, Professor Waltraud Schelkle, Martin Wolf | The idea of a common currency materialised with the Maastricht Treaty thirty years ago. But soon after it was tested in a major crisis in 1992/93, with more to come. This panel will discuss whether the reforms since 2010 have been sufficient to make the Euro a "mature" currency. Meet our speakers and chair Paul De Grauwe (@pdegrauwe) is John Paulson Chair in European Political Economy at the LSE European Institute. Prior to joining LSE, Paul was Professor of International Economics at the University of Leuven, Belgium. He was a member of the Belgian parliament from 1991 to 2003. Waltraud Schelkle is Professor in Political Economy at the European Institute and has been at LSE since 2001. She is also an Adjunct Professor (Privatdozentin) of Economics at the Economics Department of the Free University of Berlin. Martin Wolf (@martinwolf_) is chief economics commentator at the Financial Times. Angelo Martelli (@angelo_martelli) is Assistant Professor in European and International Political Economy at the LSE European Institute. He worked as a Consultant for the Jobs Group of the World Bank, as a Policy Fellow for the Open Innovation Team of the UK Cabinet Office and HM Treasury and as a Technical Expert for the IMF. More about this event The European Institute (@LSEEI) is a centre for research and graduate teaching on the processes of integration and fragmentation within Europe. In the most recent national Research Excellence Framework the Institute was ranked first for research in its sector. This event is part of the LSE European Institute’s 30thanniversary celebrations. Twitter Hashtag for this event: #LSEEI30
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Podcast: LSE: Public lectures and events (LS 55 · TOP 0.5% what is this?)
Episode: The Indian Economy: recent developments and prospects
Pub date: 2021-10-11
Contributor(s): Shri Shaktikanta Das, Dr Swati Dhingra, N K Singh, Martin Wolf | In this event, the Governor of the Reserve Bank of India and the Chair of the 15th Indian Finance Commission will discuss the challenges facing the economy of India and what we can expect from it in the future. Meet our speakers and chair Shri Shaktikanta Das (@DasShaktikanta), former Secretary, Department of Revenue and Department of Economic Affairs, Indian Ministry of Finance, assumed charge as the 25th Governor of the Reserve Bank of India in December 2018. Immediately prior to his current assignment, he was acting as Member, 15th Finance Commission and G20 Sherpa of India. Swati Dhingra (@swatdhingraLSE) is Associate Professor in Economics at LSE, and associate of the Centre for Economic Performance. She is currently a member of the UK’s Trade Modelling Review Expert Panel and LSE’s Economic Diplomacy Commission. She is Research Fellow at CEPR, and on the editorial boards of Journal of International Economics and Review of Economic Studies. N K Singh (@NKSingh_MP) is a prominent Indian economist, academician, and policymaker. He is the President of the Institute of Economic Growth and the Chairman of the 15th Finance Commission. Prior to this position, he presided as Chairman of the Fiscal Responsibility and Budget Management Review Committee. He also served as a member of the Upper House of the Parliament, the Rajya Sabha, from 2008 to 2014. Martin Wolf (@martinwolf_) is Associate Editor and Chief Economics Commentator at the Financial Times, London. He was awarded the CBE (Commander of the British Empire) in 2000 for services to financial journalism. His most recent publication is The Shifts and The Shocks: What we’ve learned – and have still to learn – from the financial crisis (London and New York: Allen Lane, 2014). Andrés Velasco (@AndresVelasco) is Professor of Public Policy and Dean of the School of Public Policy at the London School of Economics and Political Science. Minouche Shafik is Director of the London School of Economics and Political Science and will deliver opening remarks. Nick Stern (@lordstern1) is the IG Patel Professor of Economics and Government and will deliver closing remarks. More about this event The LSE School of Public Policy (@LSEPublicPolicy) is an international community where ideas and practice meet. Our approach creates professionals with the ability to analyse, understand and resolve the challenges of contemporary governance.
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Podcast: Business Breakdowns (LS 49 · TOP 1% what is this?)
Episode: Chipotle: Simplicity as the Recipe for Success - [Business Breakdowns, EP. 02]
Pub date: 2021-04-07
Today we will be diving into Chipotle, the fast-casual food chain known for its burritos. It was started in 1993 by Steve Els, an entrepreneur who is actually a classically trained chef and dreamed of opening a fine dining restaurant.
He started Chipotle to earn cash for that dream, but the well-known chain took off and made TexMex fast-casual food an American staple. Over the past two decades, Chipotle has expanded nationwide to over 2000 owned and operated stores. Its significant growth is tied to its simple restaurant decor and efficient operations. Nevertheless, the beloved fast-casual chain was plagued with a series of foodborne illnesses from 2015 to 2018. Since then, the chain has been adapting rapidly to regain the trust of customers nationwide.
In this breakdown, we discuss Chipotle's origin stories, its hypergrowth, its focus on simplicity and innovation. We'll also go into details around how they navigated COVID and their national food safety outbreaks.
To help me break down Chipotle, I'm joined by Zack Fuss, an investor at Continental Grain and an expert on all things food and restaurant-related.
For the full show notes, transcript, and links to mentioned content, check out the episode page here.
This episode is brought to you by Tegus. We created Business Breakdowns to uncover the lessons and frameworks behind every business, and that's what makes Tegus our perfect launch partner. Much of the foundational prep for these episodes starts with research on the Tegus platform.
With Tegus, you can learn everything you’d want to know about a company in an on-demand digital platform. Investors share their expert calls, allowing others to instantly access more than 15,000 calls on Coinbase, Hinge Health, Farfetch, or almost any company of interest. All you have to do is log in. If you're ready to go deeper on any company and you appreciate the value of primary research, head to tegus.co/breakdowns for a free trial.
Business Breakdowns is a property of Colossus, Inc. For more episodes of Business Breakdowns, visit joincolossus.com/episodes.
Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here.
Follow us on Twitter: @JoinColossus | @patrick_oshag | @jspujji | @zbfuss
Show Notes
[00:03:13] - [First question] - What is Chipotle
[00:04:24] - Chipotle’s scale compared to its competitors
[00:05:36] - The origin story of Steve Ells and Chipotle
[00:06:47] - Carving out the fast-casual restaurant niche
[00:09:02] - Unique themes that have been carried into today
[00:10:22] - Unit economics in fine dining versus fast-casual dining
[00:11:56] - Gross margins and their similarities across establishments
[00:14:53] - An ideal payback period for a restaurant
[00:16:00] - What allows for Chipotle to have such an optimized payback period
[00:18:29] - Owned and operated versus franchised
[00:20:49] - Pros and cons to franchising or being an owner-operator
[00:22:11] - Key factors to consider when choosing to franchise or not
[00:23:31] - Chipotle taking $350 million in growth capital from McDonald’s
[00:25:58] - Differences between McDonald’s and Chipotle’s food
[00:27:08] - The E Coli outbreak in late 2015
[00:28:17] - Sweetgreen, Cava, Zoes Kitchen, Noodles & Co.
[00:30:09] - Pershing Square’s investment in Chipotle post-outbreak
[00:31:51] - Technology and its effects on the restaurant industry
[00:33:39] - Digital orders and profit margin variance
[00:35:53] - Launching a Digital-Only quesadilla menu item
[00:36:33] - Internet aggregators, dark kitchens, and future food tech trends
[00:39:52] - How Chipotle beat out Qdoba
[00:41:32] - Blaze Pizza, Tasty Made, Panda Express
[00:43:38] - Dark kitchens and network expansion
[00:45:01] - Lessons builders can take away from Chipotle’s story
[00:45:01] - Lessons investors can take away from Chipotle’s story
The podcast and artwork embedded on this page are from Colossus, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Business Breakdowns (LS 49 · TOP 1% what is this?)
Episode: Wyndham Hotels: Loyalty Matters - [Business Breakdowns, EP. 24]
Pub date: 2021-09-01
Today, we will break down Wyndham Hotels, the world’s largest and most diverse hotel franchisor with more than 9,000 hotels across 20 brands in over 80 countries.
Wyndham is a brilliant example of a ubiquitous business that often goes unnoticed. In this breakdown, we’ll start by looking at just how vast Wyndham’s portfolio of hotels and brands is, how the Highway Act of 1956 played an important role in developing that scale, and explore the economics of hotel ownership, both from the franchisee and franchisor’s perspective.
Then we’ll dive into Wyndham’s growth algorithm, the factors that make the business resilient to external shocks, and the ways in which green programs are helping to drive higher cash-on-cash returns for franchisees.
To help break down Wyndham Hotels, host Patrick O’Shaughnessy is joined by Lauren Taylor Wolfe, co-founder and Managing Partner of Impactive Capital and a Wyndham shareholder.
For the full show notes, transcript, and links to the best content to learn more, check out the episode page here.
This episode is brought to you by Tegus. We created Business Breakdowns to uncover the lessons and frameworks behind every business, and that's what makes Tegus our perfect launch partner. Much of the foundational prep for these episodes starts with research on the Tegus platform.
With Tegus, you can learn everything you’d want to know about a company in an on-demand digital platform. Investors share their expert calls, allowing others to instantly access more than 15,000 calls on Coinbase, Hinge Health, Farfetch, or almost any company of interest. All you have to do is log in. If you're ready to go deeper on any company and you appreciate the value of primary research, head to tegus.co/breakdowns for a free trial.
Business Breakdowns is a property of Colossus, Inc. For more episodes of Business Breakdowns, visit joincolossus.com/episodes.
Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here.
Follow us on Twitter: @JoinColossus | @patrick_oshag | @jspujji | @zbfuss
Show Notes
[00:02:48] - [First question] - What Wyndham is and the scope of their hotel franchise
[00:04:17] - Defining what select-service hotels are compared to traditional ones
[00:04:49] - An overview of what the hotel business is and their important levers
[00:06:44] - Why Wyndham’s business model is so advantageous
[00:09:22] - Franchisee expectations and the pros of franchising the brand
[00:12:22] - Whether or not Wyndham participates in loan and debt generation
[00:13:36] - Overview of their award-winning loyalty program
[00:17:36] - Customer acquisition for their loyalty program and how it drives spending
[00:19:16] - Wyndham’s corporate history and how it affects them today
[00:22:17] - Driving growth beyond their current real-estate footprint
[00:24:30] - Possible positive or negative nonlinear events that could affect them
[00:26:09] - Overview of the sales functions inside of their business
[00:28:05] - Changes in hotel use trends as of late
[00:30:05] - What hotel management means as a business
[00:33:05] - Capital allocation and abundant free cash flow without much need for it
[00:35:48] - Considering the ESG implications when evaluating the hotel industry
[00:38:17] - Aspects of the business that make it both resilient and competitive
[00:42:14] - Big variables that could cause Wyndham to fail
[00:44:10] - What it is about Wyndham’s economic opportunity that is favorable for franchisees
[00:46:19] - Unit economics and expenses at the individual hotel level
[00:47:40] - Lessons learned about brand, investing in a brand, and identifying new brands to acquire
[00:50:22] - What she’s learned most as an investor working with Wyndham
[00:52:19] - Attractive opportunities Hilton could offer that Wyndham couldn’t
[00:53:46] - What she’s learned about being a strong operator while working at Wyndham
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Podcast: Business Breakdowns (LS 49 · TOP 1% what is this?)
Episode: Uber: The Undeletable App - [Business Breakdowns, EP. 31]
Pub date: 2021-10-20
Today, we’re breaking down Uber. Despite a corporate history that spans just over a decade, the ink spilled on Uber could have its own wing in a library. So rather than record with our typical Breakdown format, we decided to host two portfolio managers and familiar guests on the podcast, Mario Cibelli, and Ram Parameswaran, to walk through their bull cases on Uber stock.
Uber is the case study for network effects in two-sided marketplaces but a controversial corporate culture, ongoing regulatory battles, and a debate over unit economics has made it a battleground stock since going public in 2019. During our wide-ranging conversation, we cover the opportunity for Uber’s business segments, what deteriorating service means for the product, and what COVID may have revealed regarding Ubers’ financials.
While Mario and Ram are clearly Uber bulls, it’s particularly fun to hear where their views align and differ. It’s a great reminder that we can all take very different paths to arrive at the same conclusion. Please enjoy this great breakdown of Uber.
For the full show notes, transcript, and links to the best content to learn more, check out the episode page here.
This episode is brought to you by Quartr. With Quartr, you can access conference calls, investor presentations, transcripts, and earnings reports – straight from your pocket. Quartr is 100% free and includes companies from 12 markets including the US, the UK, Canada, India, and all the Scandinavian countries. Quartr is available for both iOS and Android, so check out the app today.
This episode is brought to you by Brex. Brex began as the first corporate card for startups and now offers a full financial stack built for scale. Get 10-20x higher credit limits, uncapped rewards, easy deposits and payments, and expense management all in one. Grow your business faster with Brex.
Business Breakdowns is a property of Colossus, LLC. For more episodes of Business Breakdowns, visit joincolossus.com/episodes.
Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here.
Follow us on Twitter: @JoinColossus | @patrick_oshag | @jspujji | @zbfuss
Show Notes
[00:03:16] - [First question] - What they find most interesting about Uber
[00:04:40] - Major aspects of the business today in regards to bookings and revenue
[00:07:47] - The importance of multiple use cases and what happens by exploring verticals
[00:10:47] - History of getting drivers to join the company and what matters on the supply side
[00:15:01] - Impact of higher wait times and ride prices and emerging trends in a post-covid era
[00:17:48] - Thinking about Uber as a busted but booming model
[00:22:31] - The unit economics and journey of $100 flowing into Uber
[00:29:32] - Possible concerns and needs for capital Uber may have in the future
[00:37:13] - The role that DoorDash plays in this ecosystem and where it’s a potential threat
[00:39:01] - Is Lift an equally worthy competitor compared to DoorDash
[00:41:59] - What we can learn about labor and regulation when studying Uber
[00:43:44] - Thoughts on current management and capital allocation
[00:49:56] - Parallels between Amazon Prime and Uber’s membership program
[00:52:25] - Using the accumulated data to integrate an advertising model into their app
[00:56:33] - The biggest potential threats to Uber’s growing success
The podcast and artwork embedded on this page are from Colossus, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Comedy of the Week (LS 66 · TOP 0.05% what is this?)
Episode: Alex Edelman's Peer Group
Pub date: 2021-05-31
With lockdown changing all our perspectives on what's important, Alex Edelman reflects on how things have changed for him and what he now truly values. He takes a personal look at what life's really about now that the comedy clubs are closed and he can't go anywhere, and investigates how much his existence has been improved by the arrival of a new flatmate: his girlfriend.
Written by Alex Edelman and Max Davis
With special thanks to Josh Weller Simon Alcock Charlie Dinkin Adam Brace Danny Jolles and Hannah Einbinder
Producer is Sam Michell
It is a BBC Studios Production.
The podcast and artwork embedded on this page are from BBC Radio 4, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Comedy of the Week (LS 66 · TOP 0.05% what is this?)
Episode: Just a Minute
Pub date: 2021-09-20
Sue Perkins hosts the return of Radio 4’s longest running panel show, Just a Minute. This episode was produced using remote recording technology, with the audience joining from their homes all over the world. Caroline Barlow blows the whistle.
Devised by Ian Messiter
Produced by Hayley Sterling
A BBC Studios Production
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Podcast: Web Rush (LS 39 · TOP 2% what is this?)
Episode: Episode 153: Single Page Application vs Multi Page Application with Rich Harris
Pub date: 2021-09-30
Recording date: Sep 14, 2021
John Papa @John_Papa
Ward Bell @WardBell
Dan Wahlin @DanWahlin
Craig Shoemaker @craigshoemaker
Rich Harris @Rich_Harris
Brought to you by* AG Grid * Narwhal
Visit nx.dev to get the preeminent open-source toolkit for monorepo development, today.
Resources:* Single-page application vs. multiple-page application * More on MPA vs SPA * SvelteKit * What is a SPA (Single Page App) * Rendering on the Web * SPA SEO Pitfalls to Know Before you Build your App * Server rendering and Nuxt and Vue * Server rendering and Next and React * Vercel * Canvas Charting * Netlify * Azure Static Web Apps * Rich Harris on Twitter discussing MPA and SPA * Qwik on GitHub * First Look at Qwik * Web Rush 0144 - Qwik with Misko Hevery * What is a CDN (Content Delivery Network) * What is Edge computing? * CloudFlare workers
Timejumps 01:43 Guest introduction * 03:36 What is a SPA and MPA? * 08:22 Sponsor: Ag Grid * 09:34 What role does server rendering play? * 12:17 What is client side hydration? * 15:22 What's the state of modern tooling? * 20:46 The only reason to choose an MPA is... * 26:19 Sponsor: Narwhal * 26:52 What is the next evolution of this? * 36:17* Final thoughts
Podcast editing on this episode done by Chris Enns of Lemon Productions.
The podcast and artwork embedded on this page are from Dan Wahlin, John Papa, Ward Bell, Craig Shoemaker, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Ben Shapiro Show (LS 91 · TOP 0.01% what is this?)
Episode: Blake Masters REVEALS the Future in Arizona
Pub date: 2021-09-08
Blake Masters is co-author of the #1 New York Times Bestseller Zero to One, and president of the Thiel foundation. He joins to discuss the 2022 race for Arizona Senate; the future of big tech; and the virtues of a lo-fi lifestyle.
Check out Debunked. Where Ben Shapiro exposes leftist fallacies in 15 minutes or less. Watch the full season available only on The Daily Wire: utm.io/uc9er
Subscribe to Morning Wire, Daily Wire’s new morning news podcast, and get the facts first on the news you need to know: https://utm.io/udyIF
Learn more about your ad choices. Visit megaphone.fm/adchoices
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Podcast: James Acaster's Perfect Sounds (LS 56 · TOP 0.5% what is this?)
Episode: Romesh Ranganathan & Beyoncé's Lemonade
Pub date: 2020-04-24
Romesh Ranganathan discovers the world's best-selling album of 2016... but, more importantly, the album that turned James onto pop.
Produced by Hannah Hufford Photography by Edward Moore Design by Danny Arter
The podcast and artwork embedded on this page are from BBC Radio, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: James Acaster's Perfect Sounds (LS 56 · TOP 0.5% what is this?)
Episode: Evelyn Mok & Malian hip-hop
Pub date: 2020-06-19
Evelyn discovers the Malian hip-hop artist Luka Productions, named after his own studio, and his 2016 album Mali Kady. Plus 'Kettering Is Nice' makes its debut.
Produced by Hannah Hufford Photography by Edward Moore Design by Danny Arter
The podcast and artwork embedded on this page are from BBC Radio, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: James Acaster's Perfect Sounds (LS 56 · TOP 0.5% what is this?)
Episode: Phil Wang & electronic Mexican folk
Pub date: 2020-12-04
Phil absolutely loves Me Demandó DEMOS, recorded by San Cha on the farm where her mother grew up, without the opportunity to collaborate with a producer.
The podcast and artwork embedded on this page are from BBC Radio, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Broken Record with Rick Rubin, Malcolm Gladwell, Bruce Headlam and Justin Richmond (LS 69 · TOP 0.05% what is this?)
Episode: Peter Frampton Forgets the Words
Pub date: 2021-04-27
Peter Frampton is a classic rock guitar god who rose to international fame in the late ‘70s with his chart-topping double album, Frampton Comes Alive! Justin Richmond caught up with Frampton recently to talk about his new album of cover songs, Frampton Forgets The Words, that provides insight into his expansive 50-year career. Songs like “Loving The Alien,” a David Bowie song that Frampton says is a tribute to his childhood friend who helped revitalize his career. And George Harrison’s “Isn’t It A Pity,” which reminds Frampton of the time he jammed with George in Abbey Road studios while Phil Spector looked on from the control room. And Frampton also talks about how becoming a pinup sex symbol in the late ‘70s almost sidelined his career. Plus, how managing an inflammatory muscle disease has impacted his writing and playing.
Subscribe to Broken Record’s YouTube channel to hear old and new interviews, often with bonus content: https://www.youtube.com/brokenrecordpodcast and follow us on Twitter @BrokenRecord
You can also check out past episodes here: https://brokenrecordpodcast.com
Check out a playlist of our favorite Peter Frampton songs HERE.
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Podcast: Broken Record with Rick Rubin, Malcolm Gladwell, Bruce Headlam and Justin Richmond (LS 69 · TOP 0.05% what is this?)
Episode: Extended Cut: Brian Eno and Rick Rubin
Pub date: 2021-08-17
The Broken Record team has been revisiting some of our favorite episodes and releasing new extended cuts via PushNik, our Apple Podcasts subscription program. Today we’re giving you a taste of what those cuts sound like with the extended, ad-free cut of Rick Rubin's conversation with Brian Eno. This episode was recorded just before the debut of Eno’s Sonos radio station, The Lighthouse, where Eno’s programmed days worth of unreleased tracks from various eras in his career. In this episode we get to hear Rick and Eno discuss the works that changed Eno’s conception of art (1:08:47), the way lyrics generally hold very little water for the both of them (34:39), and more on the way Eno incorporates randomness into his music 9:42).
To hear more extended cuts of our conversations with artists like The Beastie Boys, Questlove, Brandi Carlile, Tanya Tucker, and Moby, subscribe to PushNik on Apple Podcasts. For $4.99 a month, you’ll get exclusive content like the Broken Record extended cuts and uninterrupted, ad-free listening across 14 shows in the Pushkin Industries catalog, including Malcolm Gladwell’s Revisionist History and The Happiness Lab with Dr. Laurie Santos. Search for Broken Record in Apple Podcasts, visit our show page, and sign up there. You can try it free for seven days.
Subscribe to Broken Record’s YouTube channel to hear all of our interviews: https://www.youtube.com/brokenrecordpodcast and follow us on Twitter @BrokenRecord
You can also check out past episodes here: https://brokenrecordpodcast.com
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The podcast and artwork embedded on this page are from Pushkin Industries, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Revisionist History (LS 89 · TOP 0.01% what is this?)
Episode: Hallelujah
Pub date: 2016-07-28
How does genius emerge? An exploration of different types of innovation—through the lens of Elvis Costello’s extraordinary song “Deportee,” once utterly forgettable and then, through time and iteration, a work of beauty and genius.
If you're looking to go deeper into the subjects on Revisionist History, visit Malcolm's collection on iBooks at http://www.apple.co/MalcolmGladwell -- iBooks will update the page every week with new recommendations.
To learn more about the topics covered in this episode, visit www.RevisionistHistory.com
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The podcast and artwork embedded on this page are from Pushkin Industries, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: hanging out with audiophiles (LS 51 · TOP 0.5% what is this?)
Episode: HOWA EP 39 - ALEXIS TAYLOR
Pub date: 2019-03-04
I’m so fortunate to have one of my favourite UK musicians on the show, Mr Alexis Taylor.
A founding member of the now legendary Hot Chip, he’s been releasing music for good whee while now and by gosh it’s all superb. His clothing choices are also second to none. The man marches to the beat of his own drum machine.
I REALLY hope you’re not aware of Alexis thus far so you get to experience the joy of opening up his catalog as it were the first snow you’d ever seen on your lawn. The majesty!!!
Hello Alexis!
GIG ALERT
Alexis has got a seriously cool show coming up very soon in NYC March 15th and 16th
https://www.metmuseum.org/events/programs/met-live-arts/alexis-taylor-19
Needless to say, this is a MUST for any New Yorkers. He’s playing together with the amazing Money Mark, my super talented friend Jonny Lam, Annie Hart and visual master Nick Relph at the Met! Bloody hell. EPIC!
Today’s Nitty is a levels game.
I ask the potentially expensive question “does a DI/mic pre combo make eurorack belt harder?”
Find out!
Music for this episode comes courtesy of Rory Simmons
https://www.rorysimmons.com
https://rorysimmons.bandcamp.com
soundcloud.com/rory_simmons
He’s a superb player and has already worked with a massively talent bunch of people like Jamie Cullum, The 1975, Bonobo, Mount Kimbie, Bat for lashes and many more! SWEET
Sponsors of todays show are the wonderous https://www.spitfireaudio.com
Makers of some of the finest sample libraries in the known universe. Just unreal and yet so so real!!
How you doing?
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Podcast: Broken Record with Rick Rubin, Malcolm Gladwell, Bruce Headlam and Justin Richmond (LS 69 · TOP 0.05% what is this?)
Episode: BROCKHAMPTON's Kevin Abstract
Pub date: 2021-07-06
BROCKHAMPTON's original members formed after posting on a Kanye West fan message board in 2010, and then started making music in The Woodlands, Texas. Since then, they’ve turned the idea of a boy band on its head. With as many as 14 members at times, BROCKHAMPTON's deconstructed the traditional pop star/rap ethos by being unapologetically inclusive of racial and sexual identities.
Today BROCKHAMPTON's frontman Kevin Abstract talks through the group’s sixth and latest album, Roadrunner: New Light, New Machine with Rick Rubin. We’ll hear Kevin talk about how the project slowly evolved from a pop album to something darker and more rap driven. Kevin also explains how supporting one of the group’s members through losing their dad became the album’s creative thrust, and explains why their next album will probably be their last.
Subscribe to Broken Record’s YouTube channel to hear all of our interviews: https://www.youtube.com/brokenrecordpodcast and follow us on Twitter @BrokenRecord
You can also check out past episodes here: https://brokenrecordpodcast.com
Check out a playlist of our favorite BROCKHAMPTON and Kevin Abstract tracks HERE.
Learn more about your ad-choices at https://www.iheartpodcastnetwork.comSee omnystudio.com/listener for privacy information.
The podcast and artwork embedded on this page are from Pushkin Industries, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Undercurrents (LS 38 · TOP 2.5% what is this?)
Episode: Episode 85: The global human rights system, and responding to ransomware
Pub date: 2021-08-13
The COVID-19 pandemic, new systems of digital repression and the strengthening of authoritarian governments around the world have put significant strain on the international human rights system. To consider the prospects for multilateral human rights protection and the role that civil society activism can play in this, Ben is joined by Dr Agnès Callamard, the Secretary General of Amnesty International.
Then Amrit discusses new developments in cybercrime with International Security Programme colleagues Esther Naylor and Isabella Wilkinson. They assess the prevalence and impact of ransomware attacks, and how governments and the private sector can respond.
Read the Chatham House expert comment:
Closing the space between cybercrime and cybersecurity
Credits:
Speakers: Agnès Callamard, Esther Naylor, Isabella Wilkinson
Hosts: Ben Horton, Amrit Swali
Sound Editor: Jamie Reed
Recorded and produced by Chatham House.
The podcast and artwork embedded on this page are from Chatham House, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Undercurrents (LS 38 · TOP 2.5% what is this?)
Episode: Episode 83: Afghanistan, and Indian democracy under Modi
Pub date: 2021-07-08
In this week's episode, Mariana is joined by Hameed Hakimi from the Asia-Pacific Programme to discuss the implications of the US troop withdrawal from Afghanistan. They consider, among other things, the strength of Afghan governance structures, the prospects for the Taliban, and the complexity of responding to widespread population movement in the region.
Then Amrit speaks to Debashish Roy Chowdhury and John Keane about the state of Indian democracy under PM Narendra Modi. Their latest book, To Kill A Democracy, describes India's daily struggles for democratic survival, and explains how lived social injustices and unfreedoms rob elections of their meaning, while at the same time feeding the decadence and iron-fisted rule of its governing institutions.
Read the World Today article:
Afghanistan: America pulls out the dagger
Credits:
Speakers: Hameed Hakimi, Debashish Roy Chowdhury, John Keane
Hosts: Amrit Swali, Mariana Vieira
Editor: Jamie Reed
Recorded and produced by Chatham House.
The podcast and artwork embedded on this page are from Chatham House, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: This Week in Startups (LS 63 · TOP 0.1% what is this?)
Episode: Robinhood earnings breakdown, Amazon launching department stores + Mark Suster deep dives VC | E1268
Pub date: 2021-08-19
Jason breaks down the 2 metrics he uses to to assess Robinhood's Q2 earnings (1:43) and Amazon entering Department Store retail (16:39). Then, Mark Suster from Upfront Ventures joins (24:07) to discuss venture metrics, community as a moat, why high valuations don't necessarily mean a bubble & more.
The podcast and artwork embedded on this page are from Jason Calacanis, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: James Acaster's Perfect Sounds (LS 56 · TOP 0.5% what is this?)
Episode: Nish Kumar & Bon Iver's 22, A Million
Pub date: 2020-07-03
Bon Iver and folk music fan Nish Kumar listens for the first time to the band's 2016 experimental electronic masterpiece 22, A Million.
The podcast and artwork embedded on this page are from BBC Radio, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: James Acaster's Perfect Sounds (LS 56 · TOP 0.5% what is this?)
Episode: Nish Kumar & Kevin Abstract's American Boyfriend: A Suburban Love Story
Pub date: 2021-01-29
Nish is blown away by this concept album from Brockhampton's Kevin Abstract. The album tells the story of him growing up in Texas with homophobic parents and a racist boyfriend.
The podcast and artwork embedded on this page are from BBC Radio, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Friday Night Comedy from BBC Radio 4 (LS 75 · TOP 0.05% what is this?)
Episode: Party's Over: ep4 Parish Council 13th August 2021
Pub date: 2021-08-13
What happens when the Prime Minister suddenly stops being Prime Minister?
One day you're the most powerful person in the country, the next you're irrelevant, forced into retirement 30 years ahead of schedule and find yourself asking 'What do I do now?'
Miles Jupp stars as Henry Tobin - Britain's shortest serving and least popular post war PM (he managed 8 months).
We join Henry soon after his crushing election loss. He’s determined to not let his disastrous defeat be the end of him. Instead Henry's going to get back to the top - he's just not sure how and in what field.
This week, Henry meets a local nemesis as he tries to make some home and garden improvements so Christine steps in with a plan.
Henry Tobin... Miles Jupp Christine Tobin... Ingrid Oliver Natalie... Emma Sidi Jones... Justin Edwards Albert...Joseph Marcell
Written by Paul Doolan and Jon Hunter
Produced by Richard Morris and Simon Nicholls Production co-ordinator: Caroline Barlow Sound design: Marc Willcox
A BBC Studios Production
The podcast and artwork embedded on this page are from BBC Radio 4, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Acquired (LS 63 · TOP 0.1% what is this?)
Episode: Special: Solana (with CEO Anatoly Yakovenko)
Pub date: 2021-07-19
We sit down with the hottest new protocol layer in crypto today: Solana, and its cofounder Anatoly Yakovenko, who is the CEO of Solana Labs. If you listened to our Ethereum episode or follow crypto even at a cursory level, you've likely heard of Solana and its ability to scale transactions thousands of times higher than Ethereum. And, unlike other so-called "ETH killers", Solana is doing so in production today with large and real applications. We dive into the project's history coming out of the 2017-18 crypto winter, how it works and what's ahead now that they've recently raised $314m (yes that is Pi $million) from a16z and Polychain Capital, with their native SOL tokens currently trading at a market cap around $10B (!).
If you love Acquired and want more, join our LP Community for access to over 50 LP-only episodes, monthly Zoom calls, and live access for big events like our Book Clubs. We can't wait to see you there. Join here at: https://acquired.fm/lp/
Sponsors:
- Thanks to SoftBank Latin America for being our presenting sponsor for this special episode. SoftBank has truly been the first and best large capital allocator in the Latin American startup ecosystem, and we are very excited to work with and learn from them over the rest of 2021. If you are an entrepreneur, employee, other investor or simply someone who's interested in learning about the best young companies in LatAm right now, get in touch with them at: https://bit.ly/acquiredsoftbanklatam and tell them that you heard about them on Acquired.
- Shu's Twitter (which we highly recommend following!): https://twitter.com/snyatta
- Paulo's LinkedIn: https://www.linkedin.com/in/paulo-passoni-67195/
- Thank you as well to Modern Treasury and to Fundrise. You can learn more about them at:
- https://bit.ly/acquiredmoderntreasury (and you can find our reverse interview with them at https://www.moderntreasury.com/acquired )
- https://bit.ly/acquiredfundrise
Topics covered:
- Anatoly's background as a wireless engineer at Qualcomm, and how it led to a fundamental discovery of how to improve crypto system scalability
- Solana's role in the crypto protocol ecosystem and why there's a need for it (and why it can and will exist) alongside Ethereum versus "killing" it
- Starting Solana during the 2017-18 crypto winter, and how it forced them to focus just on building and shipping versus raising and posturing
- Bootstrapping adoption with the mining community (Solana's "true believers") and the early and ardent support they provided
- Where Solana falls on the Vitalik "Scalability - Security - Decentralization" trilemma, and why Solana's superpower of maintaining composability is so attractive
Links:
- Solana: https://solana.com
- Solana on Twitter: https://twitter.com/solana
- Solana Hackathon submissions: https://airtable.com/shriNT26cAZeDJagn/tbl5fZ4E1BBbVAttW
- FTX: https://ftx.us
- Audius: https://audius.co
Note: Acquired hosts and guests may hold assets discussed in this episode. This podcast is not investment advice, and is intended for informational and entertainment purposes only. You should do your own research and make your own independent decisions when considering any financial transactions.
The podcast and artwork embedded on this page are from Ben Gilbert and David Rosenthal, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Evolving for the Next Billion by GGV Capital (LS 45 · TOP 1% what is this?)
Episode: Peter De Caluwe of Thunes: Building a Cross-Border Payments Network for Emerging Markets
Pub date: 2020-09-16
Today on the show, we have Peter De Caluwe, the Executive Chairman and CEO of Thunes. Thunes is a B2B cross-border payments network for emerging markets. It provides transfer of funds between payment systems, including mobile wallet providers, money transfer operators and banks, in more than 100 countries and 60 currencies. We recorded this interview a while back, the company recently announced its $60 million Series B, which GGV Capital also participated as an existing investor. With more than 25 years of experience in FinTech, Peter is a specialist in electronic payments, e-commerce, credit cards in emerging markets. He was previously the CEO of Ogone, Naspers Payments and PayU. Having graduated with a Bachelor's in Marketing from Group T Leuven, a Belgium-based college, he has risen through the ranks to become a serial entrepreneur and investor. For the full transcript of the show, go to nextbn.ggvc.com Join our listeners' community, go to nextbn.ggvc.com/engage
The podcast and artwork embedded on this page are from GGV Capital, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Evolving for the Next Billion by GGV Capital (LS 45 · TOP 1% what is this?)
Episode: Binny Bansal: Inside the Success of Flipkart
Pub date: 2021-04-07
Today on the show we have Binny Bansal. Binny cofounded Flipkart in 2007 and played a pivotal role in scaling it to a market leading e-commerce space which still has so many fundamental customer and supply problems along the way. From a small beginning, Binny along with his friend Sachin Bansal turned Flipkart into a massive online commerce venture that was bought over by Walmart in 2018, for a whopping $16 billion. He's also a prolific angel investor and mentor, with over 30 investments in startups ecosystem in India.
This episode is co-hosted by GGV Colleague Madhu Yalamarthi.
For the full transcript of the show, go to nextbn.ggvc.com Join our listeners' community, go to nextbn.ggvc.com/community
The podcast and artwork embedded on this page are from GGV Capital, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: In Our Time (LS 75 · TOP 0.05% what is this?)
Episode: Booth's Life and Labour Survey
Pub date: 2021-06-10
Melvyn Bragg and guests discuss Charles Booth's survey, The Life and Labour of the People in London, published in 17 volumes from 1889 to 1903. Booth (1840-1916), a Liverpudlian shipping line owner, surveyed every household in London to see if it was true, as claimed, that as many as a quarter lived in poverty. He found that it was closer to a third, and that many of these were either children with no means of support or older people no longer well enough to work. He went on to campaign for an old age pension, and broadened the impact of his findings by publishing enhanced Ordnance Survey maps with the streets coloured according to the wealth of those who lived there.
The image above is of an organ grinder on a London street, circa 1893, with children dancing to the Pas de Quatre
With
Emma Griffin Professor of Modern British History at the University of East Anglia
Sarah Wise Adjunct Professor at the University of California
And
Lawrence Goldman Emeritus Fellow in History at St Peter’s College, University of Oxford
Producer: Simon Tillotson
The podcast and artwork embedded on this page are from BBC Radio 4, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: In Our Time (LS 75 · TOP 0.05% what is this?)
Episode: Edward Gibbon
Pub date: 2021-06-17
Melvyn Bragg and guests discuss the life and ideas of one of the great historians, best known for his History of the Decline and Fall of the Roman Empire (published 1776-89). According to Gibbon (1737-94) , the idea for this work came to him on 15th of October 1764 as he sat musing amidst the ruins of Rome, while barefooted friars were singing vespers in the Temple of Jupiter. Decline and Fall covers thirteen centuries and is an enormous intellectual undertaking and, on publication, it became a phenomenal success across Europe.
The image above is of Edward Gibbon by Henry Walton, oil on mahogany panel, 1773.
With
David Womersley The Thomas Wharton Professor of English Literature at St Catherine’s College, University of Oxford
Charlotte Roberts Lecturer in English at University College London
And
Karen O’Brien Professor of English Literature at the University of Oxford
Producer: Simon Tillotson
The podcast and artwork embedded on this page are from BBC Radio 4, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: In Our Time (LS 75 · TOP 0.05% what is this?)
Episode: Shakespeare's Sonnets
Pub date: 2021-06-24
Melvyn Bragg and guests discuss the collection of poems published in 1609 by Thomas Thorpe: Shakespeare’s Sonnets, “never before imprinted”. Yet, while some of Shakespeare's other poems and many of his plays were often reprinted in his lifetime, the Sonnets were not a publishing success. They had to make their own way, outside the main canon of Shakespeare’s work: wonderful, troubling, patchy, inspiring and baffling, and they have appealed in different ways to different times. Most are addressed to a man, something often overlooked and occasionally concealed; one early and notorious edition even changed some of the pronouns.
With:
Hannah Crawforth Senior Lecturer in Early Modern Literature at King’s College London
Don Paterson Poet and Professor of Poetry at the University of St Andrews
And
Emma Smith Professor of Shakespeare Studies at Hertford College, Oxford
Producer: Simon Tillotson
The podcast and artwork embedded on this page are from BBC Radio 4, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: In Our Time (LS 75 · TOP 0.05% what is this?)
Episode: Doggerland (Summer Repeat)
Pub date: 2021-07-22
Melvyn Bragg and guests discuss the people, plants and animals once living on land now under the North Sea, now called Doggerland after Dogger Bank, inhabited up to c7000BC or roughly 3000 years before the beginnings of Stonehenge. There are traces of this landscape at low tide, such as the tree stumps at Redcar (above); yet more is being learned from diving and seismic surveys which are building a picture of an ideal environment for humans to hunt and gather, with rivers and wooded hills. Rising seas submerged this land as glaciers melted, and the people and animals who lived there moved to higher ground, with the coasts of modern-day Britain on one side and Denmark, Germany, The Netherlands, Belgium and France on the other.
With
Vince Gaffney Anniversary Professor of Landscape Archaeology at the University of Bradford
Carol Cotterill Marine Geoscientist at the British Geological Survey
And
Rachel Bynoe Lecturer in Archaeology at the University of Southampton
Producer: Simon Tillotson
The podcast and artwork embedded on this page are from BBC Radio 4, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Acquired (LS 63 · TOP 0.1% what is this?)
Episode: Meituan
Pub date: 2021-03-10
We dive into the history behind Meituan, the juggernaut Chinese "super-app" which dominates China's services economy, offering consumers everything from food delivery, restaurant reviews, travel booking, bike-sharing, movie ticketing, and countless other entertainment and lifestyle services all at the touch of a button. Already China's 3rd largest tech company by market cap (behind just Tencent and Alibaba), Meituan did $15 billion in net revenue in FY2019 and continues to grow rapidly. What makes it so special, and how were they able to become the market leader in such a competitive space? This story is packed with lessons that apply equally beyond China tech to high-growth company building and investing everywhere.
If you love Acquired and want more, join our LP Community for access to over 50 LP-only episodes, monthly Zoom calls, and live access for big events like emergency pods and book club discussions with authors. We can't wait to see you there. Join here at: https://acquired.fm/lp/
Sponsors:
- Thanks to Tiny for being our presenting sponsor for all of Acquired Season 8. Tiny is building the "Berkshire Hathaway of the internet" — if you own a wonderful internet business that you want to sell, or know someone who does, you should get in touch with them. Unlike traditional buyers, they commit to quick, simple diligence, a 30-day or less process, and will leave your business to do its thing for the long term. You can learn more about Tiny here: http://bit.ly/acquiredtiny
- Thank you as well to Vouch and to Capchase. You can learn more about them at:
- https://bit.ly/acquired-vouch
- http://bit.ly/acquiredcapchase
The Meituan Playbook:
(also available on our website at https://www.acquired.fm/episodes/meituan )
-
Adding product offerings (post initial product-market fit) isn't losing focus. It's smart business.
-
A huge part of Meituan's success and longterm defensibility versus its literally thousands of past competitors is its ability to cross-sell customers across many different product lines. Meituan can afford to spend much more on acquiring and retaining a new user who'll end up purchasing food delivery, groceries, hotels, travel and more through the platform vs. standalone competitors in each vertical. Most western companies woefully misunderstand this dynamic. (Amazon being a notable exception)
- Meituan enjoys an average of 26 transactions per user per year (vs e.g. Airbnb users which book an average of 0.5 transactions/year). With each additional offering, Meituan increases the number of revenue streams it can amortize its CAC over, while also offering superior experiences to customers.
-
Key to making this strategy work is having the discipline to follow the same playbook as any startup: launch new initiatives quickly, test and improve based on real customer feedback, don't let perfect be the enemy of shipped, and kill what's not working and move on. Meituan and Amazon's new initiatives often lack polish — but they either quickly bring in $billions of revenue, or they die and the company goes on to the next one. Again with few exceptions, western tech companies completely misunderstand how to execute this playbook effectively.
-
When you spot a market that's both large and growing fast — ride that wave!!
-
Chinese e-commerce was a 20% saturation industry in 2017 and still growing nicely. However real world services was only 5% online, and poised to grow even faster. Staying nimble to capitalize on this online to offline (or "O2O") trend allowed Meituan to accelerate while Alibaba was caught flat-footed. Today Meituan (along with its fellow Tencent portfolio company Pinduoduo) represents probably the biggest threat Alibaba has faced in its entire history.
-
Many still don't realize what a powerful moat (trusted) reviews provide in online platforms.
-
Once it merged with Dianping, review data became Meituan's biggest competitive advantage vs other food delivery (and other product line) competitors. A deep database of reviews creates an incredible barrier to entry: any competitor can standup a set of listings, but without trusted reviews those listings are just "flat". This same dynamic helped Airbnb successfully defend against European clones early in its life.
-
Old news, but always worth repeating: the days of China simply cloning American tech companies are long gone. Today it's China, not the US, that's leading innovation on mobile and the internet more broadly across many categories.
-
Ironically, Meituan's founder Wang Xing started his career as perhaps China's top Web 2.0 company cloner, and Meituan itself began as a Groupon knockoff. But to say the the tables have turned today is a massiveunderstatement, haha.
-
Meituan capitalized on the secular trend of China's growing middle class and mobile-first economy.
-
Meituan's growth followed the growth of China's middle class. They were able to capitalize on the emergence of Tier 2 and 3 cities that provided newly addressable populations.
- Meituan was smart to pay attention to these non-Tier 1 cities from the very beginning. Founder Wang Xing realized that smaller cities where people were beginning to access the internet via mobile phones and internet cafes were a good fit for their initial group-discount platform.
