Machine Learning – Software Engineering Daily: Recent Episodes
Machine Learning – Software Engineering Daily
SED News is a monthly podcast from Software Engineering Daily where hosts Gregor Vand and Sean Falconer break down the biggest stories shaping software engineering, Silicon Valley, and the broader tech industry.
In this episode, Gregor and Sean dig into a wave of “runaway AI” stories, including Anthropic and OpenAI disclosing that their models accessed outside organizations during cyber evaluations, and Amazon reporting a staggering budget overrun blamed on bad agent loops. They explore why most of these incidents trace back to human decisions rather than models breaking free, and the awkward reality that today’s systems can bill you for tokens without reliably counting them.
They also talk about the “Kimi moment.” Moonshot AI‘s open weight model has closed the gap with frontier models like ChatGPT and Claude at a remarkable pace, and the hosts unpack what it means for open weight strategies and how chip scarcity is pushing Chinese labs to innovate.
As always, the episode wraps up with a few standout Hacker News threads, including a JetBrains test of a “caveman speak” skill that promised big token savings, how refactoring can cut input token costs, the release of CodePen 2.0, and a build of Doom that renders through SQL queries.
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The terminal has been a constant in software development for decades. It has remained largely unchanged while everything around it transformed. However, as AI agents have become central to the developer workflow, the terminal is emerging as a natural home for agentic development, and a new category of tooling is forming around it.
Warp is a popular Rust-based terminal and agentic development environment. The company recently open-sourced its codebase and launched Oz, its cloud agent infrastructure product aimed at helping enterprises automate software development at scale.
Zach Lloyd is the co-founder and CEO of Warp, and a former principal engineer at Google where he led engineering on the Google Docs suite. In this episode, Zach joins Gregor Vand to discuss how Warp has evolved over the years, why the terminal is better suited than the IDE for agentic development, how Oz approaches the governance and auditability challenges enterprises face with AI agents, and more.
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Open source software underpins virtually every modern application. That ubiquity is a superpower for developers, but it is also an expanding attack surface. Software supply chain attacks were once rare but are now happening daily, with malicious actors exploiting the trust developers place in public registries, package managers, and CI/CD pipelines.
Chainguard is a secure software supply chain platform. The company started with hardened container images and has expanded to cover domains including VMs, language libraries, GitHub Actions, and agent skills.
Matt Moore is a co-founder and CTO of Chainguard, and a veteran of Google’s open source, container, and security infrastructure work. In this episode, Matt joins Gregor Vand to discuss lessons from recent supply chain attacks, why CI/CD pipelines are now a primary attack surface, the challenge of meaningful software inventories, the EU Cyber Resilience Act, and what the arrival of Anthropic’s Mythos model means for the pace of vulnerability discovery and the urgency of patching at machine speed.
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The most useful coding agents can mutate their environments by downloading packages, writing files, and connecting to services across the network. However, that freedom also presents dangers, and promises to usher in a new wave of security threats.
Docker recently announced Docker Sandboxes, which give each agent its own isolated micro VM while preserving the familiar ergonomics of a container. A standard container shares the host’s kernel, but a micro VM emulates hardware and runs its own kernel, giving a stronger security boundary around code that cannot be trusted.
Mark Cavage is the President and COO of Docker, and he previously worked at companies including Stripe, AWS and Oracle. In this episode, Mark joins Gregor Vand for a wide-ranging conversation that includes why agents break the immutability assumptions containers were built on, how micro VMs differ from both containers and traditional VMs, and the still-unsolved challenge of giving agents scoped, trustworthy access to sensitive services and data.
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Golden Gate Ventures is one of Southeast Asia’s most established early-stage venture firms, having backed companies across the region since 2011. They have invested in companies that are now household names in the region. Examples include Gojek, which started as a motorcycle taxi service in Indonesia and grew into a super app spanning payments, food delivery, and logistics, and Grab, which began as a ride-hailing service in Singapore and similarly expanded across Southeast Asia into food delivery and financial services.
Jeffrey Paine is a co-founder and partner at Golden Gate Ventures. In this episode, Jeffrey joins Gregor Vand to discuss how the Southeast Asian startup ecosystem has evolved, why the regional copycat model is giving way to globally ambitious technical founders, what companies like Manus and Supabase reveal about Singapore’s emerging role in the global tech landscape, and what advice Jeffrey gives founders today who want to build something that matters beyond their home market.
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AI agents have shown remarkable potential to function as persistent digital assistants that are capable of monitoring data, managing communications, and taking action autonomously over long periods. OpenClaw was one of the first serious attempts to fulfill that vision, connecting frontier coding agents to messaging platforms like Slack and WhatsApp and letting them run continuously in the background. However, OpenClaw largely set aside questions of security to pursue that vision, leaving credentials exposed in the agent’s environment and giving agents broad access to data and services far beyond what any given task required.
NanoClaw is an open source project that takes a zero trust approach to agent orchestration. Rather than relying on instructions to constrain agent behavior, it isolates each agent in its own Docker container, keeps credentials entirely outside the agent’s environment, and enforces human-in-the-loop approval for sensitive actions.
Gavriel Cohen is the founder of NanoClaw and he joins Kevin Ball to discuss the security architecture behind NanoClaw, how the agent sandbox and proxy model work in practice, how agents communicate with each other and with the host orchestration process, how the project approaches context window management and long-lived agent sessions, and more.