Links:
- Meituan's English language walkthrough video: https://www.youtube.com/watch?v=5wxgQVjDviQ
- The Tech Buzz China podcast: https://www.techbuzzchina.com
- GGV's Evolving for the Next Billion podcast: https://nextbn.ggvc.com/podcasts/
- Bernard Leong's Analyse Asia: https://analyse.asia
Carve Outs:
- Extraterrestrial: https://www.amazon.com/Extraterrestrial-First-Intelligent-Beyond-Earth-ebook/dp/B081TTY4NX/
- John Luttig's newsletter: https://luttig.substack.com
Episode Sources:
- Episode 258: Meituan Dianping with Liza Lin - Analyse Asia
- http://meituan.todayir.com/attachment/20180907112826231236667_en.pdf
- http://meituan.todayir.com/attachment/2020041708160280279238680_en.pdf
- http://www.yawenlei.com/uploads/4/4/3/4/44340649/asr_lei.pdf
- http://www.yourtechstory.com/2019/04/06/wang-xing-chinese-billionaire-businessman-founder-meituan/
- https://about.meituan.com/en
- https://analyse.asia/2018/07/28/episode-258-meituan-dianping-with-liza-lin/
- https://archive.org/details/aisuperpowerschi0000leek/page/22/mode/2up
- https://chinatechinvestor.simplecast.com/episodes/43-alibaba-has-a-meituan-problem-can-they-solve-it-with-11-billion-nTvmG0A5
- https://cn.reuters.com/article/instant-article/idUKTRE7433HI20110504
- https://daxueconsulting.com/o2o-food-delivery-market-in-china/
- https://en.wikipedia.org/wiki/Ele.me
- https://en.wikipedia.org/wiki/Meituan
- https://en.wikipedia.org/wiki/Wang_Xing
- https://medium.com/@Loisinbeijing/online-food-delivery-market-in-china-and-why-ele-me-is-losing-the-food-delivery-wars-17ef912d8f53
- https://money.cnn.com/2018/09/20/technology/meituan-dianping-ipo/index.html
- https://nextbn.ggvc.com/opinions/meituan-dianpings-path-towards-profitability/
- https://nextbn.ggvc.com/podcast/s1-ep-5-tao-zhang-of-dianping-on-merging-with-meituan-groupon/
- https://secure.fundsupermart.com/fsm/article/view/rcms202620/meituan-dianping-the-unicorn-that-might-one-day-become-china-s-next-ten-bagger
- https://secure.fundsupermart.com/fsm/article/view/rcms204700/meituan-dianping-the-undisputed-king-of-china-s-45-billion-dollar-online-food-delivery-industry
- https://seekingalpha.com/instablog/49925729-dongtalk/5288005-three-campaigns-of-meituan-dianping-in-2018
- https://techcrunch.com/2015/01/19/meituan-700m/?_ga=2.56564267.1010056541.1614018328-150822071.1609868284
- https://techcrunch.com/2015/06/30/baidu-offline-to-online-20-billion-cny/?_ga=2.59578797.1010056541.1614018328-150822071.1609868284
- https://techcrunch.com/2015/10/08/meituan-and-dianping-chinas-top-group-deals-sites-merge-in-multi-billion-dollar-deal/
- https://thehustle.co/01272021-bytedance-valuation/#:~:text=ByteDance is currently valued at,%24800B)
- https://venturebeat.com/2008/05/26/chinese-local-review-site-dianping-a-lot-more-than-a-yelp-for-china/
- https://web.archive.org/web/20170615042020/http://www.fox14tv.com/story/35003690/meituan-dianping-becomes-the-first-worldwide-on-demand-delivery-platform-to-process-more-than-10-million-orders-and-deliveries-per-day
- https://www.caixinglobal.com/2017-02-22/video-of-brawling-deliverymen-sets-chinese-internet-abuzz-101057682.html
- https://www.forbes.com/global/2011/0509/companies-wang-xing-china-groupon-friendster-cloner.html?sh=517b2d5955a6
- https://www.ft.com/content/05686da9-60f8-4a3a-a5c5-95155bd01ffe
- https://www.marketwatch.com/story/alibaba-raises-11-billion-in-hong-kong-secondary-listing-2019-11-20
- https://www.statista.com/statistics/1155778/china-number-of-wechat-mini-program-daily-active-users/
- https://www.techbuzzchina.com/episodes/ep-10-meituan-the-super-app-that-won-against-a-thousand-clones
- https://www.techbuzzchina.com/episodes/ep-25-the-o2o-local-services-war-alibaba-vs-meituan-part-1-eleme
- https://www.techbuzzchina.com/episodes/ep-26-the-o2o-local-services-war-alibaba-vs-meituan-part-2-koubei
- https://www.techinasia.com/5000-group-buy-sites-in-china-but-no-ones-making-money
- https://www.techinasia.com/china-online-food-ordering-startup-eleme-raises-80-million-dollars
- https://www.techinasia.com/chinas-successful-founders-afraid-copycat
- https://www.theworldofchinese.com/2017/08/wheel-life-china-the-fast-and-the-furious/
- https://www.wsj.com/articles/BL-MBB-58175
- https://www.wsj.com/articles/chinas-dianping-valued-at-4-billion-1427962959
- https://www.wsj.com/articles/chinas-meituan-dianping-files-for-ipo-reveals-loss-of-nearly-3-billion-in-2017-1529895226
- https://www.wsj.com/articles/chinas-meituan-dianping-raises-3-3-billion-in-biggest-startup-round-ever-1453211614?mod=article_inline
- https://www.wsj.com/articles/chinese-app-meituan-raises-4-2-billion-in-ipo-1536819691
- https://www.wsj.com/articles/chinese-startups-meituan-com-and-dianping-near-multibillion-dollar-merger-1444188561
- https://www.wsj.com/articles/investors-gain-billions-from-chinese-tech-ipo-1538041120
- https://www.wsj.com/articles/investors-including-tencent-priceline-pump-4-billion-into-online-lifestyle-platform-1508413127
- https://www.wsj.com/articles/offering-discounts-and-delivery-meituan-wants-to-become-chinas-next-internet-giant-1529578801
- https://www.youtube.com/watch?v=5wxgQVjDviQ
- https://www.youtube.com/watch?v=ruyCPdUz1J0
- https://youtu.be/IgDeiGpmXaQ
- https://youtu.be/z9NI-UAZDvw
The podcast and artwork embedded on this page are from Ben Gilbert and David Rosenthal, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Syntax - Tasty Web Development Treats (LS 62 · TOP 0.1% what is this?)
Episode: Serverless / Cloud Functions - Part 1
Pub date: 2020-02-19
In this episode of Syntax, Scott and Wes talk about serverless and cloud providers - the benefits, limitations, providers and more!
.TECH Domains - Sponsor If you need eyes on your project, you’ll need a domain, and .TECH is perfect for representing your brand. Find out if your .TECH domain is available at go.tech/syntax2020. Use the coupon code Syntax2020 and get 90% off 1- 5- and 10-year domain names.
Freshbooks - Sponsor Get a 30 day free trial of Freshbooks at freshbooks.com/syntax and put SYNTAX in the “How did you hear about us?” section.
Show Notes 4:05 - What is Serverless?
- URL driven
- Startup/shut down (Heroku works this way)
- Digital Ocean droplet works differently
8:15 - What are the benefits?
- Scale up specific functions rather than everything - aka potentially cheaper
- Security - your singular server instance being hacked is not a possibility
- Less knowledge overhead required
- You don’t need to manage your own server
- Empowers front-end devs to do more
- Faster deploys
- Only re-deploy the code that changed
17:05 - What can you host on Serverless?
- Static Files - SPA (React)
- Single functions
- It can be in JS, Python, GO, PHP
18:07 - What can’t you host on Serverless?
- Entire applications
- Large apps have slow coldstarts
- 500mb limit
23:40 - Raw Providers
- Google Cloud
- Azure
- AWS Lambda
- SAP
- Red Hat
- IBM Cloud Functions
- Cloudflare Workers
- Kind of cool because they work like service workers where you can intercept any HTTP request
27:33 - Easy Providers + Frameworks
- Begin + Arc.codes
- Zeit Now + Next.js
- Anything + Serverless
- Netlify
- AWS Amplify
- Apex Up - TJ Holowaychuk
- Open Faas + Digital Ocean
Links * Heroku * Digital Ocean * Meteor Galaxy * Codepen Radio: Preprocessors and Lambda * Zeit Now * Wes’ tweet about serverless * @maxsteenbergen * uses.tech * Google Cloud * Azure * AWS Lambda * SAP * Red Hat * IBM Cloud Functions * Cloudflare Workers * Begin * Arc.codes * Severless * Netlify * AWS Amplify * Apex Up * Open Faas * @tjholowaychuk * Scott tries Begin.com * SyntaxFM Reddit
××× SIIIIICK ××× PIIIICKS ××× * Scott: Matt McMuscles YouTube Channel * Wes: Modern Vintage Gamer
Shameless Plugs * Scott: Scott’s YouTube Channel * Wes: Beginner Javascript Course - Use the coupon code ‘Syntax’ for $10 off!
Tweet us your tasty treats! * Scott’s Instagram * LevelUpTutorials Instagram * Wes’ Instagram * Wes’ Twitter * Wes’ Facebook * Scott’s Twitter * Make sure to include @SyntaxFM in your tweets
The podcast and artwork embedded on this page are from Wes Bos & Scott Tolinski - Full Stack JavaScript Web Developers, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Syntax - Tasty Web Development Treats (LS 62 · TOP 0.1% what is this?)
Episode: More on Severless - Databases × Files × Secrets × Auth × More!
Pub date: 2020-03-04
In this episode of Syntax, Scott and Wes do a part 2 about Serverless — databases, files, secrets, auth, and more!
Sanity - Sponsor Sanity.io is a real-time headless CMS with a fully customizable Content Studio built in React. Get a Sanity powered site up and running in minutes at sanity.io/create. Get an awesome supercharged free developer plan on sanity.io/syntax.
Freshbooks - Sponsor Get a 30 day free trial of Freshbooks at freshbooks.com/syntax and put SYNTAX in the “How did you hear about us?” section.
Show Notes 2:47 - Wes tried Cloudflare Workers
- Also this is so cool:
Hey Wes, just listened to the latest Syntax episode on the serverless setup. Not sure if it’s an episode idea or not, but if you wanna do a bit of a dive on Cloudflare’s service workers, I’m currently leading an “invisible infrastructure migration” right now from a legacy WordPress setup to a new Storyblok/Netlify setup. We’re using Cloudflare’s service workers to basically “stitch” the headers/menus/footers from the old WordPress site into our new Netlify pages, but serving the page back as if it was part of the normal domain. This means we can migrate from the old to the new slowly without massively disrupting SEO, doing a lengthy/costly rebuild, etc.
- A word on Digital Ocean
- Kubernetes + FAAS allows you to scale up/down
13:54 - Secret management
- Some have a great UI
- Some have a CLI
- Some only have production
- Some have dev/staging/prod
16:24 - Vendor lock-in
- Two kinds of vendor lock-in
- Lock into a low-level provider (Like AWS, or MongoDB)
- Lock into a framework
- Questions to ask:
- Can I go, take my app as-is, and host it on another provider?
- Can I refactor the config and run my code as-is?
- Do I need to refactor my code for it to run on other platforms?
- Next.js will only run on Now
- There is a community package
- Begin all runs on Arc.codes
- Firebase is locked in?
25:12 - Sharing dependencies
- Each function will have its own package.json, which can be a pain
- Publish utils a private module
- AWS Layers
- Import/export
- Bundle and tree shake
30:26 - Local development
- Now dev
- NPX sandbox
- Wrangler for Cloudflare workers
36:40 - Existing applications
- Difficult to move with many routes, but easy to move a Graphql API that has one single route
- Maybe do piece by piece instead of all at once
- Begin has http express method
45:21 - Data
- Any DB you want
- Dynamo DB integrated into many
- Firebase
- KV Storage for Cloudflare workers
- Fauna
48:14 - File storage
- Generally files go in the associated file place like Amazon S3, Backblaze B2, Cloudinary
- Many also have this integrated as well
52:18 - Auth
- Serverless is ephemeral and stateless
- JWT likely as sessions will work, but doesn’t really make sense
Links * Cloudflare Workers * Akamai * MongoDB Stitch * Hitler uses Kubernetes * Digital Ocean * Kubernetes * Firebase * Google Cloud * Architect * Next.js * Now.sh * Begin * Netlify * Now * Wrangler * Apollo Federation * Monaco * Postman * Codesandbox * DynamoDB * Amazon S3 * Backblaze B2 * Cloudinary
××× SIIIIICK ××× PIIIICKS ××× * Scott: The Power of Bad by John Tierney * Wes: Socket Organizer
Shameless Plugs * Scott: Animating React with Framer Motion - Sign up for the year and save 25%! * Wes: All Courses - Use the coupon code ‘Syntax’ for $10 off!
Tweet us your tasty treats! * Scott’s Instagram * LevelUpTutorials Instagram * Wes’ Instagram * Wes’ Twitter * Wes’ Facebook * Scott’s Twitter * Make sure to include @SyntaxFM in your tweets
The podcast and artwork embedded on this page are from Wes Bos & Scott Tolinski - Full Stack JavaScript Web Developers, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Syntax - Tasty Web Development Treats (LS 62 · TOP 0.1% what is this?)
Episode: Hasty Treat - Authentication: LocalStorage vs Cookies vs Sessions vs Tokens
Pub date: 2019-03-04
In this Hasty Treat, Scott and Wes talk about authentication — the difference between localStorage, cookies, session, tokens and more!
LogRocket - Sponsor LogRocket lets you replay what users do on your site, helping you reproduce bugs and fix issues faster. It’s an exception tracker, a session replayer and a performance monitor. Get 14 days free at https://logrocket.com/syntax.
Show Notes 4:20 - How should we track users?
- Token based - generally stored in the client
- Session based - stored on the server
- Token Based (JWT)
6:00 - Token-based auth
- Stateless - the server does not maintain a list of logged in users
- Scalable - you can use serverless functions easily
- Cross domain
- Data can be stored in JWT
- Easy to use on non-web sites like mobile apps
- Hard to expire tokens — you must maintain a list of blacklisted tokens
7:48 - Session-based auth
- Stateful - generally you maintain a list of session IDs
- Passive - once signed in, no need to send token again
- Easy to destroy sessions
10:48 - How do we identify the user on each request? localStorage or Cookies?
- A common misconception is that localStorage is for tokens while cookies is for sessions
- With localStorage, we need to grab the token and send them along on each request
- With cookies, the data is sent along on each request
11:25 - Security Issues
- XSS for Tokens - make sure bad actors can’t run code on your site
- Sanitize inputs
- XSRF - CSRF tokens are needed
Links * Cookies vs Tokens: The Definitive Guide
Tweet us your tasty treats! * Scott’s Instagram * LevelUpTutorials Instagram * Wes’ Instagram * Wes’ Twitter * Wes’ Facebook * Scott’s Twitter * Make sure to include @SyntaxFM in your tweets
The podcast and artwork embedded on this page are from Wes Bos & Scott Tolinski - Full Stack JavaScript Web Developers, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Syntax - Tasty Web Development Treats (LS 62 · TOP 0.1% what is this?)
Episode: Potluck - Svelte × Bleeding-Edge Tech × Git Process × Screencasts × Government Jobs × Permissions-Based APIs × Rescript × More!
Pub date: 2021-07-21
It’s another Potluck! In this episode, Scott and Wes answer your questions about Svelte, bleeding-edge tech, best Git processes, Create React App, screencast software, FitBit API, government jobs, Syntax sponsors, and more!
.TECH Domains - Sponsor .TECH is taking the tech industry by storm. A domain that shows the world what you are all about! If you’re looking for a domain name for your startup, portfolio, or your own project like we did with uses.tech, check out .tech Domains. Syntax listeners can snap their .TECH Domains at 80% off on five-year registration by visiting go.tech/syntaxistech and using the coupon code “syntax5”.
LogRocket - Sponsor LogRocket lets you replay what users do on your site, helping you reproduce bugs and fix issues faster. It’s an exception tracker, a session re-player and a performance monitor. Get 14 days free at logrocket.com/syntax.
Mux - Sponsor Mux Video is an API-first platform that makes it easy for any developer to build beautiful video. Powered by data and designed by video experts, your video will work perfectly on every device, every time. Mux Video handles storage, encoding, and delivery so you can focus on building your product. Live streaming is just as easy and Mux will scale with you as you grow, whether you’re serving a few dozen streams or a few million. Visit mux.com/syntax.
Show Notes 03:15 - I was wondering what you guys think about using the latest of Svelte (svelte-next) in serious projects? Does the improved devEx makes up for the small (but growing) community and lack of libraries? Do you think svelte-next is here to stay or maybe we will get a revamp that breaks backward compatibility in a couple of years, like svelte 2 -> svelte 3?
8:48 - Git question: My process is often that I want to be able to use my last project as a starting point for my next project, with the new project having absolutely no connection or relationship to the old project. What steps can I take to completely sever any ties to the old project? Bonus question: In the new project I would love to eliminate all commits from the old project and start the new project having just one commit, the initial commit with all the code from the old project.
11:05 - Is CRA still useful for building actual production-level web apps these days? People seem to be reaching for Next or Gatsby most of the time, and I feel CRA is mainly used for actually learning React/building personal small websites. Your thoughts? Also, for normal CSR, I feel it is better to use something like Next, and fetch data inside your component (eg: for a dashboard) rather than building one with CRA. Am I wrong?
19:40 - What are your favorite screencast tools? (Linux? Mac? Windows?)
25:53 - Is it a bad trait for beginners to “give up” easily? By that, I mean instead of taking the time to think of the answer to a problem, they would instead rely on googling the solution and try to understand how it worked afterward.
27:55 - In pursuit of better health I want to track my weight daily using a smart digital scale. The idea is to automate the process of logging my own weight (e.g. stepping on the scale will update my Apple Health and any other integrations I have). After some searching around I landed on the Aria Air (mostly because I like the design and it has the coolest name). One small problem - it does not sync with Apple Health as it is a product from FitBit. They have an API so I’m thinking about running a serverless function daily, around 8 a.m. after I weigh in, to hit the FitBit API, get the data and push it to Apple Health. This way I can stay in the Apple eco-system whilst happily getting this nice, aesthetic digital scale. Any thoughts on how you would personally implement something like this? P.S. My girlfriend thinks I’m crazy, but I know the tinkerer inside Wes will love this.
30:26 - I work for the government with good pay and benefits and love where I work, but I feel like I’m missing out. Working in government we are not always working on the bleeding edge of technology. I do try and learn on my own, but it’s hard sometimes if I don’t put it into practice. I do peek at other job openings and get excited about the tech stack and the things they’re doing. I’m just afraid if I leave I won’t have the stability and benefits I would get from working in government. Any tips or thoughts would be appreciated.
34:24 - Unpopular opinion: Authentication isn’t that hard, but authorization is! What systems have you built to handle when users with specific permissions are allowed (or disallowed) to take actions within your system? What advice would you give to other developers developing permissions-based APIs, assuming their users can have 5-10 different levels of permissions?
40:21 - What are your thoughts on ReScript as an alternative to TypeScript?
44:43 - How come you guys moved to two sponsors on a Hasty and three on a Tasty? Not that it’s a big deal - was just curious of it was to keep up with costs or just because you could and then you’d make more? Either way, the show is awesome and really appreciate your opinions on everything!
48:01 - Have you tried Angular 12? I’d think you’d be pleasantly surprised if you gave it a chance!
52:20 - I have to copy and paste hundreds of products with six rows of details from a spreadsheet into a web interface because there is no API or CSV upload function for this program. Any recommendation on how to automate data entry into web inputs, navigate pages / click buttons, and toggle between applications? BTW, I scored my first web developer job and have to give you guys credit for steering me in the right direction.
Links * Svelte * Create React App * Next.js * Vercel * iShowU * Descript * Screenflow * Aria Air * FitBit * Apple Health * https://www.gov.uk/ * Keystone * rescript * TypeScript * Angular * Syntax 359: Hasty Treat - Making a Vaccine Bot with JavaScript * Puppeteer * uses.tech * wes.tech
××× SIIIIICK ××× PIIIICKS ××× * Scott: SvelteKit * Wes: Wyze Sprinkler Controller
Shameless Plugs * Scott: Svelte Components Course - Sign up for the year and save 25%! * Wes: All Courses - Use the coupon code ‘Syntax’ for $10 off!
Tweet us your tasty treats! * Scott’s Instagram * LevelUpTutorials Instagram * Wes’ Instagram * Wes’ Twitter * Wes’ Facebook * Scott’s Twitter * Make sure to include @SyntaxFM in your tweets
The podcast and artwork embedded on this page are from Wes Bos & Scott Tolinski - Full Stack JavaScript Web Developers, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Syntax - Tasty Web Development Treats (LS 62 · TOP 0.1% what is this?)
Episode: ShopTalk x Syntax
Pub date: 2021-07-28
In this episode of Syntax, Scott and Wes do a collaboration with Chis Coyier and Dave Rupert from ShopTalk Show! They talk about favorite tech stacks, podcasting, learning new tech, dealing with FOMO, and more!
Prismic - Sponsor Prismic is a Headless CMS that makes it easy to build website pages as a set of components. Break pages into sections of components using React, Vue, or whatever you like. Make corresponding Slices in Prismic. Start building pages dynamically in minutes. Get started at prismic.io/syntax.
Sentry - Sponsor If you want to know what’s happening with your code, track errors and monitor performance with Sentry. Sentry’s Application Monitoring platform helps developers see performance issues, fix errors faster, and optimize their code health. Cut your time on error resolution from hours to minutes. It works with any language and integrates with dozens of other services. Syntax listeners new to Sentry can get two months for free by visiting Sentry.io and using the coupon code TASTYTREAT during sign up.
Cloudinary - Sponsor Cloudinary is the best way to manage images and videos in the cloud. Edit and transform for any use case, from performance to personalization, using Cloudinary’s APIs, SDKs, widgets, and integrations.
Show Notes 07:23 - What’s your favorite stack right now?
28:52 - What are your thoughts on WordPress? Do you still use it?
33:59 - What do you want for listeners of Syntax?
38:21 - How do you deal with FOMO / the pressure to learn new tech?
Links * https://shoptalkshow.com/469/ * Chris Coyier * Dave Rupert * Syntax 372: CSS Container Queries, Layers, Scoping and More with Miriam Suzanne * https://svelte.dev/ * https://kit.svelte.dev/ * https://mercurius.dev/ * https://www.prisma.io/ * https://keystonejs.com/ * https://graphql.org/ * https://redwoodjs.com/ * https://nuxtjs.org/ * https://astro.build/ * https://vercel.com/ * https://wordpress.org/ * https://dayoneapp.com/ * https://automattic.com/ * https://mongoosejs.com/ * https://www.blink182.com/ * https://newsroom.spotify.com/2021-02-22/a-new-era-for-podcast-advertising/ * Chase Reeves YouTube Channel * https://xdebug.org/
××× SIIIIICK ××× PIIIICKS ××× * Dave: + 1: Haikyu!! + 2: Nintendo Garage * Chris: Ray App * Wes: + 1: Connor Ward YouTube Channel + 2: Ryan Knorr YouTube Channel
Shameless Plugs * Scott: All Courses - Sign up for the year and save 25%! * Wes: All Courses - Use the coupon code ‘Syntax’ for $10 off!
Tweet us your tasty treats! * Scott’s Instagram * LevelUpTutorials Instagram * Wes’ Instagram * Wes’ Twitter * Wes’ Facebook * Scott’s Twitter * Make sure to include @SyntaxFM in your tweets
The podcast and artwork embedded on this page are from Wes Bos & Scott Tolinski - Full Stack JavaScript Web Developers, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Acquired (LS 63 · TOP 0.1% what is this?)
Episode: Special: 2021 China Tech Trends (with Tech Buzz China)
Pub date: 2021-05-05
We team up with two of the very best English-language analysts covering China tech today, Rui Ma and Ying Lu from the Tech Buzz China podcast, to talk about the big trends happening on the ground in China right now. We've had Rui and Ying's episodes on repeat in our own podcast players for many years as we researched our Meituan, PDD, Tencent and Alibaba episodes, and we're so excited to have them finally join us live. We had a blast and learned much more about what's actually happening in the world's largest market than the relative trickle of news Western audiences normally receive. Tune in!
LP Book Club Announcement!
The Acquired LP Book Club is officially returning! We are super excited to have Brad Stone join us on May 21st to discuss his sequel to the Everything Store, Amazon Unbound. We'll be interviewing Brad on Zoom with Acquired LPs “live in the audience”, and Q+A to follow. You can join and become an LP here: https://acquired.fm/lp/
Sponsors:
- Thanks to Kevel for being our presenting sponsor for this special episode. Kevel provides API infrastructure to quickly build custom ad platforms for sponsored listings, internal promotions, native ads, and more — customers include Yelp, Rappi, OfferUp, Mozilla, Strava, and many other large apps and platforms. In true Acquired fashion, Kevel and CEO James Avery have put together a fun page showcasing the company's "history & facts", which you can find here: http://bit.ly/acquiredkevel !
- Thank you as well to Masterworks and to Perkins Coie. You can learn more about them at:
- http://bit.ly/acquiredmasterworks (use code “Acquired” to skip the waitlist)
- http://bit.ly/acquiredperkins
Topics and trends covered:
- How Rui and Ying stay on top of trends in China tech remotely from the US
- The rise of “tech company like” CPG and other consumer brands in China and extremely fast product development and iteration: e.g., Genki Forest, Perfect Diary and Shein
- Community group buying and the reinvention of commerce in rural China (along with an eye-opening discussion of what qualifies as “rural” in China... which is very different from the West!)
- Autonomous and electric vehicle design and production in China (which is the world's largest car market), and the government's push for China to become a global leader in both
- The current state of anti-trust in China and why investors and operators on the ground in China are optimistic about recent developments
Links:
- Tech Buzz China: https://www.techbuzzchina.com
- TBC's fantastic Insider community for investors and operators: https://www.techbuzzchina.com/insider
The podcast and artwork embedded on this page are from Ben Gilbert and David Rosenthal, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Acquired (LS 63 · TOP 0.1% what is this?)
Episode: Ethereum
Pub date: 2021-07-06
We close out Season 8 with the most ambitious organization we've ever covered on Acquired: Ethereum, and it's celebrity wunderkind founder Vitalik Buterin. If you thought Mark Zuckerberg IPO-ing Facebook at $100B by age 27 was something, just wait until you hear the story of this high school junior creating $500B (!!) of market cap by the same age — and oh yeah, maybe seeding the future dethroning of Facebook, Google, Amazon and all of big tech in the process. Regardless whether you're a crypto neophyte, a die-hard bull, or a skeptical bear, this is a story you need to hear, and Ethereum is an innovation you need to understand. Buckle in for a wild ride... and some special surprises from a few Acquired friends. :)
If you love Acquired and want more, join our LP Community for access to over 50 LP-only episodes, monthly Zoom calls, and live access for big events like emergency pods and book club discussions with authors. We can't wait to see you there. Join here at: https://acquired.fm/lp/
Sponsors:
- Thanks to Tiny for being our presenting sponsor for all of Acquired Season 8. Tiny is building the "Berkshire Hathaway of the internet" — if you own a wonderful internet business that you want to sell, or know someone who does, you should get in touch with them. Unlike traditional buyers, they commit to quick, simple diligence, a 30-day or less process, and will leave your business to do its thing for the long term. You can learn more about Tiny here: http://bit.ly/acquiredtiny
- Thank you as well to Vouch and to Capchase. You can learn more about them at:
- https://bit.ly/acquired-vouch
- http://bit.ly/acquiredcapchase
Links:
- The Ethereum Whitepaper: https://ethereum.org/en/whitepaper/
- Yung Spielburg https://open.spotify.com/artist/273so0X2Yuo93vfeX2nLDI and Mike Taylor https://open.spotify.com/artist/30ejUciK31BCg0IVCbt1dW on Spotify
Carve Outs:
- Arthur C. Clarke: https://www.amazon.com/Arthur-C-Clarke/e/B000APF21M? (especially Rendezvous with Rama and The City & The Stars)
- Disney-Marvel's Loki: https://www.disneyplus.com/series/loki/6pARMvILBGzF
- The Diamond Age by Neal Stephenson: https://www.amazon.com/Diamond-Age-Illustrated-Primer-Spectra/dp/0553380966
- Nier Automata: https://en.wikipedia.org/wiki/Nier:_Automata
- Magic the Gathering IRL: https://magic.wizards.com/en/events/event-types/friday-night-magic
Episode Sources:
- https://www.amazon.com/Infinite-Machine-Crypto-hackers-Building-Internet/dp/0062886142/
- http://www.gjermundbjaanes.com/understanding-ethereum-smart-contracts/
- https://bitcoinmagazine.com/technical/bootstrapping-a-decentralized-autonomous-corporation-part-i-1379644274
- https://bitcointalk.org/index.php?topic=4916.msg72174#msg72174
- https://bitinfocharts.com/comparison/ethereum-transactionfees.html#6m
- https://blog.ethereum.org/2015/07/30/ethereum-launches/
- https://coinmarketcap.com
- https://cointelegraph.com/news/ico-market-2018-vs-2017-trends-capitalization-localization-industries-success-rate
- https://consensys.net/about/
- https://decrypt.co/36641/who-are-ethereums-co-founders-and-where-are-they-now
- https://defipulse.com
- https://en.wikipedia.org/wiki/Aether_(classical_element)
- https://en.wikipedia.org/wiki/Bitcoin_Magazine
- https://en.wikipedia.org/wiki/Bitcointalk
- https://en.wikipedia.org/wiki/Charles_Hoskinson
- https://en.wikipedia.org/wiki/Cynthia_Dwork
- https://en.wikipedia.org/wiki/Dai_(cryptocurrency)
- https://en.wikipedia.org/wiki/Decentralized_autonomous_organization
- https://en.wikipedia.org/wiki/Decentralized_finance
- https://en.wikipedia.org/wiki/Emin_Gün_Sirer
- https://en.wikipedia.org/wiki/EOS.IO
- https://en.wikipedia.org/wiki/Ethereum
- https://en.wikipedia.org/wiki/Ethereum
- https://en.wikipedia.org/wiki/Gavin_Wood
- https://en.wikipedia.org/wiki/History_of_Russia_(1991–present)
- https://en.wikipedia.org/wiki/Ian_Goldberg
- https://en.wikipedia.org/wiki/Jed_McCaleb
- https://en.wikipedia.org/wiki/Joseph_Lubin_(entrepreneur)
- https://en.wikipedia.org/wiki/MetaMask
- https://en.wikipedia.org/wiki/Proof_of_work
- https://en.wikipedia.org/wiki/Smart_contract
- https://en.wikipedia.org/wiki/The_DAO_(organization)
- https://en.wikipedia.org/wiki/Vitalik_Buterin
- https://ethereum.org/en/developers/docs/gas/
- https://ethereum.org/en/whitepaper/
- https://finance.yahoo.com/news/eip-1559-nears-ethereum-upgrade-070058011.html
- https://info.etherscan.com/understanding-an-ethereum-transaction/
- https://joincolossus.com/episodes/14242194/drake-ethereum-into-the-ether?tab=transcript
- https://media.consensys.net/ethereum-gas-fuel-and-fees-3333e17fe1dc
- https://medium.com/@ConsenSys/a-101-noob-intro-to-programming-smart-contracts-on-ethereum-695d15c1dab4
- https://medium.com/the-capital/layer-1-vs-layer-2-what-you-need-to-know-about-different-blockchain-layer-solutions-69f91904ce40
- https://novicedock.com/learn/cryptocurrency/ethereum
- https://open.spotify.com/episode/0I8O5IW4lDaFdUNrhXJZ6k?si=tt199jqFThKyk__DSgb08w&context=spotify%3Ashow%3A41TNnXSv5ExcQSzEGLlGhy&dl_branch=1
- https://uniswap.org/blog/uniswap-history/
- https://vessenes.com/more-ethereum-attacks-race-to-empty-is-the-real-deal/
- https://www.bloomberg.com/news/articles/2021-03-07/crypto-coin-outperforming-bitcoin-is-about-to-see-supply-reduced
- https://www.coindesk.com/2016-ico-blockchain-replace-traditional-vc
- https://www.entrepreneur.com/article/358661
- https://www.fool.com/investing/2021/05/27/what-is-ethereum-20-and-when-will-it-happen/
- https://www.investopedia.com/articles/investing/031416/bitcoin-vs-ethereum-driven-different-purposes.asp
- https://www.reddit.com/r/ethereum/comments/4oi2ta/i_think_thedao_is_getting_drained_right_now/
- https://www.reddit.com/r/ethereum/comments/m07kof/ethereum_to_become_a_deflationary_asset_eip1559/
- https://www.reddit.com/r/MakerDAO/comments/as40d0/why_would_someone_choose_dai_over_tether/
- https://www.statista.com/statistics/807195/ethereum-market-capitalization-quarterly/
- https://www.youtube.com/watch?v=3x1b_S6Qp2Q
- https://www.youtube.com/watch?v=K4UNOv6SUcQ&t=2354s
- https://www.youtube.com/watch?v=WSN5BaCzsbo
The podcast and artwork embedded on this page are from Ben Gilbert and David Rosenthal, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Capital Allocators (LS 58 · TOP 0.5% what is this?)
Episode: Private Equity Masters 3: Robert F. Smith – Vista Equity Partners (Capital Allocators, EP.202)
Pub date: 2021-07-05
Robert F. Smith is the Founder, Chairman and CEO, Vista Equity Partners. Vista is a private investment firm that focuses entirely on enterprise software companies and manages $75 billion in assets across private equity, permanent capital, credit and public vehicles. Taken together, Vista’s current portfolio companies are about 70 in number and house 70,000 employees, 700,000 customers across 175 countries, and 200 million global users. Its combined revenue would make the portfolio one of the largest enterprise software companies in the world. Our conversation covers Robert’s background, the special characteristics of enterprise software, screening potential targets, adding value through industry expertise, assessing management teams, employing operational and financial leverage, and exiting investments. We then turn to managing Vista and the competitive landscape, and close with reflections on Robert’s past mistakes and impact through philanthropy.
Learn More Subscribe: Apple | Spotify | Google Follow Ted on Twitter at @tseides or LinkedIn Subscribe Monthly Mailing List Read the Transcript
The podcast and artwork embedded on this page are from Ted Seides, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Capital Allocators (LS 58 · TOP 0.5% what is this?)
Episode: Private Equity Masters 4: David Rubenstein – Carlyle Group (Capital Allocators, EP.203)
Pub date: 2021-07-12
David Rubenstein is the Co-Founder and Co-Chairman of The Carlyle Group. Founded back in 1987, Carlyle is one of the world’s largest and most diversified global investment firms with $260 billion in asset under management across three business segments and twenty-nine offices around the world. Our conversation covers David’s start in private equity, growing Carlyle from the early days, raising capital skillfully, recruiting talent globally, and managing a public company. We then turn to David’s outlook for the industry, advice for CIOs, and his recent activity across his family office, writing, philanthropy, and interviewing. Learn More Subscribe: Apple | Spotify | Google Follow Ted on Twitter at @tseides or LinkedIn Subscribe Monthly Mailing List Read the Transcript
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Podcast: The Documentary Podcast (LS 67 · TOP 0.05% what is this?)
Episode: Rebuilding Beirut’s village in a city
Pub date: 2021-07-29
A year ago Johnny Khawand saw the home he grew up in ripped apart by the massive explosion in a chemical dump in the port of Beirut, Lebanon – one of the largest non-nuclear blasts in history. For hours Johnny fought to save neighbours trapped in the rubble, seeing some die in front of him. Now, after months of restoration work, he’s coming back to try to rebuild his life, hoping that the unique spirit of his close-knit, multi-faith neighbourhood – Karantina – will survive. As he enters his house again for the first time, memories flood back – both comforting and distressing. Johnny and other survivors have formed close bonds with some of the volunteers, including engineers and architects, who’ve spent the last year rebuilding the district for free. They’re passionate about restoring its ancient buildings exactly as they were before. But they’re angry that they’ve received no help from the Lebanese state, which is accused of negligence over the explosion. And Johnny and others now fear that wider redevelopment plans will bring in big money and change Karantina’s character forever. Tim Whewell asks if Beirut’s “village in a city”, with its many layers of history and memory, can survive?
Reporter and producer: Tim Whewell Producer: Mohamad Chreyteh Editor: Bridget Harney
(Image: Beirut explosion survivors Manal Ghaziri and Johnny Khawand outside the ruins of a neighbours' house in the Karantina district. Credit: Mohamad Chreyteh/BBC)
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Podcast: Acquired (LS 63 · TOP 0.1% what is this?)
Episode: WhatsApp
Pub date: 2020-01-29
Join the Acquired Limited Partner program! https://glow.fm/acquired/ (works best on mobile)
We kick off Season 6 with a long-awaited Acquired Classic: Facebook’s $22B purchase of WhatsApp in 2014, which still ranks as the largest acquisition of a private VC-backed startup in history. Yet despite that enormous pricetag and all its associated fanfare, as we sit here 5+ years later WhatsApp actually generates LESS revenue than the meager ~$20m it was bringing in at the time of acquisition. Was this this worst acquisition of all-time, or a brilliant strategic chess move by Mark Zuckerberg & co? Tune in as we render Acquired’s judgement!
Note: Unfortunately David’s audio quality in this episode was impacted by a technical glitch which we didn’t discover until after recording. Our editors worked super hard to fix in post-production, but it’s still not totally perfect. We hope you’ll give it a listen regardless, and we’re working on getting a transcript made ASAP, which we’ll post to the website when it’s ready. Thanks for bearing with us,
-Ben & David
Carve Outs:
- Ben: Computer glasses: https://www.amazon.com/s?k=computer+glasses
- David: Reebok Floatrides: https://www.amazon.com/Reebok-Forever-Floatride-Energy-Black/dp/B07NYBRQ96/
Sponsor:
- Thanks to Silicon Valley Bank for being our banner sponsor for Acquired Season 6. You can learn more about SVB here: https://www.svb.com/next
- Thank you as well to Wilson Sonsini - You can learn more about WSGR at: https://www.wsgr.com/
Sources:
- https://en.wikipedia.org/wiki/WhatsApp
- https://www.forbes.com/sites/parmyolson/2014/02/19/exclusive-inside-story-how-jan-koum-built-whatsapp-into-facebooks-new-19-billion-baby/#64c1c94b2fa1
- https://www.forbes.com/sites/parmyolson/2014/03/04/inside-the-facebook-whatsapp-megadeal-the-courtship-the-secret-meetings-the-19-billion-poker-game/#63d8b5c4350f
- https://www.wired.com/2015/09/whatsapp-serves-900-million-users-50-engineers/
- https://youtu.be/v6PbymjXsto
- https://youtu.be/X4YsJt4rIOI
- https://overcast.fm/+WorS9-a74
- https://youtu.be/-2CAWS7M_0w
- https://youtu.be/X4YsJt4rIOI
- https://www.wired.co.uk/article/whats-app-owner-founder-jan-koum-facebook
- https://www.buzzfeednews.com/article/ryanmac/whatsapp-brian-acton-delete-facebook-stanford-lecture
- https://www.forbes.com/sites/parmyolson/2018/09/26/exclusive-whatsapp-cofounder-brian-acton-gives-the-inside-story-on-deletefacebook-and-why-he-left-850-million-behind/#7475a0213f20
- https://www.bloomberg.com/news/features/2017-06-28/tencent-rules-china-the-problem-is-the-rest-of-the-world
- https://techcrunch.com/2013/07/16/whatsapp-free/
- http://allthingsd.com/tag/jan-koum/
- http://allthingsd.com/20130510/whatsapp-ceo-jan-koum-hates-advertising-and-the-tech-rumor-mill-full-dive-video/
- https://techcrunch.com/2018/01/31/whatsapp-hits-1-5-billion-monthly-users-19b-not-so-bad/
- https://blog.whatsapp.com/10000633/Building-for-People-and-Now-Businesses
- https://techcrunch.com/2017/09/05/whatsapp-business-app/
- https://techcrunch.com/2014/02/21/whatsapp/
- https://www.washingtonpost.com/business/economy/whatsapp-founder-plans-to-leave-after-broad-clashes-with-parent-facebook/2018/04/30/49448dd2-4ca9-11e8-84a0-458a1aa9ac0a_story.html
- https://www.wsj.com/articles/whatsapp-backs-off-controversial-plan-to-sell-ads-11579207682
- https://www.wsj.com/articles/behind-the-messy-expensive-split-between-facebook-and-whatsapps-founders-1528208641?mod=article_inline
- https://blogs.wsj.com/digits/2014/06/05/whatsapp-co-founder-stresses-independence-from-facebook/?mod=article_inline
- https://bgr.com/2020/01/17/whatsapp-ads-2020-facebook-canceled-plans-to-bring-ads-to-status-bar/
- https://www.vox.com/2018/5/8/17329524/whatsapp-new-ceo-facebook-cofounder-jan-koum-departs
- https://www.linkedin.com/in/chdaniels/
- https://www.linkedin.com/in/jkoum/
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Podcast: Money Talks from The Economist (LS 59 · TOP 0.5% what is this?)
Episode: Money Talks: Uncertainty principles
Pub date: 2021-07-21
Financial markets are rattled by fears about the rapidly spreading Delta variant of covid-19. But another threat also looms: can the economic recovery survive the end of emergency stimulus? Plus, why America’s shale-oil tycoons are now fracking as little as possible. And, our correspondent meets bitcoin miners in rural China to find out why they are packing up and shipping out. Simon Long hosts
Subscribers to The Economist can join our finance reporters John O’Sullivan, Buttonwood columnist, and Alice Fulwood, Wall Street correspondent, on July 29th for a live event unpicking the inner workings of financial markets and how to make sense of them. Register and submit your questions at economist.com/marketsevent
For full access to print, digital and audio editions as well as exclusive live events, subscribe to The Economist at economist.com/podcastoffer
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Podcast: Money Talks from The Economist (LS 59 · TOP 0.5% what is this?)
Episode: Money Talks: Where have all the workers gone?
Pub date: 2021-05-19
Businesses are struggling to fill vacancies at the same time as millions of people are out of work. Host Patrick Lane investigates this conundrum. Also, each year almost 10% of global tax revenue is lost through companies shifting their income to tax havens. How can governments get the world’s most profitable companies to cough up? And, Patrick Collison, co-founder and CEO of Stripe, on the rise of America’s biggest ever unlisted firm.
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Podcast: Startup School by Y Combinator (LS 45 · TOP 1% what is this?)
Episode: Running Your Startup by Patrick Collison
Pub date: 2018-10-03
YC Partner Adora Cheung, along with Patrick Collison, the founder of well known YC alumnus Stripe, discuss how to most effectively run a startup company towards success.
- Transcript
- Video Link
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Podcast: Broken Record with Rick Rubin, Malcolm Gladwell, Bruce Headlam and Justin Richmond (LS 69 · TOP 0.05% what is this?)
Episode: Archie Shepp: Activist of the Avant-Garde
Pub date: 2021-06-29
Archie Shepp is a tenor saxophonist and composer who's spent over half a century contributing to the evolution of Black music. Shepp has long fought for Black musicians to get their fair share of credit, recognition and recompense for their contributions to popular music. Shepp's been considered a leader of avant-garde jazz since the 60's. He's famously played alongside John Coltrane, Lee Morgan, and the great free jazz pianist, Cecil Taylor. But 84-year-old Shepp doesn’t consider the music he plays jazz at all. He calls it “African American music” to acknowledge the Black Americans who created the tradition.
On today’s episode, Justin Richmond talks to Archie Shepp about how an assignment he received in the third grade sparked the activism that's been ever present in his 60-year career. Shepp also talks about his relationship with Coltrane, who he says never took his horn out of his mouth. And he also recalls the rhetorical power of Malcolm X and the lasting image of seeing him speak to a sea of black heads on the streets of Harlem.
Subscribe to Broken Record’s YouTube channel to hear all of our interviews: https://www.youtube.com/brokenrecordpodcast and follow us on Twitter @BrokenRecord
You can also check out past episodes here: https://brokenrecordpodcast.com
Check out a playlist of our favorite Archie Shepp tracks HERE.
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Podcast: Broken Record with Rick Rubin, Malcolm Gladwell, Bruce Headlam and Justin Richmond (LS 69 · TOP 0.05% what is this?)
Episode: Mark Ronson Finds The Perfect Sound
Pub date: 2021-07-13
Mark Ronson's big break as a producer came from working with Amy Winehouse to find the perfect sound for her career defining album, Back To Black. Since then, Ronson has gone on to win an Oscar, a Golden Globe and seven Grammys for producing chart-topping hits for artists like Lady Gaga, Adele and Bruno Mars. Ronson’s sound is often associated with danceable, driving rhythm sections—which makes sense, considering his background as a renowned DJ.
In today’s interview with Rick Rubin we’ll hear Mark talk about the day he met Amy Winehouse and how she might’ve confused him for Rick Rubin. Mark also talks about the night he fell in love with DJing, growing up with his step-dad in Foreigner, and how being isolated from his studio during the pandemic caused him to think that his days as a pop music producer might be over.
Subscribe to Broken Record’s YouTube channel to hear all of our interviews: https://www.youtube.com/brokenrecordpodcast and follow us on Twitter @BrokenRecord
You can also check out past episodes here: https://brokenrecordpodcast.com
Check out a playlist of our favorite Mark Ronson tracks HERE.
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Podcast: Song Exploder (LS 74 · TOP 0.05% what is this?)
Episode: Fousheé - Deep End
Pub date: 2021-07-14
The story of how the song "Deep End" came into existence and became a hit is kind of wild. One person who really didn’t see it coming is the person who created it, Fousheé. She’s a singer and songwriter from New Jersey. You might have seen her competing on The Voice in 2018. Soon after that, she got asked to make a pack of vocal samples for the music platform Splice, where users can download samples and include them in their own songs, royalty-free. Coming up, Foushée tells the story of what happened with one of those samples, and how that led to her making "Deep End." That song has now been streamed over 385 million times. Fousheé became the first Black female artist to hit the Top 10 Alternative Chart in over 30 years.