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AI agents have become capable of reasoning across large amounts of data, calling tools, and taking sequences of actions autonomously. These qualities make them well suited to some of the most persistent pain points in DevOps, including the on-call engineer woken at 3 AM to diagnose an incident, the build failure that takes hours to trace back to a root cause, and the operational toil of modern software delivery. Agentic DevOps is the emerging paradigm that applies these capabilities to the full software lifecycle, with the goal of matching the velocity of modern software delivery with an equally capable operational layer.
Neha Gaswamy leads Agentic DevOps at AWS and has been at Amazon for over twenty years. In this episode, she joins Matt Merrill to discuss the AWS approach to agentic DevOps, how Amazon dogfoods its own DevOps tooling, how their DevOps agent works from alarm to root cause, why determinism still matters in an agentic world, creative MCP integrations customers are building, and what the future holds for SRE engineers as agents take on more of the operational work.
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AI agents have transformed how software gets written, but the operational side of running software in production has not yet experienced a similar revolution. The same teams responsible for keeping systems healthy, investigating incidents, and managing reliability are still doing much of that work manually.
Mezmo is a Production AI company that makes autonomous operations fast, efficient, and safe. Their open source project, AURA, is a declarative agent framework specifically designed for SRE and platform engineering workflows. It takes a Kubernetes-inspired approach where teams define what they want agents to do rather than scripting every step of how to do it.
Andre Elizondo is the head of product at Mezmo, and he has a background in systems engineering, SRE, and observability. In this episode, Andre joins Kevin Ball to discuss what makes SRE agent workflows fundamentally different from coding agents, how AURA handles context engineering, AURA’s declarative configuration model, the spectrum of agent autonomy, and where the role of the SRE is headed as agents take on more of the operational work.
Full Disclosure: This episode is sponsored by Mezmo.
Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space.
Please click here to see the transcript of this episode.
Sponsorship inquiries: sponsor@softwareengineeringdaily.com
The post AURA and Open-Source Agents for Production Operations appeared first on Software Engineering Daily.
Eric Ries is the creator of the Lean Startup method and the author of the New York Times bestseller The Lean Startup, which transformed how a generation of founders and engineers think about building products. It introduced concepts like the MVP, the pivot, and build-measure-learn that are now so widely adopted they feel obvious. Over two decades of working with founders, CEOs, and investors, Eric has observed that some companies built on those principles eventually betray the very customers and engineers who made them great. His new book, Incorruptible: Why Good Companies Go Bad and How Great Companies Stay Great, is his attempt to answer the question of whether it is possible to build a company that resists that fate.
In this episode, Eric joins Gregor Vand for a wide-ranging discussion about why so many great companies lose their way, and what software engineers and founders can do today to build or find companies that are genuinely resistant to corruption.
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SED News is a monthly podcast from Software Engineering Daily where hosts Gregor Vand and Sean Falconer break down the biggest stories shaping software engineering, Silicon Valley, and the broader tech industry.
In this episode, Gregor and Sean dig into the growing tension around restricted AI models, including Anthropic‘s Fable being pulled from the Claude platform days after launch. They explore what Sean calls “vibe regulations” and the risk foreign governments and enterprises face when a model they depend on can be cut off. They also cover the FT’s reporting on London’s “DeepMind mafia,” a vibe-coding clone controversy involving YC-backed Corgi and Papermark, SpaceX‘s acquisitions of Cursor and Mesh, and Anthropic’s launch of Claude Science.
They also take on the latest round of the IDE wars, and explore who owns your dev toolchain, the vendor lock-in that now comes from context and memory rather than the model itself, and the widening cost gap between frontier tools and open weight models. As always, the episode wraps up with a few standout Hacker News threads.
The post SED News: Restricted Models, IDE Wars, and the DeepMind Mafia appeared first on Software Engineering Daily.
Advanced software systems have long been more complex than any single engineer can fully understand. Observability is the established solution to this problem, but with AI agents now generating code, deploying changes, and operating autonomously, the challenge of understanding large software systems is entering a new dimension.
Grafana is an open source observability platform, and one of the most widely used in the world. The company builds tools that help teams collect, visualize, and act on telemetry data across logs, metrics, and traces. They are now extending that capability into the agentic era with AI-powered investigation and monitoring tools.
Anthony Woods is a co-founder of Grafana Labs. In this episode, he joins Matt Merrill to discuss how AI-generated code is straining software operations, why telemetry data volume has become as much a problem as a solution, how Grafana is adapting to a world where agents are the primary consumers of observability data, and what keeps him up at night about where the industry is headed.
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The post Grafana’s Approach to AI-Native Observability appeared first on Software Engineering Daily.
Building great software always involves technical problem solving, but the best software goes beyond function. It feels fluid, coherent, and genuinely fun to use. This quality lives at the intersection of engineering and design, and very few teams know how to reliably produce it.
Metalab is an engineering and design studio that has worked with some of the most successful companies in tech, including Apple, Slack, Uber, and Instacart. The studio is known for bringing together software engineering and design craft in a way that few studios can match.
Wesley Yu is the VP of Engineering at Metalab, where he leads the teams that design and build digital products for early-stage companies. In this episode, Wesley joins Josh Goldberg to discuss how Metalab approaches tech stack selection for client projects, why agency work demands a bias toward boring and stable technology, how iterative development and deliberately ugly apps lead to better final products, and how AI tools are changing the boundary between design and engineering.
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The post Building Software That People Love appeared first on Software Engineering Daily.