For more visit, songexploder.net/foushee.
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Podcast: Song Exploder (LS 74 · TOP 0.05% what is this?)
Episode: Sparks - This Town Ain't Big Enough for Both of Us
Pub date: 2021-06-16
Sparks are the brothers Ron and Russell Mael, a legendary duo from Los Angeles. Over the last 50 years, they’ve released 25 albums. They’ve collaborated with Giorgio Moroder and Franz Ferdinand, and they’ve influenced bands like Joy Division, Faith No More, Björk, and countless others. Director Edgar Wright, whose films include Shaun of the Dead, Hot Fuzz, Baby Driver, and Scott Pilgrim vs the World, has made a documentary about the band called The Sparks Brothers. It premiered at Sundance, and comes out in theaters on Friday, June 18th. In this episode, Ron and Russell break down their hit, “This Town Ain’t Big Enough for Both of Us," which came out in 1974, and changed their careers forever.
To learn more, visit songexploder.net/sparks
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Podcast: Song Exploder (LS 74 · TOP 0.05% what is this?)
Episode: Arlo Parks - Black Dog
Pub date: 2021-06-02
Arlo Parks is a singer and songwriter from London. In January 2021, she released her debut album, Collapsed in Sunbeams. It hit number three on the UK charts, and she won this year’s BRIT award for Breakthrough Artist. Last year, NME called her song "Black Dog" the year’s "most devastating song." In this episode, Anaïs breaks down “Black Dog," which she made with producer Gianluca Buccellati. ("But I just call him Luca.") Here’s Arlo Parks on Song Exploder.
If you’re thinking about suicide, or if you have a friend who is, or if you just need someone to talk to right now, you can get support by calling the National Suicide Prevention Lifeline at 1-800-273-TALK (8255) or by texting HOME to 741-741, which is the Crisis Text Line. If you're outside of the U.S., check out the list of international hotlines at suicide.org.
For more about “Black Dog,” visit songexploder.net/arlo-parks
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Podcast: Invest Like the Best with Patrick O'Shaughnessy (LS 67 · TOP 0.05% what is this?)
Episode: David Sacks - How to Operate a SaaS Startup - [Invest Like the Best, EP. 234]
Pub date: 2021-07-13
My guest today is David Sacks, General Partner at Craft Ventures and founding COO of PayPal. During our conversation, we explore what differentiates Enterprise SaaS from DTC subscriptions, what makes for a magical product launch event, and what key growth metrics David uses to measure success. David has written extensively on his idea of operating cadence, and we explore how that applies to the various functions within an organization. As time goes on, I am more and more impressed at the talent that existed within the original PayPal mafia, and I couldn’t help but ask David to highlight the superpowers for a few of his early partners. This was an incredibly informative conversation with fun threads throughout. Please enjoy my conversation with David Sacks.
For the full show notes, transcript, and links to the best content to learn more, check out the episode page here.
This episode is brought to you by Canalyst. Canalyst is the leading destination for public company data and analysis. If you've been scrambling to keep up with the deluge of IPOs and SPACs these days, Canalyst has models on Robinhood, Marqeta, Grab, and everything in between. Learn more and try Canalyst for yourself at canalyst.com/patrick.
This episode is brought to you by Eight Sleep. Eight Sleep's new Pod Pro Cover is the easiest and fastest way to sleep at your perfect temperature. Simply add the Pod Pro Cover to your current mattress and start sleeping as cool as 55°F or as hot as 110°F. To embrace the future of sleep and get $150 off your new mattress go to eightsleep.com/patrick or use code "Patrick".
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Show Notes
[00:03:30] - [First question] - Defining what it means when a company can explode
[00:05:39] - What it would look like if a company didn’t have what it takes to explode
[00:06:17] - Key factors that make for a strong product hook
[00:08:51] - Whether or not there has been a divergence at the early stage of B2B investing compared to B2C
[00:11:36] - Reasons why products that make people collaborate are always stronger
[00:14:14] - Nuances between team subscriptions and team product use
[00:15:37] - Describing the burn multiple metric and how it can be applied to companies
[00:18:18] - The gross margin problem and issues for businesses in this area writ large
[00:22:34] - Common practices amongst sales programs that have and haven’t worked
[00:24:22] - What about new founders makes him most excited
[00:25:58] - Explaining cadence and why he groups product and marketing as one bucket and sales and finance as another
[00:30:44] - The anatomy of a great product launch
[00:32:17] - Ways in which external dependencies can be landmines for growing companies
[00:34:06] - Whether or not he’s willingness to invest in a business with regulatory variables
[00:36:59] - What he’s seen in company culture that breaks a company as they scale
[00:39:55] - Things a founder actually does in order to reign in and tame their culture
[00:42:08] - Unique traits of founders who are both investors and operators
[00:44:12] - Peter Thiel’s superpower
[00:44:57] - Max Levchin’s superpower
[00:45:38] - Elon Musk’s superpower
[00:46:07] - Roelof Botha’s superpower
[00:47:15] - Reid Hoffman’s superpower
[00:47:43] - Keith Rabois’ superpower
[00:48:41] - What zones of change in the world have his attention writ large
[00:51:43] - Why teams want to be pushed and how we can apply that to business
[00:55:25] - The kindest thing anyone has ever done for him
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Podcast: Village Global's Venture Stories (LS 46 · TOP 1% what is this?)
Episode: Fintech and Investment Strategy with Addie Lerner
Pub date: 2021-05-24
Addie Lerner (@addielerner), founder of Avid Ventures, joins Erik to discuss:
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Why she started Avid Ventures and how her experiences at growth-stage funds influences her investing strategy at earlier stages.
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Her take on the “great barbell” in venture capital and how she thinks about investing in an environment with outsized valuations. She says that firms are now investing in seed stage companies at Series B prices.
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The origins of her bullishness on fintech and why “every company is becoming a fintech company.”
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Why she thinks there can be multiple winners in the global remote work space.
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Why X for Y businesses in international geographies can work, if there is a local angle to the business that makes it uniquely suited for a particular geography.
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How she thinks about crypto and why she’s looking to back eldercare companies.
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Her investment thesis and why she wants to back founders who "believe they were put on earth to build their company."
Thanks for listening — if you like what you hear, please review us on your favorite podcast platform.
Check us out on the web at www.villageglobal.vc or get in touch with us on Twitter @villageglobal.
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Podcast: Village Global's Venture Stories (LS 46 · TOP 1% what is this?)
Episode: Education, The Great Stagnation, and Innovation with Noah Smith
Pub date: 2021-06-08
Noah Smith (@noahpinion), Bloomberg Opinion writer and author of the Noahpinion Substack, joins Erik to discuss:
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Why colleges should try to emulate the Cal State and CUNY systems, which Noah says provide the best value for dollars in education.
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Why the US should want to copy the Japanese and Korean healthcare systems, and the power that a national health insurance program has to drive cost down.
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Why the oil shock precipitated the great stagnation, and the evolution (and non-evolution) of energy sources over the years.
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What climate economics got wrong and why the revolution in green energy is will not only be about reducing carbon emissions but rather the abundance of cheap energy.
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What people get wrong about inflation and monetary policy and how the fed really works.
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What the US should do to increase innovation, and Noah’s take on whether science and commercialization of discoveries is slowing down or not.
Thanks for listening — if you like what you hear, please review us on your favorite podcast platform.
Check us out on the web at www.villageglobal.vc or get in touch with us on Twitter @villageglobal.
Want to get updates from us? Subscribe to get a peek inside the Village. We’ll send you reading recommendations, exclusive event invites, and commentary on the latest happenings in Silicon Valley. www.villageglobal.vc/signup
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Podcast: Invest Like the Best with Patrick O'Shaughnessy (LS 67 · TOP 0.05% what is this?)
Episode: Bill Gurley – All Things Business and Investing - [Invest Like the Best, EP.137]
Pub date: 2019-07-02
My guest this week is Bill Gurley, a general partner at Benchmark Capital and one my favorite investment thinkers. As you’ll hear, despite enormous success through his career, Bill is clearly still in love with business and investing. Where many might discuss past glories, I’ve been incredibly impressed with how both Bill and his partners emphasize the current portfolio and market landscape. I’m thankful to have had the chance to speak with him in this format. I hope you enjoy our conversation.
For more episodes go to InvestorFieldGuide.com/podcast.
Sign up for the book club, where you’ll get a full investor curriculum and then 3-4 suggestions every month at InvestorFieldGuide.com/bookclub.
Follow Patrick on Twitter at @patrick_oshag
Show Notes
1:13 - (First Question) – The idea of increasing returns
1:21 – Competiting Technologies, Increasing Returns, and Lock-in By Historical Events
2:07 – Complex Systems Theory – Santa Fe Institute
4:35 – Markers that could be a sign of network effect in a company
6:27 – The opportunities for companies to capture network effect
8:46 – Are there certain teams/leaders that are more conducive to leading a network effect company
11:55 – Liquidity quality
13:35 – How important is the revenue model at the beginning
15:59 – Fascination with Nextdoor
17:56 – Paradox of Choice
18:39 – Finding opportunities
20:17 – Potential marketplaces and assets that could be commoditized
20:20 – All Markets Are Not Created Equal: 10 Factors To Consider When Evaluating Digital Marketplaces
21:39 – Usage yield on the world’s assets
23:50 – Has technology changed the world of value investing
26:28 – Hyper niche marketplaces
27:52 – Challenges of labor marketplaces
30:12 – User generated content businesses
32:44 – People who are capable of building UGC businesses
33:16 – His interest in Discord
34:31 – Factors of a healthy marketplace
37:57 – Fools’ gold in marketplace businesses
39:04 – How influx of cash is impacting the marketplace business landscape
40:43 – All Revenue is Not Created Equal: The Keys to the 10X Revenue Club
43:20 – How does the influx of money into the space impact him
46:44 – Spending money to attack top brands
50:32 – Regulatory capture
53:36 – His thoughts on the IPO market
57:49 – How did he realize this was his passion
1:00:42 – Qualifying his passion
1:01:52 – Favorite thing about working with entrepreneurs
102:48 – Honing your craft
1:04:33 – Making yourself a good mentor
1:05:56 – Kindest thing anyone has done for him
Learn More
For more episodes go to InvestorFieldGuide.com/podcast.
Sign up for the book club, where you’ll get a full investor curriculum and then 3-4 suggestions every month at InvestorFieldGuide.com/bookclub
Follow Patrick on twitter at @patrick_oshag
The podcast and artwork embedded on this page are from Colossus, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Twenty Minute VC: Venture Capital | Startup Funding | The Pitch (LS 57 · TOP 0.5% what is this?)
Episode: 20VC: 7 Powers: The Foundations of Business Strategy: Deconstructing Economies of Scale, How To Assess True Market Size, Assessing Risk vs Uncertainty, What “Brand As Power” Really Means with Hamilton Helmer, Managing Partner and Chief Investment Offi
Pub date: 2020-05-18
Hamilton Helmer is the Managing Partner & Chief Investment Officer @ Strategy Capital a long-only public equity fund that selects securities for investment based on Power Dynamics, a proprietary model of fundamental value, developed by Hamilton over decades of strategy consulting with clients such as Hewlett-Packard, Adobe and more. Hamilton is also the author of one of my favourite books, 7 Powers. If that was not enough, Hamilton is also an Instructor in the Economics Department @ Stanford University.
In Today’s Episode You Will Learn:
1.) How Hamilton made his way into the world of investing from advising some of the largest tech titans on the planet with Adobe and HP?
2.) What is a strategy mental model? What makes the most effective strategic models? What characteristics do they have? How should founders balance between sticking to models and being willing to change them? What are Hamilton's biggest takeaways from working with Netflix?
3.) "All strategy begins with invention", what did Hamilton mean by this? How does Hamilton explain the success of copycats in markets? How does Hamilton separate between first mover and creator? How does Hamilton analyse the transition from startup to pricer with scale economics?
4.) How does Hamilton advise founders to view and approach competition? Why does Hamilton totally disagree with the requirement of being 10x better than your competition? Is product innovation alone enough without brand or business model innovation?
5.) How does Hamilton define "brand"? Is brand an attainable strategy alone or is it the byproduct of something else? How transferable is brand in one category to alternative categories? How does a brand truly know when they have sustainable leverage and power?
Items Mentioned In Today’s Show:
Hamilton’s Fave Book: The Road to Reality: A Complete Guide to the Laws of the Universe, Stardust
As always you can follow Harry and The Twenty Minute VC on Twitter here!
Likewise, you can follow Harry on Instagram here for mojito madness and all things 20VC.
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Podcast: Invest Like the Best with Patrick O'Shaughnessy (LS 67 · TOP 0.05% what is this?)
Episode: Hamilton Helmer – Power + Business - [Invest Like the Best, EP.174]
Pub date: 2020-05-19
My guest today is Hamilton Helmer, the Co-Founder and Chief Investment Officer of Strategy Capital and the author of one of the best business books in history called 7 Powers, which is the topic of much of our conversation. He has spent his career as a practicing business strategist: advising companies, investing based on strategic insights and teaching strategy. In the last three decades, he has also utilized his strategy concepts as a public equity investor. In this conversation we cover all seven business powers, from counter-positioning to scale economies, and how companies earn and keep those powers. Any investor or businessperson should understand these concepts, and 7 Powers is the best work I’ve seen that explains them in depth. Please enjoy our conversation.
For more episodes go to InvestorFieldGuide.com/podcast.
Sign up for the book club, where you’ll get a full investor curriculum and then 3-4 suggestions every month at InvestorFieldGuide.com/bookclub.
Follow Patrick on Twitter at @patrick_oshag
Show Notes
(1:31) – (First question) - What power means to him
(5:05) – Benefits being more common than barriers in the power equation
(6:28) – How early-stage companies develop their barriers
(11:23) – The power of counter positioning and how he’s seen it applied
(14:47) – The product side of counter positioning
(16:39) – Daniel Ek Podcast episode
(17:27) – Applying the idea of counter positioning to yourself
(20:40) – A cornered resource
(23:49) – A look at google as a cornered resource
(27:12) – Unique power of network economies
(31:18) – What subtleties disqualify network effects
(32:54) – Nuances of scale economies
(35:56) – Learning economies and who can scale it better
(37:07) – Building a switching cost and barrier into your business
(40:10) – Branding as power
(44:27) – Defining process power and how it differs from scale economies
(46:40) – The notion of the time lag and cash flow
(50:42) – Why is so much power concentrated in technology businesses
(52:07) – What does power mean for customers
(53:43) – Developing power as an art vs science, and the best power artists
(55:08) – The kindest thing anyone has done for him
Learn More
For more episodes go to InvestorFieldGuide.com/podcast.
Sign up for the book club, where you’ll get a full investor curriculum and then 3-4 suggestions every month at InvestorFieldGuide.com/bookclub
Follow Patrick on Twitter at @patrick_oshag
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Podcast: The NFX Podcast (LS 42 · TOP 1.5% what is this?)
Episode: The Founders' List: Mind the Moat: Notes on 7 Powers (written by Hamilton Helmer) from Flo Crivello (Founder & CEO Teamflow)
Pub date: 2021-04-30
This is The Founders' List - audio versions of essays from technology’s most important leaders, selected by the founder community.
We’ve recently published a podcast and essay focused on Hamilton Helmer’s 7 Powers book, regarded as a core insight behind the success of companies like Spotify, Netflix, Stripe, and Twilio.
For this episode of the Founders’ List, we’ll be sharing notes and personal thoughts on the book titled “Mind the Moat, a 7 Powers Review” by Flo Crivelo, Founder & CEO of TeamFlow.
Read Flo's essay here - https://florentcrivello.com/index.php/2018/07/29/mind-the-moat-a-7-powers-review/
The podcast and artwork embedded on this page are from NFX, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The NFX Podcast (LS 42 · TOP 1.5% what is this?)
Episode: The 7 Powers with Hamilton Helmer & Jeff Lawson
Pub date: 2021-04-29
In this special NFX Podcast episode, Jeff Lawson (Co-Founder & CEO at Twilio) interviews Hamilton Helmer to analyze the 7 Powers, getting at the core question: What is it about certain businesses that keeps the force of competition away? Helmer’s framework reveals that you can intentionally craft the conditions for persistent & durable differential returns.
7 Powers is Hamilton’s cognitive guide to building an enduringly great business and breaks fresh ground by constructing a comprehensive strategy toolset that is easy for you to learn, communicate and quickly apply. Hamilton has led over 200 strategy projects with major clients such as Adobe Systems, Hewlett-Packard, Netflix, Raychem, and Spotify.
Jeff Lawson is a Co-Founder and CEO at Twilio, a cloud communications platform that adds messaging, voice and video to web and mobile apps. He is a serial inventor with over 15 years of entrepreneurial and product experience.
Read the full NFX Essay here - https://www.nfx.com/post/seven-powers/
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Podcast: Masters in Business (LS 65 · TOP 0.1% what is this?)
Episode: An Interview With Bill Janeway: Masters in Business (Audio)
Pub date: 2016-01-04
Jan. 4 (Bloomberg) -- Bloomberg View columnist Barry Ritholtz interviews William H. Janeway, Managing Director of Warburg Pincus and author of “Doing Capitalism in the Innovation Economy: Markets Speculation and the State”, published by Cambridge University Press in October 2012. They discuss the history of financial bubbles. This interview aired on Bloomberg Radio.
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Podcast: The MoneyWeek Podcast (LS 51 · TOP 0.5% what is this?)
Episode: Andy Haldane: bitcoin as money is a fanciful idea that should fill us with horror
Pub date: 2021-06-25
The Bank of England's outgoing chief economist, Andy Haldane, tells Merryn why he isn't a fan of bitcoin as money; why the BoE is actively considering its own digital cash (and what that would mean for you); plus Covid and our stellar economic recovery, where inflation could go next, and how we avoid ending up like the 1970s.
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Podcast: a16z Podcast (LS 62 · TOP 0.1% what is this?)
Episode: One on One with Marc and Ben
Pub date: 2021-02-17
On social audio app Clubhouse, Marc Andreessen and Ben Horowitz are hosting a new live show called "One on One with A and Z", where they go deep on questions submitted via Twitter. The show is based in part by a newspaper column that Andy Grove did in the 80s, where readers sent in questions for him to answer in his column.
In this mega-episode of the a16z Podcast, we've combined their first two episodes into almost three hours of discussion and debate about some of the most important topics in entrepreneurship, tech, and culture. Each of these episodes also initially aired on our new show, a16z Live, which captures and share many of the live discussions and events featuring, hosted, or co-hosted by a16z partners (with outside voices too) on Clubhouse and beyond.
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Podcast: The LRB Podcast (LS 52 · TOP 0.5% what is this?)
Episode: Alan Bennett: Diary From the Pandemic Year
Pub date: 2021-05-25
Alan Bennett reads selections from his diary from March 2020 to March 2021.
Read more Alan Bennett in the LRB here: lrb.me/alanbennettpod
Alan Bennett's pandemic diary will be published as a signed, numbered London Review Bookshop limited edition at the end of June. Pre-order a copy at lrb.me/housearrest
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Podcast: The Tim Ferriss Show (LS 81 · TOP 0.01% what is this?)
Episode: #506: Balaji Srinivasan on The Future of Bitcoin and Ethereum, How to Become Noncancelable, the Path to Personal Freedom and Wealth in a New World, the Changing Landscape of Warfare, and More
Pub date: 2021-03-25
Balaji Srinivasan on The Future of Bitcoin and Ethereum, How to Become Noncancelable, the Path to Personal Freedom and Wealth in a New World, the Changing Landscape of Warfare, and More | Brought to you by Wealthfront automated investing, Athletic Greens all-in-one nutritional supplement, and Helix Sleep premium mattresses. More on all three below.
“If code scripts machines, media scripts human beings.” — Balaji Srinivasan
Balaji S. Srinivasan (@balajis) is an angel investor and entrepreneur. Formerly the CTO of Coinbase and General Partner at Andreessen Horowitz, he was also the co-founder of Earn.com (acquired by Coinbase), Counsyl (acquired by Myriad), Teleport (acquired by Topia), and Coin Center.
He was named to the MIT Technology Review’s “Innovators Under 35”, won a Wall Street Journal Innovation Award, and holds a BS/MS/PhD in Electrical Engineering and an MS in Chemical Engineering, all from Stanford University. Balaji also teaches the occasional class at Stanford, including an online MOOC in 2013, which reached 250,000+ students worldwide.
To learn more about Balaji’s most recent project, visit 1729.com, a newsletter that pays you. They’re giving out $1,000 in BTC each day for completing tasks and tutorials. Subscribers also receive chapters from Balaji’s new (free) book, The Network State.
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Smart investing should not feel like a rollercoaster ride. Let the professionals do the work for you. Go to Wealthfront.com/Tim and open a Wealthfront account today, and you’ll get your first $5,000 managed for free, for life. Wealthfront will automate your investments for the long term. Get started today at Wealthfront.com/Tim.
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This episode is also brought to you by Athletic Greens. I get asked all the time, “If you could only use one supplement, what would it be?” My answer is usually Athletic Greens, my all-in-one nutritional insurance. I recommended it in The 4-Hour Body in 2010 and did not get paid to do so. I do my best with nutrient-dense meals, of course, but AG further covers my bases with vitamins, minerals, and whole-food-sourced micronutrients that support gut health and the immune system.
Right now, Athletic Greens is offering you their Vitamin D Liquid Formula free with your first subscription purchase—a vital nutrient for a strong immune system and strong bones. Visit AthleticGreens.com/Tim to claim this special offer today and receive the free Vitamin D Liquid Formula (and five free travel packs) with your first subscription purchase! That’s up to a one-year supply of Vitamin D as added value when you try their delicious and comprehensive all-in-one daily greens product.
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Podcast: Fintech Insider Podcast by 11:FS (LS 53 · TOP 0.5% what is this?)
Episode: 538. News: Are Stripe and Shopify THE biggest fintech companies right now?
Pub date: 2021-06-21
Our expert hosts, Adam Davis and David Brear, are joined by some great guests to talk about the most notable fintech, financial services and banking news from the past week. This week's guests include:
- Gus Tomlinson, General Manager Identity Fraud Europe, at GBG
- Sammy Fry, Net Zero Lead at Tech Nation
Soundbites:
- Matt Henderson, EMEA Business Lead at Stripe
- Kathryn Petralia, co-founder of Kabbage
We cover the following stories from the fintech and financial services space:
- Stripe moves beyond payments with Stripe Identity 4:22
- UK tech leaders team up to fight climate crisis 14:50
- AmEx opens its first business accounts with Kabbage 29:01
- Shopify expands its one-click checkout, Shop Pay, to any merchant on Facebook or Google 37:10
- Laybuy brings in store BN/PL to the UK 44:50
- Bitcoin tops $40,000 after Musk says Tesla could use it again 46:50
- Fintech giant Wise push button on long-awaited listing 48:51
- Web founder Berners-Lee to auction source code as NFT 51:09
This episode is sponsored by Temenos. Temenos is the world’s leader in banking software, helping over 3,000 banks serve over 1.2 billion people. Our purpose is to make banking better. Together with our community, we make banks more successful, individuals better banked, and society better served. With our software banks can create more human digital experiences, hyper-efficient business models, and transform their back-office. Our clients are the highest performing banks with cost-income ratios which are twice better than the industry average.
Learn more at Temenos.com.
This episode is brought to you by Visa. one of the world’s leaders in digital payments. Visa’s Fintech Fast Track program is a quick and easy way to connect to the Visa network and issue payment credentials. Whether you’re an up and coming neobank, modernizing B2B payments, or launching a new crypto solution - amazing things can happen when your innovation is combined with the power of one of the world's largest payment networks. Learn more about the possibilities at partner.visa.com
This episode is also sponsored by YouGov. With a global consumer panel of 15 million registered members, 11+ years’ historic single-source data, and proprietary technology that connects data and simplifies research, YouGov is home to the largest collection of constant, entirely permissioned consumer opinion and rich behavioural intelligence in the world.
YouGov’s latest report measures the effect of COVID-19 on consumer attitudes, behaviours, and preferences when it comes to financial services. Beyond the pandemic, it examines the payments landscape, investments, sustainability & ethics and more in 17 markets.
Download On the money: YouGov’s Global Banking & Finance Report 2021
Fintech Insider by 11:FS is a podcast dedicated to all things fintech, banking, technology and financial services. Hosted by a rotation of 11:FS experts including David Brear, Simon Taylor, Jason Bates and Sarah Kocianski and joined by a range of brilliant guests, we cover the latest global news, bring you interviews from industry experts or take a deep dive into subject matters such as APIs, AI or digital banking.
If you enjoyed this episode, don't forget to subscribe and please leave a review
Follow us on Twitter: www.twitter.com/fintechinsiders where you can ask the hosts questions, alternatively email podcasts@11fs.com!
Special Guests: Gus Tomlinson, Kathryn Petralia, Matt Henderson, and Sammy Fry.
Links:
- Stripe moves beyond payments with Stripe Identity
- UK tech leaders team up to fight climate crisis
- AmEx opens its first business accounts with Kabbage
- Shopify expands its one-click checkout, Shop Pay, to any merchant on Facebook or Google
- Laybuy brings in store BN/PL to the UK
- Bitcoin tops $40,000 after Musk says Tesla could use it again
- Fintech giant Wise push button on long-awaited listing
- Web founder Berners-Lee to auction source code as NFT
The podcast and artwork embedded on this page are from 11:FS, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Fintech Insider Podcast by 11:FS (LS 53 · TOP 0.5% what is this?)
Episode: 539. Insights: Lending - the next evolution of fintech?
Pub date: 2021-06-25
Adam Davis is joined by a fantastic panel of guests to discuss lending.
Lending has traditionally been the fastest and arguably most lucrative route for banks to make money, and we want to look at why so many new fintechs are turning to lending as the next product in their arsenal, and how competition is hotting up in this space.
We also want to take a look at the new lending models and companies coming onto the scene and how they are finding new and innovative ways to better serve the end customer or business, vs the traditional banks.
Our expert guests are:
- Valentina Kristensen, Director of Marketing and Comms, OakNorth
- Tucker Haas, CEO and founder, Quo
- Mark Mullen, CEO Atom Bank
This episode is sponsored by Temenos. Temenos is the world’s leader in banking software, helping over 3,000 banks serve over 1.2 billion people. Our purpose is to make banking better. Together with our community, we make banks more successful, individuals better banked, and society better served. With our software banks can create more human digital experiences, hyper-efficient business models, and transform their back-office. Our clients are the highest performing banks with cost-income ratios which are twice better than the industry average.
Learn more at Temenos.com.
This episode is brought to you by Visa, one of the world’s leaders in digital payments. Visa’s Fintech Fast Track program is a quick and easy way to connect to the Visa network and issue payment credentials. Whether you’re an up and coming neobank, modernizing B2B payments, or launching a new crypto solution - amazing things can happen when your innovation is combined with the power of one of the world's largest payment networks. Learn more about the possibilities at http://partner.visa.com/.
This episode is also sponsored by YouGov. With a global consumer panel of 15 million registered members, 11+ years’ historic single-source data, and proprietary technology that connects data and simplifies research, YouGov is home to the largest collection of constant, entirely permissioned consumer opinion and rich behavioural intelligence in the world.
YouGov’s latest report measures the effect of COVID-19 on consumer attitudes, behaviours, and preferences when it comes to financial services. Beyond the pandemic, it examines the payments landscape, investments, sustainability & ethics and more in 17 markets. Download On the money: YouGov’s Global Banking & Finance Report 2021
Fintech Insider by 11:FS is a podcast dedicated to all things fintech, banking, technology and financial services. Hosted by a rotation of 11:FS experts including David Brear, Simon Taylor, Jason Bates and Sarah Kocianski and joined by a range of brilliant guests, we cover the latest global news, bring you interviews from industry experts or take a deep dive into subject matters such as APIs, AI or digital banking.
If you enjoyed this episode, don't forget to subscribe and please leave a review
Follow us on Twitter: www.twitter.com/fintechinsiders where you can ask the hosts questions, alternatively email podcasts@11fs.com!
Special Guests: Mark Mullen, Tucker Haas, and Valentina Kristensen.
The podcast and artwork embedded on this page are from 11:FS, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Infinite Loops (LS 47 · TOP 1% what is this?)
Episode: Alex Danco — Everyone's Job is World Building (EP.53)
Pub date: 2021-06-24
As our recurring guest, Alex Danco of Shopify returns to Infinite Loops for the third time! We talk about: * Age of Scarcity vs. Abundance * Everyone's job is World Building * Early days of the Internet * 'The Office' — and it's parallels with the real world * Hierarchies in corporate and cultural America * and a LOT more!
Follow Alex on Twitter at https://twitter.com/Alex_Danco and read his amazing essays at https://alexdanco.com/
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Podcast: Acquired (LS 63 · TOP 0.1% what is this?)
Episode: Berkshire Hathaway Part III
Pub date: 2021-06-07
It's time. We wrap our Berkshire Hathaway trilogy with Warren and Charlie entering a new era: the age of the internet. Can they and Berkshire adapt to this brave new world? We find out. And, after 9+ hours, we render our final judgments on Berkshire and Warren's career. Is "Never bet against America" still the right longterm approach? Or is there another, even bigger Snowball out there that Warren may be missing?
If you love Acquired and want more, join our LP Community for access to over 50 LP-only episodes, monthly Zoom calls, and live access for big events like our recent Book Club event with Brad Stone. We can't wait to see you there. Join here at: https://acquired.fm/lp/
Sponsors:
- Thanks to Tiny for being our presenting sponsor for all of Acquired Season 8. Tiny is building the "Berkshire Hathaway of the internet" — something they're so dedicated to, they even make and sell bronze busts of Warren & Charlie online! if you own a wonderful internet business that you want to sell, or know someone who does, you should get in touch with them. Just like Berkshire, they commit to quick, simple diligence, a 30-day or less process, and will leave your business to do its thing for the long term. You can learn more about Tiny here: http://bit.ly/acquiredtiny and find their Berkshire Nerds store here: http://bit.ly/acquiredbrknerds
- Thank you as well to Vouch and to Capchase. You can learn more about them at:
- https://bit.ly/acquired-vouch
- http://bit.ly/acquiredcapchase
The Berkshire Hathaway Playbook:
(also available on our website at https://www.acquired.fm/episodes/berkshire-hathaway-part-iii )
-
The Berkshire Hathaway "Culture"
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Berkshire Hathaway really only has three key cultural tenants that stretch across its huge array of operating businesses and investments:
- Don't put Berkshire's reputation at risk (i.e., don't be Salomon Brothers).
- Don't take money out of the business (i.e., re-invest and avoid or defer paying tax whenever possible).
- Funnel all excess cash back to Omaha for re-allocation (i.e., if you can't find a good use for excess cash, give it back to Warren).
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You need different strategies at different company scales and points in time.
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Berkshire's greatest longterm strength has been its ability to adapt and employ different strategies as it and the world has changed. From the transition from cigar butts to wonderful businesses as we saw in our last episode, to diluting the equity portfolio with fixed income assets from Gen Re before the internet bubble crash, to focusing on preferred equity during the financial crisis and ultimately making a non-controlling stake Apple the largest asset in the whole Berkshire portfolio, Warren and Charlie have demonstrated remarkable flexibility during their investing careers.
-
There are huge advantages to a company structure where one person makes all decisions.
-
Warren's ability to make $10 billion+ decisions on his own and within an hour (usually with input from Charlie) is truly unique in the global history of business, and allows Berkshire the flexibility to capitalize on opportunities that no one else can act upon, like the financial crisis. At the same time this setup obviously carries risk — not so much in Warren making bad decisions (cough, airlines), but in missing other opportunities simply due to lack of diversity in thought. Which leads us to our last playbook theme...
-
Never Bet Against The Internet. (aka the "Rosenthal doctrine")
-
Andrew Marks of TQ Ventures perhaps sums up Warren's career best: he's the greatest "status quo investor" that's ever lived, as embodied in his "never bet against America" philosophy. As long as the future looks mostly like the present, nobody is better than Warren at handicapping probabilities and picking winners. But that's no longer the world we live in today. As Doug Leone laid out in our Sequoia Part II episode, we now live in a world of accelerating change: what works today is unlikely to keep working tomorrow. And where is that dynamic baked into the very fabric of existence? The Internet. Never bet against it.
Links:
- Bill Gates 1996 Wired interview: https://youtu.be/VFFlO7yBIBM?t=1056
- Jeff Bezos's 2008 AWS == electricity talk at YC Startup School: https://www.youtube.com/watch?v=6nKfFHuouzA
- Charlie's 2021 Shareholder Meeting "slip-up": https://www.youtube.com/watch?v=6gyqElEG6Uo
Carve Outs:
- Common Stocks and Uncommon Profits: https://www.amazon.com/Common-Stocks-Uncommon-Profits-Writings/dp/0471445509
- Xbox Game Pass: http://xbox.com/gamepass
- Goodfellas: https://www.imdb.com/title/tt0099685/
- The Goodfellas soundtrack: https://open.spotify.com/playlist/0xVpgEngjrg6FOw5vEFHRp
Episode Sources:
- https://archive.fortune.com/magazines/fortune/fortune_archive/1999/11/22/269071/index.htm
- https://berkshirehathaway.com/2020ar/2020ar.pdf
- https://companiesmarketcap.com/berkshire-hathaway/marketcap/
- https://cunninghamjeff.medium.com/don-keough-mel-gibson-and-the-buffett-gang-abcb8b06e9b3
- https://en.wikipedia.org/wiki/Ajit_Jain
- https://en.wikipedia.org/wiki/Bear_Stearns
- https://en.wikipedia.org/wiki/Berkshire_Hathaway
- https://en.wikipedia.org/wiki/Donald_Keough
- https://en.wikipedia.org/wiki/Howard_Graham_Buffett
- https://en.wikipedia.org/wiki/List_of_assets_owned_by_Berkshire_Hathaway
- https://en.wikipedia.org/wiki/Marmon_Group
- https://en.wikipedia.org/wiki/New_Coke
- https://en.wikipedia.org/wiki/Roberto_Goizueta
- https://en.wikipedia.org/wiki/Ted_Weschler
- https://en.wikipedia.org/wiki/The_Giving_Pledge
- https://en.wikipedia.org/wiki/Todd_Combs
- https://finance.yahoo.com/news/bank-america-become-one-warren-141016560.html
- https://fortune.com/2011/09/12/meet-ted-weschler-buffett-auction-winner-berkshires-new-hire/
- https://fs.blog/2009/11/the-crisis-the-decline-of-berkshire-hathaways-stock-from-triple-a-status/
- https://givingpledge.org/About.aspx
- https://markets.businessinsider.com/news/stocks/warren-buffett-invested-3-billion-general-electric-ge-2008-crisis-2020-6-1029327040
- https://omaha.com/business/ajit-jain-s-role-at-berkshire-expands-new-ceo-at-reinsurer-gen-re-will-report/article_e2c6283e-d64a-57aa-85bf-c6f540f41231.html
- https://omaha.com/business/berkshire-hathaways-bnsf-railway-seems-to-pull-its-own-weight/article_59b29e97-aa57-5826-9c1f-8e2cf85180cd.html
- https://rationalwalk.com/revisiting-berkshire-hathaways-acquisition-of-bnsf/
- https://rationalwalk.com/revisiting-berkshires-wrigley-investments-brka-brkb/
- https://realmoney.thestreet.com/investing/kass-apple-is-the-most-consequential-investment-that-warren-buffett-ever-made-15220462
- https://sabercapitalmgt.com/warren-buffett-1997-email-exchange-on-microsoft/
- https://sabercapitalmgt.com/wp-content/uploads/2019/12/BuffettRaikesemails.pdf
- https://seekingalpha.com/article/4175060-time-for-berkshire-and-mclane-to-part-ways
- https://static.fmgsuite.com/media/documents/1bae1ba7-c2f2-4af5-ac1f-c0429dc7e5f0.pdf
- https://theoraclesclassroom.com/blog/berkshire-hathaway-intrinsic-value-calculation-q3-2020/
- https://waitbutwhy.com/2015/01/artificial-intelligence-revolution-1.html
- https://www.ajc.com/business/keough-affable-but-tough-coke-leader/3mJH6FkDAXijjEscdeEQMK/
- https://www.amazon.com/Complete-Financial-History-Berkshire-Hathaway/dp/0857199129
- https://www.amazon.com/Poor-Charlies-Almanack-Charles-Expanded/dp/1578645018
- https://www.amazon.com/Snowball-Warren-Buffett-Business-Life/dp/0553805096
- https://www.berkshirehathaway.com/2008ar/2008ar.pdf
- https://www.berkshirehathaway.com/2020ar/2020ar.pdf
- https://www.bostonglobe.com/business/2017/02/27/buffett-apple-airline-wagers-highlight-emergence-deputies/nV0z0YHUmR8SjYi1XZ6eLK/story.html
- https://www.cnbc.com/2019/12/31/this-decade-saw-warren-buffett-finally-exit-ibm-jump-big-into-apple.html
- https://www.cnbc.com/2019/12/31/this-decade-saw-warren-buffett-finally-exit-ibm-jump-big-into-apple.html
- https://www.cnbc.com/2020/02/24/warren-buffett-says-apple-is-probably-the-best-business-i-know-in-the-world.html
- https://www.economist.com/leaders/2021/05/08/warren-buffett-should-step-aside-for-his-chosen-successor
- https://www.einpresswire.com/article/251597070/donald-r-keough-1926-2015
- https://www.fool.com/investing/2018/02/18/the-3-biggest-mistakes-warren-buffett-made-with-ib.aspx
- https://www.fool.com/investing/2019/11/19/heres-how-much-warren-buffett-has-made-on-coca-col.aspx
- https://www.fool.com/investing/2019/12/29/heres-how-much-money-warren-buffett-has-made-in-ge.aspx
- https://www.forbes.com/sites/joewalsh/2021/05/01/buffett-doesnt-regret-selling-airline-stocks-last-year---and-he-still-doesnt-want-to-invest-in-them/?sh=48cbba1e6dfa
- https://www.goldmansachs.com/our-firm/history/moments/2008-buffett-investment.html
- https://www.himcap.com/#Management
- https://www.investopedia.com/ask/answers/021615/what-difference-between-berkshire-hathaways-class-and-class-b-shares.asp
- https://www.macrotrends.net/stocks/charts/BRK.A/berkshire-hathaway/stock-price-historyhttps://www.cnbc.com/2019/02/25/warren-buffett-says-berkshire-stock-managers-weschler-and-combs-have-trailed-the-sp-500.html
- https://www.nytimes.com/1995/02/15/business/worldbusiness/IHT-buffett-quietly-amasses-10-stake-in-amex.html
- https://www.nytimes.com/2008/04/28/business/28gum-web.html
- https://www.reuters.com/article/us-berkshire-buffett-precisioncastparts/warren-buffetts-10-billion-mistake-precision-castparts-idUSKCN2AR0MZ
- https://www.theguardian.com/business/2011/apr/30/warren-buffett-big-mistake-david-sokol-lubrizol
- https://www.wsj.com/articles/BL-DLB-32821
- https://www.wsj.com/articles/SB10001424052702303341904575576373008860754
- https://www.wsj.com/articles/SB10001424052702303467004575574630162624198
- https://www.wsj.com/articles/SB10001424052748703740004574513191915147218
- https://www.wsj.com/articles/SB10001424052748703977004575393180048272028
- https://www.wsj.com/articles/SB10001424053111904353504576569102534356770
- https://www.wsj.com/articles/warren-buffett-recounts-his-role-in-2008-financial-crisis-1536314400
- https://www.youtube.com/watch?v=6gyqElEG6Uo
- https://www.youtube.com/watch?v=6nKfFHuouzA
- https://youtu.be/QSGz4Y8CP2I
- https://youtu.be/VFFlO7yBIBM?t=1056
- https://youtu.be/VFFlO7yBIBM?t=1056
- https://www.sec.gov/Archives/edgar/data/109694/0000898430-96-001695.txt
The podcast and artwork embedded on this page are from Ben Gilbert and David Rosenthal, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Acquired (LS 63 · TOP 0.1% what is this?)
Episode: Berkshire Hathaway Part II
Pub date: 2021-05-12
In Part II of our Berkshire Hathaway Trilogy (!), we pick up the story with Warren wandering in the woods of Omaha, searching for his life's next chapter after retiring from the professional investing business at the top of his game at age 39. How does he emerge from those woods anew, transforming from Ben Graham's cigar-butt cocoon into the butterfly collector of Berkshire's wonderful businesses? (Spoiler: Charlie Munger.) And how did one rotten-to-the-core business nearly bring it all down — everything he'd ever worked for — in the span of one terrible week? Tune in!
If you love Acquired and want more, join our LP Community for access to over 50 LP-only episodes, monthly Zoom calls, and live access for big events like our upcoming Book Club event with Brad Stone. We can't wait to see you there. Join here at: https://acquired.fm/lp/
Sponsors:
- Thanks to Tiny for being our presenting sponsor for all of Acquired Season 8. Tiny is building the "Berkshire Hathaway of the internet" — something they're so dedicated to, they even make and sell bronze busts of Warren & Charlie online! if you own a wonderful internet business that you want to sell, or know someone who does, you should get in touch with them. Just like Berkshire, they commit to quick, simple diligence, a 30-day or less process, and will leave your business to do its thing for the long term. You can learn more about Tiny here: http://bit.ly/acquiredtiny and find their Berkshire Nerds store here: http://bit.ly/acquiredbrknerds
- Thank you as well to Vouch and to Capchase. You can learn more about them at:
- https://bit.ly/acquired-vouch
- http://bit.ly/acquiredcapchase
The Charlie Munger Playbook: (also available on our website at https://www.acquired.fm/episodes/berkshire-hathaway-part-ii )
-
Change your mind. Evolve. Reinvent.
-
Without Charlie's influence, Warren may have stuck to chasing cigar butts his entire career, and missed out on wonderful businesses like See's Candy, The Washington Post, Capital Cities, Geico (for the longterm) and Coca-Cola.
-
Charlie's life experience taught him that the world can change on a dime, and what worked in the past won't necessarily work in the future. To succeed over the longterm you have to be a constant learning machine — which sounds obvious, but the difficult part is being willing to question your own deeply held assumptions and beliefs, and then discard them when they no longer fit reality.
-
Focus on getting a few simple things right — and the rest takes care of itself.
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Adapting his beloved grandfather's motto ("Concentrate on the task immediately in front of you, and control your spending."), Charlie learned early on that there are only a few bedrock sort of things in life that never change — and that if you just focus on getting those right, you'll do well. Find a great spouse who makes you better in life; buy wonderful businesses at fair prices; never get into a position where you're over-extended; be philanthropic when you can; have fun along the way. It's hard to argue much else matters.
-
Reflecting back on his and Warren's success, Charlie says, "It isn't that we were so good at doing things that were difficult. We were good at avoiding things that were difficult — finding things that are easy."
-
Risk ≠ volatility. Risk = chance of going out of business.
-
The Efficient Market Hypothesists of the 1970s-80s proposed that all investing risk could be reduced to "beta", or volatility relative to the market. This led to the 1980s' explosion of debt, derivatives and other "weapons of mass financial destruction" which people believed "riskless" because their volatility was hedged. Charlie and Warren recognized before anyone else that to the contrary, these instruments greatly ratcheted risk in the system! Operating with so much leverage, a single small but unexpected event could topple the whole house of cards. Unfortunately Warren and Charlie didn't listen to their own advice when entering the Salomon Brothers saga...
-
Never wrestle with a pig. You both get dirty and the pig likes it.
-
Some people (and companies or even whole industries) are addicted to "getting dirty" — deceiving, betraying, evading, cheating, belittling, and generally pursuing their own self-interest above all else. It can be tempting to engage with such people, because they often have or promise great financial rewards. But you can't win in the long run. As the saying goes — you'll both get dirty, and the pig will like it. Unfortunately again, Warren and Charlie didn't always listen to their own advice...