Yacht Club Games is the studio behind the acclaimed Shovel Knight franchise. Their latest release is Mina the Hollower, which is a top-down action RPG inspired by classic Zelda and Castlevania titles. After many years in development, the game recently launched to widespread critical acclaim.
David D’Angelo is a lead programmer at Yacht Club Games. In this episode, David joins Joe Nash to discuss the custom C++ engine built for Mina the Hollower, how the team approached Game Boy Color art constraints and audio in a modern rendering pipeline, the game’s Castlevania-inspired combat philosophy, how the open world manages saving and collision without load screens, and more.
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Predictive modeling is a core element in modern systems, and powers capabilities such as fraud detection, loan approvals, and recommendation systems. These systems typically operate on structured, relational data stored in enterprise databases, with rows, columns, and interlinked tables. While computer vision and natural language processing have undergone a neural network revolution, the tabular data layer underpinning predictive modeling still largely relies on manual feature engineering and task-specific models.
Relational deep learning proposes a new approach. It treats databases as graphs and applies transformer-style attention mechanisms directly over structured relational data. Researchers are now building foundation models for tabular data that aim to generalize across predictive tasks without painstaking feature engineering.
Jure Leskovec is a Professor of Computer Science at Stanford University and he previously served as Chief Scientist at Pinterest and was an investigator at the Chan Zuckerberg Biohub. Most recently, he co-founded the machine learning startup, Kumo.AI.
In this episode, Jure joins Sean Falconer to discuss the limitations of traditional predictive modeling, why structured enterprise data requires its own modality-specific neural architectures, how graph transformers generalize attention to relational databases, and more.
The post Foundation Models for Structured Data appeared first on Software Engineering Daily.
Modern web development requires an ever-growing collection of tools including formatters, linters, bundlers, and plugins. Each tool typically has its own configuration, dependencies, and performance cost. As applications grow more complex, the overhead of maintaining this toolchain becomes a real burden.
Biome is an open source toolchain for web projects that brings formatting and linting together in a single fast, opinionated tool. It’s built in Rust and is designed to be a drop-in replacement for Prettier and ESLint, with sensible defaults, minimal configuration, and consistent behavior across the CLI and editor environments. Biome also introduces a module graph that enables cross-file analysis, and type-aware lint rules that don’t require the TypeScript compiler.
Emanuele Stoppa, known as Ema, is a Senior Systems Engineer at Cloudflare, a lead at Astro, and the creator and lead maintainer of Biome. In this episode, Ema joins Josh Goldberg to discuss the history of Biome, how linters and formatters work under the hood, what makes Biome’s architecture fundamentally different from the tools it replaces, and what’s coming next for the project and its community.
Josh Goldberg is an independent full time open source developer in the TypeScript ecosystem. He works on projects that help developers write better TypeScript more easily, most notably on typescript-eslint: the tooling that enables ESLint and Prettier to run on TypeScript code. Josh regularly contributes to open source projects in the ecosystem such as ESLint and TypeScript. Josh is a Microsoft MVP for developer technologies and the author of the acclaimed Learning TypeScript (O’Reilly), a cherished resource for any developer seeking to learn TypeScript without any prior experience outside of JavaScript. Josh regularly presents talks and workshops at bootcamps, conferences, and meetups to share knowledge on TypeScript, static analysis, open source, and general frontend and web development.
Please click here to see the transcript of this episode.
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The post Biome and the Future of JavaScript Tooling appeared first on Software Engineering Daily.
Most of the cryptography securing the internet today rests on mathematical problems that classical computers cannot solve in any reasonable timeframe. That assumption is now being tested. Recent advances in quantum computing have dramatically compressed timelines, and many in the industry have set a target of full post-quantum security by 2029, meaning a complete migration to algorithms designed to remain secure against quantum attacks.
Bas Westerbaan is a cryptography engineer at Cloudflare, where he leads the company’s efforts to migrate to post-quantum cryptography. In this episode, Bas joins Kevin Ball to discuss how quantum computers threaten public key cryptography, what post-quantum algorithms actually are and how they work, the timeline shifts that have made quantum readiness feel so urgent, and what software engineers need to do now to prepare their systems.
Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space.
Please click here to see the transcript of this episode.
Sponsorship inquiries: sponsor@softwareengineeringdaily.com
The post Preparing for Q-Day appeared first on Software Engineering Daily.
Multiplayer games are among the hardest software systems to build, requiring developers to synchronize state across unreliable networks while maintaining fairness, performance, and a responsive player experience. Latency, cheating, server costs, and debugging distributed game logic all introduce complexity that single-player games never encounter.
Dome Keeper is a minimalist tower defense game with roguelike elements where players must protect a fragile glass dome from relentless waves of alien attackers. The game was developed with the Godot Engine and released in 2022. More recently, the development team embarked on the challenge of adding multiplayer to the game.
René Habermann is the founder of Bippinbits and the creator of Dome Keeper. Chris Ridenour is the founder of KAR Games, which is Godot focused studio that developed Drift: Space Survival. Chris is now working with the Dome Keeper team to bring multiplayer to the game. René and Chris join the show to talk about the origins of Dome Keeper, developing the game, and the process of adding multiplayer to a Godot game.
Joe Nash is a developer, educator, and award-winning community builder, who has worked at companies including GitHub, Twilio, Unity, and PayPal. Joe got his start in software development by creating mods and running servers for Garry’s Mod, and game development remains his favorite way to experience and explore new technologies and concepts.
Please click here to see the transcript of this episode.
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The post Developing Multiplayer Games in Godot appeared first on Software Engineering Daily.
SED News is a monthly podcast from Software Engineering Daily where hosts Gregor Vand and Sean Falconer unpack the biggest stories shaping software engineering, Silicon Valley, and the broader tech industry.
In this episode, they cover Apple‘s uncertain path beyond the iPhone. They also discuss Google‘s agentic pivot at Google I/O, a surge in DuckDuckGo traffic following Google’s default switch to AI mode, and payroll platform Remote surpassing 300 million in ARR with flat headcount.
Gregor and Sean also dig into why consumer subscriptions don’t seem to correspond to actual costs, how enterprise is quietly subsidizing the AI economy, why the true moat has shifted from model quality to context management and agentic harness, and what the coming wave of token cost optimization might look like as companies start scrutinizing their AI bills.
Finally, they highlight standout threads from Hacker News including Doom running on a travel router touchscreen, a viral post asking whether AI productivity gains should translate to a day off, YouTube‘s move to automatically label AI-generated content, and SimCity 3000 running in 4K.
Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn.
Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn.
Please click here to see the transcript of this episode.
Sponsorship inquiries: sponsor@softwareengineeringdaily.com
The post SED News: Apple’s AI Problem, The Real Business Model of AI, and Token Cost Reckoning appeared first on Software Engineering Daily.
The web has quietly become one of the most capable platforms for game development. Advances in WebAssembly, WebGL, and WebGPU have given developers tools that rival native desktop performance, while game engines like Unity and Godot have added robust web export pipelines. However, building games for the browser comes with its own set of constraints including file size, browser compatibility, and the need to quickly capture and maintain the player’s attention.
Erik Dubbelboer is a Principal Engineer at Poki which is a web games platform serving over 100 million monthly users. He’s also a game developer himself, with titles including Silly Skies and Village Builder. His unusual position building developer tools that power the platform, while also shipping games on it, gives him a rare perspective on what it actually takes to succeed in web game development.
In this episode, Erik joins Joe Nash to discuss the history of web games from the Flash era to today’s renaissance, how WebAssembly and WebGPU have transformed what is possible in the browser, the tradeoffs between different game engines for web publishing, and more.
Joe Nash is a developer, educator, and award-winning community builder, who has worked at companies including GitHub, Twilio, Unity, and PayPal. Joe got his start in software development by creating mods and running servers for Garry’s Mod, and game development remains his favorite way to experience and explore new technologies and concepts.
Please click here to see the transcript of this episode.
Sponsorship inquiries: sponsor@softwareengineeringdaily.com
The post Web Native Game Development appeared first on Software Engineering Daily.
Software engineering has developed powerful tools for observability, data management, and continuous testing, but hardware engineering has largely not kept pace. The feedback loops, tooling, and infrastructure that software engineers take for granted simply do not exist in most hardware programs.
Nominal is a data platform built to help hardware organizations move at the same speed as software teams. It manages the hardware data supply chain end to end, from ingesting high-frequency sensor data off physical assets to enabling real-time control room monitoring, post-test analysis, and simulation correlation.
Jason Hoch is the co-founder and CTO of Nominal, and he has a background spanning distributed data systems at Palantir and cloud infrastructure at Vercel. In this episode, Jason joins Kevin Ball to discuss why hardware engineering has lagged so far behind software in tooling and observability, the unique data challenges of working with high-frequency time series sensor data, how Nominal handles both real-time control room workflows and post-test analysis, why AI agents are transforming software development but have not yet made the same leap in hardware, and what it would take to close that gap.
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The post The Hardware Bottleneck AI Can’t Fix appeared first on Software Engineering Daily.
Autonomous drone delivery has long been the stuff of science fiction, but ongoing advances have moved the space from experimental to operational. Zipline is one of the leading companies in this space, with drones that charge between missions and fly autonomously to deliver packages directly to customers.
Kyle Madonia is the VP of Application Software and IT at Zipline, and she previously spent a decade as an engineer at SpaceX. In this episode, Kyle joins Gregor Vand to discuss how Zipline’s software stack powers end-to-end autonomous delivery, the engineering challenges of managing drone fleets at scale, and how the team approaches software releases for safety-critical systems.
Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn.
Please click here to see the transcript of this episode.
Sponsorship inquiries: sponsor@softwareengineeringdaily.com
The post Autonomous Drone Delivery at Scale appeared first on Software Engineering Daily.
Europe’s startup ecosystem is maturing rapidly, with companies like Revolut, Lovable, and Legora demonstrating that world-class technology businesses can be built and scaled on the continent. While the US remains the dominant force in venture-backed software as home to the largest markets, the deepest capital pools, and the most ambitious exit culture, a growing number of European founders are choosing to build at home.
Edward Keelan is a Partner at Octopus Ventures, one of Europe’s largest and most active venture capital firms, where he has spent over 16 years leading the B2B software and enterprise AI fund. His portfolio spans seed through Series C, with a focus on European founders building in AI, vertical SaaS, and enterprise software. This long-view experience gives him a rare perspective on what it takes to build enduring technology companies in Europe.
In this episode, Edward joins Elena Boroda to discuss what separates great founders from the rest, how AI is reshaping the software landscape and threatening established players, the state of the European startup ecosystem and what it needs to compete globally, and what engineers and founders should be thinking about as the industry enters a new era.