Links:
- Chuck Rickershauser's corporate flow chart: (left half) (right half)
Carve Outs:
- The Sopranos: https://www.hbo.com/the-sopranos
- Macklemore on Armchair Expert: https://armchairexpertpod.com/pods/macklemore
Episode Sources:
- http://www.studioz7.com/stamps.html
- https://cmqinvesting.substack.com/p/damn-right-behind-the-scenes-with
- https://cmqinvesting.substack.com/p/damn-right-behind-the-scenes-with
- https://dealbook.nytimes.com/2014/03/12/with-deal-for-tv-station-buffett-shrinks-ties-to-graham-family/
- https://en.wikipedia.org/wiki/Ajit_Jain
- https://en.wikipedia.org/wiki/Berkshire_Hathaway
- https://en.wikipedia.org/wiki/Black_Monday_(1987)
- https://en.wikipedia.org/wiki/Blue_Chip_Stamps
- https://en.wikipedia.org/wiki/Charlie_Munger
- https://en.wikipedia.org/wiki/Eugene_Meyer_(financier)
- https://en.wikipedia.org/wiki/Fritz_Beebe
- https://en.wikipedia.org/wiki/Harvey_Seeley_Mudd
- https://en.wikipedia.org/wiki/John_Gutfreund
- https://en.wikipedia.org/wiki/John_J._Byrne
- https://en.wikipedia.org/wiki/John_Meriwether
- https://en.wikipedia.org/wiki/Katharine_Graham
- https://en.wikipedia.org/wiki/Liar's_Poker
- https://en.wikipedia.org/wiki/Michael_Lewis
- https://en.wikipedia.org/wiki/Nebraska_Furniture_Mart
- https://en.wikipedia.org/wiki/Phil_Graham
- https://en.wikipedia.org/wiki/Salomon_Brothers
- https://en.wikipedia.org/wiki/See's_Candies
- https://en.wikipedia.org/wiki/Thomas_Charles_Munger
- https://fortune.com/1997/10/27/warren-buffett-salomon/
- https://fundooprofessor.wordpress.com/2012/12/06/httpsdl-dropbox-comu28494399bloglinksfloats_and_moats-pdf/
- https://markets.businessinsider.com/news/stocks/warren-buffett-berkshire-hathaway-dream-business-is-sees-candies-2019-7-1029916323
- https://moiglobal.com/tom-murphy-2018/
- https://moneyisboring.com/2019/10/03/a-case-study-of-why-warren-buffett-bought-disney-in-1966/
- https://seekingalpha.com/article/4175060-time-for-berkshire-and-mclane-to-part-ways
- https://static.fmgsuite.com/media/documents/1bae1ba7-c2f2-4af5-ac1f-c0429dc7e5f0.pdf
- https://www.amazon.com/gp/product/0471446912/
- https://www.amazon.com/Liars-Poker-Norton-Paperback-Michael-ebook/dp/B003E20ZRY
- https://www.amazon.com/Poor-Charlies-Almanack-Charles-Expanded/dp/1578645018
- https://www.amazon.com/Snowball-Warren-Buffett-Business-Life/dp/0553805096
- https://www.berkshirehathaway.com/1999ar/FortuneMagazine.pdf
- https://www.fool.com/investing/2019/12/29/heres-how-much-money-warren-buffett-has-made-in-ge.aspx
- https://www.fool.com/investing/best-warren-buffett-quotes.aspx
- https://www.gurufocus.com/news/1344156/why-warren-buffetts-blue-chip-stamps-deal-was-so-revolutionary-
- https://www.multpl.com/s-p-500-pe-ratio/table/by-month
- https://www.sees.com/timeline/
- https://www.washingtonpost.com/business/warren-buffett-to-step-down-from-washington-post-co-board/2011/01/20/ABWJ9NR_story.html
- https://www.youtube.com/watch?v=jMuX_-hE7SQ
- https://youtu.be/QSGz4Y8CP2I
The podcast and artwork embedded on this page are from Ben Gilbert and David Rosenthal, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: La Story (LS 57 · TOP 0.5% what is this?)
Episode: Bourse : les SPAC à la folie
Pub date: 2021-05-25
Pour « La Story », le podcast d’actualité des « Echos », Pierrick Fay et ses invités décryptent cette pratique boursière de « Special purpose acquisition company », plus connue sous le terme de SPAC, qui agite Wall Street et tente de s’installer en Europe.
La Story est un podcast des « Echos » présenté par Pierrick Fay. Cet épisode a été enregistré en mai 2021. Rédaction en chef : Clémence Lemaistre. Invités : Eric Benoist (analyste technologie et data chez Natixis), Laurence Boisseau et Bastien Bouchaud (journalistes au service Marchés des « Echos »). Réalisation : Willy Ganne. Musique : Théo Boulenger. Identité graphique : Upian. Photo : Getty Images. Sons : CNBC, Metropolitan Films, tastytrade, Cassius Cuvée.
Voir Acast.com/privacy pour les informations sur la vie privée et l'opt-out.
The podcast and artwork embedded on this page are from Les Echos, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: La Story (LS 57 · TOP 0.5% what is this?)
Episode: Israël, de crise en crise
Pub date: 2021-05-18
Pour « La Story », le podcast d’actualité des « Echos », Pierrick Fay et ses invités décryptent les raisons de la flambée de violences qui plonge de nouveau Israéliens et Palestiniens dans un conflit ouvert et meurtrier.
Spécial La Story : abonnez-vous à -50% et accédez à nos enquêtes, éditos, newsletters en exclusivité et en avant-première.
https://abonnement.lesechos.fr/?origin=WO60APROP&utm_source=ancrage&utm_medium=site&utm_campaign=podcast
La Story est un podcast des « Echos ». Cet épisode a été enregistré en mai 2021. Rédaction en chef : Clémence Lemaistre. Invités : Catherine Dupeyron (correspondante des « Echos » en Israël) et Yves Bourdillon (journaliste aux « Echos »). Réalisation : Willy Ganne. Musique : Théo Boulenger. Identité graphique : Upian. Photo : REUTERS/Mohammed Salem. Sons : Euronews, France 24, Sky News, i24NEWS, Le Trio Joubran.
Voir Acast.com/privacy pour les informations sur la vie privée et l'opt-out.
The podcast and artwork embedded on this page are from Les Echos, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Acquired (LS 63 · TOP 0.1% what is this?)
Episode: Berkshire Hathaway Part I
Pub date: 2021-04-21
It's time. After 150+ episodes on great companies, we tackle the granddaddy of them all — Berkshire Hathaway. One episode alone isn't nearly enough to do Warren and Poor Charlie justice, so today we present Part I: Warren's story. How did a folksy, middle-class kid from Omaha become the single greatest capitalist of all-time? Why, like Jordan, did he retire (twice!) at the top of his game, only to reinvent himself and come back stronger than ever? As always, we dive in. Let's dance.
If you love Acquired and want more, join our LP Community for access to over 50 LP-only episodes, monthly Zoom calls, and live access for big events like emergency pods and book club discussions with authors. We can't wait to see you there. Join here at: https://acquired.fm/lp/
Sponsors:
- Thanks to Tiny for being our presenting sponsor for all of Acquired Season 8. Tiny is building the "Berkshire Hathaway of the internet" — something they're so dedicated to, they even make and sell bronze busts of Warren & Charlie online! if you own a wonderful internet business that you want to sell, or know someone who does, you should get in touch with them. Just like Berkshire, they commit to quick, simple diligence, a 30-day or less process, and will leave your business to do its thing for the long term. You can learn more about Tiny here: http://bit.ly/acquiredtiny and find their Berkshire Nerds store here: http://bit.ly/acquiredbrknerds
- Thank you as well to Vouch and to Capchase. You can learn more about them at:
- https://bit.ly/acquired-vouch
- http://bit.ly/acquiredcapchase
The Warren Buffett Playbook:
(also available on our website at https://www.acquired.fm/episodes/berkshire-hathaway-part-i )
-
Money can create more money. (aka "Compounding")
-
Very early in life, Warren figured out something most people never truly grasp: money can be used to generate more money. It's sounds simple, but once you fully internalize this concept, you'll never see the world the same again. A given sum no longer represents what you could buy with it — a coffee, a phone, a car, a house, etc — but rather what it could grow to become over time. At the extreme, people like Warren are "cursed", seeing prices for goods not as whatever the sticker says, but 5x, 10x, 20x higher — because that's what the opportunity cost of parting with the capital represents.
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If you own an asset that's compounding at a high rate with no obvious reason it will stop... dear lord do not interrupt it!! Most people are tempted to meddle: lock in gains, cover other losses, actively trade, or otherwise "manage" their investments. In the long run these actions are almost assuredly all value-destructive behaviors if you own truly great businesses.
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Align incentives: be a doctor, not a prescriptionist.
-
Warren likened stockbrokers — who got paid based on volume of trades placed, not investment performance — to "prescriptionist" doctors who were paid by their number and type of pills prescribed, versus actual patient outcomes. Once Warren created his investment partnerships (and then later transformed Berkshire Hathaway into something similar), he not only unlocked hugely better outcomes for his"patients", but allowed created a path to pursue his own dream and become fabulously wealthy in the process.
-
You can't expect to control other people's emotions around money (or anything else).
-
However with the right "ground rules", you can mitigate the impact of others on your business and decision making — and even use them to your advantage.
-
Warren's early partnerships had a few ground rules and norms: partners will not know what securities are held, trading in/out is allowed only 1 day / year, and Warren will consistently set low expectations (leaving himself ample room to over-deliver). These set the stage for nearly complete freedom for Warren to operate as he saw fit — to the immense gain of his limited partners.
-
Sins of omission (selling or passing) nearly always cost more than sins of commission (buying).
-
Warren is almost without doubt the greatest investor of all time. However even he made three incredibly stupid "unforced errors" early in his career that cost hundreds of billions in future gains: selling GEICO, selling American Express, and passing on the opportunity to invest in Intel with Arthur Rock.
- That said, Warren's fourth great mistake (and in his estimation his greatest) was certainly a sin of commission: buying Berkshire Hathaway itself. Warren estimates this single blunder totaled $200B+ in opportunity cost over his lifetime.
Carve Outs:
- Ben: Year One of Not Boring: https://www.notboring.co/p/a-not-boring-adventure-one-year-in
- David: Balaji Srinivasan on The Tim Ferriss Show: https://tim.blog/2021/03/24/balaji-srinivasan/
Episode Sources:
- https://berkshirehathaway.com/reports.html
- https://einvestingforbeginners.com/warren-buffetts-ground-rules/
- https://en.wikipedia.org/wiki/Alice_Schroeder
- https://en.wikipedia.org/wiki/Benjamin_Graham
- https://en.wikipedia.org/wiki/Berkshire_Hathaway
- https://en.wikipedia.org/wiki/Howard_Buffett
- https://en.wikipedia.org/wiki/List_of_public_corporations_by_market_capitalization#2021
- https://en.wikipedia.org/wiki/Oliver_Chace
- https://en.wikipedia.org/wiki/Robert_Noyce
- https://en.wikipedia.org/wiki/Salad_Oil_scandal
- https://en.wikipedia.org/wiki/Samuel_Slater
- https://en.wikipedia.org/wiki/Seabury_Stanton
- https://en.wikipedia.org/wiki/Union_Pacific_Railroad
- https://en.wikipedia.org/wiki/Valley_Falls_Company
- https://en.wikipedia.org/wiki/Wall_Street_Crash_of_1929
- https://en.wikipedia.org/wiki/William_J._Ruane
- https://fundooprofessor.wordpress.com/2012/07/09/flirting-with-floats-part-i/
- https://fundooprofessor.wordpress.com/2012/07/16/flirting-with-floats-part-ii/
- https://fundooprofessor.wordpress.com/2012/12/06/httpsdl-dropbox-comu28494399bloglinksfloats_and_moats-pdf/
- https://medium.com/@madmedic11671/how-salad-oil-almost-crashed-the-u-s-economy-c3ed3c2cb797
- https://minesafetydisclosures.com/blog/2017/4/16/berkshire-hathaway-brkb
- https://novelinvestor.com/happy-hour-wild-ride-geico/
- https://qz.com/emails/quartz-obsession/1269094/
- https://static.fmgsuite.com/media/documents/1bae1ba7-c2f2-4af5-ac1f-c0429dc7e5f0.pdf
- https://www.amazon.com/Buffett-American-Capitalist-Roger-Lowenstein/dp/0812979273
- https://www.amazon.com/Poor-Charlies-Almanack-Charles-Expanded/dp/1578645018
- https://www.amazon.com/Snowball-Warren-Buffett-Business-Life/dp/0553805096
- https://www.berkshirehathaway.com/letters/1995.html
- https://www.cnbc.com/2019/01/31/warren-buffett-on-his-successful-relationship-with-charlie-munger.html
- https://www.hbomax.com/feature/urn:hbo:feature:GWEW13AjEq0vCwwEAAAAH
- https://www.nationalindemnity.com/About_History.aspx
- https://www.nytimes.com/2009/02/04/business/04buffett.html
- https://www.tilsonfunds.com/BRK.pdf
- https://www.youtube.com/watch?v=fjXZbW8ALRA&t=463s
- https://www.youtube.com/watch?v=FsDYatBvwYI&t=127s
- https://www.youtube.com/watch?v=oFEwN7j0IWw
- https://www.youtube.com/watch?v=UZNqLWe5o2Q&t=171s
- https://www.youtube.com/watch?v=ZJzu_xItNkY
- https://www2.census.gov/prod2/popscan/p60-001.pdf
- https://yale.app.box.com/s/8lb7yqca5tmfcjbjhhuw5xft7i1ddttj
The podcast and artwork embedded on this page are from Ben Gilbert and David Rosenthal, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Sticky Notes: The Classical Music Podcast (LS 61 · TOP 0.5% what is this?)
Episode: Beethoven Symphony No. 1
Pub date: 2021-04-29
Today begins a pretty massive project for Sticky Notes - a complete Beethoven cycle over the next few weeks! We start of course with Beethoven's 1st symphony. Some people tend to think of Beethoven’s 1st as a cautious foray into the symphonic world, but I couldn’t disagree more. It is a bold, confident leap into the genre, a genre that Beethoven would end up changing for good. All of the elements that make Beethoven's symphonies so fantastic are already present in this symphony, so let's begin the journey!
The podcast and artwork embedded on this page are from Joshua Weilerstein, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Electronic Music (LS 33 · TOP 5% what is this?)
Episode: Don Lewis - The Untold Story Of A Synthesizer Pioneer
Pub date: 2020-11-12
Chapters
00:39 - Introduction
01:18 - How does it feel to have a movie made about you?
04:16 - Why make a film about Don?
05:45 - Why create LEO, the Live Electronic Orchestra?
09:18 - Is it about taking control?
11:25 - The documentary process
15:22 - A whole lotta love and struggles
20:26 - The technology of LEO
26:29 - Have you ever been tempted to move LEO from hardware to software?
29:52 - Gathering testimonies
32:12 - The documentary production
36:21 - How have you found pitching this movie to distributors?
40:31 - How LEO inspired MIDI
48:21 - Where can we watch the movie?
49:28 - Ending
Don Lewis BiogDon Lewis is a gifted musician and educator whose mission is to inspire and empower audiences to achieve their dreams. Whether presenting a solo concert or collaborating with other musicians and artists, Don's music brings a message of hope, respect, and community. Don began playing piano as a Dayton, Ohio high school student. Later, at Tuskegee Institute, he accompanied and sang with the Tuskegee Chorus and played for Dr. Martin Luther King, Jr.’s Freedom Rallies.
Uniting his interest in engineering with his musical talent, Don became one of the pioneers in synthesizer use and technology. In 1977, he designed and built a synthesizer system, Live Electronic Orchestra (LEO) that was an inspiration for Musical Instrument Digital Interface (MIDI), now on display at the Museum of Making Music in Carlsbad, CA.
Don has performed at concerts worldwide and has appeared as a soloist with many symphony orchestras. As a studio artist, he has worked with such greats as Quincy Jones, Sergio Mendez and Michael Jackson. Don has also created scores for film and television productions including the award winning Rainbow's End and Were You There series featured on PBS. In addition he has scored commercials for such clients as Nissan, Pacific Telephone, and Digital Equipment Corp.
Also an enthusiastic teacher, Don has taught courses in the history of Gospel Music, multimedia, and synthesizer technology at University of California at Berkeley Extension, San Jose State University and Stanford University. In 1987, Don combined his love of children, education and music to create Say “Yes” to Music! Since then, he has delighted thousands of students, teachers and school administrators across the United States and Canada with his inspiring musical assemblies.
Throughout the years Don has been a concert artist and consultant with various musical instrument manufacturers including Hammond, Arp, Yamaha, Roland Corporation, and Rodgers Instruments. He continues to delight and touch the hearts of his fans in his concerts at home and throughout the world.
Ned Augustenborg BiogNed Augustenborg has produced a wide range of content in the television industry, having produced or directed for ESPN, MSNBC, CNN, The Mountain Sports Network, Canadian Sports Network, Prime Ticket, CNBC and Sports Net.
Following his formal education at the University of Southern California and the University of Arizona, Augustenborg began his career in computer animation while collaborating on experimental video projects at the Long Beach Museum of Art Video Annex, followed by producing documentaries on such diverse subjects as the California Department of Corrections, a struggling Los Angeles Latino rock band and the early life of Nobel Prize winner Glenn T. Seaborg.
In addition to his freelance production career, Augustenborg also managed several studios for a variety of Cable TV operators throughout Southern California. A recipient of multiple Emmy and Cable ACE Awards in the categories of Entertainment, Documentary, Experimental Video, News; Augustenborg most recently produced and directed for Time Warner Cable’s newly developed sports channels in Southern California for which he received two Emmy nominations for "Best Live Sports Coverage" in 2014.
Links / Credits
All music clips produced, arranged, composed and performed by Don Lewis and taken from the soundtrack to the film, “The Ballad of Don Lewis” © Don Lewis Music 2020.
“Fall in Pleasanton”
“Hold On”
“Be-Noun-Chi”
Original Don Lewis LEO performances are available on the Album “Twelve Gates to the City” Available from Apple Music and Amazon.
Film website: https://www.theballadofdonlewis.com/ Trailer: https://vimeo.com/442861162 Available from: https://4bri.net/newReleasesx5.asphttps://donlewismusic.com/https://www.augustenborgproductions.com/
Rob Puricelli BiogRob Puricelli is a Music Technologist and Instructional Designer who has a healthy obsession with classic synthesizers and their history. In conjunction with former Fairlight Studio Manager, Peter Wielk, he fixes and restores Fairlight CMI’s so that they can enjoy prolonged and productive lives with new owners.
He also writes reviews and articles for his website, failedmuso.com, and other music-related publications, and has guested on a number of music technology podcasts and shows. He can often be found at various synthesizer shows demonstrating his own collection of vintage music technology.www.failedmuso.com Twitter: @failedmuso Instagram: @failedmuso Facebook: https://www.facebook.com/failedmuso/
The podcast and artwork embedded on this page are from Sound On Sound, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Business Breakdowns (LS 49 · TOP 1% what is this?)
Episode: Visa: The Original Protocol Business - [Business Breakdowns, EP. 07]
Pub date: 2021-05-05
Today we will be diving into Visa. Starting in 1958 as a BankAmericard credit card program in Fresno, California, it then became a non-profit consortium of banks that operated the Visa network. Over the first few decades of its existence, Visa became the protocol layer that allowed essentially all the banks in the world to communicate with one another.
In 2007, Visa completed a corporate restructuring that took it public and now boasts a larger market cap than all of the banks that previously owned it as part of the consortium.
In this Breakdown, we set the stage with Visa's role in a card transaction, describe the lifeblood of Visa’s revenue, interchange, and then dive into its unique history as a consortium turned multi-hundred billion-dollar public business. We then explore Visa’s unique moat and network effect, how Visa makes money today, and look at the potential threats from other businesses and macroeconomic forces. Visa is a fascinating business, and I recommend you check out our website at JoinColossus.com, where we provide additional articles, books, and podcasts for those who want to keep unpacking the Visa story.
To help me break down Visa, I'm joined by Alex Rampell, a general partner at Andreessen Horowitz, where he focuses on investing in financial services. Prior to joining Andreessen, Alex co-founded multiple companies, including Affirm and TrialPay, which was acquired by Visa in 2015.
For the full show notes, transcript, and links to mentioned content, check out the episode page here.
This episode is brought to you by Tegus. We created Business Breakdowns to uncover the lessons and frameworks behind every business, and that's what makes Tegus our perfect launch partner. Much of the foundational prep for these episodes start with research on the Tegus platform.
With Tegus, you can learn everything you’d want to know about a company in an on-demand digital platform. Investors share their expert calls, allowing others to instantly access more than 15,000 calls on Coinbase, Hinge Health, Farfetch, or almost any company of interest. All you have to do is log in. If you're ready to go deeper on any company and you appreciate the value of primary research, head to tegus.co/breakdowns for a free trial.
Business Breakdowns is a property of Colossus, Inc. For more episodes of Business Breakdowns, visit joincolossus.com/episodes.
Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here.
Follow us on Twitter: @JoinColossus | @patrick_oshag | @jspujji | @zbfuss
Show Notes
[00:03:20] - [First question] - Key players and functionality of a credit transaction
[00:05:50] - How $3 would be split up amongst the network after a $100 purchase is made
[00:10:55] - How Visa came to be a central player and why banks don’t talk to each other
[00:16:26] - Other businesses that have dominating protocol effects in fragmented sectors
[00:19:47] - What the internals of a business like Visa looks like and
[00:24:48] - Visa’s topline revenue is almost entirely exclusive to transaction fees
[00:26:11] - Thinking of Visa as a tax and simultaneous enabler of commerce writ large
[00:30:48] - Why concentration poses a risk to their business model
[00:34:56] - How international standards may play a role in Visa’s future
[00:41:52] - Would it be worth it for merchants to build something competitive
[00:44:33] - Thoughts on new value transfer tech companies and their relevance to Visa
[00:48:59] - Plaid’s role in the payment ecosystem and as a potential competitor
[00:50:40] - Parallels between the crypto space, their protocols, and open-source payments
[00:52:54] - Business lessons for entrepreneurs when studying Visa’s history
[00:54:44] - Lessons learned that can be applied to investing when studying Visa’s history
[00:55:37] - Books to learn more; A Piece of the Action, One for Many
The podcast and artwork embedded on this page are from Colossus, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Invest Like the Best with Patrick O'Shaughnessy (LS 67 · TOP 0.05% what is this?)
Episode: Alyssa Ravasio - Supply, Demand, and the Outdoors - [Founder’s Field Guide, EP. 31]
Pub date: 2021-04-29
My guest today is Alyssa Ravasio, co-founder and CEO of Hipcamp, a platform to discover and book your next camping trip. I was excited for this conversation as it combines two of my favorite passions – the outdoors and internet marketplaces. In our discussion, we cover how Alyssa bootstrapped demand in the early days of Hipcamp, the importance of creating not just great experiences but magical ones, and the evolution of Hipcamp’s business model. After listening to this episode, I’m sure the first thing you’ll want to do is get outside in nature. Please enjoy my conversation with Alyssa Ravasio.
For the full show notes, transcript, and links to mentioned content, check out the episode page here.
This episode is brought to you by Vanta. Vanta has built software that makes it easier to get and maintain your SOC 2 report at a fraction of the typical cost. Founder’s Field Guide listeners can redeem a $1k off coupon at vanta.com/patrick.
This episode is brought to you by DigitalOcean. DigitalOcean provides founders and creators with the platform they need to get their website and apps off the ground, all with low-bandwidth pricing to save them money over other cloud providers.
If you are looking for the best place to build web apps or API backends on robust infrastructure, DigitalOcean is the place for you. They provide a fully managed solution that handles your infrastructure, operating systems, databases, and other dependencies on their new App Platform product. App Platform makes it easy to build, deploy, and scale apps. Get started for free at do.co/founders.
Founder's Field Guide is a property of Colossus, Inc. For more episodes of Founder's Field Guide, visit joincolossus.com/episodes.
Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here.
Follow us on Twitter: @patrick_oshag | @JoinColossus
Show Notes
[00:02:57] - [First question] - The original insight that led to founding Hipcamp
[00:06:15] - What she studied in college and primary lessons learned there
[00:08:18] - Specific features of studying the internet that influenced her trajectory
[00:10:14] - Deciding what to attack first when developing Hipcamp’s infrastructure
[00:14:12] - Dimensions and market size of the outdoor industry
[00:15:48] - Organizing fragmented data into a useable schema and data set
[00:18:24] - What customers cared about most in the early days of Hipcamp
[00:20:00] - Managing focus and liquidity of the initial marketplace
[00:22:58] - Helping landowners manage the logistics of their property placements
[00:25:16] - Liability insurance in the outdoor industry
[00:27:35] - The biggest challenge faced in developing an outdoor marketplace
[00:30:03] - Amplifying and accelerating naturally grooved distribution
[00:32:32] - Identifying the top correlates of making magic happen
[00:34:55] - An ideal future for Hipcamp five years from now
[00:38:28] - What bad supply looks like and creating quality standards
[00:42:08] - Their business model and revenue stream
[00:44:45] - Deciding on their take rate and the debate around booking fees
[00:46:14] - Lessons learned along the way as a leader
[00:47:42] - Unit economics from the host’s perspective
[00:52:33] - The most interesting businesses built on top of Hipcamp
[00:53:35] - Why is Hipcamp’s mission so important to her
[00:57:11] - The kindest thing anyone has ever done for her
The podcast and artwork embedded on this page are from Colossus, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Acquired (LS 63 · TOP 0.1% what is this?)
Episode: The New York Times Company
Pub date: 2021-02-18
For the entire 20th Century, you’d be hard pressed to find a better business than an American newspaper — Warren Buffett famously described them as “franchises” — and no American newspaper stood taller than the New York Times. Controlled by a single family bound by a legal oath “to maintain the editorial independence and integrity of The New York Times and to continue it as an independent newspaper, entirely fearless, free of ulterior influence and unselfishly devoted to the public welfare”, the Times served as the paper of record for generations of Americans and people around the world. But no good thing lasts forever, and the dawn of the 21st Century saw both the Times and this once-mighty industry devastated by the dual disruptive forces of the internet and the 2008 financial crisis. And yet by 2021, The Times, essentially alone of its former peers, has reemerged from the American newspaper wreckage and transformed itself into a thriving digital business with an order of magnitude more subscribers than its print heyday. Curious how it all happened? We dive into 170 years of history to find out!
If you love Acquired and want more, join our LP Community for access to over 50 LP-only episodes, monthly Zoom calls, and live access for big events like emergency pods and book club discussions with authors. We can't wait to see you there. Join here at: https://acquired.fm/lp/
Sponsors:
- Thanks to Tiny for being our presenting sponsor for all of Acquired Season 8. Tiny is building the "Berkshire Hathaway of the internet" — if you own a wonderful internet business that you want to sell, or know someone who does, you should get in touch with them. Unlike traditional buyers, they commit to quick, simple diligence, a 30-day or less process, and will leave your business to do its thing for the long term. You can learn more about Tiny here: http://bit.ly/acquiredtiny
- Thank you as well to Vouch and to Capchase. You can learn more about them at:
- https://bit.ly/acquired-vouch
- http://bit.ly/acquiredcapchase
The New York Times Company Playbook:(also available on our website at https://www.acquired.fm/episodes/the-new-york-times-company )
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When you find yourself sitting in front of a big approaching demand wave... ride it!!
-
The New York (Daily) Times was founded during the newspaper boom of the 1850s, and similarly Adolph Ochs took over the local Chattanooga paper at the start of that city’s mining boom.
- The NYT made huge investments in its reporting during the two World Wars as the public’s appetite for news exploded, while its rivals missed the ball worrying over preserving advertising space. Likewise NYT launched The Daily (which would become the biggest podcast in the world) immediately following Trump’s inauguration in early 2017.
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Arguably NYT’s biggest business mistake was missing the cable wave -- which Rupert Murdoch leveraged brilliantly to build Fox News into the most valuable news media franchise in the world.
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Where there’s an entrepreneurial will, there’s an entrepreneurial way.
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Adolph Ochs bought the Chattanooga Times with $250 and sellers’ notes, and then acquired The New York Times out of bankruptcy with no personal money down and $100k of real estate debt. And turned them both into successes on a level no one (even himself at times) believed possible.
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Recurring Acquired theme: the media business is still the second-best business of all time, behind technology.
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Media’s ability to generate dual revenue streams (advertising and subscription) from the same content product generates enormous leverage on investment, AND most of those costs are fixed vs. variable (especially in a digital environment).
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This is why “content is king” has always been true in the media industry.
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NYT’s version of this strategy has always been to invest more in high-quality journalism than any of its peers. It was true in 1896 when Ochs took over, true during the World Wars and the Pentagon Papers, and perhaps has never been more true than today when NYT employs 1,700 journalists around the world and pays them an average of >2x the rest of the industry.
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That said, distribution is critical as well. To build a world-class media organization you must be great at both content AND distribution.
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In the old media landscape, NYT built great distribution through its printing and delivery operations, as well as savvy investments like the Index which led to libraries and researchers across the country relying on the Times as the “paper of record”.
- However in today’s media landscape, the task of building great distribution falls on the newsroom and journalists themselves. The job is no longer finished once you hit publish -- reporters and editors must own the responsibility of getting their work in front of readers via social media and shareable story elements.
Links:
- The 2014 NYT Innovation Report: https://archive.org/details/pdfy-59s-4-I2qSvG6MnA/mode/2up
- Mine Safety Disclosures’ NYT presentation: https://minesafetydisclosures.com/blog/newyorktimes
Carve Outs:
Ben:
- Titan by Ron Chernow: https://www.amazon.com/Titan-Life-John-Rockefeller-Sr-ebook/dp/B000XUDGHG
- Iteratively: https://iterative.ly
David:
- Sabaa Tahir’s Ember in the Ashes series: https://www.amazon.com/Ember-Ashes-3-Book/dp/B074VDZB17
Episode Sources:
- http://www.internethistorypodcast.com/2015/10/martin-nisenholtz-on-bringing-the-new-york-times-online/
- https://archive.nytimes.com/www.nytimes.com/books/99/09/19/daily/092299tifft-book-review.html?module=inline
- https://archive.nytimes.com/www.nytimes.com/learning/general/onthisday/bday/0312.html
- https://archive.org/details/pdfy-59s-4-I2qSvG6MnA/mode/2up
- https://archives.cjr.org/cover_story/sulzberger_at_the_barricades.php
- https://en.wikipedia.org/wiki/Adolph_Ochs
- https://en.wikipedia.org/wiki/Arthur_Hays_Sulzberger
- https://en.wikipedia.org/wiki/Battle_of_Fort_Sumter
- https://en.wikipedia.org/wiki/Daniel_Ellsberg
- https://en.wikipedia.org/wiki/Dotdash
- https://en.wikipedia.org/wiki/Edwin_D._Morgan
- https://en.wikipedia.org/wiki/George_Jones_(publisher)
- https://en.wikipedia.org/wiki/Henry_Jarvis_Raymond
- https://en.wikipedia.org/wiki/Iphigene_Ochs_Sulzberger
- https://en.wikipedia.org/wiki/List_of_assets_owned_by_The_New_York_Times_Company#Television_stations
- https://en.wikipedia.org/wiki/List_of_The_New_York_Times_employees
- https://en.wikipedia.org/wiki/Martin_Nisenholtz
- https://en.wikipedia.org/wiki/The_New_York_Times
- https://en.wikipedia.org/wiki/The_New_York_Times_Building
- https://en.wikipedia.org/wiki/The_New_York_Times_Company
- https://en.wikipedia.org/wiki/Yellow_journalism
- https://fintel.io/so/us/nyt
- https://media.foxcorporation.com/wp-content/uploads/prod/2019/09/18223214/Fox-Annual-Report-2019_Mid.pdf
- https://minesafetydisclosures.com/blog/newyorktimes
- https://nymag.com/intelligencer/2015/08/new-york-times-heirs.html
- https://nymag.com/news/features/40647/index4.html
- https://nymag.com/news/media/51015/
- https://nytco-assets.nytimes.com/2021/02/Press-Release-12.27.2020-Final-for-posting.pdf
- https://stratechery.com/2020/an-interview-with-buzzfeed-ceo-jonah-peretti/?utm_source=Memberful&utm_campaign=f14650dd37-daily_update_2020_11_24&utm_medium=email&utm_term=0_d4c7fece27-f14650dd37-110888309
- https://www.amazon.com/dp/B0058Z4NOQ/ref=dp-kindle-redirect?_encoding=UTF8&btkr=1
- https://www.amazon.com/gp/product/0316836311/ref=ppx_yo_dt_b_asin_title_o04_s00?ie=UTF8&psc=1
- https://www.arcgis.com/apps/Cascade/index.html?appid=86354f1b322a4ec2a548e58ac3e83d49
- https://www.bostonglobe.com/business/2012/05/11/new-york-times-sells-its-remaining-stake-boston-red-sox/ey4kwU4m6Xn2PYfcblrMcL/story.html
- https://www.enwoven.com/collections/view/1277/timeline
- https://www.fool.com/earnings/call-transcripts/2021/02/04/new-york-times-co-nyt-q4-2020-earnings-call-transc/
- https://www.forbes.com/sites/jonathanberr/2020/09/30/failing-new-york-times-stock-is-on-a-tear/?sh=57459cfd6247
- https://www.library.illinois.edu/hpnl/tutorials/antebellum-newspapers-city/
- https://www.macrotrends.net/stocks/charts/NYT/new-york-times/revenuehttps://www.presscouncil.org.au/uploads/52321/ufiles/The_New_York_Times_Innovation_Report_-_March_2014.pdf
- https://www.npr.org/sections/codeswitch/2014/05/15/312850571/a-complicated-first-a-black-editor-takes-the-helm-at-the-gray-lady
- https://www.nytco.com/company/history/our-history/
- https://www.nytco.com/person/a-g-sulzberger/
- https://www.nytco.com/person/joseph-kahn/
- https://www.nytco.com/person/kathleen-kingsbury/
- https://www.nytco.com/person/meredith-kopit-levien/
- https://www.nytimes.com/2004/04/25/weekinreview/the-public-editor-paper-of-record-no-way-no-reason-no-thanks.html
- https://www.nytimes.com/2009/01/20/business/media/20times.html
- https://www.nytimes.com/2012/10/02/opinion/nocera-how-punch-protected-the-times.html
- https://www.nytimes.com/2016/09/17/business/media/new-york-times-reinstates-managing-editor-role-appoints-joseph-kahn.html
- https://www.nytimes.com/2018/01/22/reader-center/ag-sulzberger-publisher-reader-questions.html
- https://www.nytimes.com/2018/09/20/insider/times-womens-section-female-reporters.html
- https://www.nytimes.com/2020/01/28/business/media/ben-smith-buzzfeed-new-york-times.html
- https://www.nytimes.com/2020/03/01/business/media/ben-smith-journalism-news-publishers-local.html
- https://www.nytimes.com/interactive/2018/opinion/editorialboard.htmlhttps://www.nytco.com/person/dean-baquet/
- https://www.quora.com/Why-is-The-New-York-Times-called-the-gray-lady
- https://www.scribd.com/doc/224608514/The-Full-New-York-Times-Innovation-Report?campaign=SkimbitLtd&ad_group=1025X1162200X86792d9062cfc602c27b4a78b6a20b8f&keyword=660149026&source=hp_affiliate&medium=affiliate
- https://www.statista.com/statistics/315041/new-york-times-company-digital-subscribers/
- https://www.wsj.com/articles/american-history-and-the-new-york-times-11602093219
- https://www.wsj.com/articles/SB123660214438270341
- https://www.youtube.com/watch?v=WVH0Yz0OMT0
The podcast and artwork embedded on this page are from Ben Gilbert and David Rosenthal, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Acquired (LS 63 · TOP 0.1% what is this?)
Episode: Special: Amazon Unbound (with Brad Stone)
Pub date: 2021-05-27
Brad Stone joins us to discuss the making of the modern Amazon, and how it's morphed from the "flywheel company" of The Everything Store into a set of interlocking and self-reinforcing businesses that extended both wider and deeper into the global economy than anyone ever imagined. (except perhaps Jeff Bezos) Is Amazon the Standard Oil of our time, or maybe something much, much bigger? Tune in as we dive in!
If you love Acquired and want more, join our LP Community for access to over 50 LP-only episodes, monthly Zoom calls, and live access for big events including upcoming Book Clubs like these! We can't wait to see you there. Join here at: https://acquired.fm/lp/
Sponsors:
- Thanks to Kevel for being our presenting sponsor for this special episode. Kevel provides API infrastructure to quickly build custom ad platforms for sponsored listings, internal promotions, native ads, and more — customers include Yelp, Rappi, OfferUp, Mozilla, Strava, and many other large apps and platforms. In true Acquired fashion, Kevel and CEO James Avery have put together a fun page showcasing the company's "history & facts", which you can find here: http://bit.ly/acquiredkevel !
- Thank you as well to Masterworks and to Perkins Coie. You can learn more about them at:
- http://bit.ly/acquiredmasterworks (use code “Acquired” to skip the waitlist)
- http://bit.ly/acquiredperkins
Topics covered:
- When and why Brad decided The Everything Store needed a sequel
- The process of writing the book and access he got at Amazon, including S-Team executives like Dave Clark
- The evolution of Amazon's core strategy from the flywheel into a set of "interlocking and self-reinforcing businesses", and how Brad landed on that as the key theme for the book
- Amazon's culture and the evolution from "Jeff-bots", and its embodiment in S-team members and company leaders beyond
- Amazon's investments in Video and why Bezos was ahead of the pack in realizing its strategic importance (including the rumored as-of-recording MGM deal)
- Amazon's secretive "Campfire" event and why Amazon does it despite its very un-Amazon price tag
- Brad's take on the future of three major Amazon business lines: Video, International and Marketplace / 3rd Party Sellers
- Amazon and Bezos's intense focus on competitors, despite the "theater" of their mantra to only focus on customers
- The Bezos "lapses of judgment" in 2018-19 and what it was like reporting on all the craziness around it
- Tracking down the "voice of Alexa" Nina Rolle, and Bezos's relationship with Elon!
Links:
- Amazon Unbound (on Amazon, natch): https://www.amazon.com/Amazon-Unbound-Invention-Global-Empire/dp/1982132612/
The podcast and artwork embedded on this page are from Ben Gilbert and David Rosenthal, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Acquired (LS 63 · TOP 0.1% what is this?)
Episode: Slack + Salesforce Emergency Pod with Packy McCormick of Not Boring
Pub date: 2020-12-02
Acquired is live on the scene covering Salesforce's blockbuster $27.7B acquisition of Slack, with the help of the internet's #1 Slack bull (and top internet analyst in his own right), Packy McCormick of Not Boring. We dissect the deal itself, Slack's relatively short life as a public company, the impact of Microsoft and Teams, and most importantly what this means for enterprise SaaS startups broadly. And oh yeah — we have a ton of fun too. :)
Note: you can find our full June 2019 episode on Slack's history and their DPO here: https://www.acquired.fm/episodes/the-slack-dpo
If you want more more Acquired and the tools + resources to become the best founder, operator or investor you can be, join our LP Program for access to our LP Show, the LP community on Slack and Zoom, and our live Book Club discussions with top authors. Join here at: https://acquired.fm/lp/
Sponsors:
- Thanks to Tiny for being our presenting sponsor for all of Acquired Season 7. Tiny is building the "Berkshire Hathaway of the internet" — if you own a wonderful internet business that you want to sell, or know someone who does, you should get in touch with them. Unlike traditional buyers, they commit to quick, simple diligence, a 30-day or less process, and will leave your business to do its thing for the long term. You can learn more about Tiny here: http://tinycapital.com
- Thank you as well to Bamboo Growth and to Perkins Coie. You can learn more about them at:
- https://growwithbamboo.com
- https://www.perkinscoie.com/
Playbook Themes from this Episode:
(also available on our website at https://www.acquired.fm/episodes/slack-salesforce-emergency-pod-with-packy-mccormick-of-not-boring )
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Distribution is still key when it comes to selling enterprise products at the highest levels.
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SaaS startups can (and do!) land deals with big companies all the time now through the bottoms-up motion of individual teams adopting the product and paying by credit card. And they also can (and do!) expand those deals into large, enterprise-wide contracts. But the massive power of Microsoft, Salesforce, and to a lesser-degree Oracle and Google's salesforces + bundling distribution abilities enables deals to happen at a scale that most independent companies find difficult to match.
-
Enterprise products are like icebergs — 90% of the work is below the surface.
-
This is true both at the tactical level (integrations, permissions, security, etc) and the strategic: providing seamless connective tissue between work apps inside and across organizations is what makes Microsoft so powerful as an enterprise player — not necessarily because their products are better.
-
This is why Slack Connect was such an important initiative for Slack, and why Microsoft trained the full firepower of its Teams marketing against it, while mostly ignoring Zoom even though Zoom is much more directly competitive on the product feature front.
-
Telling your story well always matters, no matter how big you get.
-
Perhaps the biggest reason Slack "failed" as a public company was its inability to effectively communicate what it did that was special, why that was important, and why it was defensible enough to withstand the assault from Teams. Arguably, great answers existed to all of those questions — and the company kept posting impressive numbers to back them up — but Wall Street never bought the story Slack sold.
-
Enterprise collaboration is moving deeper into work apps themselves.
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Today's native SaaS tools like Figma, Coda, Notion and others are embedding collaboration and chat natively into apps themselves — which reduces the primacy of a centralized platform like Slack, Teams or Discord. While on the one hand this is a threat to dedicated collaboration platforms, it also presents a massive opportunity: if they (or someone else) can decouple the core "collaboration layer" service from their own dedicated apps and also embed it directly into those work apps via APIs, it exponentially increases the surface area of workplace productivity that they can address.
-
The "Outsiders playbook" of growing through acquisition once your original product approaches market saturation works just as well in tech as it did in other sectors like media and industrials.
-
As Will Thorndike outlined in The Outsiders, many of the best CEO capital allocators of all-time utilized the grow-through-acquisition strategy very effectively. Salesforce (and other big tech companies like Facebook) are clearly adopting the same approach.
Links:
- Not Boring: https://notboring.substack.com
- Packy on Twitter: https://twitter.com/packyM
- Packy's bull thesis on Slack from November 2020: https://notboring.substack.com/p/slack-the-bulls-are-typing
The podcast and artwork embedded on this page are from Ben Gilbert and David Rosenthal, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Knowledge Project with Shane Parrish (LS 69 · TOP 0.05% what is this?)
Episode: #5 Chris Dixon: The State of Venture Capital
Pub date: 2015-11-13
In this episode, a16z partner Chris Dixon and I discuss the history of venture capital, artificial intelligence, what makes a great entrepreneur, and why companies fail.
Go Premium: Members get early access, ad-free episodes, hand-edited transcripts, searchable transcripts, member-only episodes, and more. Sign up at: https://fs.blog/membership/
Every Sunday our newsletter shares timeless insights and ideas that you can use at work and home. Add it to your inbox: https://fs.blog/newsletter/
Follow Shane on Twitter at: https://twitter.com/ShaneAParrish
The podcast and artwork embedded on this page are from Farnam Street, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Invest Like the Best with Patrick O'Shaughnessy (LS 67 · TOP 0.05% what is this?)
Episode: Chris Dixon – The Future of Tech - [Invest Like the Best, EP.69]
Pub date: 2017-12-26
My guest this week is Chris Dixon, who has written some of my favorite essays on technology and venture investing. Chris is a prolific investor and thinker, having been an entrepreneur, angel investor, and now partner at the well-known venture capital firm Andreessen Horowitz. Our conversation focuses on major trends in technology, including cryptocurrencies and the future of autonomous vehicles and drones. Chris has a rule of thumb for technology trends: find out what smart people are working on during the weekend, and you’ll know what other will be doing years in the future. After surveying his old essays, it’s clear you use Chris’s writings as a similar litmus test.
Hash Power is presented by Fidelity Investments Please enjoy this great conversation with Chris Dixon on the future of tech.
For more episodes go to InvestorFieldGuide.com/podcast.
Sign up for the book club, where you’ll get a full investor curriculum and then 3-4 suggestions every month at InvestorFieldGuide.com/bookclub.
Follow Patrick on Twitter at @patrick_oshag
Books Referenced
Technological Revolutions and Financial Capital: The Dynamics of Bubbles and Golden Ages
Who Controls the Internet?: Illusions of a Borderless World
Links Referenced
Douglas Hofstadter
Daniel Dennett
How Aristotle Created the Computer
New Yorker Cover on automation
The World of Numbers website
Jerry Neumann podcast episode
David Tisch podcast
ERC-20 Token Standard
Eleven Reasons To Be Excited About The Future of Technology
Show Notes
2:04 (First Question) – Why did Chris choose to study philosophy
2:23 – Douglas Hofstadter
2:24 – Daniel Dennett
3:20 – How Aristotle Created the Computer
3:35 – Where has his thinking and viewpoints changed the most having been in the real world
4:42 – What is the real driving force behind all of the technology that we are creating and will automation kill all of the jobs
6:16 – New Yorker Cover on automation
6:57 – The World of Numbers website
8:36 – A look at his history in networks and network design
11:03 – Technological Revolutions and Financial Capital: The Dynamics of Bubbles and Golden Ages
11:07 – Jerry Neumann podcast episode
12:32 – Who Controls the Internet?: Illusions of a Borderless World
13:06 – What are the market and technological forces that make it difficult to regulate software hardware companies
14:39 – The best features of proprietary centralized networks and open networks
16:40 – What things are better centralized vs decentralized
22:30 – David Tisch podcast
23:03 – When it comes to cryptocurrencies, what are the concerns that the protocols themselves hold value and could this lead to centralization of the system problems
24:02 – Block size debate (topic)
26:40 – ERC-20 Token Standard
27:23 – Is the blockchain the answer to the stagnation of the big tech players
32:47 – Does Chris find investment in individual crypto tokens analogous to seed funding in companies
34:39 - How does Chris think about the dichotomy of investing in people vs technologies
34:59 – Eleven Reasons To Be Excited About The Future of Technology
37:45 – What organizational structures of companies are most compelling
41:50 – Any major trends in technology a cause for concern for Chris
42:34 – Any interesting trends by people looking to disrupt the centralization of internet power to a small few
44:09 – What major trends is Chris passionately pursuing
51:15 – If everyone agrees on a future trend of technology, can you still make money investing in them
52:20 – How do you encourage younger people to approach the world and a career differently in this ever-changing world
57:39 – Kindest thing anyone has done for Chris
Learn More
For more episodes go to InvestorFieldGuide.com/podcast.
Sign up for the book club, where you’ll get a full investor curriculum and then 3-4 suggestions every month at InvestorFieldGuide.com/bookclub
Follow Patrick on twitter at @patrick_oshag
The podcast and artwork embedded on this page are from Colossus, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Undercurrents (LS 38 · TOP 2.5% what is this?)
Episode: Episode 78: President Biden's first 100 days
Pub date: 2021-04-29
Despite a rapidly mounting domestic agenda, President Biden has already set up an ambitious foreign policy programme, from taking the United States back into the Paris Agreement to announcing the withdrawal troops from Afghanistan.
To explore what we’ve learnt from the first three months of US foreign policy under President Biden, Ben is joined by Leslie Vinjamuri.
Visit the Chatham House microsite:
America's Global Role
Credits:
Speaker: Leslie Vinjamuri
Host: Ben Horton
Editor: Jamie Reed
Recorded and produced by Chatham House
The podcast and artwork embedded on this page are from Chatham House, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: In Our Time (LS 75 · TOP 0.05% what is this?)
Episode: Ovid
Pub date: 2021-04-29
Melvyn Bragg and guests discuss the Roman poet Publius Ovidius Naso (43BC-17/18AD) who, as he described it, was destroyed by 'carmen et error', a poem and a mistake. His works have been preserved in greater number than any of the poets of his age, even Virgil, and have been among the most influential. The versions of many of the Greek and Roman myths we know today were his work, as told in his epic Metamorphoses and, together with his works on Love and the Art of Love, have inspired and disturbed readers from the time they were created. Despite being the most prominent poet in Augustan Rome at the time, he was exiled from Rome to Tomis on the Black Sea Coast where he remained until he died. It is thought that the 'carmen' that led to his exile was the Art of Love, Ars Amatoria, supposedly scandalising Augustus, but the 'error' was not disclosed.
With
Maria Wyke Professor of Latin at University College London
Gail Trimble Brown Fellow and Tutor in Classics at Trinity College at the University of Oxford
And
Dunstan Lowe Senior Lecturer in Latin Literature at the University of Kent
Producer: Simon Tillotson
The podcast and artwork embedded on this page are from BBC Radio 4, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Acquired (LS 63 · TOP 0.1% what is this?)
Episode: Airbnb
Pub date: 2020-12-11
Over 13 years after its founding, one of the defining startup companies of the past decade finally makes its public debut — and boy was it a big one. But for all the hype (and all the legitimately great things Airbnb has accomplished), this is a company that looks very different today than in the past. Even before COVID, Airbnb's once-exponential bookings growth had declined to linear levels while the company's costs continued to balloon at accelerating rates. What’s going on here? Are public investors smart to bet on a permanent shift in travel behavior coming out of the pandemic? Or is this a case of unrealistic expectations? As always, we dive in.
If you want more more Acquired and the tools + resources to become the best founder, operator or investor you can be, join our LP Program for access to our LP Show, the LP community on Slack and Zoom, and our live Book Club discussions with top authors. Join here at: https://acquired.fm/lp/
Sponsors:
- Thanks to Tiny for being our presenting sponsor for all of Acquired Season 7. Tiny is building the "Berkshire Hathaway of the internet" — if you own a wonderful internet business that you want to sell, or know someone who does, you should get in touch with them. Unlike traditional buyers, they commit to quick, simple diligence, a 30-day or less process, and will leave your business to do its thing for the long term. You can learn more about Tiny here: http://tinycapital.com
- Thank you as well to Bamboo Growth and to Perkins Coie. You can learn more about them at:
- https://growwithbamboo.com
- https://www.perkinscoie.com/
Playbook Themes from this Episode:
(also available on our website at https://www.acquired.fm/episodes/airbnb )
-
If you can create value for all sides in a market ("expand the efficient frontier"), you really can’t help but be successful.
-
Born out of the 2008 financial crisis, Airbnb was able to fundamentally change the nature of the travel market and provide guests with more quality for less money, while also enabling hosts to earn meaningful extra income during a very difficult economic period. This led to incredible market adoption of the service, at times almost despite the company's own actions and activities.
-
When you create a market, you have an opportunity to set the terms.
-
By virtue of creating a whole new class of supply that had never participated in the travel market before, Airbnb was able to enact much more platform-favorable payment terms versus the hotel industry. Unlike Booking.com and the OTAs, guests pay Airbnb at the time of booking, and Airbnb keeps that cash (including fees) until after check-in — which could occur weeks or even months later.
-
This created an enormously beneficial cashflow dynamic for Airbnb that allowed them to grow while burning much less cash than otherwise would have been required.
-
When you don't fly low to the ground, you aren't forced to operate at the lowest level of detail.
-
Unlike DoorDash which needed to create an enormously efficient operational machine just in order to survive, Airbnb's capital-light business model, low operational intensity and favorable cashflow dynamics meant they've never had to operate in a particularly cost-disciplined or product-focused manner. While the core business has been insulated from competition due to its global network effects, the company has missed or poorly executed on Amazon-like opportunities to expand into adjacent markets and services that could have continued to drive new growth.
-
Relying solely direct/organic traffic is both a gift and a curse.
-
Undeniably, direct/organic customer acquisition is a wonderful goal for any business to strive for. Who wouldn't want to acquire customers without paying for them? However, if you don't also build the muscle for profitable and reliable growth through paid channels, you can be left vulnerable vulnerable when organic growth slows, as it inevitably will.
Carve Outs:
- David — San Francisco Ballet's Nutcracker: https://www.sfballet.org/productions/nutcracker-online/
- Ben — Star Wars Lofi HipHop: https://open.spotify.com/playlist/5iu1sp3UBb1rjf8KNKETtJ?si=WxPdJcRpSnelHN9qh0xYSw
Sources:
- http://www.critbuns.com/index.html
- http://www.paulgraham.com/airbnb.html
- https://avc.com/2011/03/airbnb/
- https://diff.substack.com/p/understanding-airbnb
- https://en.wikipedia.org/wiki/Airbnb
- https://en.wikipedia.org/wiki/Brian_Chesky
- https://en.wikipedia.org/wiki/CouchSurfing
- https://en.wikipedia.org/wiki/Joe_Gebbia
- https://en.wikipedia.org/wiki/Timeline_of_Airbnb
- https://gadgets.ndtv.com/internet/features/airbnb-ipo-ceo-brian-chesky-nasdaq-december-debut-stock-market-2328441
- https://growthhackers.com/growth-studies/airbnb
- https://hbr.org/2019/04/research-when-airbnb-listings-in-a-city-increase-so-do-rent-prices
- https://medium.com/traveltechmedia/airbnb-vs-booking-holdings-51e79b8cc489
- https://news.airbnb.com/brian-cheskys-open-letter-to-the-airbnb-community-about-building-a-21st-century-company/
- https://news.airbnb.com/designing-the-future-of-airbnb/
- https://nextviewventures.com/blog/airbnb-s-1-part-1-so-how-profitable-is-this-thing-really/
- https://thegeneralist.substack.com/p/airbnb-the-disaster-artist
- https://twitter.com/danprimack/status/1337101820007768064
- https://www.amazon.com/Upstarts-Airbnb-Battle-Silicon-Valley/dp/0316388416/
- https://www.bloomberg.com/news/articles/2020-12-09/airbnb-s-3-1-billion-ipo-hinges-on-hosts-who-make-rentals-feel-like-home
- https://www.cnbc.com/2019/12/10/airbnb-gitlab-considering-direct-listings-and-bankers-coming-around.html
- https://www.cnbc.com/2020/04/14/airbnb-raises-another-1-billion-in-debt.html
- https://www.cnbc.com/2020/12/09/airbnb-sells-shares-at-68-in-ipo-pricing-above-range.html
- https://www.epi.org/publication/the-economic-costs-and-benefits-of-airbnb-no-reason-for-local-policymakers-to-let-airbnb-bypass-tax-or-regulatory-obligations/
- https://www.forbes.com/sites/davidjeans/2020/11/16/airbnb-cofounders-own-nearly-42-of-covid-dented-business-ipo-filing-shows/
- https://www.linkedin.com/in/brianchesky/
- https://www.npr.org/2017/10/19/543035808/airbnb-joe-gebbia
- https://www.nytimes.com/2019/09/20/technology/airbnb-employees-ipo-payouts.html
- https://www.nytimes.com/2020/07/17/technology/airbnb-coronavirus-layoffs-.html
- https://www.phocuswire.com/booking-holdings-expedia-group-marketing-spend-2019
- https://www.sec.gov/Archives/edgar/data/1559720/000119312520294801/d81668ds1.htm
- https://www.sec.gov/Archives/edgar/data/1559720/000119312520306257/d81668ds1a.htm
- https://www.theinformation.com/articles/10-questions-airbnbs-ipo-investors-should-ask
- https://www.theinformation.com/articles/airbnbs-biggest-ipo-winners?utm_content=article-5102&utm_campaign=article_email&utm_source=sg&utm_medium=email
- https://www.theinformation.com/video/313?utm_campaign=Live_Video_QA_Call_P&utm_content=565410&utm_medium=email&utm_source=cio&utm_term=197334
- https://www.wsj.com/articles/airbnb-expected-to-price-ipo-above-56-to-60-a-share-range-11607527468?mod=hp_lead_pos5
- https://www.wsj.com/articles/airbnb-operating-chief-to-step-down-join-board-11574439482?mod=article_inline
- https://www.wsj.com/articles/airbnb-paying-more-than-10-interest-on-1-billion-financing-announced-monday-11586297484?mod=rsswn
- https://www.youtube.com/watch?v=efNyRmTLbjQ
- https://www.linkedin.com/in/blecharczyk/
- https://www.linkedin.com/in/jgebbia/
- https://news.airbnb.com/brian-cheskys-open-letter-to-the-airbnb-community-about-building-a-21st-century-company/
The podcast and artwork embedded on this page are from Ben Gilbert and David Rosenthal, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: La Story (LS 57 · TOP 0.5% what is this?)
Episode: Nicola Sturgeon, la femme qui veut libérer l’Ecosse
Pub date: 2021-04-12
Pour « La Story », le podcast d’actualité des « Echos », Pierrick Fay et ses invités font le portrait de la Première ministre écossaise, qui incarne le cauchemar de Boris Johnson dans sa bataille pour organiser, si elle est réélue le 6 mai, un nouveau référendum pour l’indépendance de son pays permettant son retour au sein de l’UE.
Spécial La Story : abonnez-vous à -50% et accédez à nos enquêtes, éditos, newsletters en exclusivité et en avant-première.
https://abonnement.lesechos.fr/?origin=WO60APROP&utm_source=ancrage&utm_medium=site&utm_campaign=podcast
La Story est un podcast des « Echos ». Cet épisode a été enregistré en avril 2021. Rédaction en chef : Clémence Lemaistre. Invités : Olivier de France (directeur de recherche à l’Iris, responsable du programme Europe) et Alexandre Counis (correspondant des « Echos » à Londres). Réalisation : Willy Ganne. Musique : Théo Boulenger. Identité graphique : Upian. Photo : ANDREW MILLIGAN/POOL/AFP. Sons : The Guardian, « Les Indestructibles » (2004), Notsensibles « I’m in Love With Margaret Thatcher » (1979), Franz Ferdinand « Take Me Out » (2004), Euronews, France 3.
Voir Acast.com/privacy pour les informations sur la vie privée et l'opt-out.
The podcast and artwork embedded on this page are from Les Echos, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: La Story (LS 57 · TOP 0.5% what is this?)
Episode: Coinbase, l’IPO fracassante
Pub date: 2021-04-20
La première journée en Bourse de Coinbase a confirmé l’emballement pour les monnaies virtuelles sur les Marchés. Pour « La Story », le podcast d’actualité des « Echos », Pierrick Fay et Nessim Aït-Kacimi reviennent sur l’introduction record au Nasdaq de la plateforme de trading de cryptomonnaies.
Spécial La Story : abonnez-vous à -50% et accédez à nos enquêtes, éditos, newsletters en exclusivité et en avant-première.
https://abonnement.lesechos.fr/?origin=WO60APROP&utm_source=ancrage&utm_medium=site&utm_campaign=podcast
La Story est un podcast des « Echos ». Cet épisode a été enregistré en avril 2021. Rédaction en chef : Clémence Lemaistre. Invité : Nessim Ait-Kacimi (spécialiste des changes aux « Echos »). Réalisation : Willy Ganne. Musique : Théo Boulenger. Identité graphique : Upian. Photo : Richard B. Levine/Newscom/SIPA. Sons : CNBC, Jean-Louis Aubert « Milliers, Millions, Milliards », Nas - I Am « Money Is My Bitch », Wriggles « Plouf », Koh-Lanta.
Voir Acast.com/privacy pour les informations sur la vie privée et l'opt-out.
The podcast and artwork embedded on this page are from Les Echos, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Rachman Review (LS 44 · TOP 1.5% what is this?)
Episode: Spying in the digital age
Pub date: 2021-03-18
Helen Warrell, the FT’s defence and security editor, talks to cybersecurity expert Dmitri Alperovitch about the SolarWinds and Microsoft hacks. How extensive was the damage inflicted and how should the west respond to such attacks?
Clips: CBS, CNN, NBC
Podcast: The NFX Podcast (LS 42 · TOP 1.5% what is this?)
Episode: The Founders' List: Valve's New Employee Handbook
Pub date: 2021-04-08
This is The Founders' List - audio versions of essays from technology’s most important leaders, selected by the founder community.
This is Valve’s new employee handbook, delivered to every new person working at the iconic gaming company. It is an abbreviated encapsulation of Valve's guiding principles. As Valve continues to grow, they hope that these principles will serve each new person joining the ranks as an employee.
Read the full handbook here - https://cdn.cloudflare.steamstatic.com/apps/valve/Valve_NewEmployeeHandbook.pdf
The podcast and artwork embedded on this page are from NFX, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The NFX Podcast (LS 42 · TOP 1.5% what is this?)
Episode: The Founders' List: 1997 Letter to Amazon Shareholders
Pub date: 2021-04-20
This is The Founders' List - audio versions of essays from technology’s most important leaders, selected by the founder community.
Amazon's approach to building sustainable long-term value should be a lesson to all companies - not just Internet companies. When Jeff Bezos took Amazon public 24 years ago, he sent this letter to all Shareholders and is still considered a playbook for building a great company - read by NFX.
"1997 was indeed an incredible year. We at Amazon.com are grateful to our customers for their business and trust, to each other for our hard work, and to our shareholders for their support and encouragement."
The podcast and artwork embedded on this page are from NFX, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Axios Re:Cap (LS 56 · TOP 0.5% what is this?)
Episode: Organized labor’s two high-profile failures — and what comes next
Pub date: 2021-04-20
Workers at Amazon’s warehouse in Bessemer, Alabama, rejected unionizing by more than a 2-1 margin earlier this month, despite a surge of national support for their efforts, including from President Biden. This followed a failed effort to get Uber, Lyft and DoorDash drivers recognized as employees and not contractors.
Dan talks to two of the organizers involved about what went wrong, legislation in Congress that might bolster the power of unions, and where organized labor goes from here.
Learn more about your ad choices. Visit megaphone.fm/adchoices
The podcast and artwork embedded on this page are from Axios, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Acquired (LS 63 · TOP 0.1% what is this?)
Episode: Special: Sequoia Capital's Investment Playbook (with Alfred Lin)
Pub date: 2021-02-01
We cover Sequoia Capital a lot on this show. Not only across our now four(!) dedicated episodes, but across a stunning nearly 50% of recent season companies where Sequoia was a primary or only investor — the most of any venture firm by an enormous margin. Today in this very special episode, we dive into the principles that have led to the firm's 49 years of unparalleled success in venture, and the playbook behind how they identify markets and companies that create outcomes worthy of the firm's namesake tree.
If you love Acquired and want more, join our LP Community for access to over 50 LP-only episodes, monthly Zoom calls, and live access for big events like emergency pods and book club discussions with authors. We can't wait to see you there. Join here at: https://acquired.fm/lp/
Sponsors:
- Thanks to MITIMCo for being our presenting sponsor for this special episode. They are truly some of the best and most well-known investors in the LP communit, and their investment performance supports MIT's cutting-edge research, and world-class education. If you or someone you know is starting a fund or recently launched, get in touch with them at: http://bit.ly/acquiredmitimco , and tell them that you heard about MITIMCo on Acquired.
- Thank you as well to Masterworks and to Perkins Coie. You can learn more about them at:
- http://bit.ly/acquiredmasterworks (use code “Acquired” to skip the waitlist)
- http://bit.ly/acquiredperkins
The Sequoia Capital Playbook:
(also available on our website at https://www.acquired.fm/episodes/special-sequoia-capitals-investment-playbook-with-alfred-lin )
-
Bring a prepared mind.
-
Founders (as they should) typically think more about solving a problem in the world, and less about the market context around what they're doing. Sequoia has always focused on the market — which allows them to bring a prepared mind to conversations with founders both pre and post investment. Great partnerships and great investments lie at the intersection of these two perspectives.
-
Focusing on the market takes many forms at Sequoia. It includes building and maintaining market landscapes, constantly looking for white spaces, and convening quarterly "blue sky" sessions within the firm.
-
The two questions that matter are "Why now?" and "Who cares?".
-
Early-stage is different from other forms of investing. As Don would say, it's predicated on investing in markets undergoing significant change: today's solutions are wrong for tomorrow. A good answer to "why now" upends the Warren Buffet quote about reputations of businesses with bad economics surviving intact. For example, DoorDash and Instacart had great “why now's” (ability to access a whole new class of labor through mobile devices), whereas Webvan (also a Sequoia investment) did not.
-
Similarly, the key to evaluating market size in the context of early-stage venture is to focus on the opportunity size tomorrow, not today. "Who cares" is a great lens to predict and distill this: if this new solution were widely known and available who (how many people/customers, what segments, with what buying power) will care (how much will it improve their lives or businesses)?
-
The goal is not buying low and selling high. The goal is compounding capital.
-
In a compounding environment, gains from the next few years will always dwarf all cumulative gains from years prior. The goal is to invest in companies that are able to become compounders, help them do so, and enjoy the returns as long as possible.
-
Identifying compounding (and whether it will continue) is hard to get right. The question Sequoia asks is whether the future for a given market, company or investment looks brighter than today. When the answer is yes: 💎🙌
-
Venture is a humbling business.
-
You can make money even if you get the investment thesis wrong, lose money even if you get the investment thesis right, and you realize your losses many years before your gains. (Alfred has been at Sequoia for over 10 years and only just had his first two IPOs: Airbnb and DoorDash.)
- To succeed in venture over the long run you need all three of high IQ, high EQ, and hustle. (We would also add high patience to the list!) What you don't need are specific qualifications: Michael Moritz was a journalist, Roelof Botha was an actuary, Don and Doug were sales guys and Alfred was a COO. Greatness can come from anywhere.
- There will always be too much capital chasing too few good deals. It's true today, it was true when Alfred started 10 years ago, and it was true when Michael Moritz started 20 years before that. But the winning companies will always generate outsized returns by using that capital to their unfair advantage. Your job as a VC is very simple, but devilishly hard: invest in those companies.
Links:
- Our two-part Sequoia history:
- Part I: https://www.acquired.fm/episodes/sequoia-capital-part-1
- Part II: https://www.acquired.fm/episodes/sequoia-capital-part-ii-with-doug-leone
- Don Valentine's talk at Stanford GSB: https://www.youtube.com/watch?v=nKN-abRJMEw&t=2555s
- Our conversation about Sequoia's Black Swan Memo with Roelof Botha: https://www.acquired.fm/episodes/sequoias-black-swan-memo
The podcast and artwork embedded on this page are from Ben Gilbert and David Rosenthal, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Version One with Maurice Cherry
Episode: Guillermo Rauch, CEO and Co-Founder of Vercel
Pub date: 2021-04-20
Vercel is the best place to deploy any frontend app, and the platform enables frontend teams to do their best work. It works with over 30 Jamstack frameworks and is used by teams at Airbnb, Twilio, and The Washington Post (to name a few).
Guillermo Rauch, the founder and CEO of Vercel is our guest this week on Version One. From his early start with tech and open source in Argentina to becoming a part of the startup community in San Francisco, we'll follow Guillermo on his international journey that led to creating the Vercel platform.
The podcast and artwork embedded on this page are from CodeSandbox BV, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Version One with Maurice Cherry
Episode: Matt Biilmann, CEO and Co-Founder of Netlify
Pub date: 2021-04-06
Netlify is known as one of the fastest-growing platforms for high-performance websites and apps. Dozens of big companies use Netlify -- including Figma, Shopify, Mailchimp, and Nike -- and to date, they have onboarded over a million other businesses and developers to their platform.
On this week's episode of Version One, we take a look at Netlify's beginning product journey from its CEO and co-founder, Matt Biilmann. Listen as Matt talks about his early career as a music journalist, partnering up with his best friend (and Netlify co-founder) Chris Bach, and learn about the triumphs and challenges they faced while building their way to the successful first version of their product.
The podcast and artwork embedded on this page are from CodeSandbox BV, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Life Scientific (LS 60 · TOP 0.5% what is this?)
Episode: Theresa Marteau on how to change behaviour
Pub date: 2021-04-13
We all know how to be more healthy. And yet we are also remarkably good at NOT doing what we know is good for us. We keep meaning to get fit, but the sofa seems so much more appealing than a run. We know we shouldn’t have another slice of cake, but we do. Behavioural psychologist, Professor Dame Theresa Marteau wants to understand why, despite the best of intentions, so many of us fail to adopt healthier lifestyles. She talks to Jim Al-Khalili about her life and work and why, after studying the evidence she changed her mind about how to change our behaviour. Back in the 90s, it seemed reasonable to assume that telling people they were at high risk of dying would jolt them into eating more healthily and taking more exercise. Now we know better. Thanks in large part to research pioneered by Theresa, we have a much more sophisticated understanding of what drives our behaviour. It turns out that small scale interventions to redesign our environment can exert a big influence on our behaviour by nudging us all into make better decisions, in ways that are beyond our awareness. Spoiler alert - smaller wine glasses really do make you drink less! Responses to Covid-19 show that nations can act rapidly and radically in response to immediate threats to health, even at huge cost. Can we do the same to tackle other threats to global health? Producer: Anna Buckley
The podcast and artwork embedded on this page are from BBC Radio 4, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Great Lives (LS 59 · TOP 0.5% what is this?)
Episode: Chinua Achebe, Nigerian novelist
Pub date: 2021-04-15
Yasmin Alibhai-Brown picks Nigerian novelist, Chinua Achebe, the author of Things Fall Apart. With archive contributions from Chimamanda Ngozi Adichie and Chinua Achebe himself. He was born in Nigeria in 1930 and Yasmin Alibhai Brown met him twice in Uganda in the 1960s and remains deeply impressed by both his books and his life.
The presenter is Matthew Parris, the producer is Miles Warde
The podcast and artwork embedded on this page are from BBC Radio 4, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Unchained (LS 63 · TOP 0.1% what is this?)
Episode: Vitalik Buterin on Ethereum's Five-Year Anniversary - Ep.183
Pub date: 2020-07-28
Vitalik Buterin, co-founder of Ethereum, shares his thoughts on the five-year anniversary of Ethereum, challenges with ETH 2.0 and criticisms about DeFi. We discuss:
- his thoughts and feelings ahead of Ethereum’s five-year anniversary
- how to address the high gas fees on the Ethereum network
- whether the complexity of Ethereum 2.0 creates a risk for the security of the network
- whether staking will lead to a higher focus on ETH price and issues such as monetary policy
- how he looks at the ETH price with its significance for security in Ethereum 2.0
- how proof of stake systems can be more democratic
- whether staking lends itself to the kind of securitization that looks similar to mortgage-backed securities
- his concerns about DeFi
- the most optimal way for DeFi projects to distribute their tokens
- his thoughts about Bitcoin on Ethereum
- inherent risks to Ethereum users connected to China’s Blockchain Service Network (BSN)
- his views on DCEP, other CBDCs and Libra
- the indictment of Ethereum Foundation staff member Virgil Griffith for allegedly helping North Korea to circumvent sanctions
- how he plans to make the Ethereum Foundation more transparent
- whether lack of diversity would impact the success of Ethereum in the long term
- where he would like to see Ethereum in the next five years
Thank you to our sponsors!
Crypto.com: https://www.crypto.com
Tezos: https://tquorum.com/
Episode links:
Vitalik Buterin: https://twitter.com/VitalikButerin
Ethereum: https://ethereum.org/en/
Vitalik’s blog: https://vitalik.ca
Median gas price: https://blockchair.com/ethereum/charts/median-gas-price
Average gas price: https://blockchair.com/ethereum/charts/average-gas-price
Solutions to gas price problem: https://www.coindesk.com/ethereum-developers-consider-new-fee-model-as-gas-costs-climb
Reddit AMA with the Ethereum 2.0 Research team, including Vitalik: https://old.reddit.com/r/ethereum/comments/ho2zpt/ama_we_are_the_efs_eth_20_research_team_pt_4_10/
Paper on how DeFi lending could undermine security in a POS system: https://arxiv.org/abs/2001.00919
Vitalik tweet on yield farming: https://twitter.com/VitalikButerin/status/1278337657194655744
Abra settlement with SEC and CFTC: https://www.coindesk.com/sec-cftc-hit-crypto-app-abra-with-300k-in-penalties-over-illegal-swaps
Maya Zehavi’s comments on what this could mean for DeFi: https://twitter.com/mayazi/status/1282696180741414918
China’s BSN using public chains, including Ethereum: https://www.coindesk.com/chinas-blockchain-infrastructure-to-extend-global-reach-with-six-public-chains
Unchained interview about DCEP: https://unchainedpodcast.com/why-china-aims-to-replace-cash-with-the-digital-yuan/
Charges against Virgil Griffith: https://www.justice.gov/usao-sdny/pr/manhattan-us-attorney-announces-arrest-united-states-citizen-assisting-north-korea
More on Virgil Griffith case: https://www.coindesk.com/usa-v-virgil-griffith-what-we-know-and-dont-in-the-bombshell-crypto-sanctions-case
Unchained interview with human rights activist Yeonmi Park on what life is like in North Korea: https://unchainedpodcast.com/yeonmi-park-on-why-doing-business-with-north-korea-is-like-buying-a-ticket-to-a-concentration-camp/
Unchained interview on why North Korea is interested in cryptocurrency: https://unchainedpodcast.com/why-north-korea-is-interested-in-cryptocurrency/
Case against Steven Nerayoff: https://www.justice.gov/usao-edny/pr/two-arrested-extortion-startup-cryptocurrency-company
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Podcast: Bobby and Jens (LS 49 · TOP 1% what is this?)
Episode: James Hayden
Pub date: 2021-04-16
Bobby Julich and Jens Voigt sit down with bikepacking legend James Hayden. Find out what he overcame to seal victory at the Transcontinental Race and how he used his cycling knowledge to lose robbers on horseback in Kyrgyzstan!
This episode was a Velonews production in association with Shocked Giraffe. This episode was produced by Mark Payne and edited by Kirk Warner.
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Podcast: La Grande table (LS 51 · TOP 0.5% what is this?)
Episode: Ballaké Sissoko et Vincent Segal, cordes sensibles
Pub date: 2021-04-15
durée : 00:26:51 - La Grande table culture - par : Chloë Cambreling - Rencontre avec Ballaké Sissoko et Vincent Segal, à l'occasion de la sortie du nouvel album "Djourou" de Ballaké Sissoko, sur le label No Format! - réalisation : Thomas Beau - invités : Ballaké Sissoko Musicien (kora); Vincent Ségal violoncelliste et bassiste
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Podcast: The Documentary Podcast (LS 67 · TOP 0.05% what is this?)
Episode: Shipping’s dirty secret
Pub date: 2021-03-25
The shipping industry is worth millions to the world economy and we depend on it for most of our goods. Assignment lifts the lid on the dangerous and polluting world of shipbreaking and investigates why ships once owned by UK companies end their lives on beaches in India, Pakistan and Bangladesh.
(Image: Bangladeshi labourers and docked ships at a shipbreaking yard. Credit: Farjana Khan Godhuly/AFP via Getty Images)
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Podcast: People & Music Industry (LS 32 · TOP 5% what is this?)
Episode: 50 Years Of Eventide
Pub date: 2021-03-04
Chapters00:00 - Introduction
00:27 - 50 Years Of Eventide
01:04 - The Early Days
02:52 - Introducing Digital Technology To The Studio
04:20 - H910 Harmonizer
07:49 - The Birth Of The Effects Unit
09:23 - Eventide Everywhere!
10:58 - The Strangest Use For A Harmonizer
11:33 - Auto-Tune Before Auto-Tune
12:50 - SP2016 Array Processor
15:21 - Eventide's Weirdest Product
16:51 - Rare Items / The S1066 Effects Unit
18:52 - Adapting Algorithms22:35 - Physion / Structural Effects
24:21 - The Use Of Software In All Tech
26:12 - Audio Networking
27:30 - Ending
Eventide BiogEventide have spent the past 50 years creating technical solutions for various industries. Richard Factor initially founded the business in 1971 to create custom-made solutions for studio engineers. The first product was a tape search unit for the Ampex MM1000, built for New York Producer Steve Katz to assist his workflow in the studio. This led to Ampex themselves requesting units and a range of small electronic projects followed. One of those projects became the 1745 Digital Delay Line with the introduction of RAM and later, pitch change. In 1972 Tony Agnello joined the company and developed the H910 Harmonizer® which became a huge success and was followed by the H949 with ‘deglitch’ feature, allowing for cleaner pitch control. At this time they started to develop products for the broadcast market, including the Monstermat and the Mono Stereo Matrix unit. This led on to them developing HP compatible RAM boards, HPIB buffers and ethernet cards. In the 80s they returned to their original idea of developing a general purpose digital audio processor utilising DSP and the Eventide SP2016 was created. Following a move to larger premises, Eventide became involved in developing moving maps for aviation use. They also solved another problem for the broadcast and customer service industry by creating the Logging Recorder, a DVD-RAM storage media. Today their tech is used extensively within the broadcast, music, aviation and customer service industries.https://www.eventideaudio.com/
Sam Inglis BiogEditor In Chief Sam Inglis has been with Sound On Sound for more than 20 years. He is a recording engineer, producer, songwriter and folk musician who studies the traditional songs of England and Scotland, and the author of Neil Young's Harvest (Bloomsbury, 2003) and Teach Yourself Songwriting (Hodder, 2006).https://www.soundonsound.com
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Podcast: The LRB Podcast (LS 52 · TOP 0.5% what is this?)
Episode: Analogous Patisseries
Pub date: 2021-02-23
Mary-Kay Wilmers, who retired as editor of the LRB last month, talks to Andrew O'Hagan about her career, first at Faber and Faber, then the Listener, then for 42 years at the London Review of Books. She talks about working with T.S. Eliot, the importance of being teased, and how a joke by Alan Bennett changed her life.
The episode also contains extracts from Wilmers's 1988 diary for the LRB, 'Putting in the Commas', and O'Hagan's piece about Wilmers in the latest issue of the paper. Read and listen to them in full here:
Mary-Kay Wilmers: Putting in the Commas
Andrew O'Hagan: Miss Skippit
Subscribe to the LRB from just £1 per issue: https://mylrb.co.uk/podcast20b
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Podcast: Undercurrents (LS 38 · TOP 2.5% what is this?)
Episode: Episode 17: Alastair Campbell on New Labour and Brexit, Alistair Darling on the Financial Crisis
Pub date: 2018-09-20
In the latest volume of his diaries, out this week, Alastair Campbell (former Director of Communications for Tony Blair) looks back on Gordon Brown's time in office between 2007 and 2010. Ben met Alastair to discuss among other things the art of political leadership, Brexit, the state of the Labour Party and policies to combat mental illness. Listen from 5:17.
This month marked 10 years since the 2008 financial crisis, an event that still weighs heavily on political systems across the world. Agnes visited London's Guildhall to discuss the aftermath of the crash with Alistair Darling (former Chancellor of the Exchequer), who was speaking at an event organised by Chatham House's Global Economy and Finance Department. Listen from 35:47.
Read the book:
Alastair Campbell Diaries Volume 7: From Crash to Defeat 2007-2010
Find out more about the event:
A Decade on from the Financial Crisis: the Legacy and Lessons of 2008
We apologise for the sound quality at the start of this episode, please stick with it - it improves!
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Podcast: Frontiers of Psychotherapist Development Podcast by Daryl Chow, Ph.D.
Episode: #7. A Tribute to K. Anders Ericsson
Pub date: 2020-06-24
We pay tribute to K. Anders Ericsson in today's episode.
He is known by many to be "the expert on expertise." His work, along with his colleagues had a profound impact on a wide array of professional domains such as sports, music, chess, and more recently in the field of psychotherapy. His four decades worth of research also informed the hugely popular book by Malcolm Gladwell, Outlier. (Though the "10,000hr" rule thing got misrepresented by others).
~~~ In this episode, you'd hear
-
the impact he had on me personally.
-
the foreword that he wrote for our book, Better Results (co-authored with Scott Miller and Mark Hubble, APA, 2020), and
-
a short poem I wrote in Oct 23, 2010 that was dedicated to him.
Better Results book was also dedicated to Dr. Ericsson. ~~~ Show Notes:
Peak by Ericsson and Pool
Ericsson, Krampe and Teach-Römer's seminal article on The Role of Deliberate Practice in the Acquisition of Expert Performance
The Music of Psychotherapy: Learning in a Wicked Environment (a blogpost I wrote in Aug 2019 about my first encounter with Ericsson in Kansas City) Scott Miller's recent interview with Ericsson.
A meta-analysis on deliberate practice by Macnamara et al. 2014
Our reanalysis of the 2014 meta-analysis Music, Right in Front of Me, by Daryl Chow and produced by DC and Joel Louie.
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Podcast: The James Altucher Show (LS 67 · TOP 0.05% what is this?)
Episode: 341 - Anders Ericsson [Anniversary Episode]: 7 Secrets of Mastery
Pub date: 2018-04-12
Anders K. Ericsson discovered the “10,000” hour rule. I had him on my podcast in 2016 to talk about peak performance. (One of my favorite topics.) And he broke down the steps every individual should follow to learn and MASTER a skill.
Links and Resources
Peak: Secrets from the New Science of Expertise by Dr. K Anders Ericsson and Robert Poole
Also Mentioned
Outliers: The Story of Success by Malcolm Gladwell
The Polgar SIsters (the 3 best female Hungarian chess players ever)
Laszlo Polgar (chess teacher who raised 3 chess prodigies his daughters)
The Inner Game of Tennis: The Classic Guide to the Mental Side of Peak Performance
Mozart - considered the most talented prodigy in music history, Anders disputes this
Magnus Carlsen - the best chess player in the world at age 12
Michelangelo
Picasso - one of the best painters of his time
Andy Warhol - in the 1950s he was a master illustrator
The Boston Marathon
I write about all my podcasts! Check out the full post and learn what I learned at jamesaltucher.com/podcast.
Thanks so much for listening! If you like this episode, please subscribe to “The James Altucher Show” and rate and review wherever you get your podcasts:
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Podcast: The NFX Podcast (LS 42 · TOP 1.5% what is this?)
Episode: Adam Grant on Anti-Patterns of 10x Thinking with Pete Flint
Pub date: 2021-02-04
"Doubt what you know, be curious about what you don't, and update your views based on new data."
NFX partner Pete Flint recently got together with organizational psychologist and bestselling author Adam Grant to discuss his new book, Think Again, about the counterintuitive and competitive advantages of rethinking.
Adam and Pete share the frameworks for: - Rethinking vs. Contrarian Thinking - Techniques from the World’s Best Superforecasters - Jeff Bezos’s 2×2 Decision Framework - The Top Killers of Co-founder Relationships - Why Confident Humility Is A Founder Superpower - & more
Read the NFX Essay - https://www.nfx.com/post/anti-patterns-of-10x-thinking/
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Podcast: Axios Re:Cap (LS 56 · TOP 0.5% what is this?)
Episode: Nicole Perlroth on the cyber-weapons arms race
Pub date: 2021-02-16
The U.S. government last year discovered that it was the victim of the largest-ever cybersecurity breach in its history, in which Russian hackers allegedly used a software exploit to access a deep trove of sensitive information. It was the latest escalation in a digital battle that is only expected to escalate, via a global black market where governments can buy everything from ways to hack laptop cameras to power grids.
Dan goes deeper with Nicole Perlroth, a New York Times cybersecurity reporter who just published a book titled "This Is How They Tell Me The World Ends."
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Podcast: Sound Check with Diwash Gahatraj
Episode: Sahana Bajpaie
Pub date: 2020-08-14
Sahana Bajpaie is a contemporary Rabindra Sangeet singer with a massive fan following in Bengal, Bangladesh and Bengali diaspora. In this podcast she spoke about how she left the country when she was one of the most prominent upcoming artistes in the music scene and her rediscovery of herself during her stay in London.
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Podcast: Philharmonia Orchestra Video Podcasts (LS 29 · TOP 10% what is this?)
Episode: Universal Notes: A Musical Collaboration Between India and the UK
Pub date: 2016-09-06
The Philharmonia Orchestra and Darbar have collaborated on an exciting cross-cultural music project called Universal Notes. The new piece was created in a year of workshops in India and the UK and will premiere at Darbar Festival on 16 Sept 2016. Taking inspiration from India’s raga mode of melody, the project moves away from jamming and ‘fusion’ styles, with an ambition to create music that brings two great classical traditions together equally. This film, shot on location in Bangalore and Mumbai, introduces the project and its participants.
The concert premiere of Universal Notes takes place at the Darbar Festival at Royal Festival Hall, London, Friday 16 September 2016, 6.30pm. philharmonia.co.uk/concerts/1632
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Podcast: Song Exploder (LS 74 · TOP 0.05% what is this?)
Episode: Vagabon - Water Me Down
Pub date: 2020-01-29
When Laetitia Tamko started making the second Vagabon album, she really wanted to produce the entire thing on her own. It would be a new sound, and producing was still a relatively new skill to her, but she wanted to tackle it head on, and do it all herself. On this song, though, "Water Me Down," Laetitia actually has a co-producer, Eric Littman. It’s the one exception to her otherwise entirely self-produced album. In this episode, she breaks down how she and Eric made the song, and why it was worth making that exception.
songexploder.net/vagabon
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Podcast: Song Exploder (LS 74 · TOP 0.05% what is this?)
Episode: Reissue: Michael Kiwanuka - Black Man in a White World
Pub date: 2020-06-04
Instead of a new episode this week, revisiting this episode originally published in May 2017. Please consider donating to local and national organizations engaged in the work of racial equality. Here are some links:
- American Civil Liberties Union
- NAACP Legal Defense and Educational Fund
- Community Bail Funds
Michael Kiwanuka is a singer/songwriter from London. His second album, Love and Hate, came out in 2016, and was named one of the Best Albums of the Year from the BBC, NME, The Guardian, GQ, and more. One of the songs on the album was used as the theme for the hit HBO series Big Little Lies. In this episode, Michael breaks down the song "Black Man in a White World."
songexploder.net/michael-kiwanuka
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Podcast: Song Exploder (LS 74 · TOP 0.05% what is this?)
Episode: Billie Eilish - Everything I Wanted
Pub date: 2020-11-18
Billie Eilish started releasing music when she was 14 years old. Her debut album came out last year, when she was 17. It debuted at Number 1 on Billboard, went triple platinum, and won five Grammys. Billie made that record with her brother and creative partner, producer Finneas O’Connell, in their parents’ house in Highland Park, Los Angeles.
While working on that album, they also started writing this song, “Everything I Wanted,” which came out as a single in November 2019. It was Billie’s second top ten hit, and it went double platinum, too. In this episode, you’ll hear some of the original voice memos Billie and Finneas made while writing, and the two of them explain why the song was almost never finished.
songexploder.net/billie-eilish
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Podcast: Song Exploder (LS 74 · TOP 0.05% what is this?)
Episode: HAIM - Summer Girl
Pub date: 2021-01-27
HAIM is a band from Los Angeles, made up of the sisters Danielle, Este, and Alana Haim. They’ve released three albums, and they’ve been nominated for three Grammys. Over the years, they’ve worked extensively with Grammy-winning producer Ariel Rechtshaid. Danielle and Ariel share the emotional backstory of the song “Summer Girl,” from HAIM’s third album, Women in Music Pt. III. In this episode, they break down their experience creating the song, along with Este Haim and the song’s co-producer and co-writer Rostam.
For more, visit songexploder.net/haim.
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Podcast: Behind The Idea (LS 40 · TOP 2% what is this?)
Episode: Behind The Idea # 16: Aswath Damodaran Talks Facebook, Valuation, And Stories
Pub date: 2018-05-15
Professor Aswath Damodaran joins Behind the Idea this week to discuss Facebook as a follow-up to our episode reviewing his article (BTI #12). He also sheds light on his investing philosophy, why he dislikes the Dean of Valuation moniker, and the importance of faith and testing your faith in investing. Topics covered: Why the 'Dean of Valuation' is a terrifying title - 1:10 minute mark Recapping Damodaran's Facebook (FB) thesis - 2:00 How do you deal with the risk to Facebook's dominance? - 3:45 The piece W
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Podcast: The Investopedia Express with Caleb Silver (LS 47 · TOP 1% what is this?)
Episode: The Dean of Valuation has a Warning for Value Investors and Bitcoin Lovers
Pub date: 2021-01-25
Can traditional fundamental analysis be trusted anymore? 2020 turned our ideas about valuing companies upside down. Aswath Damodaran, NYU Professor, best-selling author and a wizard in the world of valuing companies, says we have been doing it wrong for years and he has a dire warning for value investors. He also calls out the Oracle of Omaha, the Fed and Bitcoin enthusiasts. Plus, why the educated investor needs to know about free cash flow this week.
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Podcast: The Prof G Pod with Scott Galloway (LS 64 · TOP 0.1% what is this?)
Episode: State of Play: GameStop
Pub date: 2021-01-29
Aswath Damodaran, a professor of finance at NYU Stern, joins Scott to discuss the news surrounding GameStop and the short squeeze. Aswath also shares his thoughts on SPACs, the markets more broadly, and the importance of diversifying your portfolio. Follow him on Twitter, @AswathDamodaran.
Additional music by:https://www.davidcuttermusic.com / @dcuttermusic
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Podcast: Guiding Assets (LS 43 · TOP 1.5% what is this?)
Episode: Aswath Damodaran on Valuing the Disruptors and Disrupted
Pub date: 2020-01-15
Episode #395. We live in disruptive times. But as valuation expert Aswath Damodaran often says, disruption is easy, but making money from disruption is hard. In this episode, the author of five books on valuation discusses the "disruption dilemma," shares his advice for investors trying to value the disruptors, and gives viewers and listeners a preview of what attendees can expect from an extended session on valuation that he will be teaching at the 73rd CFA Institute Annual Conference in Atlanta in May 2020.
The podcast and artwork embedded on this page are from CFA Institute, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Monocle 24: The Menu (LS 43 · TOP 1.5% what is this?)
Episode: Food Neighbourhoods 212: Recipe edition, Thomas Keller
Pub date: 2020-11-24
The owner and chef of one of the world’s most successful restaurants, The French Laundry, shares one of his favourite recipes with us.
The podcast and artwork embedded on this page are from Monocle, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: La Story (LS 57 · TOP 0.5% what is this?)