Elena Boroda focuses on GTM for developer tools and AI startups, with experience in observability and building tools for MCP servers. She is based in Berlin.
https://www.linkedin.com/in/elena-boroda
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React Native is an open source framework developed by Meta that allows engineers to build mobile applications for both iOS and Android using a single JavaScript codebase. The framework bridges the gap between web development and native mobile, which lets teams ship to both platforms simultaneously without sacrificing the look and feel of a truly native app.
Manjiri Moghe is a Staff Software Engineer at Coinbase, where she has spent five years building and scaling one of the world’s most demanding React Native applications. Her work spans performance optimization, reliability engineering, and the developer tooling that keeps large engineering teams moving quickly without sacrificing quality.
In this episode, Manjiri joins Josh Goldberg to discuss why React Native has become the framework of choice for high-velocity mobile teams, how Coinbase measures app health, how to handle data fetching and loading in production, how AI coding agents are changing the day-to-day workflow for mobile engineers, and more.
Josh Goldberg is an independent full time open source developer in the TypeScript ecosystem. He works on projects that help developers write better TypeScript more easily, most notably on typescript-eslint: the tooling that enables ESLint and Prettier to run on TypeScript code. Josh regularly contributes to open source projects in the ecosystem such as ESLint and TypeScript. Josh is a Microsoft MVP for developer technologies and the author of the acclaimed Learning TypeScript (O’Reilly), a cherished resource for any developer seeking to learn TypeScript without any prior experience outside of JavaScript. Josh regularly presents talks and workshops at bootcamps, conferences, and meetups to share knowledge on TypeScript, static analysis, open source, and general frontend and web development.
Please click here to see the transcript of this episode.
Sponsorship inquiries: sponsor@softwareengineeringdaily.com
The post React Native at Scale appeared first on Software Engineering Daily.
Formal methods are a branch of mathematics and computer science focused on proving the correctness of systems, and they have long promised a more rigorous foundation for software. However, their complexity has kept them confined to a small community of specialists. That is now changing as agentic AI systems take on increasingly autonomous roles. The question of how to define, enforce, and verify what those agents are allowed to do has become urgent, and automated reasoning is emerging as a critical part of the answer.
Byron Cook is a VP and Distinguished Scientist at AWS, a professor at University College London, and a program manager at DARPA. He founded the Automated Reasoning Group at AWS over a decade ago, where his team built the foundations behind products like IAM Access Analyzer, VPC Reachability Analyzer, and Bedrock Guardrails.
In this episode, Byron joins Sean Falconer to discuss how automated reasoning works and why it scales so well with AI, the rise of neurosymbolic approaches that combine formal logic with large language models, what it means to formally specify agent behavior using temporal logic, and why the convergence of agentic AI and formal methods may represent one of the most significant shifts in how software is built and verified.
Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn.
Please click here to see the transcript of this episode.
Sponsorship inquiries: sponsor@softwareengineeringdaily.com
The post Formal Methods as Agent Guardrails appeared first on Software Engineering Daily.
Open source software underpins nearly every modern application, including frameworks powering the most popular websites, to the libraries securing financial backend systems. However, while open source drives collaboration and innovation at a global scale, it also faces deep challenges in sustainability, community health, and long-term maintenance. Many of the world’s most critical dependencies are still maintained by just a handful of volunteers.
Abby Cabunoc Mayes leads Open Source Maintainer Programs at GitHub, and Brian Muenzenmeyer is a Principal Engineer, Node.js maintainer, and author of the book, Approachable Open Source. Abby and Brian join Josh Goldberg to talk about what it means to build and sustain healthy open source projects, how maintainers can foster inclusive communities, the evolving role of open source in the workplace, and how AI is reshaping the way we collaborate.
Josh Goldberg is an independent full time open source developer in the TypeScript ecosystem. He works on projects that help developers write better TypeScript more easily, most notably on typescript-eslint: the tooling that enables ESLint and Prettier to run on TypeScript code. Josh regularly contributes to open source projects in the ecosystem such as ESLint and TypeScript. Josh is a Microsoft MVP for developer technologies and the author of the acclaimed Learning TypeScript (O’Reilly), a cherished resource for any developer seeking to learn TypeScript without any prior experience outside of JavaScript. Josh regularly presents talks and workshops at bootcamps, conferences, and meetups to share knowledge on TypeScript, static analysis, open source, and general frontend and web development.
Please click here to see the transcript of this episode.
Sponsorship inquiries: sponsor@softwareengineeringdaily.com
The post Open Source Sustainability appeared first on Software Engineering Daily.
Vector search has risen to become a foundational tool in modern search and retrieval systems, including the RAG pipelines that power many AI applications. However, the demands on retrieval systems are growing more sophisticated, which is revealing the limits of relying on a single vector similarity score.
Vespa is a popular open source search and data serving engine. Central to Vespa’s architecture is tensor-based retrieval, which is an approach that represents data as tensors rather than simple vectors. Tensor-based retrieval enables richer mathematical operations and more flexible ranking functions that can surmount the limitations of a single vector similarity score.
Radu Gheorghe is a software engineer at Vespa with a background spanning nearly 12 years of consulting and training on Elasticsearch and Solr. In this episode, Radu joins Sean Falconer to discuss why vector similarity alone falls short in production, how tensor-based retrieval generalizes to support richer ranking functions, the trade-offs in chunking and multi-stage re-ranking architectures, and where AI search is headed next.
Full Disclosure: This episode is sponsored by Vespa.
Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn.