Episode: Esther Duflo, la femme qui dépoussière l’économie
Pub date: 2020-07-09
Le choix pour le prix Nobel 2019 d’économie reste une surprise de la part de l’académie suédoise. La Française Esther Duflo partage la récompense avec Abhijit Banerjee et Michael Kremer pour leurs travaux sur la pauvreté. Pour « La Story », le podcast d’actualité des « Echos », Pierrick Fay et ses invités dressent le portrait d’une chercheuse qui représente une nouvelle génération d’économistes privilégiant le terrain à la théorie.
La Story est un podcast des « Echos » présenté par Pierrick Fay. Cet épisode a été enregistré en février 2020 dans les locaux des « Echos » (Paris, 15e). Rédaction en chef : Clémence Lemaistre. Invités : Jean-Marc Vittori (éditorialiste aux « Echos ») et Véronique Le Billon (correspondante des « Echos » à New York). Réalisation : Willy Ganne. Chargée de production et d’édition : Michèle Warnet. Musique : Théo Boulenger. Identité graphique : Upian. Photo : Paul Grover/REX/SIPA. Sons : Canal-U, UCLouvain, RTBF, Les Echos - Les éditos de la rédaction, CNRS.
Voir Acast.com/privacy pour les informations sur la vie privée et l'opt-out.
Voir Acast.com/privacy pour les informations sur la vie privée et l'opt-out.
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Podcast: La Grande table (LS 51 · TOP 0.5% what is this?)
Episode: Esther Duflo, être économiste en temps de crise
Pub date: 2020-09-07
durée : 00:33:44 - La Grande table idées - par : Olivia Gesbert - Quel rôle de l'économiste dans la cité en temps de crise ? Esther Duflo, prix Nobel d'économie 2019 et co-auteure de l'ouvrage "Économie utile pour des temps difficiles" (avec Abhijit V. Banerjee, Seuil, 2020), est notre invitée. - réalisation : Thomas Beau - invités : Esther Duflo économiste
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Podcast: The Challenge of World Poverty
Episode: Lecture 1: Introduction
Pub date: 2013-01-10
Lecture 1 provides an introduction to the study of global poverty. The class watches two videos featuring opposing views of the effect of aid on poor countries.
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Podcast: The Michael Shermer Show (LS 57 · TOP 0.5% what is this?)
Episode: 59. Cass R. Sunstein — On Freedom
Pub date: 2019-03-26
In this pathbreaking book, New York Times bestselling author Cass Sunstein asks us to rethink freedom. He shows that freedom of choice isn’t nearly enough. To be free, we must also be able to navigate life. People often need something like a GPS device to help them get where they want to go — whether the issue involves health, money, jobs, children, or relationships.
In both rich and poor countries, citizens often have no idea how to get to their desired destination. That is why they are unfree. People also face serious problems of self-control, as many of them make decisions today that can make their lives worse tomorrow. And in some cases, we would be just as happy with other choices, whether a different partner, career, or place to live — which raises the difficult question of which outcome best promotes our well-being.
Accessible and lively, and drawing on perspectives from the humanities, religion, and the arts, as well as social science and the law, On Freedom explores a crucial dimension of the human condition that philosophers and economists have long missed — and shows what it would take to make freedom real.
In addition to discussing his book Sunstein and Shermer talk about what it was like to work in the Obama administration, the issue of free will and determinism in the context of his theory of libertarian paternalism and choice architecture, opt-in vs. opt-out programs related to everything from menu options to organ donations, the electoral college, term limits for Supreme Court Justices, free speech on college campuses (and trigger warnings, safe spaces, and micro aggressions), Universal Basic Income, taxes, and terrorism.
About Professor Sunstein’s principle, Dr. Shermer wrote in his book The Mind of the Market:
Libertarian paternalism makes a deeper assumption about our nature — that at our core we are moral beings with a deep and intuitive sense about what is right and wrong, and that most of the time most people in most circumstances choose to do the right thing. Thus, applying the principle of libertarian paternalism to the larger politico-economic system as a whole, I suggest that the default option should be to grant people the libertarian ideal of maximum freedom, while using the best science available to inform the policy that gives structure to the minimum number of restrictions on our freedoms. Let’s opt for more freedom and add back restrictions on freedom only where absolutely necessary and with great reluctance.
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This Science Salon was recorded on March 4, 2019.
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Podcast: The Decision Corner (LS 28 · TOP 10% what is this?)
Episode: How Fun Might Move the World: Cass Sunstein
Pub date: 2020-09-02
In today’s episode of The Decision Corner, we are joined by Cass Sunstein, the Robert Walmsley University Professor at Harvard University. Professor Sunstein is the founder and director of the Program on Behavioral Economics and Public Policy at Harvard Law School. He is a prolific writer, who has written over 40 books, and hundreds of articles, including the international bestseller and essential introduction to behavioral science, Nudge: Improving Decisions about Health, Wealth, and Happiness (with Richard H. Thaler, 2008).
He is a recipient of the Holberg Prize, which is bestowed by the Government of Norway. The Holberg Prize is recognized as a counterpart to the Nobel Prize for unparalleled contributions to scholarship in the humanities or the law. Sunstein is currently the Chair of the WHO technical advisory group on Behavioural Insights and Sciences for Health, and he advises the United Nations, the European Commission, the World Bank, and countries around the world on issues of law and public policy.
He was Administrator of the White House Office of Information and Regulatory Affairs from 2009 to 2012; subsequently, he served on the President’s Review Board on Intelligence and Communications Technologies and the Pentagon’s Defense Innovation Board. He is now working on a variety of projects involving the regulatory state, “sludge,” fake news, and freedom of speech.
In the episode, we discuss:
- What is fun?
- What kind of people have the most fun, and whether that is something worth pursuing as a society.
- The effectiveness of fun in marketing, such as Amazon’s frustration-free packaging project.
- The role of fun in policy-making: determination and playfulness in Taiwan, how jokes can lead to optimism and hope, New Zealand’s Prime Minister’s attempts at making peoples’ days better.
- Political leadership and vulnerability.
- Making mandated behavior change a more tolerable and shared enterprise.
- Fear appeals: the benefits of enhancing high stakes situations to prevent harm.
- Populism and the need for personal connections with our political leaders.
- Cass’s nuanced distinction between the first and second waves of behavioral science.
- FEAST (Fun, Easy, Attractive, Social, and Timely): Cass’s guidelines for engaging affective responses when developing policy.
- Why every revolution must tolerate dancing.
- What Cass Sunstein asked a world-class athlete about having fun under pressure.
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Podcast: LSE: Public lectures and events (LS 55 · TOP 0.5% what is this?)
Episode: A Decade of Behavioural Science at LSE: A Fireside chat with Professor Paul Dolan
Pub date: 2021-01-20
Contributor(s): Professor Paul Dolan | Join us for this fireside chat where Paul Dolan will be reflecting on ten years of behavioural science at LSE, discussing biases, narratives, happiness, resilience and more. We will be summarising the learnings from behavioural science in the last 10 years, drawing from research from LSE and beyond. We will also be looking to the future, mapping out the most important and exciting areas of study. Those that join us can expect to laugh, learn and lean into behavioural science. Meet our speaker and chair Paul Dolan (@profpauldolan) is Professor of Behavioural Science at the London School of Economics and Political Science. He is author of the Sunday Times best-selling book Happiness by Design, and Happy Ever After. Grace Lordan (@GraceLordan_) is an associate professor in behavioural science at LSE. Her research focuses on why some people have successful lives as compared to others because of factors beyond their own control. She is the founder and director of The Inclusion Initiative, a research centre at LSE and the author of Think Big: Take Small Steps and Build the Career You Want. More about this event The Department of Psychological & Behavioural Science (@LSE_PBS) is a growing community of researchers, intellectuals, and students who investigate the human mind and behaviour in a societal context. Our department conducts cutting-edge psychological and behavioural research that is both based in and applied to the real world.
The podcast and artwork embedded on this page are from London School of Economics and Political Science, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: LSE: Public lectures and events (LS 55 · TOP 0.5% what is this?)
Episode: SHORTCAST | Behavioural Science and a Post-COVID World
Pub date: 2021-01-28
Contributor(s): Professor Nick Chater, Professor Paul Dolan, Dr Grace Lordan, Professor Tali Sharot, Rory Sutherland | The impacts of COVID-19 on society post-COVID and how we deal with them hinge on how politicians, firms and the public respond. What valuable lessons can we learn from behavioural science in a post-COVID-19 world? These unique insights are crucial to mitigating the societal impacts of COVID-19. Nick Chater (@NickJChater) is Professor of Behavioural Science, Warwick Business School. He is co-founder of Decision Technology Ltd, and is a member of the UK's Committee on Climate Change. Paul Dolan (@profpauldolan) is Professor of Behavioural Science at the London School of Economics and Political Science. He is author of the Sunday Times best-selling book Happiness by Design, and Happy Ever After. Grace Lordan is an associate professor in behavioural science at the LSE. Her research focuses on why some people have successful lives as compared to others because of factors beyond their own control. She is the founder and director of The Inclusion Initiative, a research centre at the LSE & the author of Think Big: Take Small Steps and Build the Career You Want. Tali Sharot is a Professor Cognitive Neuroscientist at the Department of Experimental Psychology, University College London, where she is the director of the Affective Brain Lab. She is the author of The Optimism Bias and The Influential Mind, both of which received the British Psychological Society Book Award. She was also awarded fellowships from the Wellcome Trust and the British Academy. Rory Sutherland (@rorysutherland) is the Vice Chairman of Ogilvy, an attractively vague job title which has allowed him to co-found a behavioural science practice within the agency. Before founding Ogilvy Change, Rory was a copywriter and creative director at Ogilvy for over 20 years, having joined as a graduate trainee in 1988. He has variously been President of the IPA, Chair of the Judges for the Direct Jury at Cannes, and has spoken at TED Global. He writes regular columns for the Spectator, Market Leader and Impact, and also occasional pieces for Wired. Simon Hix (@simonjhix) is Pro-Director (Research) and Harold Laski Professor of Political Science at LSE. He is one of the leading researchers, teachers, and commentators on European and comparative politics in the UK. He has published over 100 books and articles and has won several prestigious prizes and fellowships for his research, including from the US-UK Fulbright Commission, the American Political Science Association, and the UK Economic and Social Research Council. He is also a prize-winning teacher, and continues to teach “Introduction to Political Science” to over 300 first-year undergraduate students. This event forms part of LSE’s Shaping the Post-COVID World initiative, a series of debates about the direction the world could and should be taking after the crisis. Twitter Hashtag for this event: #LSECOVID19 For more information
Event posting LSE’s Shaping the Post-COVID World initiative
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Podcast: Notion Capital - enterprise tech startups (LS 29 · TOP 10% what is this?)
Episode: P306 - Talking product strategy, consumer science and culture with Gibson Biddle
Pub date: 2019-11-20
When we think of the very best tech companies on the planet – Amazon, Facebook, Google and Netflix – what sets them apart is that innate ability to innovate. Gib was previously VP Product at Netflix and Chief Product Officer at Chegg and is now one of the most highly valued - and well-travelled – speakers and thinkers in product strategy on the planet today.
Highlights:
·Why culture is so much more powerful than a bunch of rules
·Building great products is messy, that’s why strategy and customer science are so important
·Why you should never lose your punk startup risk-taking muscles
Interviewed by Paul Papadimitriou and Stephen Millard.
Read more: https://notion.vc/resources/talking-product-strategy-consumer-science-and-culture-with-gibson-biddle/
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Podcast: Inside Intercom (LS 46 · TOP 1% what is this?)
Episode: Conversations on Support
Pub date: 2021-01-21
In this week's episode, we're taking a look at some of the conversations we've had with some of the most highly regarded support leaders in SaaS.
See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
The podcast and artwork embedded on this page are from Intercom, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Fintech Insider Podcast by 11:FS (LS 53 · TOP 0.5% what is this?)
Episode: 495. Insights: The VC gender investment gap
Pub date: 2021-01-22
As COVID-19, the ensuing economic crisis, and recent calls for racial justice show, the cost of complacency toward matters of equity is reaching an inflection point. With statistics that show that both those making the decisions and those receiving the benefits are overwhelmingly white and male, the VC industry clearly has a diversity problem that needs to be addressed from top to bottom.
In this episode of Fintech Insider: Insights, Simon Tayor is joined by some great guests to dive into the topic:
- Jillian Williams - Investment Principal at Anthemis & Head of BLCK VC NYC
- David Mott - Chair of the Venture Capital Committee at the British Private Equity and Venture Capital Association & Co-Founder of Oxford Capital
- Jennifer Neundorfer - Co-Founder & General Partner at January Ventures
This podcast is brought to you by Jack Henry Digital the pioneer and creator of personal digital banking that helps community financial institutions strategically differentiate their digital offerings from those of MegaBanks, BigTechs and FinTechs.
This podcast is also brought to you by Mitek (NASDAQ: MITK). Mitek is a global leader in mobile capture and digital identity verification solutions built on the latest advancements in computer vision, artificial intelligence and machine learning. Mitek’s identity verification solutions enable an enterprise to verify a user’s identity during a digital transaction, which assists businesses operating in highly regulated markets to reduce financial risk and meet regulatory requirements while increasing revenue from digital channels. Financial services, marketplaces and other organizations around the world use Mitek to reduce friction creating the digital experiences their customers expect. Mobile Deposit® and Mobile Verify® are used by millions of consumers for check deposit, new account opening and more. The company is based in San Diego with offices in New York, London, Amsterdam, Barcelona, Paris and St Petersburg. Learn more at www.miteksystems.com.
Banking as a Service is deconstructing the banking stack.
It's enabling brands to embed finance more easily, and to tailor financial products to specific customer needs.
This is presenting new opportunities for specialised providers and offers banks extra revenue streams.
Download our report for a comprehensive, no BS view of what Banking as a Service is and what it means for the industry.
Head to bit.ly/bankingasaservice.
Fintech Insider by 11:FS is a podcast dedicated to all things fintech, banking, technology and financial services. Hosted by a rotation of 11:FS experts including David Brear, Simon Taylor, Jason Bates and Sarah Kocianski and joined by a range of brilliant guests, we cover the latest global news, bring you interviews from industry experts or take a deep dive into subject matters such as APIs, AI or digital banking.
If you enjoyed this episode, don't forget to subscribe and please leave a review
Follow us on Twitter: www.twitter.com/fintechinsiders where you can ask the hosts questions, alternatively email podcasts@11fs.com!
Special Guests: David Mott, Jennifer Neundorfer , and Jillian Williams .
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Podcast: Greymatter (LS 45 · TOP 1% what is this?)
Episode: Shishir Mehrotra on Scaling YouTube and The 10 Things That Matter | Blitzscaling 13
Pub date: 2016-03-02
This is session 13 of Technology-enabled Blitzscaling, a Stanford University class taught by Reid Hoffman, John Lilly, Allen Blue, and Chris Yeh. This class features a guest lecture by Shishir Mehrotra, who helped guide YouTube through hypergrowth after its acquisition by Google. At the end of the class, Shishir is interviewed by Allen Blue.
The podcast and artwork embedded on this page are from Greylock Partners, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Greymatter (LS 45 · TOP 1% what is this?)
Episode: Frameworks For Driving Sustainable Growth w/ Mike Duboe, Brian Balfour, Shaun Clowes | Greymatter
Pub date: 2019-07-23
Greylock Investor Mike Duboe, Reforge CEO Brian Balfour, and Mulesoft SVP of Product Shaun Clowes on developing a healthy and successful growth strategy.
Growth is fundamental to a company's survival. To compete in today's crowded business environment, it is vital for founders to implement powerful strategies that drive healthy and sustainable growth. Successful growth strategies start with a methodical framework tailored to your product and target audience that will increase your user base and activation rate.
This episode of Greymatter is the first in a mini-series of growth discussions featuring Greylock Investor Mike Duboe. In this discussion Reforge Founder and CEO Brian Balfour, and Metromile Chief Product Officer Shaun Clowes chat with Mike about frameworks and foundational concepts needed to develop a successful growth strategy. The three growth/product leaders discuss the skills and traits to look for in a startup's first growth hire, why growth should be a cross-functional discipline vs a set of channels or tactics, how to assess healthy vs unhealthy growth, conceptualizing loops vs funnels, and the applicability of B2C growth concepts in B2B organizations.
The podcast and artwork embedded on this page are from Greylock Partners, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Greymatter (LS 45 · TOP 1% what is this?)
Episode: Building & Measuring Sustainable Growth Marketing Strategies | Greymatter
Pub date: 2019-07-30
Greylock Investor Mike Duboe, Right Side Up CEO Tyler Elliston, and KeepTruckin VP of Data Science Kim Larsen discuss frameworks and tactics for successful growth marketing.
This episode of Greymatter is the second in a series of growth discussions featuring Greylock investor Mike Duboe. In this discussion, Right Side Up CEO Tyler Elliston and KeepTrucking VP of Data Science Kim Larsen chat with Mike about the foundational concepts and tactics to build and measure effective growth marketing strategies. Mike, Tyler, and Kim discuss the importance of diversification in marketing channels, best practices for measurement including incrementality testing, how to avoid common pitfalls of budget allocation, and building a growth marketing team in-house versus partnering with an agency. This podcast goes into many more concepts and frameworks, but below are just a few takeaways from the discussion.
The podcast and artwork embedded on this page are from Greylock Partners, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Village Global's Venture Stories (LS 46 · TOP 1% what is this?)
Episode: Growth and Go-To-Market with Brian Balfour and Pascal Levy-Garboua
Pub date: 2020-12-08
Brian Balfour (@bbalfour), of Reforge, and Long Journey Ventures, and Pascal Levy-Garboua (@2pasc) of Long Journey Ventures, join Erik on this episode.
They discuss:
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Common mistakes that early-stage companies make in their go-to-market strategy and how to avoid them.
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Advice on partnership strategies and go-to-market.
-
Frameworks for who to hire and why.
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Misconceptions that founders have around loops.
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Advice for enterprise companies.
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Advice on building a business development function within the org.
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What the industry has learned about growth over the last several years.
-
How community fits within growth.
Thanks for listening — if you like what you hear, please review us on your favorite podcast platform.
Check us out on the web at www.villageglobal.vc or get in touch with us on Twitter @villageglobal.
Want to get updates from us? Subscribe to get a peek inside the Village. We’ll send you reading recommendations, exclusive event invites, and commentary on the latest happenings in Silicon Valley. www.villageglobal.vc/signup
The podcast and artwork embedded on this page are from Village Global, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Village Global's Venture Stories (LS 46 · TOP 1% what is this?)
Episode: Reid Hoffman and Chamath Palihapitiya on Angel Investing and The Future of Venture
Pub date: 2020-11-19
Chamath Palihapitiya (@chamath), CEO of Social Capital, and Reid Hoffman (@reidhoffman), partner at Greylock, joined Village Global co-founder and partner Ben Casnocha at a special Village Global event.
Angel Island brought together 100+ angel investors for talks from world-class investors, discussions, and opportunities to make new connections.
In this session they discussed:
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What is broken about venture and how to fix it.
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Why Reid is backing new experiments in venture.
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Their thoughts on the solo GP phenomenon.
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How to think about risk when evaluating an investment.
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Whether they're long or short Silicon Valley.
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Why SPACs are here to stay.
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How to think about diversification in an angel portfolio.
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The state of pricing across different rounds.
Thanks for listening — if you like what you hear, please review us on your favorite podcast platform.
Check us out on the web at www.villageglobal.vc or get in touch with us on Twitter @villageglobal.
Want to get updates from us? Subscribe to get a peek inside the Village. We’ll send you reading recommendations, exclusive event invites, and commentary on the latest happenings in Silicon Valley. www.villageglobal.vc/signup
The podcast and artwork embedded on this page are from Village Global, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Village Global's Venture Stories (LS 46 · TOP 1% what is this?)
Episode: Keith Rabois’s Advice For Angel Investors
Pub date: 2020-11-15
Keith Rabois (@rabois), partner at Founders Fund, joins Erik on this episode. It was recorded as part of an On Deck event. They discuss:
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How he would compare the state of angel investing when he was getting started years ago to angel investing today.
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His learnings about building a better filter for whether to take a meeting or not, and what he looks for in a person’s LinkedIn profile.
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Portfolio allocation and how much to allocate to angel investing.
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What differentiates people who are an executive and angel versus those who are full-time VCs.
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Whether he plans to invest in crypto.
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Thoughts on pricing and the differences in pricing considerations between angels and VCs.
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How he uses Twitter.
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Trends he sees and ideas he’s exploring.
Thanks for listening — if you like what you hear, please review us on your favorite podcast platform.
Check us out on the web at www.villageglobal.vc or get in touch with us on Twitter @villageglobal.
Want to get updates from us? Subscribe to get a peek inside the Village. We’ll send you reading recommendations, exclusive event invites, and commentary on the latest happenings in Silicon Valley. www.villageglobal.vc/signup
The podcast and artwork embedded on this page are from Village Global, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Village Global's Venture Stories (LS 46 · TOP 1% what is this?)
Episode: How To Start and Grow a Newsletter with Lenny Rachitsky and Nadia Eghbal
Pub date: 2020-08-27
Nadia Eghbal (@nayafia), of Substack, and author of Working in Public, and Lenny Rachitsky (@lennysan), author of a newsletter on product and growth and former Airbnb PM, join Erik on this episode to discuss:
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The On Deck Writer’s Fellowship in partnership with Substack.
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The evolution of newsletters as a medium over time.
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Nadia and Lenny’s journeys on Substack.
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Their advice for people who want to get started writing their own newsletters.
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How they think about what to write about and the right cadence for their newsletters.
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How to improve as a writer.
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How to think about pricing.
Thanks for listening — if you like what you hear, please review us on your favorite podcast platform.
Check us out on the web at villageglobal.vc or get in touch with us on Twitter @villageglobal.
The podcast and artwork embedded on this page are from Village Global, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Greymatter (LS 45 · TOP 1% what is this?)
Episode: Lessons Learned Growing Successful Marketplaces w/ Mike Duboe, Lenny Rachitsky, & Dan Hockenmaier
Pub date: 2019-12-18
This episode of Greymatter is the third in a series of growth discussions featuring Greylock investor Mike Duboe. Lenny Rachitsky, former growth lead at Airbnb, and Dan Hockenmaier, founder of Basis One and former director of growth marketing at Thumbtack, join Mike to share lessons learned when growing marketplaces from startup to scaleup. They discuss how to design a growth org, processes and frameworks around experiments, building growth models, understanding marketplace liquidity, how to think about disintermediation, and parallels between B2C /B2B marketplaces.
The podcast and artwork embedded on this page are from Greylock Partners, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Village Global's Venture Stories (LS 46 · TOP 1% what is this?)
Episode: All Things Marketplaces with Dan Hockenmaier, Casey Winters, and Lenny Rachitsky
Pub date: 2020-03-05
Joining Erik on this episode are:
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Dan Hockenmaier (@danhockenmaier), founder of Basis One
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Casey Winters (@onecaseman), Chief Product Officer at Eventbrite
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Lenny Rachitsky (@lennysan), former founder as well as former growth PM at Airbnb
They discuss:
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How marketplaces have evolved over the last several years.
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The trend towards owning more of a user’s experience, and the pros and cons of that approach.
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The possibilities for new vertical marketplaces.
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Where they would be investing if they were running a fund focused on the space.
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Why some of the biggest companies in the space were initially overlooked.
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How to think about building liquidity in a marketplace.
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Some of the interesting debates in the field, and their takes on them.
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The most surprising learnings they’ve had while working in the space.
Thanks for listening — if you like what you hear, please review us on your favorite podcast platform.
Check us out on the web at villageglobal.vc or get in touch with us on Twitter @villageglobal.
The podcast and artwork embedded on this page are from Village Global, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Song Exploder (LS 74 · TOP 0.05% what is this?)
Episode: Dua Lipa - Levitating
Pub date: 2020-10-07
Dua Lipa is a Grammy-winning singer and songwriter from London. Her second album, Future Nostalgia, came out in March 2020. It hit #1 on the charts in thirteen countries, and it was shortlisted for the UK’s Mercury Prize.
Dua co-wrote the song "Levitating" with some of her closest collaborators, including producer Stephen Kozmeniuk, AKA Koz. In this episode, Dua and Koz break down “Levitating” and how Dua’s childhood memories shaped its sound.
songexploder.net/dua-lipa
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Podcast: All Songs Considered (LS 67 · TOP 0.05% what is this?)
Episode: Let's Celebrate Beethoven's 250th
Pub date: 2020-12-15
On this edition of All Songs Considered, we celebrate the life of Beethoven and the lasting powers of his music in the 250 years since his birth.
The podcast and artwork embedded on this page are from NPR, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: All Songs Considered (LS 67 · TOP 0.05% what is this?)
Episode: Viking's Choice 2020: The Year In Loud, Weird And Wonderful
Pub date: 2021-01-06
Spanish punk, metal-sampling rap, Kenyan grindcore, Russian synth pop — this is how NPR Music's resident Viking coped, raged and soothed himself in 2020.
Songs featured on this episode:
• Accidente: "Lo que importa" from Canibal
• Backxwash: "Spells" from God Has Nothing To Do With This Leave Him Out Of It
• Aisha Orazbayeva: "Oliver Leith: Blurry Wake Song" from Music for Violin Alone
• Maral: "Bushehr" from Push
• Moor Mother: "Circuit Break" from Circuit City
• Стереополина: "Последнее свидание" from Институт культуры и отдыха
• Claire Rousay: "Two Things" from Both
• Meurtrieres: "Alienor" from Meurtrières
For more, follow the Viking's Choice playlist and subscribe to the newsletter.
The podcast and artwork embedded on this page are from NPR, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: hanging out with audiophiles (LS 51 · TOP 0.5% what is this?)
Episode: EP 78 - FOUR TET
Pub date: 2021-01-27
Kieran Hebden is one of the most uniquely talented UK electronic artists working today. He’s produced a wonderful stack of LPS and a remixed some of the best, from Aphex to Radiohead to Jamie Lidell. He’s walked an unconventional path but the seemingly strange choices of gear and methods he’s landed on are all very much considered. He is literally building a physical line of records that will stand as his legacy and that’s at the forefront of his thinking. There’s this drive to be finishing music and finding ways to keep inspired and making the whole thing feel free and live. He finds an amazing balance in his work between meticulousness and chaos. The collaborations with Jazz legend Steve Reid in particular showed how live electronics could be just that. LIVE! Messy, ever changing and satisfyingly intense. Unsurprisingly, he works best outside the traditional music making spaces, favouring his home and small, manageable setups over the monster rooms. Minimal amount of gear but huge amounts of vinyl!!!! We toured the states in 2005. I was opening for Kieran and it was a hell of a run. I’ve very fond memories of that time. He’s a super smart and warm man that is the only person I know that plays cool edit like a violin.
Please welcome onto hanging out with audiophiles
Four Tet! ______________
Music on the show comes from
Colin Fry. Find his music here.
Kams on Metr music. Find the goodies here!
METR MUSIC Website | Soundcloud | Bandcamp
______________
In the nitty I delve in the beautifully noisy needlescape that can be made with the Gakken toy record maker ! Lots of fun but I fried mine somehow though power issues. This nitty was the last time it shall be heard. Shame. I was just getting the hang of it. Here’s a link if you fancy building one (instructions in Japanese
______________
Special thanks to Jake Aron for this tremendous help getting the interview sounding good and building a new vocal chain. I love the changes. Love Jake! If you need mix/production work he’s your guy. So talented and great to work with Find him here
jakearon.net
______________
Thank you also to the show sponsors Spectrasonics Makers of SERIOUS soft synth magic! Check them out! https://www.spectrasonics.net
Check these ace YouTube sessions to see the quality and the sheer playing! It’s wild
KEYSCAPE
TRILIAN 1.5
So good!
HAPPY HOLS TO ALL OF YOU!!
The podcast and artwork embedded on this page are from hanging out with audiophiles, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Product Experience (LS 40 · TOP 2% what is this?)
Episode: The Habits of Successful Product Managers – Dave Martin on The Product Experience
Pub date: 2021-01-06
We learn something from every guest that joins us on the podcast, but it’s not that often that something they say makes it onto our wall. Dave Martin (co-founder of Right to Left) chatted with us about building great habits for product people, and how to make sure they stick. He also tells us about [...] Read more »
The post The Habits of Successful Product Managers – Dave Martin on The Product Experience appeared first on Mind the Product.
The podcast and artwork embedded on this page are from Mind the Product, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Reith Lectures (LS 55 · TOP 0.5% what is this?)
Episode: From Climate Crisis to Real Prosperity
Pub date: 2020-12-23
Mark Carney, the former Governor of the Bank of England, argues that the roots of the climate change threat lie in a deeper crisis of values. He suggests that we can create an ecosystem in which society’s values broaden the market’s conceptions of value. In this way, individual creativity and market dynamism can be channelled to achieve broader social goals including, inclusive growth and environmental sustainability.
Presenter: Anita Anand Producer: Jim Frank Editor: Hugh Levinson Production Coordinator: Brenda Brown Studio Manager: Rod Farquhar
The podcast and artwork embedded on this page are from BBC Radio 4, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Reith Lectures (LS 55 · TOP 0.5% what is this?)
Episode: From Covid Crisis to Renaissance
Pub date: 2020-12-16
Mark Carney, the former Governor of the Bank of England, observes that the pandemic has forced states to confront how we value health, wealth and opportunity. During the first few months of the crisis, most states chose to value human life more than the economic well-being of the nation-state. But if that seems to be changing how do we assess value in this sense?
Dr Carney elucidates surprising differences in the financial value put on a human life in different nations – and goes on to argue that this reductionist approach fails to take into account deeper thinking about the worth of human existence.
Presenter: Anita Anand Producer: Jim Frank Editor: Hugh Levinson
The podcast and artwork embedded on this page are from BBC Radio 4, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Reith Lectures (LS 55 · TOP 0.5% what is this?)
Episode: From Credit Crisis to Resilience
Pub date: 2020-12-09
Mark Carney, the former Governor of the Bank of England, takes us back to the high drama of the financial crisis of 2008, which ended a period when bankers saw themselves as unassailable Masters of the Universe. More than a decade on, how much have the bankers changed their ways? How far has the financial sector changed? Dr Carney says that we must remain vigilant and resist the “three lies of finance.” If we don’t, he warns, we will live with a system which is ill-prepared for the next crisis.
Presenter: Anita Anand Producer: Jim Frank Editor: Hugh Levinson
The podcast and artwork embedded on this page are from BBC Radio 4, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The LRB Podcast (LS 52 · TOP 0.5% what is this?)
Episode: Haiti's Revolution
Pub date: 2020-11-17
Pooja Bhatia talks to Thomas Jones about the Haitian revolution of 1791, the world-historical debut of the movement for Black liberation. They discuss the early insurrections, the leadership of Toussaint Louverture and his complicated legacy, the post-revolutionary land reforms and their traces in modern Haiti's mango industry, and how Bhatia managed to get an interview with former president Jean-Bertrand Aristide after his return from exile.
Find more by Pooja Bhatia on Haiti in the LRB here: https://lrb.me/haitirevolutionpod
Subscribe to the LRB from just £1 per issue: https://mylrb.co.uk/podcast20b
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Podcast: The LRB Podcast (LS 52 · TOP 0.5% what is this?)
Episode: John Lanchester: Twenty Types of Human
Pub date: 2020-12-29
John Lanchester reads his review of Kindred: Neanderthal Life, Love, Death and Artby Rebecca Wragg Sykes.
Read the piece here: lrb.me/neanderthalspod
Subscribe to the LRB from just £1 per issue: https://mylrb.co.uk/podcast20b
Podcast: The Documentary Podcast (LS 67 · TOP 0.05% what is this?)
Episode: The Hindu bard
Pub date: 2020-12-29
In 1914 a 19-year-old Indian student caused a sensation when she was awarded the top prize - the bardic chair - at the 1914 University College of Wales Eisteddfod held in Aberystwyth. All the entries in the prestigious Welsh language and literature contest were submitted under pseudonyms. When the winner was awarded to "Shita", for an ode written in English, Dorothy Bonarjee revealed herself as the author, and received a "deafening ovation". It was the first time ever that the competition had been won by a non-European, or even by a woman.
The podcast and artwork embedded on this page are from BBC World Service, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Reith Lectures (LS 55 · TOP 0.5% what is this?)
Episode: From Moral to Market Sentiments
Pub date: 2020-12-02
Mark Carney’s Reith 2020 Lectures chart how we have come to esteem financial value over human value and how we have gone from market economies to market societies. He argues that this has contributed to a trio of crises: of credit, Covid and climate. And the former Bank of England Governor will outline how we can turn this around.
In this lecture, recorded with a virtual audience, he reflects that whenever he could step back from what felt like daily crisis management, the same deeper issues loomed. What is value? How does the way we assess value both shape our values and constrain our choices? How do the valuations of markets affect the values of our society?
Dr Carney argues that society has come to embody Oscar Wilde’s aphorism: “Knowing the price of everything but the value of nothing.”
Presenter: Anita Anand Producer: Jim Frank Editor: Hugh Levinson
The podcast and artwork embedded on this page are from BBC Radio 4, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: In Our Time (LS 75 · TOP 0.05% what is this?)
Episode: Macbeth
Pub date: 2020-10-01
Melvyn Bragg and guests discuss one of Shakespeare’s greatest tragedies. When three witches prophesy that Macbeth will be king one day, he is not prepared to wait and almost the next day he murders King Duncan as he sleeps, a guest at Macbeth’s castle. From there we explore their brutal world where few boundaries are distinct – between safe and unsafe, friend and foe, real and unreal, man and beast – until Macbeth too is slaughtered.
The image above shows Nicol Williamson as Macbeth in a 1983 BBC TV adaptation.
With:
Emma Smith Professor of Shakespeare Studies at Hertford College, University of Oxford
Kiernan Ryan Emeritus Professor of English Literature at Royal Holloway, University of London
And
David Schalkwyk, Professor of Shakespeare Studies and Director of Global Shakespeare at Queen Mary, University of London
Producer: Simon Tillotson
The podcast and artwork embedded on this page are from BBC Radio 4, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: In Our Time (LS 75 · TOP 0.05% what is this?)
Episode: Albrecht Dürer
Pub date: 2020-11-12
Melvyn Bragg and guests discuss the great German artist Albrecht Dürer (1471-1528) who achieved fame throughout Europe for the power of his images. These range from his woodcut of a rhinoceros, to his watercolour of a young hare, to his drawing of praying hands and his stunning self-portraits such as that above (albeit here in a later monochrome reproduction) with his distinctive A D monogram. He was expected to follow his father and become a goldsmith, but found his own way to be a great artist, taking public commissions that built his reputation but did not pay, while creating a market for his prints, and he captured the timeless and the new in a world of great change.
With
Susan Foister Deputy Director and Curator of German Paintings at the National Gallery
Giulia Bartrum Freelance art historian and Former Curator of German Prints and Drawings at the British Museum
And
Ulinka Rublack Professor of Early Modern European History and Fellow of St John’s College, University of Cambridge
Studio production: John Goudie
The podcast and artwork embedded on this page are from BBC Radio 4, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Tim Ferriss Show (LS 81 · TOP 0.01% what is this?)
Episode: #286: The Man Who Taught Me How to Invest
Pub date: 2017-12-16
Mike Maples, Jr. (@m2jr) is the man who taught me how to invest. He's one of my favorite people and a personal mentor.
He is a partner at Floodgate, a venture capital firm that specializes in micro-cap investments in startups. He has been on the Forbes Midas List since 2010 and named one of Fortune magazine's "8 Rising VC Stars." Before becoming a full-time investor, Mike was inolved as a founder and operating executive at back-to-back starup IPOs, including Tivoli Systems (acquired by IBM) and Motive (acquired by Alcatel-Lucent). Some of Mike's investments include Twitter, Twitch.tv. ngmoco, Weebly, Chegg, Bazaar-voice, Spiceworks, Okta, and Demandforce.
Enjoy!
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If you enjoy the podcast, would you please consider leaving a short review on Apple Podcasts/iTunes? It takes less than 60 seconds, and it really makes a difference in helping to convince hard-to-get guests. I also love reading the reviews!
For show notes and past guests, please visit tim.blog/podcast.
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Past guests on The Tim Ferriss Show include Jerry Seinfeld, Hugh Jackman, Dr. Jane Goodall, LeBron James, Kevin Hart, Doris Kearns Goodwin, Jamie Foxx, Matthew McConaughey, Esther Perel, Elizabeth Gilbert, Terry Crews, Sia, Yuval Noah Harari, Malcolm Gladwell, Madeleine Albright, Cheryl Strayed, Jim Collins, Mary Karr, Maria Popova, Sam Harris, Michael Phelps, Bob Iger, Edward Norton, Arnold Schwarzenegger, Neil Strauss, Ken Burns, Maria Sharapova, Marc Andreessen, Neil Gaiman, Neil de Grasse Tyson, Jocko Willink, Daniel Ek, Kelly Slater, Dr. Peter Attia, Seth Godin, Howard Marks, Dr. Brené Brown, Eric Schmidt, Michael Lewis, Joe Gebbia, Michael Pollan, Dr. Jordan Peterson, Vince Vaughn, Brian Koppelman, Ramit Sethi, Dax Shepard, Tony Robbins, Jim Dethmer, Dan Harris, Ray Dalio, Naval Ravikant, Vitalik Buterin, Elizabeth Lesser, Amanda Palmer, Katie Haun, Sir Richard Branson, Chuck Palahniuk, Arianna Huffington, Reid Hoffman, Bill Burr, Whitney Cummings, Rick Rubin, Dr. Vivek Murthy, Darren Aronofsky, and many more.
See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
The podcast and artwork embedded on this page are from Tim Ferriss: Bestselling Author, Human Guinea Pig, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Startup Grind (LS 38 · TOP 2.5% what is this?)
Episode: Building for Impact with Mike Maples of Floodgate VC
Pub date: 2016-09-14
Today we have a conversation with Mike Maples and early investor in Twitter, Chegg, Digg, and other great companies through his firm Floodgate VC
Mike Maples Jr has been on the Forbes Midas List since 2010 and was also named one of "8 Rising Stars" by FORTUNE Magazine. Before becoming a full-time investor, Mike was involved as a founder and operating executive at back-to-back startup IPOs, including Tivoli Systems (IPO TIVS, acquired by IBM) and Motive (IPO MOTV, acquired by Alcatel-Lucent.) Some of Mike's investments include Twitter, Twitch.tv, ngmoco, Weebly, Chegg, Bazaarvoice, Spiceworks, Okta, and Demandforce. Mike is known for coining the term "Thunder Lizards," which is a metaphor derived from Godzilla that describes the tiny number of truly exceptional companies that are wildly disruptive capitalist mutations. Mike likes to think of himself as a hunter of the "atomic eggs" that beget these companies. He received his BS, from Stanford University and his MBA from the Harvard Business School. His hobbies include shooting clays, cinematography, and calligraphy.
Lets listen into Mike Maples interviewed in Silicon Valley by Startup Grind’s founder Derek Andersen.
This podcast is brought to you by HBX Harvard Business School’s digital learning initiative. Introducing “Disruptive Strategy with Clayton Christensen,” an engaging and interactive online learning experience from Harvard Business School’s HBX. Learn to create winning strategies to position your organization for longterm success by applying proven disruption and innovation theories from world renowned strategist Clay Christensen. Disruptive Strategy encourages active learning and peer collaboration, and requires a commitment of approximately thirty hours over six weeks. Applications are being accepted for upcoming cohorts in August and October. To learn more, visit disruptivestrategy.org
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Podcast: The Stack Overflow Podcast (LS 46 · TOP 1% what is this?)
Episode: React, Vue, jQuery: what flavor do you like your Vanilla JS?
Pub date: 2020-11-24
You can find Ferdinandi's post and video here.
12 years ago, back when Stack Overflow was a brand new site with just a few thousand users, someone asked a basic question: What is the difference between a framework and a library?
FreeCodeCamp has its own take on this question with a pretty interesting answer. "When you use a library, you are in charge of the flow of the application. You are choosing when and where to call the library. When you use a framework, the framework is in charge of the flow. It provides some places for you to plug in your code, but it calls the code you plugged in as needed."
There was no Lifeboat badge to call out this week, so we honored a Lifejacket winner instead. Shout out to Andreas for answering the queston: Are byte arrays initialised to zero in Java?
The podcast and artwork embedded on this page are from The Stack Overflow Podcast, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: a16z Podcast (LS 62 · TOP 0.1% what is this?)
Episode: Fintech for Gen Z and Millennials
Pub date: 2020-10-01
Millennials and Gen Z have been hard-hit by the one-two punch of the 2008 and 2020 financial crises. That experience has radically shaped their approach to finances and their mindset around credit and debt. This episode explores how fintech founders are now designing products tailored to the financial challenges of younger consumers, from managing and avoiding student loans to building credit to saving and budgeting apps.
Historically, students have largely been overlooked by traditional banks. Due to a combination of economic forces, predatory lending practices, and uninformed decisions, millennials have more outstanding student loans—and owe more money—than any prior generation. According to a poll conducted this month by the data intelligence company Morning Consult, just 46 percent of millennials believe their student debt was worth attending college.
Amira Yahyaoui wants to change that. She’s the founder and CEO of Mos, a platform that allows students to apply for every government college financial aid program with a single application. In this episode, Amira joins host Lauren Murrow and a16z fintech partners Anish Acharya and Seema Amble to discuss how fintech can cut through bureaucracy, downsize student debt, and optimize—and ultimately automate—consumers’ financial futures from an early age.
The podcast and artwork embedded on this page are from Andreessen Horowitz, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: a16z Podcast (LS 62 · TOP 0.1% what is this?)
Episode: How to Decide, Convey vs. Convince, & More
Pub date: 2020-10-08
It seems like investors are especially obsessed with the psychology of decision making -- high stakes, after all -- but all kinds of decisions, whether in life or business -- like dating, product management, what to eat or watch on Netflix -- are an "investment portfolio" of decisions... even if you sometimes feel like you're making one big decision at a time (like, say, marriage or what product to develop next or who to hire).
Obviously, not all decisions are equal; in fact, sometimes we don't even have to spend any time deciding. So how do we know which decisions to apply a robust decision process too, which ones not to? What are the strategies, mindsets, tools to help us decide? How can we operationalize a good decision process and decision hygiene into our teams and organizations? After all, we're tribal creatures -- our opinions are infectious (for better and for worse) -- so how do we convey vs. convince, and not necessarily agree but inform to decide? Especially given common pitfalls (resulting, hindsight bias, etc.), and "the paradox of experience", including even (and more so) winning vs. losing.
Decision expert (and leading poker player) Annie Duke comes back on the a16z Podcast -- after our first conversation with her for Thinking in Bets, which focused mainly on WHY our decision making gets so frustrated -- to talk about her new book, which picks up where the last left off, on HOW to Decide: Simple Tools for Better Choices. In conversation with a16z managing partner Jeff Jordan (and former CEO of OpenTable and former GM of eBay among other things) -- so, from all sides of investing, operating, life -- Annie shares tips for decision makers of all kinds making decisions under uncertainty... really, all of us.
The views expressed here are those of the individual AH Capital Management, L.L.C. (“a16z”) personnel quoted and are not the views of a16z or its affiliates. Certain information contained in here has been obtained from third-party sources, including from portfolio companies of funds managed by a16z. While taken from sources believed to be reliable, a16z has not independently verified such information and makes no representations about the enduring accuracy of the information or its appropriateness for a given situation. In addition, this content may include third-party advertisements; a16z has not reviewed such advertisements and does not endorse any advertising content contained therein.
This content is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. You should consult your own advisers as to those matters. References to any securities or digital assets are for illustrative purposes only, and do not constitute an investment recommendation or offer to provide investment advisory services. Furthermore, this content is not directed at nor intended for use by any investors or prospective investors, and may not under any circumstances be relied upon when making a decision to invest in any fund managed by a16z. (An offering to invest in an a16z fund will be made only by the private placement memorandum, subscription agreement, and other relevant documentation of any such fund and should be read in their entirety.) Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z, and there can be no assurance that the investments will be profitable or that other investments made in the future will have similar characteristics or results. A list of investments made by funds managed by Andreessen Horowitz (excluding investments for which the issuer has not provided permission for a16z to disclose publicly as well as unannounced investments in publicly traded digital assets) is available at https://a16z.com/investments/.
Charts and graphs provided within are for informational purposes solely and should not be relied upon when making any investment decision. Past performance is not indicative of future results. The content speaks only as of the date indicated. Any projections, estimates, forecasts, targets, prospects, and/or opinions expressed in these materials are subject to change without notice and may differ or be contrary to opinions expressed by others. Please see https://a16z.com/disclosures for additional important information.
The podcast and artwork embedded on this page are from Andreessen Horowitz, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: a16z Podcast (LS 62 · TOP 0.1% what is this?)