Please click here to see the transcript of this episode.
Sponsorship inquiries: sponsor@softwareengineeringdaily.com
The post Vespa AI and Surpassing the Limits of Vector Search appeared first on Software Engineering Daily.
SED News is a monthly podcast from Software Engineering Daily where hosts Gregor Vand and Sean Falconer unpack the biggest stories shaping software engineering, Silicon Valley, and the broader tech industry.
In this episode, they cover Anthropic’s controversial “Mythos” security model and what it means for vulnerability discovery at scale. They also discuss recent layoffs at Snap and Meta, and how AI investment pressures are reshaping hiring, organizational priorities, and the economics of big tech.
Gregor and Sean then zoom out to examine the massive wave of AI infrastructure spending—hundreds of billions in capex across Amazon, Google, Microsoft, and Meta, and what it signals about the future of cloud platforms, model providers, and the engineers who build on top of them. They explore the emerging entanglement between model labs and infrastructure providers, the evolving role of engineers in an AI-native world, and the growing gap between rapid AI adoption and security readiness.
Finally, they highlight standout threads from Hacker News, including creative uses of AI coding tools to revive abandoned side projects, new approaches to training smaller yet highly capable models, surprising demographic data visualizations, and even the mathematics of “cheating” at Tetris.
Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn.
Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn.
Please click here to see the transcript of this episode.
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AI coding tools have dramatically accelerated the pace of development, and the bottleneck in the software development lifecycle has shifted to code validation and testing. However, the conventional tools and workflows that QA teams have relied on were not designed for a world where a single engineer can generate thousands of lines of code in a day.
SmartBear is a software quality platform spanning test automation, API lifecycle management, and observability. The company recently launched an AI-native QA platform called BearQ, which deploys autonomous agents that explore web applications, learns their structure and behavior, and authors and maintains test cases continuously.
Fitz Nowlan is the VP of AI and Architecture at SmartBear and the co-founder of Reflect, which is a web testing platform acquired by SmartBear in 2024. In this episode, Fitz joins Kevin Ball to discuss why web UI testing is uniquely challenging, how BearQ’s multi-agent architecture coordinates exploration and testing, why test data management becomes a hard distributed systems problem at scale, and what agentic development means for the future of QA.
Full Disclosure: This episode is sponsored by SmartBear.
Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space.
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Artificial intelligence is transforming warfare faster than the legal and ethical frameworks designed to govern it. Militaries around the world are deploying AI-powered decision support systems to identify targets, assess proportionality, and direct weapons. The gap between what is technically possible and what international law can effectively regulate is widening by the day.
Yuval Shany is a law professor at Hebrew University of Jerusalem and a research fellow at the Oxford Ethics in AI Institute. He also served on the UN Human Rights Committee, where he first encountered the legal and ethical challenges posed by autonomous weapons systems. His research focuses on the intersection of international humanitarian law, human rights, and emerging military technologies.
In this episode, Yuval joins Matt Merrill for a wide-ranging conversation. They cover topics including how close we are to fully autonomous lethal weapons, the accountability gap that AI-mediated warfare creates, and what lessons software engineers can draw from these challenges when building consequential AI systems of any kind.
Matt Merrill is a software engineering leader with over 20 years of experience building and scaling software teams across enterprise and product-focused organizations. His background is in backend development, cloud architecture, and distributed systems design. He currently architects and delivers software products and leads a team of engineers at DEPT® Agency. You can learn more about his work at code.theothermattm.com.
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Open-weight models are AI systems whose trained parameters are publicly released, which allows developers to run, fine-tune, and deploy them independently rather than accessing them only through a hosted API. While closed-weight models from companies like OpenAI or Anthropic are delivered as managed services, open-weight models give organizations direct control over how the models are deployed and used. Importantly, the performance of these models is steadily improving and they’ve become credible alternatives for production workloads, with advantages in customization and data privacy.
Fireworks AI is building a platform focused on serving and customizing open-weight models at scale. The platform includes optimized inference infrastructure, multi-hardware support across NVIDIA and AMD, and reinforcement fine-tuning capabilities.
Benny Chen is a Co-Founder of Fireworks AI. In this episode, he joins Gregor Vand to discuss his path from Meta’s ML infrastructure teams to co-founding Fireworks AI, why open-weight models are becoming increasingly competitive, how custom kernels and speculative decoding improve performance, reinforcement fine-tuning, and much more.
Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn.
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AI coding tools have gone from novelty to core infrastructure in under three years. Today, many devs use AI daily, a substantial share of new code is AI-generated, and expectations for automation are rapidly increasing.
Sonar is a company specializing in analysis of code quality and security, and they recently released a new survey – the State of Code Developer Survey. The survey provides a deep examination of how developers are using AI in real production environments, and where the real-world gaps and risks still exist.
Chris Grams is the CVP of Corporate Marketing at Sonar, and Manish Kapur is the VP of Product Marketing and Developer Relations at Sonar. In this episode, they join Matt Merrill to discuss what the survey reveals about AI-assisted development, why 96% of developers still don’t fully trust AI-generated code, how deterministic verification layers fit into agent-driven workflows, and what engineering leaders should prioritize as AI shifts from experimentation to production infrastructure.
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AI agents are increasingly capable of reasoning and performing autonomous work over long periods. However, as agents take on more complex, longer-horizon tasks, keeping them supplied with the right information becomes the core engineering challenge. The industry is moving away from pre-loading context upfront toward a model where agents dynamically navigate and retrieve the data they need, when they need it.
Redis is approaching context management using a context engine, which is an architecture built around four pillars: on-demand context retrieval, data that is always current, fast retrieval, and a memory layer that improves over time. In practice this means building materialized views of data with a semantic layer on top, rather than giving agents direct access to production databases. A memory system sits alongside this, extracting and compacting information asynchronously as the agent works.
Simba Khadder leads AI strategy at Redis, and he previously co-founded the feature store platform FeatureForm, which was acquired by Redis in 2025. In this episode, Simba joins Kevin Ball to discuss why context has become the defining challenge in agentic AI, how context engines differ from traditional RAG architectures, how materialized views underpin reliable agent data pipelines, how memory systems can improve through async extraction and compaction, and how engineering teams need to adapt their practices as AI-driven development accelerates.
Full Disclosure: This episode is sponsored by Redis.
Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space.
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AI agents are evolving from individual productivity tools into distributed systems components inside enterprises. The next frontier is coming into focus, and it involves large-scale ecosystems of collaborating agents embedded directly into business processes. However, multi-agent architectures introduce serious challenges around orchestration, state management, trust, governance, and observability.
Eric Broda is a veteran of the software industry, and he’s the co-author of the new O’Reilly book, Agentic Mesh: The GenAI-Powered Autonomous Agent Ecosystem.
In this episode, Eric joins Sean Falconer to discuss the architectural challenges of deploying agents as core infrastructure, how distributed computing principles apply to multi-agent systems, why trust and explainability are foundational, and what enterprises may look like as agents become full participants in business processes.
Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn.
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Observability emerged from the need to understand complex software systems, and involves tracking metrics, logs, and traces so engineers can detect and diagnose problems before they affect users. However, modern applications often encompass hundreds of services, containers, and dependencies, generating more observability data than dashboards and alerts alone can effectively surface.
New Relic is a leading observability platform, with a history that spans the full arc of modern software operations. Today they are working to apply AI to move observability beyond passive monitoring toward active intelligence, where systems can surface what matters, reduce alert noise, and ultimately take autonomous action before problems reach engineers or users.
Nic Benders is the Chief Technology Strategist at New Relic, where he has worked for 16 years. In this episode, Nic joins Lee Atchison to discuss the evolution of observability from dashboards and alerts to AI-driven intelligence, how LLMs and statistical tools work together to surface meaningful signals from massive datasets, the emerging challenge of observing AI systems themselves, and what the rise of AI means for the future of software engineering as a profession.
This episode is hosted by Lee Atchison. Lee Atchison is a software architect, author, and thought leader on cloud computing and application modernization. His best-selling book, Architecting for Scale (O’Reilly Media), is an essential resource for technical teams looking to maintain high availability and manage risk in their cloud environments.Lee is the host of his podcast, Modern Digital Business, an engaging and informative podcast produced for people looking to build and grow their digital business with the help of modern applications and processes developed for today’s fast-moving business environment. Listen at mdb.fm. Follow Lee at softwarearchitectureinsights.com, and see all his content at leeatchison.com.
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Mobile apps have become a primary interface for critical services, including banking, payments, and healthcare. Unlike web applications, much of the logic and intellectual property in a mobile app lives directly on the user’s device, which is an environment the developer doesn’t control. That makes mobile apps uniquely exposed to reverse engineering, runtime manipulation, and fraud.
As more critical functionality shifts to mobile, the need to harden apps against sophisticated attackers continues to grow. Guardsquare builds tools to protect and test mobile applications against both static and dynamic threats. Its platform has features including layered code obfuscation, runtime application self-protection, mobile-specific security testing, threat monitoring, and API attestation.
Ryan Lloyd is the Chief Product Officer at Guardsquare. In this episode, he joins Gregor Vand to discuss why mobile security differs from desktop and web security, how reverse engineering tools have evolved, the role of compiler-based obfuscation and runtime protections, common mobile app vulnerabilities, and how LLMs are reshaping the attacker landscape.
Full Disclosure: This episode is sponsored by Guardsquare.
Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn.
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The Model Context Protocol, or MCP, gives developers a common way to expose tools, data, and capabilities to large language models, and it has quickly become an important standard in agentic AI. FastMCP is an open source project stewarded by the team at Prefect, which is an orchestration platform for AI and data workflows. The FastMCP project builds on MCP to provide high-level, ergonomic abstractions for Python developers to rapidly build and deploy MCP servers and applications.
Jeremiah Lowin is the founder and CEO of Prefect, and Adam Azzam is the VP of Product at the company. In this episode, Jeremiah and Adam join Gregor Vand to discuss the origin story of FastMCP, the three pillars of the framework, the architectural decisions behind FastMCP 3.0, and much more.
Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn.
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SED News is a monthly podcast from Software Engineering Daily where hosts Gregor Vand and Sean Falconer unpack the biggest stories shaping software engineering, Silicon Valley, and the broader tech industry.
In this episode, they cover the resurgence of ARM and CPUs as serious compute infrastructure for running local AI agents, a supply chain attack on LiteLLM that exposed API credentials across thousands of developer environments, and the arrival of OpenCode as a fully open source alternative to Claude Code and Codex. They also discuss the diverging strategies of Anthropic and OpenAI following the Pentagon contract controversy, and what it signals about where each company is positioning itself in the enterprise and government markets. Gregor and Sean then dive deep into what the AI coding boom actually means for shipping software.