Episode: a16z Podcast: Beyond Disruption Theory
Pub date: 2019-07-04
with Marc Andreessen (@pmarca), Ben Horowitz (@bhorowitz), and Michael Copeland
Continuing our 10-year anniversary series since the founding of Andreessen Horowitz (aka "a16z"), we’re resurfacing some of our previous episodes featuring Andreessen Horowitz founders Marc Andreessen and Ben Horowitz.
This episode was actually recorded in 2014, on the 5-year anniversary of the firm, and features Michael Copeland interviewing Ben and Marc about disruption theory, as well as key traits of entrepreneurs.
You can find other episodes in this series at a16z.com/10.
The podcast and artwork embedded on this page are from Andreessen Horowitz, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Maker (LS 56 · TOP 0.5% what is this?)
Episode: #58 - Mention : Booster sa rétention de 60 à 80% en 1 an, avec Gautier Husson
Pub date: 2019-02-06
✨ Notre nouveau format...sur Youtube !
Gautier a rejoint Mention il y a maintenant 2 ans, où il a évolué tout d'abord dans l'équipe marketing avant de "créé" l'équipe Data Operations. Cette équipe à un seul objectif : faire grossir le MRR en permettant à toutes les équipes d'avoir des insights quantitatives.
Mention est la première startup "acquise" passant sur le podcast, ce qui apporte un angle différent sur la culture et son évolution à travers un rachat.
Gautier hérite donc de l' "ère Cabane", d'où l'importance majeure du tracking et de l'analyse de données. Mention est une entreprise qui, très tôt, a hyper-connectée son back-end et son produit afin de faire remonter un maximum d'évènements aux bonnes personnes (sales, produit, marketing, customers success, etc.). Toutes les startups devraient prendre quelques minutes et faire une haie d'honneur à ce genre de pratique, qui facilite par la suite toute prise de décision.
On parle marketing et inbound, puisque 80% des leads entrant viennent de leur stratégie SEO mise en place il y a plusieurs années maintenant, et comment ils restent au top en abordant des sujets techniques qui intéressent leur lecteurs ou encore comment ils ont créé une boucle d'acquisition grâce à du user generated content.
On plonge ensuite dans le sujet majeur de l'interview : booster sa rétention grâce au customer success (et donc à Salesmachine). En quelques chiffres, ils sont passés de 60 à 80% de rétention revenu en 1 an et on même réussi à atteindre 95% sur un mois (grâce au projet 410...).
Au programme :
- Comment Mention à booster sa rétention de 60% à 80% en 1 an.
- Comment évolue la culture dans une startup après un rachat.
- Pourquoi Gautier n'a pas de KPI chiffrés.
Ressources de l'épisode : bit.ly/growthmakers58
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S'inscrire sur Growth Makers pour ne rater aucun épisode.
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Travailler avec moi par le biais de l'agence : Bryte.
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Podcast: Inside Intercom (LS 46 · TOP 1% what is this?)
Episode: Scale: How Sprout Social increased retention with customer-centric marketing (S02:E02)
Pub date: 2019-11-21
Sprout Social's Director of Marketing Tara Robertson tells Intercom's Director of Content John Collins how building an empathetic, data-driven customer journey helps to ensure customer retention.
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The podcast and artwork embedded on this page are from Intercom, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Knowledge Project with Shane Parrish (LS 69 · TOP 0.05% what is this?)
Episode: #94 Chamath Palihapitiya: Understanding Yourself
Pub date: 2020-10-13
The Founder and CEO of Social Capital Chamath Palihapitiya sits down with Shane Parrish to chat about what it means to be an observer of the present, how to think in first principles, the psychology of successful investing, his thoughts on the best public company CEO’s and much more.
-- Want even more? Members get early access, hand-edited transcripts, member-only episodes, and so much more. Learn more here: https://fs.blog/membership/
Every Sunday our Brain Food newsletter shares timeless insights and ideas that you can use at work and home. Add it to your inbox: https://fs.blog/newsletter/
Follow Shane on Twitter at: https://twitter.com/ShaneAParrish
The podcast and artwork embedded on this page are from Farnam Street, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Masters in Business (LS 65 · TOP 0.1% what is this?)
Episode: Ben Horowitz Discusses Culture and Success
Pub date: 2019-12-06
Bloomberg Opinion columnist Barry Ritholtz interviews Andreessen Horowitz cofounder Ben Horowitz, whose latest book is "What You Do Is Who You Are: How to Create Your Business Culture."
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The podcast and artwork embedded on this page are from Bloomberg, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Tim Ferriss Show (LS 81 · TOP 0.01% what is this?)
Episode: #392: Ben Horowitz — What You Do Is Who You Are >> Lessons from Silicon Valley, Andy Grove, Genghis Khan, Slave Revolutions, and More
Pub date: 2019-10-24
"One of the key insights from Bushido is that a culture is not a set of beliefs, it's a set of actions." — Ben Horowitz
Ben Horowitz (@bhorowitz) is a cofounder and general partner at the venture capital firm Andreessen Horowitz. He is the author of The New York Times bestseller, The Hard Thing About Hard Things, and the upcoming Harper Business book, What You Do Is Who You Are, available October 29th. He also created the a16z Cultural Leadership Fund to connect cultural leaders to the best new technology companies, and enable more young African Americans to enter the technology industry.
Prior to a16z, Ben was cofounder and CEO of Opsware (formerly Loudcloud), which was acquired by Hewlett-Packard for $1.6 billion in 2007. Previously, Ben ran several product divisions at Netscape Communications, including the widely acclaimed Directory and Security product line.
Ben has an MS and BA in Computer Science from UCLA and Columbia University, respectively.
This podcast is brought to you by Athletic Greens. I get asked all the time, "If you could only use one supplement, what would it be?" My answer is, inevitably, Athletic Greens. It is my all-in-one nutritional insurance. I recommended it in The 4-Hour Body and did not get paid to do so.
As a listener of The Tim Ferriss Show, you'll get a free 20-count travel pack (valued at $79) with your first order at athleticgreens.com/tim.
This episode is also brought to you by Hello Monday with Jessi Hempel, LinkedIn's podcast now in its second season, and it is full of advice you can start using today.
Each week, Jessi sits down with featured guests to investigate the role work plays in our lives, and how to make it work for us. This season, one of the first episodes I recommend checking out is with Jerry Colonna. I've worked with Jerry in the past, and he is one of the start-up world's most in-demand executive coaches. In the episode, Jerry shares his approach to meetings, explains how to ask good open-ended questions, and he also goes through his approach to daily journaling.
Whether you're starting your first job or gearing up for retirement, Hello Monday helps you tackle Monday — and the rest of the workweek — with tactics and strategies you can use. Find Hello Monday with Jessi Hempel on Apple Podcasts or wherever you listen to podcasts.
If you enjoy the podcast, would you please consider leaving a short review on Apple Podcasts/iTunes? It takes less than 60 seconds, and it really makes a difference in helping to convince hard-to-get guests. I also love reading the reviews!
For show notes and past guests, please visit tim.blog/podcast.
Sign up for Tim’s email newsletter (“5-Bullet Friday”) at tim.blog/friday.
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Follow Tim:
Twitter: twitter.com/tferriss
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Past guests on The Tim Ferriss Show include Jerry Seinfeld, Hugh Jackman, Dr. Jane Goodall, LeBron James, Kevin Hart, Doris Kearns Goodwin, Jamie Foxx, Matthew McConaughey, Esther Perel, Elizabeth Gilbert, Terry Crews, Sia, Yuval Noah Harari, Malcolm Gladwell, Madeleine Albright, Cheryl Strayed, Jim Collins, Mary Karr, Maria Popova, Sam Harris, Michael Phelps, Bob Iger, Edward Norton, Arnold Schwarzenegger, Neil Strauss, Ken Burns, Maria Sharapova, Marc Andreessen, Neil Gaiman, Neil de Grasse Tyson, Jocko Willink, Daniel Ek, Kelly Slater, Dr. Peter Attia, Seth Godin, Howard Marks, Dr. Brené Brown, Eric Schmidt, Michael Lewis, Joe Gebbia, Michael Pollan, Dr. Jordan Peterson, Vince Vaughn, Brian Koppelman, Ramit Sethi, Dax Shepard, Tony Robbins, Jim Dethmer, Dan Harris, Ray Dalio, Naval Ravikant, Vitalik Buterin, Elizabeth Lesser, Amanda Palmer, Katie Haun, Sir Richard Branson, Chuck Palahniuk, Arianna Huffington, Reid Hoffman, Bill Burr, Whitney Cummings, Rick Rubin, Dr. Vivek Murthy, Darren Aronofsky, and many more.
See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
The podcast and artwork embedded on this page are from Tim Ferriss: Bestselling Author, Human Guinea Pig, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Out of Hours: The Podcast (LS 40 · TOP 2% what is this?)
Episode: Benedict's Newsletter: Building a newsletter, with Benedict Evans.
Pub date: 2020-07-16
Brough to you by Out of Hours - the community for people with side projects. Sign up to our newsletter here.
Today's guest is Benedict Evans - a venture capitalist and analyst, who has grown his own personal newsletter on tech and media to over 150,000 subscribers.
Benedict has spent over 20 years analysing mobile, digital media and technology, working in equity research, strategy and venture capital. He spent the last 6 years working for the Silicon Valley venture fund Andreessen Horowitz, famous for their high conviction investing - having invested in some of the best known companies of our time: Oculus, Buzzfeed, Medium, Pinterest, Slack and Airbnb to name a few.
He’s now back in London, working as venture partner at company builder Entrepreneur First, as well as at Mosaic Ventures.
His side project is his weekly newsletter - all about tech and media, selecting what he calls “the 10-20 pieces of news that actually matter” and explaining why they matter. It launched in 2013, and now has over 150,000 subscribers.
We talk about why side projects can help you with job interviews, the future bundling and unbundling of media, his golden rules for posting online and why even he can get imposter syndrome sending out his newsletter.
Sign up to Out of Hours here: outofhours.org
Sign up to the newsletter here: ben-evans.com
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The podcast and artwork embedded on this page are from Georgia Ritter, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Out of Hours: The Podcast (LS 40 · TOP 2% what is this?)
Episode: London Sock Exchange: Building a successful ecommerce store, with Dan Zell
Pub date: 2020-08-05
Today I’m speaking to Dan Zell - cofounder of the London Sock Exchange.
The London Sock Exchange is a ecommerce store for socks. They started as a subscription business, and have since been stocked in John Lewis, as well as shops in New York, sell over 200,000 pairs a year and, been featured in the Financial Times and The Guardian. Dan still runs it as a side project alongside his job as Managing Partner at Decoded. We talk about why you might not want to take your project full time, the tricks of running an e-commerce store, how to start lean and why side projects can help you learn and discover what you’re good at. It’s full of practical tips and tricks especially with anyone with product idea, but useful for everyone.
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The podcast and artwork embedded on this page are from Georgia Ritter, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: On the Media (LS 74 · TOP 0.05% what is this?)
Episode: Joe Rogan: Debate Moderator?
Pub date: 2020-09-16
Earlier this year we aired a profile of Joe Rogan. The unbelievably popular podcast host was in the headlines because then-presidential candidate Bernie Sanders had gone on his show — resulting in a kerfuffle in the progressive camp, because of Rogans misogyny and racism. He's back in the headlines again this week after Trump tweeted that he would gladly participate in a debate hosted by Rogan.
The fact that Joe Rogan wields so much influence is itself a kind of a head-scratcher for many coastal media observers. “Why Is Joe Rogan So Popular?” is the title of a profile in The Atlantic by Devin Gordon, a writer who immersed himself in Joe Rogan's podcast and lifestyle to understand his enormous popularity. In this segment, first aired in January, he and Brooke discuss Rogan's complicated appeal.
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Podcast: Revisionist History (LS 89 · TOP 0.01% what is this?)
Episode: Analysis, Parapraxis, Elvis
Pub date: 2018-07-19
The one song The King couldn’t sing.
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The podcast and artwork embedded on this page are from Pushkin Industries, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Revisionist History (LS 89 · TOP 0.01% what is this?)
Episode: Malcolm Gladwell's 12 Rules for Life
Pub date: 2018-06-28
Crucial life lessons from the end of hockey games, Idris Elba, and some Wall Street guys with a lot of time on their hands.
Learn more about your ad-choices at https://www.iheartpodcastnetwork.comSee omnystudio.com/listener for privacy information.
The podcast and artwork embedded on this page are from Pushkin Industries, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Product Podcast (LS 48 · TOP 1% what is this?)
Episode: Basing Decisions on Data by PayPal Group Product Manager
Pub date: 2020-10-12
The Product Podcast interview series is back with the fourth season! Featuring the brightest minds in the Product world, our latest episodes will highlight our guest's insights, methods, and strategies that are being used at the top tech companies around the world.
Episode #1
Deb Dutta, Group Product Manager at PayPal, will talk about basing decisions on data, what it's like being a Sr PM and how to break into product.
Get the FREE Product Book here
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Podcast: The LRB Podcast (LS 52 · TOP 0.5% what is this?)
Episode: Really Hot Hands
Pub date: 2020-10-13
To mark the publication of the latest LRB Collection of essays, about sport, David Runciman, on loan from Talking Politics, talks to Ben Markovits about Michael Jordan, home advantage, how basketball has tackled racial inequality, the difference between writing about sport in fiction and non-fiction, and why it turns out that players really are sometimes hot and sometimes not.
Pre-order the LRB's collection of sports writing here: https://lrb.me/sport
Find the pieces mentioned in this episode here: https://lrb.me/sportpod
Subscribe to the LRB from just £1 per issue: https://mylrb.co.uk/podcast20b
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Podcast: Azeem Azhar's Exponential View (LS 60 · TOP 0.5% what is this?)
Episode: How GPT-3 Is Shaping Our AI Future
Pub date: 2020-10-07
OpenAI stunned the world with the release of Generative Pre-trained Transformer 3 (GPT-3), the world’s most impressive language-generating AI. OpenAI CEO Sam Altman joins Azeem Azhar to reflect on the huge attention generated by GPT-3 and what it heralds for the future research and development toward the creation of a true artificial general intelligence (AGI).
They also explore:
- How AGI could be used both to reduce and exacerbate inequality.
- How governance models need to change to address the growing power of technology companies.
- How Altman’s experience leading Y Combinator informed his leadership of OpenAI.
Further reading:
- “The messy, secretive reality behind OpenAI’s bid to save the world” (Wired, 2020)
- “Sam Altman’s Manifest Destiny” (New Yorker, 2016)
- “Governance in the Age of AI” (Exponential View Podcast, 2019)
- “Trillions of Words Analyzed, OpenAI Sets Loose AI Language Colossus” (Bloomberg, 2020)
- “OpenAI’s Text Generator Is Going Commercial” (WIRED, 2020)
@Sama
@Azeem
@exponentialview
Exponential View newsletter
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Podcast: a16z Podcast (LS 62 · TOP 0.1% what is this?)
Episode: So You Want to Launch a Newsletter: Tips From Substack Writers
Pub date: 2020-09-17
This episode, part one in a two-part series on the Creator Economy, explores the process and economics behind creating an independent newsletter. In this candid conversation, host Lauren Murrow talks with four Substack writers—an artist, a technologist, a journalist, and a clinical researcher-turned-psychedelics scholar—about how to find and foster an audience, the calculus behind going paid versus unpaid, the pressure to produce, and financial benchmarks for making a living from newsletter writing.
The pandemic has prompted a reckoning within traditional media and, in parallel, a surge in the newsletter ecosystem. On Substack, readership and active writers both doubled from January through April. The newsletter hosting platform now has more than 100,000 paying subscribers.
This episode reveals the behind-the-scenes experiences of four newsletter creators, all of whom launched roughly within the past year:
Software engineer Lenny Rachitsky, most recently a growth product manager at Airbnb, whose tech-focused dispatch is called Lenny’s Newsletter.
Artist and writer Edith Zimmerman, creator of the Drawing Links newsletter, which chronicles her life and musings through comic-style illustrations.
Zach Haigney, an acupuncturist and researcher whose newsletter, The Trip Report, explores the science, policy, and business behind medicinal psychedelics.
And Patrice Peck, a freelance journalist—previously a staff writer at BuzzFeed—whose newsletter, Coronavirus News for Black Folks, highlights the pandemic’s disproportionate impact on the black community.
Listen to the end of the episode to hear more about Patrice, Zach, Edith, and Lenny's top newsletter recommendations:
Patrice’s newsletter recs:
The Intersection by Adriana Lacy
Beauty IRL by Darian Symone Harvin
Carefree Black Girl by Zeba Blay
Maybe Baby by Haley Nahman
Zach’s newsletter recs:
Stratechery by Ben Thompson
Sinocism by Bill Bishop
A Media Operator by Jacob Cohen Donnelly
Off the Chain by Anthony Pompliano
The Weekly Dish by Andrew Sullivan
Edith’s newsletter recs:
The Browser by Robert Cottrell
The Ruffian by Ian Leslie
Ridgeline by Craig Mod
Dearest by Monica McLaughlin
Why Is This Interesting? by Noah Brier and Colin Nagy
Lenny’s newsletter recs:
2PM by Webb Smith
Li’s Newsletter by Li Jin
Alex Danco’s Newsletter by Alex Danco
Turner’s Blog by Turner Novak
Next Big Thing by Nikhil Basu Trivedi
Big Technology by Alex Kantrowitz
The Profile by Polina Marinova
Everything by Nathan Baschez, Dan Shipper, Tiago Forte, and Adam Keesling
Not Boring by Packy McCormick
Illustration: Edith Zimmerman
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Podcast: DeepMind: The Podcast (LS 54 · TOP 0.5% what is this?)
Episode: AI and Neuroscience: The virtuous circle
Pub date: 2019-08-20
What can the human brain teach us about AI? And what can AI teach us about our own intelligence? These questions underpin a lot of AI research. In this first episode, Hannah meets the DeepMind Neuroscience team to explore these connections and discovers how our brains are like birds’ wings, what training a dog and an AI agent have in common, and why the simplest things for people to do are, paradoxically, often the hardest for machines.
If you have a question or feedback on the series, message us on Twitter (@DeepMind using the hashtag #DMpodcast) or email us at podcast@deepmind.com.
Further reading
- BBC: An AI playlist
- Wait But Why: The AI Revolution
- Coursera: AI for everyone
- Medium: Machine Learning for Humans
- Google Arts & Culture: 25 moments that have defined AI
- Royal Society: What is Machine Learning?
- The Algorithm: A weeklyemail newsletter from MIT Tech Review
- DeepMind blog: neuroscience and AI: a virtuous circle
- Nature: Far-sighted birds plan breakfast the evening before
Interviewees in this episode: Deepmind CEO and co-founder, Demis Hassabis; Matt Botvinick, Director of Neuroscience Research; research scientists Jess Hamrick and Greg Wayne; and Director of Research, Koray Kavukcuoglu.
Credits:
Presenter: Hannah Fry
Editor: David Prest
Senior Producer: Louisa Field
Producers: Amy Racs, Dan Hardoon
Binaural Sound: Lucinda Mason-Brown
Music composition: Eleni Shaw (with help from Sander Dieleman and WaveNet)
Commissioned by DeepMind
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Podcast: The Knowledge Project with Shane Parrish (LS 69 · TOP 0.05% what is this?)
Episode: #87 Hannah Fry: The Role of Algorithms
Pub date: 2020-07-07
Mathematician and author of Hello World and The Mathematics of Love, Hannah Fry discusses the role of maths in society, the dating world and we explore what it means to be human in the age of algorithms.
-- Want even more? Members get early access, hand-edited transcripts, member-only episodes, and so much more. Learn more here: https://fs.blog/membership/
Every Sunday our Brain Food newsletter shares timeless insights and ideas that you can use at work and home. Add it to your inbox: https://fs.blog/newsletter/
Follow Shane on Twitter at: https://twitter.com/ShaneAParrish
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Podcast: RSA Events (LS 41 · TOP 1.5% what is this?)
Episode: How to Make the World Add Up
Pub date: 2020-09-21
Statistics are vital in helping us tell stories and make sense of the world - and yet we doubt them more than ever.
But numbers - in the right hands - have the power to change the world for the better. Good statistics are not smoke and mirrors; in fact, they help us see more clearly – if we keep our wits about us.
Economist Tim Harford is an unrivalled guide to the world of numbers. Amidst a sea of disinformation and obfuscation, he shows how to seek out data with the power to inform and illuminate.
In an unmissable RSA conversation with data bias campaigner Caroline Criado Perez, we’ll learn how to look closer at how statistics are sourced and presented, and how to evaluate the claims that surround us with greater confidence, curiosity - and a healthy dose of scepticism.
#RSAnumbers
This conversation was broadcast online on the 17th September 2020. Join us at: www.thersa.org
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Podcast: RSA Events (LS 41 · TOP 1.5% what is this?)
Episode: How Did We Get Here?
Pub date: 2020-09-25
Are we still a liberal nation? Are we even pretending or aspiring to be one?
Several extraordinary years in politics and public life have shaken Britain’s image of itself as a model of liberal democracy. The status of political parties, the media, and public officials have morphed and shifted as increasingly desperate attempts have been made to contain the impulses of reactionary nationalism within mainstream political institutions. Widening inequality and the expanding role of technology have changed the way we relate to one another, eroding the sense of consensus required for liberal politics to thrive. Momentous public votes and major events have been surrounded by lying and propaganda, once met with shock, but now wearily familiar.
How was the ground laid for this liberal collapse? Professor of Political Economy William Davies reflects on this extraordinary moment as a product of a larger and longer historical context, examining the underlying preconditions for the turmoil through which we’re living, and where we might go from here. How has the UK’s response to the coronavirus pandemic shone a light on the state of its politics? And with injustice more clearly exposed and widely acknowledged than ever, could this moment pave the way for something better?
RSAliberalism
This conversation was broadcast online on the 24th September 2020. Join us at: www.thersa.org
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Podcast: Bobo and Flex (LS 60 · TOP 0.5% what is this?)
Episode: people don't care
Pub date: 2020-08-10
this week, we're diving into an extra long chaotic episode about wisdom teeth, the pressure to erode your health for the benefit of capitalism, as well as our argument for anarchy!! Enjoy!!JOIN US ON FACEBOOK:www.facebook.com/groups/boboandflexTHE BOBO&FLEX BIPOC FB GROUP:https://www.facebook.com/groups/714593526047455BOBO'S PATREON:www.patreon.com/bobosvoidFLEX'S PODCAST:https://open.spotify.com/show/5P75s7GZlPYfSWi6VDiPHD?go=1&utm_source=embed_v3&wmode=opaque&t=1690&fallback=getapp&signupPrompt=1&nd=1FOLLOW US ON INSTAGRAM:www.instagram.com/boboandflexSUPPORT US ON PATREON:www.patreon.com/boboandflexFOLLOW US ON TWITTER:twitter.com/boboandflexSUBSCRIBE TO OUR YOUTUBE CHANNEL:www.youtube.com/channel/UCplGygL_igEZA9uYvWB75SAInstagram:Bobo: www.instagram.com/bobo.matjilaFlex: www.instagram.com/flex.mamiYouTube:www.youtube.com/channel/UCMqf8MD1qkIdpsmBrgtHGQQFlex: www.youtube.com/channel/UC5GaziUPGc4bC8dP--gK15QTwitter:Bobo: www.twitter.com/BoboMatjilaFlex: www.twitter.com/flexmami
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Podcast: Live To Tape with Johnny Pemberton (LS 53 · TOP 0.5% what is this?)
Episode: 158. Irene Tu
Pub date: 2020-09-20
Omnivorous ombudsperson, wit-writing renegade, and cavorting comedian, Irene Tu, joins the executive buffet for yet another edition of the backyardcast® deep in the heat of summer. See omnystudio.com/listener for privacy information.
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Podcast: Interception (LS 48 · TOP 1% what is this?)
Episode: Cinq ans après la crise migratoire, le destin contrasté des Syriens exilés
Pub date: 2020-09-20
durée : 00:46:04 - Interception - Le tragique incendie du camp de Moria sur l’ile grecque de Lesbos nous a rappelé au début du mois de septembre le sort des centaines de milliers de réfugiés syriens contraints à l’exil par la guerre qui ravage leur pays depuis 2011.
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Podcast: Song Exploder (LS 74 · TOP 0.05% what is this?)
Episode: Caribou - Home
Pub date: 2020-02-12
Dan Snaith has been making Caribou records since 2001. He won Canada’s Polaris music prize in 2007, and this month, he’s releasing the seventh Caribou album, Suddenly.
In this episode, Dan breaks down the song “Home.” He talks about how he managed to get past several moments of creative uncertainty to figure out the final track.
songexploder.net/caribou
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Podcast: Les Pieds sur terre (LS 66 · TOP 0.05% what is this?)
Episode: Ma vie de chauffeur Uber
Pub date: 2020-09-18
durée : 00:28:33 - Les Pieds sur terre - par : Sonia Kronlund, Charlotte Bienaimé, Delphine Dhilly, Leila Djitli, Rémi Dybowski Douat, Sophie Knapp, Inès Léraud, Bahar Makooi, Pauline Maucort, Olivier Minot, Ilana Navaro, Delphine Saltel, Stéphanie Thomas, Pascale Pascariello, Valérie Borst, Martine Abat, Adila Bennedjaï-Zou, Judith Chetrit, Léa Minod, Léa Veinstein - Avec le rêve d’arrondir leur fin de mois et devenir maître de leur emploi du temps, Saladin, Malik et d'autres sont devenus conducteurs Uber. En huis clos dans une voiture, des rencontres conjurent, parfois, le sort de leur condition fragile. Deux belles histoires, avec un peu de nuance à la fin. - réalisation : Vincent Abouchar, Philippe Baudouin, Emmanuel Geoffroy, Cécile Laffon, Marie Plaçais, Alexandra Malka, Delphine Lemer, François Caunac, Clémence Gross, Anne-Laure Chanel, Yaël Mandelbaum
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Podcast: Panic with Friends - Howard Lindzon (LS 43 · TOP 1.5% what is this?)
Episode: Alex Danco of Shopify on all things Tech and Money (EP.94)
Pub date: 2020-08-26
Alex Danco works for Shopify Money and also has a masters degree in Neuroscience. He offers great takes on all things Tech and Money. Relax and enjoy.
Guest - Alex Danco of Shopify Money
howardlindzon.com, shopify.com, alexdanco.com
Twitter: @howardlindzon, @Alex_Danco, @knutjensen
fintech #invest #investment #venturecapital #stockmarket #finance
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Podcast: North Star Podcast (LS 43 · TOP 1.5% what is this?)
Episode: Alex Danco: Amazon, Cities, and Disruption
Pub date: 2019-02-18
SHOW NOTES LINKS: Find Alex online:
- Social Capital: Snippets
People mentioned:
- Steve Yegge
- Clay Christensen
- Travis Kalanick
- Dan Doctoroff
- Dr. J. Craig Venter
- Eric Reis
- Chamath Palihapitiya
Other mentions:
- Steve's Google Platforms rant
- Social Capital
- Y Combinator
- Andreessen Horowitz
- Cloud Kitchens
- Sidewalk Labs
- 100 Resilient Cities
- The Organization Man by William Whyte
- The Lean Startup
SHOW TOPICS 2:11 Amazon's Organizational Structure 4:09 Steve's Google Platform Rant 12:42 AWS & The Government 22:53 Disruption as rearrangement 30:47 Thesis-driven Discovery Team @ Social Capital 38:52 The role of transportation in shaping cities 45:47 Suburban vs Urban life 53:13 Sidewalk Labs in Toronto 1:00:11 Chief Resilience Officers in Cities 1:03:03 Alex & his interest in Biology 1:11:28 The Organization Man 1:18:58 What has Alex learned from Chamath Palihapitiya 1:22:05 Alex's writing advice 1:25:02 Ways to Think About Water
SUBSCRIBE TO MY “MONDAY MUSINGS” NEWSLETTER TO KEEP UP WITH THE PODCAST.
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Podcast: Invest Like the Best with Patrick O'Shaughnessy (LS 67 · TOP 0.05% what is this?)
Episode: Alex Danco – Scarcity, Abundance and Bubbles - [Invest Like the Best, EP.121]
Pub date: 2019-02-12
My guest this week is Alex Danco. Alex is a member of the Discover Team at Social Capital, has a background in biology, and has written about all things tech and business. While Alex is only 30, it seems like he has spent decades thinking about all the topics that we discuss, from changing business models, to railroads, to the shift from products to functions, and the rise and fall of asset bubbles. I hope you enjoy this wide ranging conversation.
For more episodes go to InvestorFieldGuide.com/podcast.
Sign up for the book club, where you’ll get a full investor curriculum and then 3-4 suggestions every month at InvestorFieldGuide.com/bookclub.
Follow Patrick on Twitter at @patrick_oshag
Show Notes
1:15 - (First Question) – A look at his day job on the discover team
2:20 – 40 problems doc
4:27 – How companies get on the list and the turnover
5:21 – Hardest problem they are looking at…housing
11:37 – The investment component that fixes housing
15:35 – Where we are in the technology cycle in the view of abundance vs scarcity
20:54 – Change in distribution and the business vs utility business idea.
28:40 – Bifurcation of small and larger businesses
32:48 – New forms of scarcity today
38:31 – The trend of massive company incumbency
41:07 – The utility of bubbles
49:08 – His favorite bubble
51:18 – Challenges and nuances of bubbles
53:35 – Zero to One Notes on Start-Ups, or How to Build the Future
1:02:22 – Future for VC funding in Silicon Valley
1:04:07 – Advice for business builders
1:08:23 – The Three True Outcomes
1:13:04 – His background in biology and innovation in that space that is coming
1:19:46 – Company examples that are of interest to him and that encapsulate his way of investing
1:24:56 – Kindest thing anyone has done for Alex
Learn More
For more episodes go to InvestorFieldGuide.com/podcast.
Sign up for the book club, where you’ll get a full investor curriculum and then 3-4 suggestions every month at InvestorFieldGuide.com/bookclub
Follow Patrick on twitter at @patrick_oshag
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Podcast: Rebank: Banking the Future (LS 43 · TOP 1.5% what is this?)
Episode: KYC as a Competitive Advantage with Fourthline
Pub date: 2020-07-24
Krik Gunning is the CEO and co-founder of Fourthline, one of fastest growing companies in Europe. Fourtline verifies the identities of millions of customers for clients like N26, ING, SolarisBank, Degiro, Flatex and many others.
Aman and Krik discuss the genesis of KYC and the constant struggle between compliance and risk at regulated institutions. They look at real world examples of how companies are stopping financial crime and how technology offerings in this commoditized market stand out.
For all of our past episodes and to sign up for our newsletter, please visit www.bankingthefuture.com.
Thank you very much for joining us today. Please welcome, Aman Ghei and Krik Gunning.
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Podcast: Rebank: Banking the Future (LS 43 · TOP 1.5% what is this?)
Episode: Breaking Down Revolut, Starling and Monzo’s Annual Results
Pub date: 2020-08-14
We’re joined by Lex Sokolin to break down recently published annual reports from Revolut, Starling and Monzo, three of the leading European digital banks.
There are some fascinating insights to be drawn from the documents, especially in the context of the broader global fintech market.
This is rich subject matter, and we surely didn’t cover everything. You may even disagree with some of our analysis! Let us know.
Please get in touch with thoughts, opinions and corrections. We’ll share all the high quality communication we receive with the entire community.
To subscribe to the Rebank newsletter, including insights, essays and written transcripts of all new episodes, please visit www.bankingthefuture.com.
To subscribe to Lex’s amazing newsletter, please visit www.fintechblueprint.com.
Thank you very much for joining us today. Please welcome, Lex Sokolin.
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Podcast: Product Decoded (LS 33 · TOP 5% what is this?)
Episode: Michael Sippey, VP Product at Medium
Pub date: 2018-05-24
Michael Sippey, Vice President of Product at Medium, shares his passion for tapping into the zeitgeist of what customers love — in his words, “to catch magic in a bottle” — to create world-class products. He is the former VP of Product at Twitter and is unique in that he is both a starter and a builder and has built both consumer and enterprise products.
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Podcast: Bestbookbits (LS 34 · TOP 5% what is this?)
Episode: On Writing - by Stephen King
Pub date: 2019-12-18
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Podcast: a16z Podcast (LS 62 · TOP 0.1% what is this?)
Episode: a16z Podcast: Crypto and the Evolution of Open Source
Pub date: 2018-08-20
with Devon Zuegel (@devonzuegel), Denis Nazarov (@iiterature), and Jesse Walden (@jessewldn)
The open source movement enabled so much in computing, including the collaborative building of libraries -- that is, building blocks of code that developers could combine together to build applications. But as these applications grew to massive scale, those libraries ended up being somewhat asymmetrical for "nights-and-weekend" developers (compared to say, the disproportionate resources of a large company with billions of users and big data).
Blockchains, however -- enabled by cryptotokens that align incentives among stakeholders -- shift open source development from libraries, to the creation of shared, open, permissionless services. Instead of being siloed and repetitively produced as if from the industrial factory era, any smart contract developed on Ethereum becomes a shared service that can interact with any other service... incentivizing developers to improve on existing services, build on top of them, and enable combinatorial innovation at greater scale than ever before.
But if decentralized networks are to win the third era of the internet, how will we resolve challenges such as single-purpose services (another form of consolidation), community conflicts, and other issues? In this video, freelance software engineer (and blockchain app developer) and writer (and urban watcher) Devon Zuegel guest-interviews a16z crypto partners Denis Nazarov and Jesse Walden, the co-founders of Mediachain Labs (which was acquired by Spotify in 2017). They draw on their past experiences leading open source development of a decentralized media attribution protocol for connecting creators to their audience, and what the implications of "services vs. libraries" could be for creatives now. And what about identity, stablecoins and crypto finance, and more? Finally, they extend their previous analogy of cities and network effects and how it fits the idea of libraries vs. services in crypto.
Please note that the a16z crypto fund is a separate legal entity managed by CNK Capital Management, L.L.C. (“CNK”), a registered investor advisor with the Securities and Exchange Commission. a16z crypto is legally independent and operationally separate from the Andreessen Horowitz family of fund and AH Capital Management, L.L.C. (“AHCM”).
In any case, the content provided here is for informational purposes only, and does NOT constitute an offer or solicitation to purchase any investment solution or a recommendation to buy or sell a security; nor it is to be taken as legal, business, investment, or tax advice. In fact, none of the information in this or other content on a16zcrypto.com should be relied on in any manner as advice. You should consult your own advisers as to legal, business, tax and other related matters concerning any investment.
Furthermore, the content is not directed to any investor or potential investor, and may not be used or relied upon in evaluating the merits of any investment and must not be taken as a basis for any investment decision. No investment in any fund advised by CNK or AHCM may be made prior to receipt of definitive offering documentation and due diligence materials. Finally, views expressed are those of the individual a16z crypto personnel quoted therein and are not the views of CNK, AHCM, or their respective affiliates.
Please see https://a16zcrypto.com/disclosures/ and https://a16zcrypto.com/disclaimers for further information.
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Podcast: Greymatter (LS 45 · TOP 1% what is this?)
Episode: Reid Hoffman | Myths About Failure
Pub date: 2020-08-20
Greylock general partner and LinkedIn co-founder Reid Hoffman and his Blitzscaling co-author Chris Yeh discuss one the most common outcomes of startups: Failure. But although failure is often the default state for startups, it is neither inevitable nor is it necessarily the end of the story. Even if you are able to bring yourself to look failure in the eye, you can take steps to maximize your chance of success. And even if you fail despite your best efforts, if you behave ethically and demonstrate that you've learned from the mistakes you might have made along the way, failure is definitely not the end. In fact, it may be the beginning.
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Podcast: Greymatter (LS 45 · TOP 1% what is this?)
Episode: Utmost | Mobilizing the Talent Continuum
Pub date: 2020-09-01
The nature of work today is increasingly dynamic, distributed, flexible, and digital-first. Relying on the extended workforce is more than a short-term, stop-gap solution: businesses are finding this population to be an efficient, viable source of talent instrumental to their core operations. That's why Utmost developed the first ever extended workforce system (EWS) to help enterprises source, engage, pay, and optimize their entire non-employee workforce, including freelancers, consultants and more. The company, which was founded in 2018 by former Workday and Groupon executives, has been a distributed organization from the start and has a unique vantage into the fast-changing nature of work. Utmost COO and co-founder Dan Beck sat down with Greylock partner Saam Motamedi to discuss. This episode is part of Greymatter's #WorkFromAnywhere series.
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Podcast: Exponent (LS 61 · TOP 0.5% what is this?)
Episode: Episode 167 — YouTube and the End of Friction
Pub date: 2019-04-05
Ben and James discuss the question of YouTube, why it’s similar and different from Facebook, and why engagement is both alluring and a potential problem.
Links
- YouTube Executives Ignored Warnings, Letting Toxic Videos Run Rampant — Bloomberg
- Ben Thompson: Apple’s Services Event — Stratechery
- Ben Thompson: Mark Zuckerberg’s Proposal, The Copyright Directive and Sunk Costs — Stratechery Daily Update
- Ben Thompson: YouTube and Toxic Videos, YouTube’s Problematic Incentives, Sins of Omission and Commission — Stratechery Daily Update
- Ben Thompson: Friction — Stratechery
- Ben Thompson: The Pollyannish Assumption — Stratechery
- Ben Thompson: The Wall Street Journal and Apple News, The Problem with Regulating Content](https://stratechery.com/2019/the-wall-street-journal-and-apple-news-the-problem-with-regulating-content-australias-terrible-new-law/)
Hosts
- Ben Thompson, @benthompson, Stratechery
- James Allworth, @jamesallworth, Harvard Business Review
Podcast Information
- Feed
- iTunes
- SoundCloud
- Feedback
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Podcast: The Kicker (LS 39 · TOP 2% what is this?)
Episode: Great escape: Nicholson Baker lets YouTube take the wheel
Pub date: 2020-07-10
When Nicholson Baker first fell in love with YouTube, it was for its “outpouring of human miscellany” and “first person journalism.” But when CJR asked him to write about YouTube as a purveyor of political information, he stumbled upon a different world—one that, in spite of recent algorithmic adjustments, makes radicalization a frictionless experience.
On this week’s Kicker, Baker and Kyle Pope, editor and publisher of CJR, discuss Baker’s YouTube experience, as well as the extraordinary discoveries he made for his new book, Baseless: My Search for Secrets in the Ruins of the Freedom of Information Act.
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Podcast: Exponent (LS 61 · TOP 0.5% what is this?)
Episode: Exponent 163 — Publishers vs Apple News
Pub date: 2019-02-15
Ben and James discuss Apple News as it is today, where Apple wants to take it in the future, and why publishers should push back.
Links
- Ben Thompson: The Cost of Apple News — Stratechery
- Ben Thompson: What Direct-to-Consumer Publishers are Selling — Stratechery Daily Update
- Apple News’s Radical Approach: Humans Over Machines — New York Times
- Publishers Chafe at Apple’s Terms for Subscription News Service — Wall Street Journal
- Ben Thompson: Apple Should Buy Netflix — Stratechery
- The Report of the 2020 Group — New York Times
Hosts
- Ben Thompson, @benthompson, Stratechery
- James Allworth, @jamesallworth, Harvard Business Review
Podcast Information
- Feed
- iTunes
- SoundCloud
- Feedback
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Podcast: Syntax - Tasty Web Development Treats (LS 62 · TOP 0.1% what is this?)
Episode: Scott Teaches Wes Svelte and Sapper
Pub date: 2020-05-20
In this episode of Syntax, Scott teaches Wes about Svelte and Sapper — general premise, best practices, and more!
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Show Notes 03:14 - General premise
- Sapper compiles away, removing framework code from build
- Component & Props based
- Easy reactivity
- Built-in tools like animation
- Template-based
07:57 - Svelte 101
- .svelte files
- Files can include , , and straight-up CSS
- Variables are used in templates via {var} - even works
- Import component and use just like React and Vue
10:49 - Stylin’
- All styles are scoped by default
- global() to wrap around global declarations
- Language type sass to use sass
12:22 - Reactivity
- Baked in
- let count = 0
- count = count + 1 will reactively update in template
- variables are essentially state
- $: double = count + 2 - to create a reactive variable that updates when another variable updates
- $: console.log(count) = will run whenever count is update a-la useEffect
- $: if (count >10) = same… reactive if
- updates let name in script
15:55 - Props
- Same was React, but need to be exported before they can be used
- Seems counterintuitive, but you get over it quickly
- EZ defaults export let answer = 'a mystery';
import Nested from './Nested.svelte'; export let answer = 'a mystery'; The answer is {answer}
20:08 - Template logic
- If statements {#if user._id} {/if}
- Loops {#each cats as kittens}
- Promise tags
{#await promise} ...waiting
{:then number} The number is {number}
{:catch error} {error.message}
{/await} 23:12 - Events
- On directive
- Functions can also be inline
- Modifiers
Click me 26:11 - Baked-in goodies
- Animation
- Dimensions
{text} * Lifecycle methods * Advanced State Via Stores + A store is simply an object with a subscribe method that allows interested parties to be notified whenever the store value changes. * import { writable } from ‘svelte/store’; * export const count = writable(0); * count.update(0) * Slots * React helmet like stuff, ie
36:39 - Sapper
- Similar to Next.js
- Folder routes
- Static export with all of the good stuff like service workers and preloading
Links * Svelte * Sapper * r/webdev * Vue.js * ScottTolinski.com * WesBos.com * Next.js * ScottTolinski.com Github Repo * Gatsby.js * Shawn Swyx
××× SIIIIICK ××× PIIIICKS ××× * Scott: Lewisia Battery Backup Solar Fountain Pump * Wes: Firefox Containers
Shameless Plugs * Scott: LevelUpTuts YouTube Channel * Wes: Wes’ New Website
Tweet us your tasty treats! * Scott’s Instagram * LevelUpTutorials Instagram * Wes’ Instagram * Wes’ Twitter * Wes’ Facebook * Scott’s Twitter * Make sure to include @SyntaxFM in your tweets
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Podcast: How to Start a Startup
Episode: 05 - Peter Thiel - Competition is for Losers
Pub date: 2016-01-06
Business Strategy and Monopoly Theory
Peter Thiel, founder of Paypal and Palantir, discusses business strategy and monopoly theory in "Competition is For Losers".
Lecture Transcript: tech.genius.com/Peter-thiel-lecture-5-business-strategy-and-monopoly-theory-annotated
See the slides and readings at startupclass.samaltman.com/courses/lec05/
Discuss this lecture: startupclass.co/courses/how-to-start-a-startup/lectures/64034
Click to view: show page on Awesound
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Podcast: How I Built This with Guy Raz (LS 85 · TOP 0.01% what is this?)
Episode: Airbnb's Joe Gebbia At The HIBT Summit
Pub date: 2018-12-06
Next in our series of episodes from the How I Built This Summit: Joe Gebbia, co-founder of Airbnb. Joe sat down with Guy Raz in front of a live audience in San Francisco, and talked about why he and his co-founders pursued their idea despite overwhelming feedback that it would never work. We're publishing another two episodes from the Summit – so keep checking your podcast feed every Thursday.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
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Podcast: How I Built This with Guy Raz (LS 85 · TOP 0.01% what is this?)
Episode: Live Episode! Dollar Shave Club: Michael Dubin
Pub date: 2018-12-17
At the end of 2010, Michael Dubin was working in marketing when he met a guy named Mark Levine at a holiday party. Mark was looking for ideas to get rid of a massive pile of razors he had sitting in a California warehouse. Michael's spontaneous idea for an internet razor subscription service grew into Dollar Shave Club, and his background in improv helped him make a viral video to generate buzz for the new brand. Just five years after launch, Unilever acquired Dollar Shave Club for a reported $1 billion. Recorded live in Los Angeles.
See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
The podcast and artwork embedded on this page are from Guy Raz | Wondery, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: How I Built This with Guy Raz (LS 85 · TOP 0.01% what is this?)
Episode: Kickstarter: Perry Chen (2018)
Pub date: 2018-12-31
In the early 2000s, Perry Chen was trying to put on a concert in New Orleans when he thought, what if fans could fund this in advance? His idea didn't work at the time, but he and his co-founders spent the next eight years refining the concept of crowdfunding creative projects. Today Kickstarter has funded over 155,000 projects worldwide. PLUS for our postscript "How You Built That," we check back in with Dustin Hogard who co-designed a survival belt that's full of tiny gadgets and thin enough to wear every day. (Original Broadcast Date: July 31, 2017.)
See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
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Podcast: How I Built This with Guy Raz (LS 85 · TOP 0.01% what is this?)
Episode: Compaq Computers: Rod Canion (2019)
Pub date: 2019-03-25
In 1981, engineer Rod Canion left Texas Instruments and co-founded Compaq, which created the first IBM-compatible personal computer. This opened the door to an entire industry of PCs that could run the same software. PLUS for our postscript "How You Built That," we check back in with Danica Lause, who turned a knitting hobby into Peekaboos Ponytail Hats: knit caps with strategically placed holes for a ponytail or bun. (Original broadcast date: May 22, 2017).
See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
The podcast and artwork embedded on this page are from Guy Raz | Wondery, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: How I Built This with Guy Raz (LS 85 · TOP 0.01% what is this?)
Episode: Wikipedia: Jimmy Wales (2018)
Pub date: 2020-01-27
During the dot-com boom of the late 1990s, Jimmy Wales was running an internet search company. That's when he began to experiment with the idea of an online encyclopedia. In 2001, Wales launched Wikipedia, a website where thousands of community members could contribute, edit, and monitor content on just about anything. Today, the non-profit has stayed true to its open source roots and is one of the ten most visited websites in the world. PLUS in our post-script "How You Built That," we check back with Leigh D'Angelo, who explains how her sister's break up inspired them to create a dating app—for dog owners.
See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
The podcast and artwork embedded on this page are from Guy Raz | Wondery, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: How I Built This with Guy Raz (LS 85 · TOP 0.01% what is this?)
Episode: Dell Computers: Michael Dell (2018)
Pub date: 2020-01-06
Before it became fashionable to start a tech company in your dorm room, Michael Dell did exactly that. In 1983, he began selling upgrade kits for PC's out of his dorm at UT Austin. A few months later he dropped out of school to focus full time on the PC business. At age of 27, he became the youngest CEO to head a Fortune 500 company. Today, Dell has sold roughly 700 million computers. PLUS in our post-script "How You Built That," we check back with Vanessa and Casey White, who turned their grandfather's pierogi recipe into Jaju Pierogi, hand-made Polish dumplings that are sold across the Northeast.
See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
The podcast and artwork embedded on this page are from Guy Raz | Wondery, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: How I Built This with Guy Raz (LS 85 · TOP 0.01% what is this?)
Episode: Minted: Mariam Naficy
Pub date: 2019-12-09
In 2000, Mariam Naficy sold her first company, an online cosmetics store called Eve.com, for $110 million. Several years later, she got the entrepreneurial itch once again: she founded Minted.com, an online stationery store that solicits designs from artists all over the world. Today Minted is one of the biggest crowdsourcing platforms on the Internet. PLUS in our post-script "How You Built That," we check back with Christopher Rannefors who created BatBnB, a sleek wooden box that hangs on your house and provides a safe home for mosquito-eating bats.
The podcast and artwork embedded on this page are from NPR, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: How I Built This with Guy Raz (LS 85 · TOP 0.01% what is this?)
Episode: Fitbit: James Park
Pub date: 2020-04-27
In 2006, James Park had what he describes as a "lightning bolt" moment when he first used a Nintendo Wii. Fascinated by its motion-tracking controller, James wondered if you could take the technology out of the living room and into the streets. Three years later, he and co-founder Eric Friedman launched the Fitbit Tracker, which allowed users to track their steps and compare progress with others. Sales took off, and Fitbit dominated the wearables market until the Apple Watch came along, forcing James and Eric to re-imagine the brand. Today, against a cloudy economic backdrop, James hopes Fitbit can grow into its role as a health and wellness service.
The podcast and artwork embedded on this page are from NPR, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Rocketship.fm (LS 52 · TOP 0.5% what is this?)
Episode: Product Journeys: Amazon Echo
Pub date: 2020-08-20
Amazon Echo (shortened to Echo) is a brand of smart speakers developed by Amazon. Echo devices connect to the voice-controlled intelligent personal assistant service Alexa, which will respond when you say "Alexa". Users may change this wake word to "Amazon", "Echo" or "Computer". The features of the device include: voice interaction, music playback, making to-do lists, setting alarms, streaming podcasts, and playing audiobooks, in addition to providing weather, traffic and other real-time information. It can also control several smart devices, acting as a home automation hub. The smart speaker needs to use Wi-Fi to connect to Internet, there is no Ethernet port.
According to confirmed reports, Amazon started developing Echo devices inside its Lab126 offices in Silicon Valley and in Cambridge, Massachusetts as early as 2010. The device represented one of first attempts to expand its device portfolio beyond the Kindle e-reader. The Echo featured prominently in Amazon's first-ever Super Bowl broadcast television advertisement in 2016.
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Podcast: Broken Record with Rick Rubin, Malcolm Gladwell, Bruce Headlam and Justin Richmond (LS 69 · TOP 0.05% what is this?)
Episode: Vagabon: Fresh Find
Pub date: 2020-08-18
This week’s Fresh Find, Vagabon, is a self taught, Cameroonian-born musician whose haunting voice and genre defying style has won over critics at both The New Yorker and Pitchfork. In this episode she talks about emigrating from West Africa to Harlem as a teenager, sneaking out of her parent’s house to play D.I.Y. punk shows in Brooklyn, and how bouts of writer’s block can cause her to dream of writing code instead of new music.
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Podcast: Dissect (LS 75 · TOP 0.01% what is this?)
Episode: S5E1 - Kendrick Lamar: DAMN.
Pub date: 2019-10-08
Season 5 is dedicated entirely to Kendrick Lamar’s Pulitzer-Prize winning album DAMN. Today’s episode dives into Lamar’s upbringing in Compton and the developing spiritual beliefs encoded in his early discography. As we’ll come to find out, these beliefs become the basis of the underlying question DAMN. serves to answer: Is it wickedness or weakness?
Say hi @dissectpodcast on Twitter and Instagram. Purchase Dissect merch at https://shop.dissectpodcast.com/. Listen to original Dissect themes on Spotify: https://spoti.fi/2k8BsZM.
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Podcast: Thinking Allowed (LS 58 · TOP 0.5% what is this?)
Episode: Ignorance
Pub date: 2020-07-15
Strategic ignorance and knowledge resistance: Laurie Taylor talks to Mikael Klintman, Professor of Sociology at the University of Lund, Sweden about our capacity for resisting insights from others. At all levels of society, he argues, our world is becoming increasingly dominated by an inability, even refusal, to engage with others' ideas. It does not bode well either for democracy or for science. They're joined by Linsey McGoey, Professor of Sociology at the University at Essex, whose new study explores the use of deliberate and wilful ignorance by elites in pursuit of the retention of power - from News International's hacking scandal to the fire at Grenfell Tower.
Producer: Jayne Egerton
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Podcast: Rebank: Banking the Future (LS 43 · TOP 1.5% what is this?)
Episode: How to Start a Bank with Joel Perlman
Pub date: 2016-11-01
Oaknorth is a startup bank in the UK.
Joel Perlman, Oaknorth's co-founder, is a UK-based serial entrepreneur.
After starting his career at McKinsey, Joel eventually left to co-found Copal Partners, a financial research outsourcing company with his business partner, Rishi Khosla. Over a period of 12 years, they grew the business to almost 3,000 people before selling it to Moody's in 2014.
Subsequently, Joel and Rishi founded OakNorth, a new UK bank focused on lending to entrepreneurs and mid-sized growth companies. From an outsider's perspective, OakNorth seems to approach banking in a pragmatic, entrepreneurial way, seeking cost effective, non-traditional ways to deliver value for customers and outcompete incumbent banks.
Formally launched in 2015, OakNorth is to date the only completely new UK bank to break even, doing so in an amazing 11 months.
The podcast and artwork embedded on this page are from Will Beeson, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Azeem Azhar's Exponential View (LS 60 · TOP 0.5% what is this?)
Episode: Disrupting Finance
Pub date: 2019-05-29
Artificial intelligence is unlocking new value in the banking and finance industry, but incumbents are struggling to keep pace. Azeem Azhar discusses what this means for the industry and its customers with Citi Research’s Global Sector Head for Banks Ronit Ghose, and the founder-CEOs of two leading innovators: Daniel Schreiber of Lemonade Insurance and Rishi Khosla of OakNorth Bank.
Paul and Azeem also discuss:
- The huge role technology debt plays in obstructing legacy banks from innovating.
- How social networks, the smartphone, and AI grew exponentially and converged to mount a challenge to the traditional banking industry.
- The ways AI is cutting friction on the customer side.
- The ability to identify the sources of client or third-party data that can generate an algorithmically-powered image of the client and provide an insight into their ‘real’ level of risk.
Further reading:
- “Digital Disruption: How FinTech is Forcing Banking to a Tipping Point” (Citi Velocity, March 2016)
- “Bank X: The New New Banks” (Citi Velocity, March 2019)
- “The Future Of Banking: Fintech Or Techfin?” (Forbes, Aug. 27, 2018)
- “Bank 4.0 Will Be All-Digital, Low-Overhead, Mobile-First” (Forbes, April 19, 2019)
Ronit Ghose @ronit_ghose
Daniel Schreiber @daschreiber
Rishi Khosla @rishi_khosla
Azeem Azhar @azeem
www.exponentialview.co
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Podcast: Fintech Insider Podcast by 11:FS (LS 53 · TOP 0.5% what is this?)
Episode: Ep. 171. Interview With Rishi Khosla, CEO of OakNorth
Pub date: 2018-01-19
OakNorth isn't perhaps the most high profile Challenger bank in the UK, solving a real problem by serving the 'missing middle' who aren't being served well by existing lenders. Their business began by exploring how they can actually solve this issue, rather than how they could become a great bank- Rishi citing this particular outlook as one of the reasons OakNorth has seen success.
Looking at OakNorth's social impact, their data shows that they've added about 2,000 to 3,000 new jobs through their lending as well as about 2,500 homes. Now perhaps some of these people may not have had a chance if it wasn't for OakNorth's human element. Their lending committee actually analyses the entrepreneurs behind the projects, rather than using a 'computer says no approach.'
Rishi clearly feels OakNorth are still in infancy when looking at their future roadmap, he states that they want to vastly expand the business as they believe they can solve problems in multiple markets. The future certainly seems bright for this challenger bank and their social impact is certainly impressive!
We hope you enjoy the show - spread the word, tell your friends and don't forget to leave us a review on iTunes.
If you want to get in touch, drop us a line at podcasts@11fs.com or on Twitter @FintechInsiders and follow us on Facebook.
Special Guest: Rishi Khosla.
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Podcast: Rebank: Banking the Future (LS 43 · TOP 1.5% what is this?)
Episode: Europe's Most Valuable Fintech with Rishi Khosla
Pub date: 2019-02-27
Rishi Khosla is co-founder and CEO of OakNorth, one of the world's best funded and fastest growing fintechs.
We've had OakNorth on the podcast a few times before. In the past, I've generally referred to OakNorth as a challenger bank, which they were, but the company has developed beyond that description.
From its savings & lending origins, OakNorth has evolved into a world-class technology platform with a banking license, recently backed by investors, including SoftBank, to the tune of $440m, the largest ever fintech investment in Europe.
Prior to starting OakNorth, Rishi and his business partner Joel Perlman founded and sold Copal Amba, an outsourced financial research company acquired by Moodys.
It's energizing talking to great entrepreneurs like Rishi. I thoroughly enjoyed this conversation, and I hope you do too.
Thank you very much for joining us today. Please welcome, Rishi Khosla.
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Podcast: The Reith Lectures (LS 55 · TOP 0.5% what is this?)
Episode: Reith Revisited: Michael Sandel on Bertrand Russell
Pub date: 2017-09-27
Sarah Montague and Michael Sandel look back at the inaugural Reith Lectures given in 1948 and 1949 by the philosopher Bertrand Russell.
In Reith Revisited, Radio 4 assesses the contributions of great minds of the past to public debate, in a dialogue across the decades with contemporary thinkers. In 1948, households across Britain gathered before the wireless as the pre-eminent public intellectual of the age, the philosopher Bertrand Russell delivered a set of lectures in honour of the BBC's founder, Lord Reith. Since then, the Reith Lectures on the Home Service and subsequently Radio 4 have become a major national occasion for intellectual debate. In this series Radio 4 revisits five of the speakers from the first ten years of the Reith Lectures.
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Podcast: Cambridge Law: Public Lectures from the Faculty of Law (LS 38 · TOP 2.5% what is this?)
Episode: 'The Rule of Law': The 2006 Sir David Williams Lecture
Pub date: 2018-08-29
On Thursday 16th November 2006, The Rt. Hon Lord Bingham of Cornhill KG, House of Lords delivered the 2006 Sir David Williams Lecture entitled "The Rule of Law".
The Sir David Williams Lecture is an annual address delivered by a guest lecturer in honour of Sir David Williams, Emeritus Rouse Ball Professor of English Law and Emeritus Vice-Chancellor of Cambridge University.
More information about this lecture, including photographs from the event, is available from the Centre for Public Law website at:
https://www.cpl.law.cam.ac.uk/sir-david-williams-lectures/rt-hon-lord-bingham-cornhill-kg-rule-law
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Podcast: Broken Record with Rick Rubin, Malcolm Gladwell, Bruce Headlam and Justin Richmond (LS 69 · TOP 0.05% what is this?)
Episode: Beastie Boys and Spike Jonze
Pub date: 2020-06-23
It's been nearly 35 years since the Beastie Boys released their classic debut album, Licensed To Ill. In this candid conversation, Rick Rubin, who started out as the Beastie's DJ, reconnects with Mike D and Ad-Rock. Spike Jonze, who directed the new Beastie Boys documentary, Beastie Boys Story, also sits in and plays moderator. It's been nearly 20 years since Ad-Rock and Rick have talked and like old friends, they jump right into a slew of inside jokes and hilarious memories of their lives leading up to the release of Licensed to Ill.
Subscribe to Broken Record's YouTube channel to hear old and new interviews, often with bonus content: https://www.youtube.com/brokenrecordpodcast
You can also check out past episodes here: https://brokenrecordpodcast.com/
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Podcast: Secret Leaders (LS 50 · TOP 1% what is this?)
Episode: OakNorth: The pathway to building a unicorn with Rishi Khosla
Pub date: 2020-02-11
Today’s guest has been a high achiever since he was a kid, completing his computer science GCSE at 11, his A-levels at 13, and receiving a Masters from LSE by the time he was 20. Some child geniuses burnout before they achieve great things, not Rishi Khosla.
Rishi, according to LL Cool J, is something of a phenomenon in the world of entrepreneurship, and we are wont to agree with him.
Co-founding OakNorth, Europe's highest valued FinTech company having raised over a billion dollars in funding to date, in only four years, Rishi is considered one of the stars of the industry.
And OakNorth is just his most recent headline.
He’s an early stage investor, investing in several businesses including PayPal and Indiabulls. He’s already co-founded a company, Copal Amba, a financial research firm which was scaled to 3,000 employees and sold to Moody’s Corporation in 2014. And he helped establish the family office of the steel magnate Lakshmi Mittal amongst many things. With OakNorth he estimates they’ve created about 12,000 new jobs in the UK and 13,000 new homes.
And has anyone ever thanked him?
“How can I put it? I guess, I've always been sprinting through life, recognition hasn't been what I focused on. I focused on results.
His advice for aspiring entrepreneurs? You need to have enough hunger inside of you, you’ve got to have fire in your stomach, to say ‘there’s only Plan A’.
We chat about:
- Working with Jack Welch at GE
- Establishing the family office of Lakshmi Mittal
- Meeting Peter Teal and Elon Musk
- Building Copal with business partner Joel, brick by brick
- Meeting Son at SoftBank and how he pitched him
- Why only 5% of unicorns are profitable
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The podcast and artwork embedded on this page are from Kindling Media, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Monocle 24: The Monocle Weekly (LS 45 · TOP 1% what is this?)
Episode: The Thinker’s Edition: politics and risk
Pub date: 2020-07-29
In the third instalment of our special new series, Andrew Mueller hosts a panel of experts and philosophers to discuss politics, risk and how the political landscape will look beyond coronavirus. Joining us this week is political scientist and president of Eurasia Group, Ian Bremmer; philosopher and political theorist, Phillip Pettit, and Associate Professor of Political Theory at The University of Oxford, Teresa M Bejan.
The podcast and artwork embedded on this page are from Monocle, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Audio Long Read (LS 64 · TOP 0.1% what is this?)
Episode: What's wrong with WhatsApp
Pub date: 2020-07-24
As social media has become more inhospitable, the appeal of private online groups has grown. But they hold their own dangers – to those both inside and out. By William Davies. Help support our independent journalism at theguardian.com/longreadpod
The podcast and artwork embedded on this page are from The Guardian, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Inside Intercom Podcast (LS 46 · TOP 1% what is this?)
Episode: Intercom on Product: Why making every day count is key to progress
Pub date: 2020-02-18
In our ninth episode of Intercom on Product, Des Traynor and Paul Adams discuss the importance of efficiency to good product principle and why making every day count is key to individual and team progress.
See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
The podcast and artwork embedded on this page are from Intercom, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Inside Intercom Podcast (LS 46 · TOP 1% what is this?)
Episode: Lean UX author Jeff Gothelf on why design must have a seat at the table
Pub date: 2019-08-15
Author and consultant, Jeff Gothelf, joins Intercom's Dee Reddy for a chat that ranged from strategies for making design part of a company’s DNA to the lessons we can learn from the human cannonball at a circus.
See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
The podcast and artwork embedded on this page are from Intercom, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: UI Breakfast: UI/UX Design and Product Strategy (LS 47 · TOP 1% what is this?)
Episode: Episode 137: Lean, Agile & Design Thinking with Jeff Gothelf
Pub date: 2019-05-03
There are so many design methodologies available these days — lean, agile & design thinking being the most popular. Could you use them side-by-side? Our guest today is Jeff Gothelf, author of Lean UX and Sense & Respond, and co-founder of Sense & Respond Press. You'll learn how to make the most out of these frameworks, help teams talk to each other, and measure customer outcomes (instead of your effort) using the right behavior metrics.
Podcast feed: subscribe to http://simplecast.fm/podcasts/1441/rss in your favorite podcast app, and follow us on iTunes, Stitcher, or Google Play Music.
Show Notes * Lean UX, Sense & Respond, Lean vs Agile vs Design Thinking — Jeff's books * Sense & Respond Press — Jeff's publishing house together with Josh Seiden * Making Progress, Hire Women — some of the latest books by Sense & Respond Press * Agile vs Lean vs Design Thinking — Jeff's original article * Episode 131: Design Sprint with Jonathan Courtney * AARRR! Dave McClure’s “Pirate Metrics And The Only Five Numbers That Matter — an article by Walter Chen * Jeff's website * Follow Jeff on Twitter: @jboogie
Today's Sponsor This episode is brought to you by Abstract — design workflow management for product design teams using Sketch. No more searching for the right version of the file, exporting and importing between tools, or trying to consolidate feedback. Now everything is in one place! Sign your team up for a free 30-day trial today by heading over to abstract.com.
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Leave a Review Reviews are hugely important because they help new people discover this podcast. If you enjoyed listening to this episode, please leave a review on iTunes. Here's how.
The podcast and artwork embedded on this page are from Jane Portman, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Product Experience (LS 40 · TOP 2% what is this?)
Episode: Shape Up – Ryan Singer on The Product Experience
Pub date: 2019-10-09
After starting out as an agency (37 Signals), those at Basecamp realised that the tools it used to manage its own projects would have value for others. What followed was a bootstrapped journey to creating an entire suite of products, using Basecamp’s own approach on how to work well together. Over the years, the company [...] Read more »
The post Shape Up – Ryan Singer on The Product Experience appeared first on Mind the Product.
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Podcast: Dollars to Donuts (LS 40 · TOP 2% what is this?)
Episode: 17. Tomer Sharon of Goldman Sachs
Pub date: 2019-03-24
In this episode of Dollars to Donuts, I talk with Tomer Sharon, the Head of User Research and Metrics at Goldman Sachs. We talk about how to assess potential hires for user research positions, infrastructure for capturing and searching a body of data, and developing a practice inside a willing, yet large, organization.
Some parts of kind of pure research are creative. Probably the biggest one is translating a set of questions that a team has into okay, what are we going to do to get answers? If it was that easy to come up with an answer to that, then anybody could do that well. That’s not the case. A lot of people are having a lot of trouble with that part. So, I think that’s a creative part. You’re not going to see a beautiful painting coming out of that, but it is creative. – Tomer Sharon
Show Links
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Tomer on LinkedIn
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Tomer on Twitter
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Goldman Sachs
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WeWork
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It’s OUR Research on Twitter
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It’s OUR Research on Amazon
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Validating Product Ideas Through Lean User Research
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Goldman Sachs Private Wealth Management
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Marcus by Goldman Sachs
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UserZoom
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UserTesting
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OKRs
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ResearchOps
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Democratizing UX (and Polaris)
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Masters In Human Factors at Bentley
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Adam Neumann, WeWork CEO
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Key Experience Indicators: How to decide what to measure? (Medium)
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Google’s HEART Framework for Measuring UX
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Face of Finance NYC 2019
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User Research London 2019
Follow Dollars to Donuts on Twitter and help other listeners find the podcast by leaving a review on iTunes.
Transcript
Steve Portigal: Greetings, humans! Thanks for listening to Dollars to Donuts, the podcast where I talk to people who lead user research in their organization.
Over the past while I’ve been putting together a household emergency kit. It’s primarily shopping exercise, and I’ve ordered a hand crank and solar powered radio, a replacement for matches, latex gloves, bandages, and air filter masks (which we made use of during a period of dangerously poor air quality recently). The last step was getting some food that will last – cans of soup and stew, crackers, single-serve breakfast cereals. There’s something satisfying about acquiring a bunch of stuff and storing it away, somewhat organized. And that led to a stray thought that I noticed – “Oh,
The podcast and artwork embedded on this page are from Steve Portigal, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Product Experience (LS 40 · TOP 2% what is this?)
Episode: Keeping Track Of Your HEART – Tomer Sharon on The Product Experience
Pub date: 2020-01-15
There’s a lot of debate in the product and research communities about quantitative vs qualitative approaches to metrics, so we sat down with Tomer Sharon (MD/Head of User Research and Metrics at Goldman Sachs) to talk about how he uses Google’s HEART approach to track what matters. Formerly of Google and WeWork, Tomer talks about [...] Read more »
The post Keeping Track Of Your HEART – Tomer Sharon on The Product Experience appeared first on Mind the Product.
The podcast and artwork embedded on this page are from Mind the Product, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Monocle 24: Monocle on Culture (LS 42 · TOP 1.5% what is this?)
Episode: ‘The Booksellers’
Pub date: 2020-07-13
We speak to DW Young and Judith Mizrachy, the director and producer of new documentary ‘The Booksellers’, which offers a glimpse into the fascinating world of New York’s book collectors and traders.
The podcast and artwork embedded on this page are from Monocle, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Rework (LS 50 · TOP 1% what is this?)
Episode: Shape Up Roundtable
Pub date: 2019-07-30
Last week's episode, an introduction to Shape Up - 00:11
Shape Up: Stop Running in Circles and Ship Work that Matters by Ryan Singer - 00:18
Ryan Singer on Twitter - 1:18
Conor Muirhead on Twitter - 1:22
Jeff Hardy on Twitter - 1:27
"Get One Piece Done" - 5:08
"Affordances before pixel-perfect screens" - 10:02
"Work is like a hill" - 12:44
"The circuit breaker" - 21:16
"Risks and Rabbit Holes" - 22:15
"Watch out for grab-bags" - 23:43
"Decide When to Stop" - 25:50
"Bets, Not Backlogs" - 28:26
The podcast and artwork embedded on this page are from Basecamp, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Fintech Insider Podcast by 11:FS (LS 53 · TOP 0.5% what is this?)
Episode: 444. Insights: African fintechs: transforming the continent's financial landscape
Pub date: 2020-07-17
Simon Taylor is joined by a fantastic panel of guests to discuss the transformation of finance in Africa and how fintechs are building up the industry. Here to discuss the matter are:
- Benjamin Fernandes - CEO, NALA
- Fara Ashiru Jituboh - Co-Founder & CEO/CTO, Okra
- Akeem Lawal - Divisional Chief Executive Officer for Transaction Processing and Enablement Business, Interswitch
In the past couple of decades, the introduction of mobile money, digital payments channels and APIs has started to reshape finance in the world’s youngest and fastest-growing continent. Focusing on East and West sub-Saharan Africa, in this show, Simon and guests discuss the concept of "leap-frogging", growing foreign interest in the African fintech market, and the implications and limitations of Africa's financial revolution for everyday people. Moreover, as the continent makes greater strides towards a digital financial future, who is still left out and what more can be done to ensure fair and equal access to the financial system?
Fintech Insider by 11:FS is a podcast dedicated to all things fintech, banking, technology and financial services. Hosted by a rotation of 11:FS experts including David Brear, Simon Taylor, Jason Bates, Leda Glyptis and Sarah Kocianski and joined by a range of brilliant guests, we cover the latest global news, bring you interviews from industry experts or take a deep dive into subject matters such as APIs, AI or digital banking.
If you enjoyed this episode, don't forget to subscribe and please leave a review
Follow us on Twitter: www.twitter.com/fintechinsiders where you can ask the hosts questions, alternatively email podcasts@11fs.com!
Special Guests: Akeem Lawal, Benjamin Fernandes , and Fara Ashiru Jituboh.
The podcast and artwork embedded on this page are from 11:FS, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Rework (LS 50 · TOP 1% what is this?)
Episode: Shape Up with Ryan Singer
Pub date: 2019-07-23
Books by Basecamp - 00:13
Shape Up: Stop Running in Circles and Ship Work that Matters - 00:17
Ryan on Twitter - 00:19
Breadboard on Wikipedia and in Shape Up - 6:30
Forrest M. Mims III on Wikipedia - 7:00
"Planning is Guessing," our episode featuring Jason Fried on six-week cycles (his segment starts at 10:03). See also his Signal v. Noise post, "What six weeks of work looks like" - 13:20
"Bets, not Backlogs," a chapter in Shape Up - 16:17
The section on appetite in Shape Up - 17:13
The section on the circuit breaker in Shape Up - 19:16
The podcast and artwork embedded on this page are from Basecamp, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Knowledge Project with Shane Parrish (LS 69 · TOP 0.05% what is this?)
Episode: #3 Sanjay Bakshi: Why Mental Models
Pub date: 2015-09-18
In this episode, I chat with professor and value investing genius Sanjay Bakshi about the power of mental models, multidisciplinary thinking, reading, and acquiring worldly wisdom.
Go Premium: Members get early access, ad-free episodes, hand-edited transcripts, searchable transcripts, member-only episodes, and more. Sign up at: https://fs.blog/membership/
Every Sunday our newsletter shares timeless insights and ideas that you can use at work and home. Add it to your inbox: https://fs.blog/newsletter/
Follow Shane on Twitter at: https://twitter.com/ShaneAParrish
The podcast and artwork embedded on this page are from Farnam Street, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: In Our Time (LS 75 · TOP 0.05% what is this?)
Episode: Lakshmi
Pub date: 2016-10-06
Melvyn Bragg and guests discuss the origins of the Hindu goddess Lakshmi, and of the traditions that have built around her for over 3,000 years. According to the creation story of the Puranas, she came to existence in the churning of the ocean of milk. Her prominent status grew alongside other goddesses in the mainly male world of the Vedas, as female deities came to be seen as the Shakti, the energy of the gods, without which they would be powerless. Lakshmi came to represent the qualities of blessing, prosperity, fertility, beauty and good fortune and, more recently, political order, and she has a significant role in Diwali, one of the most important of the Hindu festivals.
With
Jessica Frazier Lecturer in Religious Studies at the University of Kent Research Fellow at the Oxford Centre for Hindu Studies at the University of Oxford
Jacqueline Suthren-Hirst Senior Lecturer in South Asian Studies at the University of Manchester
and
Chakravarthi Ram-Prasad Professor of Comparative Religion and Philosophy at Lancaster University
Producer: Simon Tillotson.
The podcast and artwork embedded on this page are from BBC Radio 4, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: In Our Time (LS 75 · TOP 0.05% what is this?)
Episode: Nietzsche's Genealogy of Morality
Pub date: 2017-01-12
Melvyn Bragg and guests discuss Nietzsche's On The Genealogy of Morality - A Polemic, which he published in 1887 towards the end of his working life and in which he considered the price humans have paid, and were still paying, to become civilised. In three essays, he argued that having a guilty conscience was the price of living in society with other humans. He suggested that Christian morality, with its consideration for others, grew as an act of revenge by the weak against their masters, 'the blond beasts of prey', as he calls them, and the price for that slaves' revolt was endless self-loathing. These and other ideas were picked up by later thinkers, perhaps most significantly by Sigmund Freud who further explored the tensions between civilisation and the individual.
With
Stephen Mulhall Professor of Philosophy and a Fellow and Tutor at New College, University of Oxford
Fiona Hughes Senior Lecturer in Philosophy at the University of Essex
And
Keith Ansell-Pearson Professor of Philosophy at the University of Warwick
Producer: Simon Tillotson.
The podcast and artwork embedded on this page are from BBC Radio 4, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: In Our Time (LS 75 · TOP 0.05% what is this?)
Episode: Kant's Categorical Imperative
Pub date: 2017-09-21
Melvyn Bragg and guests discuss how, in the Enlightenment, Immanuel Kant (1724-1804) sought to define the difference between right and wrong by applying reason, looking at the intention behind actions rather than at consequences. He was inspired to find moral laws by natural philosophers such as Newton and Leibniz, who had used reason rather than emotion to analyse the world around them and had identified laws of nature. Kant argued that when someone was doing the right thing, that person was doing what was the universal law for everyone, a formulation that has been influential on moral philosophy ever since and is known as the Categorical Imperative. Arguably even more influential was one of his reformulations, echoed in The Universal Declaration of Human Rights, in which he asserted that humanity has a value of an entirely different kind from that placed on commodities. Kant argued that simply existing as a human being was valuable in itself, so that every human owed moral responsibilities to other humans and was owed responsibilities in turn.
With
Alison Hills Professor of Philosophy at St John's College, Oxford
David Oderberg Professor of Philosophy at the University of Reading
and
John Callanan Senior Lecturer in Philosophy at King's College, London
Producer: Simon Tillotson.
The podcast and artwork embedded on this page are from BBC Radio 4, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Rocketship.fm (LS 52 · TOP 0.5% what is this?)
Episode: Mental Models for Product Leaders
Pub date: 2019-08-15
Mental models are simple expressions of complex processes or relationships. These models are accumulated over time by an individual and used to make faster and better decisions. Today to explore Mental Models for Product Leadership and how to use them with your team every day.
This episode is also brought to you by Airtable, which is the all-in-one platform for product managers. Rocketship listeners can receive $50 in credit by signing up at Airtable.com/rocketship.
This episode is also brought to you by DigitalOcean, the cloud platform that makes it easy for startups to launch high performance modern apps and websites. Learn more about DigitalOcean and apply for Hatch at do.co/rocketship.
This episode is also brought to you by .tech, where you can secure your .tech domain name today. Rocketship listeners can receive a 90% discount on their .tech domain names by going to go.tech/rocketship and using coupon code ROCKETSHIP.
This show is a part of the Podglomerate network, a company that produces, distributes, and monetizes podcasts. We encourage you to visit the website and sign up for our newsletter for more information about our shows, launches, and events. For more information on how The Podglomerate treats data, please see our Privacy Policy.
Since you're listening to Rocketship, we'd like to suggest you also try other Podglomerate shows surrounding entrepreneurship, business, and careers like Creative Elements and Freelance to Founder.
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The podcast and artwork embedded on this page are from Rocketship / The Podglomerate, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Rocketship.fm (LS 52 · TOP 0.5% what is this?)
Episode: Product Journeys: Square
Pub date: 2020-04-23
Square was founded to solve the pain point of a well known glass blower in San Francisco who lost a sale because he couldn't accept a credit card transaction. The prototype was built in a month, and Jack raised $600 just by charging VC's to hear his pitch. Today we walk you through the Product Journey that is Square with their Hardware Lead, Jesse Dorogusker, who left Apple to join the team in 2011.
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This episode is brought to you by:
Product Institute is an online course for new and tenured product managers. Head to productinstitute.com and enter the code ROCKET at checkout, you'll receive $200 off your subscription.
LinkedIn Jobs will match the right talent with your open roll, fast. Head to Linkedin.com/rocketship to get $50 off your first job post.
Participate builds and hosts online learning communities that inspire learning, connection and growth. Head to participate.com/rocketship for a free virtual learning workshop, valued at $1,000.
Digital Ocean is a cloud provider that makes it easy for entrepreneurs and startups to deploy and scale web applications with no issues and unplanned costs. Get started for free at do.co/rocketship.
Rocketship is brought to you by The Podglomerate.
This show is a part of the Podglomerate network, a company that produces, distributes, and monetizes podcasts. We encourage you to visit the website and sign up for our newsletter for more information about our shows, launches, and events. For more information on how The Podglomerate treats data, please see our Privacy Policy.
Since you're listening to Rocketship, we'd like to suggest you also try other Podglomerate shows surrounding entrepreneurship, business, and careers like Creative Elements and Freelance to Founder.
Learn more about your ad choices. Visit megaphone.fm/adchoices
The podcast and artwork embedded on this page are from Rocketship / The Podglomerate, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Rocketship.fm (LS 52 · TOP 0.5% what is this?)
Episode: Shape Up with Ryan Singer of Basecamp
Pub date: 2019-08-22
Ryan Singer and the Basecamp team have developed their own methodology for product planning. For one, they don't do waterfall or agile or scrum. For two, they don’t line walls with Post-it notes. For three, they don’t do daily stand ups, design sprints, development sprints, or anything remotely tied to a metaphor that includes being tired and worn out at the end. No backlogs, no Kanban, no velocity tracking, none of that.
What they do instead is six week product builds followed by two weeks of cooling. Their leadership team decides how long they want to spend on tasks vs working with product estimates and if they work isn't done, it's thrown out.
Today we sit down with Ryan Singer and learn more about his most recent book Shape Up which outlines this process. Read the full book at http://basecamp.com/shapeup. You can also join Ryan and Bob Moesta at their upcoming product seminar.
This episode is brought to you by Gusto, making payroll, benefits, and HR easy for modern small businesses. Rocketship listeners get three months free at gusto.com/rocketship.
This episode is brought to you by Phrase. Do you have a product that you want to take to a global audience? Phrase will be your single point of truth for all of your translation projects. Go to phrase.com/rocketship to get your free Phrase account today.
This episode is also brought to you by DigitalOcean, the cloud platform that makes it easy for startups to launch high performance modern apps and websites. Learn more about DigitalOcean and apply for Hatch at do.co/rocketship.
This episode is also brought to you by .tech, where you can secure your .tech domain name today. Rocketship listeners can receive a 90% discount on their .tech domain names by going to go.tech/rocketship and using coupon code ROCKETSHIP.
This show is a part of the Podglomerate network, a company that produces, distributes, and monetizes podcasts. We encourage you to visit the website and sign up for our newsletter for more information about our shows, launches, and events. For more information on how The Podglomerate treats data, please see our Privacy Policy.
Since you're listening to Rocketship, we'd like to suggest you also try other Podglomerate shows surrounding entrepreneurship, business, and careers like Creative Elements and Freelance to Founder.
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The podcast and artwork embedded on this page are from Rocketship / The Podglomerate, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Disruptive Voice (LS 39 · TOP 2% what is this?)
Episode: 29. Intercom, and Architecting a Company on Jobs to Be Done
Pub date: 2019-03-04
Continuing our focus on Jobs to Be Done, Derek is joined by Intercom Co-founder and Chief Strategy Officer Des Traynor, to discuss how Intercom's model was built from the ground up with the framework. Be sure to take a look at Intercom's helpful JTBD resources.
The podcast and artwork embedded on this page are from Clay Christensen's Forum for Growth and Innovation at the Harvard Business, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Rocketship.fm (LS 52 · TOP 0.5% what is this?)
Episode: Product Journeys: Hey!
Pub date: 2020-07-09
Hey! a new email platform, many opinions...and it's creators, Jason Fried, David Heinemeier Hansson (known to the internet as DHH, the creator of Ruby on Rails) and the Basecamp team, have even more.
Hey! launched just a couple weeks ago to a flurry of controversy. From it's firm stance on tracking pixels (perceived as spy devices) to it's pre-launch fight with Apple over revenue splits, Hey! is probably the most exciting thing to happen to email since the launch of gmail over 16 years ago.
Today we take you through the ideation and launch of Hey! through the eyes of it's creators Jason Fried and DHH.
This episode is brought to you by:
Digital Ocean is a cloud provider that makes it easy for entrepreneurs and startups to deploy and scale web applications with no issues and unplanned costs. Get started for free at do.co/rocketship.
Logi Analytics is the leading platform for embedded analytics. Take your dashboard and reports to the next level. Rocketship listeners get free access to the Logi Analytics library of product demos by going to logianalytics.com/rocketship.
Earth Class Mail scans and digitizes you physical mail and makes it available to you on mobile or desktop or even Google Drive or Dropbox. Rocketship listeners get 25% off their subscription for the first 3 months by going to earthclassmail.com/rocketship.
Rocketship is brought to you by The Podglomerate.
This show is a part of the Podglomerate network, a company that produces, distributes, and monetizes podcasts. We encourage you to visit the website and sign up for our newsletter for more information about our shows, launches, and events. For more information on how The Podglomerate treats data, please see our Privacy Policy.
Since you're listening to Rocketship, we'd like to suggest you also try other Podglomerate shows surrounding entrepreneurship, business, and careers like Creative Elements and Freelance to Founder.
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The podcast and artwork embedded on this page are from Rocketship / The Podglomerate, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The Food Chain (LS 56 · TOP 0.5% what is this?)
Episode: Samin Nosrat: My life in five dishes
Pub date: 2019-12-26
The award-winning star of Netflix series 'Salt, Fat, Acid Heat' and author of the best-selling cookbook of the same name tells us about her life through five of her most memorable dishes.
The Iranian-American writer and cook has enjoyed a meteoric rise to fame in the last few years, but has struggled to come to terms with that success and says she still feels like an impostor and outsider. She very nearly took a completely different career path - she tells Emily Thomas that her dream was always to be a poet until a magical experience at a fine-dining restaurant changed everything.
Even now, though, she doesn't aspire to run a restaurant or establish a culinary empire - she doesn't like the person she becomes when put in charge of a team of chefs.
This episode was recorded at The Cookery School at Little Portland Street and was first broadcast on 30 May 2019.
(Picture: Samin Nosrat. Credit: BBC)
The podcast and artwork embedded on this page are from BBC World Service, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: How I Built This with Guy Raz (LS 85 · TOP 0.01% what is this?)
Episode: How I Built Resilience: Live with Samin Nosrat and Alice Waters & Fanny Singer
Pub date: 2020-05-14
Samin Nosrat, the author of Salt, Fat, Acid, Heat, talks with Guy about unintentionally writing the ultimate quarantine cookbook, and how she's been inspired by the camaraderie among fellow home cooks. Chez Panisse founder Alice Waters and her daughter Fanny Singer tell Guy some tips for growing a victory garden and helping local farmers stay in business. These conversations are excerpts from our How I Built Resilience series, where Guy talks online with founders and entrepreneurs about how they're navigating these turbulent times.
The podcast and artwork embedded on this page are from NPR, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Monocle 24: The Menu (LS 43 · TOP 1.5% what is this?)
Episode: Food Neighbourhoods 180: Recipe edition, Samin Nosrat
Pub date: 2020-04-14
Continuing our series of recipes from some of the world’s best chefs, we hear from US chef, author and broadcaster Samin Nosrat.
The podcast and artwork embedded on this page are from Monocle, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Home Cooking (LS 70 · TOP 0.05% what is this?)
Episode: Bean There, Done That (with Josh Malina)
Pub date: 2020-03-27
Quarantine cooking is no joke—or is it? In our inaugural episode, Hrishi makes one too many puns, Samin answers the eternal bean question (to soak or not to soak), and Josh Malina (@JoshMalina) schools us in the art of the latke.
Visit homecooking.show for a complete list of foods discussed in the episode, as well as cooking resources and a downloadable dancing can!
The podcast and artwork embedded on this page are from Samin Nosrat & Hrishikesh Hirway, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Rocketship.fm (LS 52 · TOP 0.5% what is this?)
Episode: Charlie Munger's Mental Models
Pub date: 2019-07-18
Mental models are how we understand the world. Not only do they shape what we think and how we understand but they shape the connections and opportunities that we see. Mental models are how we simplify complexity, why we consider some things more relevant than others, and how we reason.
A mental model is simply a representation of how something works. We cannot keep all of the details of the world in our brains, so we use models to simplify the complex into understandable and organizable chunks.
Today we explore some of Charlie Munger's famous mental models as outlined in his speech "The Psychology of Human Misjudgment."
This episode is brought to you by Gusto, making payroll, benefits, and HR easy for modern small businesses. Rocketship listeners get three months free at gusto.com/rocketship.
This episode is also brought to you by Airtable, which is the all-in-one platform for product managers. Rocketship listeners can receive $50 in credit by signing up at Airtable.com/rocketship.
This episode is also brought to you by DigitalOcean, the cloud platform that makes it easy for startups to launch high performance modern apps and websites. Learn more about DigitalOcean and apply for Hatch at do.co/rocketship.
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Podcast: Developer Tea (LS 57 · TOP 0.5% what is this?)
Episode: Mental Models w/ Gabriel Weinberg, CEO of DuckDuckGo (part 2)
Pub date: 2019-05-31
Today's guest, Gabriel Weinberg, the CEO of DuckDuckGo uses connections to help steer the company. What we're talking about today with Gabriel are mental models for building a team and business.
In part 2 of this interview, we dive deeper into Gabriel's mental models specifically for engineers. His book, Super Thinking, which we base the discussion on can be found here: Super Thinking.
Get in touch If you have questions about today's episode, want to start a conversation about today's topic or just want to let us know if you found this episode valuable I encourage you to join the conversation or start your own on our community platform Spectrum.chat/specfm/developer-tea
Leave a Review If you're enjoying the show and want to support the content head over to iTunes and leave a review! It helps other developers discover the show and keep us focused on what matters to you.
Subscribe to the Tea Break Challenge This is a daily challenge designed help you become more self-aware and be a better developer so you can have a positive impact on the people around you. Check it out and give it a try at https://www.teabreakchallenge.com/.
Thanks to today's sponsor: GitPrime Our sponsor GitPrime published a free book - 20 Patterns to Watch for Engineering Teams - based data from thousands of enterprise engineering teams. It’s an excellent field guide to help debug your development with data.
Go to GitPrime.com/20Patterns to download the book and get a printed copy mailed to you - for free. Check it out at GitPrime.com/20Patterns.
The podcast and artwork embedded on this page are from Jonathan Cutrell, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: Developer Tea (LS 57 · TOP 0.5% what is this?)
Episode: Mental Models w/ Gabriel Weinberg (part 1)
Pub date: 2019-05-29
Today's guest, Gabriel Weinberg, the CEO of DuckDuckGo uses connections to help steer the company. What we're talking about today with Gabriel are mental models for building a team and business.
In part 1 of this interview, we dive into Gabriel's recent book, Super Thinking. This is a big book of mental models. Don't miss part two of this interview, airing on Friday, May 31st.
Get in touch If you have questions about today's episode, want to start a conversation about today's topic or just want to let us know if you found this episode valuable I encourage you to join the conversation or start your own on our community platform Spectrum.chat/specfm/developer-tea
Leave a Review If you're enjoying the show and want to support the content head over to iTunes and leave a review! It helps other developers discover the show and keep us focused on what matters to you.
Subscribe to the Tea Break Challenge This is a daily challenge designed help you become more self-aware and be a better developer so you can have a positive impact on the people around you. Check it out and give it a try at https://www.teabreakchallenge.com/.
Thanks to today's sponsor: Sentry Sentry tells you about errors in your code before your customers have a chance to encounter them.
Not only do we tell you about them, we also give you all the details you’ll need to be able to fix them. You’ll see exactly how many users have been impacted by a bug, the stack trace, the commit that the error was released as part of, the engineer who wrote the line of code that is currently busted, and a lot more.
Give it a try for yourself at Sentry.io
The podcast and artwork embedded on this page are from Jonathan Cutrell, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Podcast: The LEADx Leadership Show with Kevin Kruse (LS 56 · TOP 0.5% what is this?)
Episode: Mental Models | Gabriel Weinberg
Pub date: 2019-08-12
Gabriel Weinberg is the CEO & Founder of DuckDuckGo, the Internet privacy company that empowers you to seamlessly take control of your personal information online, without any tradeoffs. Since 2008, Weinberg has grown DuckDuckGo from a self-funded operation out of his dusty basement into a business with over $25 million in revenue and 50 employees across multiple continents. Weinberg is a serial entrepreneur who previously founded other Internet-related companies; he is also an active angel investor. He is the author of Super Thinking: The Big Book of Mental Models.
The podcast and artwork embedded on this page are from Kevin Kruse, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.