Finally, they highlight standout threads from Hacker News, including Doom running entirely over DNS, the psychology of seafoam green in Cold War-era control rooms, a Tesla Model 3 computer assembled from salvaged crash components, and Apple’s quiet discontinuation of the Mac Pro.
Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn.
Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn.
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The post SED News: OpenCode, AI Code vs. Shipped Code, and the LiteLLM Breach appeared first on Software Engineering Daily.
FreeBSD is one of the longest-running and most influential open-source operating systems in the world. It was born from the Berkeley Software Distribution in the early 1990s, it has powered everything from high-performance networking infrastructure to game consoles and content delivery networks. Over three decades, it has evolved through major architectural shifts, from symmetric multiprocessing and kernel scalability to modern storage systems and predictable release engineering.
John Baldwin has spent more than 25 years working on FreeBSD as a developer, contributor, and consultant. In this episode, John joins Gregor Vand to discuss the origins of FreeBSD, how its governance model differs from other open-source projects, its role inside systems like Netflix’s CDN and the PlayStation 4, the challenges of maintaining a 30-year-old codebase, and much more.
Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn.
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Modern cloud-native systems are built on highly dynamic, distributed infrastructure where containers spin up and down constantly, services communicate across clusters, and traditional networking assumptions break down. Linux networking was designed decades ago around static IPs and linear rule processing, which makes it increasingly difficult to achieve scale in Kubernetes environments. At the same time, modifying the Linux kernel to keep up with these demands is slow, risky, and impractical for most organizations.
The Extended Berkeley Packet Filter, or eBPF, is a Linux kernel technology that allows sandboxed programs to run safely inside the kernel without modifying kernel source code or loading kernel modules. Cilium is an open-source, cloud-native networking platform that’s built on eBPF, and provides, secures, and observes connectivity between workloads in Kubernetes and other distributed environments.
Bill Mulligan is a maintainer in the Cilium ecosystem and a member of the team at Isovalent, the company behind Cilium. He joins the show with Gregor Vand to discuss how eBPF works under the hood, why Cilium has become one of the most widely adopted Kubernetes networking projects, and how the future of cloud-native infrastructure is being reshaped by programmable kernels.
Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn.
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Bennett Foddy is a legendary game designer known for creating wholly distinctive games such as QWOP, Getting Over It with Bennett Foddy, and the recently released Baby Steps. He’s also a former professor at the NYU Game Center, where he taught game design alongside developing his own experimental work.
In this episode, Bennett joins Joe Nash to discuss his systems-driven approach to game design, why frustration and difficulty are often misunderstood, how streaming and speedrunning have reshaped how games are played and experienced, and what makes his games stand out.
Joe Nash is a developer, educator, and award-winning community builder, who has worked at companies including GitHub, Twilio, Unity, and PayPal. Joe got his start in software development by creating mods and running servers for Garry’s Mod, and game development remains his favorite way to experience and explore new technologies and concepts.
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Developer tooling shapes how software gets written day to day, but the best tools often disappear into the background once they succeed. Formatting, linting, and build systems can either create friction and endless debate, or quietly remove entire classes of problems from a team’s workflow. Over the past decade, the JavaScript ecosystem has wrestled with both extremes as it scaled rapidly and accumulated complexity.
Prettier emerged as a response to the surprisingly human problem of engineers spending too much time debating code style instead of building software. It offers a deterministic, opinionated formatter that helped normalize automation as part of everyday development.
James Long is a design and product engineer who has worked at Mozilla and Stripe, and he’s the creator of Prettier. He joins the show with Josh Goldberg to talk about the origins of Prettier, why formatting debates are so emotionally charged, the technical challenges of building formatters, the realities of maintaining popular open-source tools, and how the JavaScript tooling ecosystem continues to evolve.
Josh Goldberg is an independent full time open source developer in the TypeScript ecosystem. He works on projects that help developers write better TypeScript more easily, most notably on typescript-eslint: the tooling that enables ESLint and Prettier to run on TypeScript code. Josh regularly contributes to open source projects in the ecosystem such as ESLint and TypeScript. Josh is a Microsoft MVP for developer technologies and the author of the acclaimed Learning TypeScript (O’Reilly), a cherished resource for any developer seeking to learn TypeScript without any prior experience outside of JavaScript. Josh regularly presents talks and workshops at bootcamps, conferences, and meetups to share knowledge on TypeScript, static analysis, open source, and general frontend and web development.
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Skateboarding games have long balanced technical precision with a sense of flow and expression, but Skate Story takes the genre in a radically different direction. It has a distinct vaporwave vibe and blends fluid skate mechanics with exploration, puzzles, and an existential narrative about freedom, pain, and obsession.
The game was created by indie developer Sam Eng, who previously released Zarvot for the Nintendo Switch. Skate Story launched to critical acclaim and was widely regarded as one of the best games of 2025.
In this episode, Sam joins the show with Joe Nash to talk about developing Skate Story.
Joe Nash is a developer, educator, and award-winning community builder, who has worked at companies including GitHub, Twilio, Unity, and PayPal. Joe got his start in software development by creating mods and running servers for Garry’s Mod, and game development remains his favorite way to experience and explore new technologies and concepts.
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Retrieval-augmented generation, or RAG, has become a foundational approach to building production AI systems. However, deploying RAG in practice can be complex and costly. Developers typically have to manage vector databases, chunking strategies, embedding models, and indexing infrastructure. Designing effective RAG systems is also a moving target, as techniques and best practices evolve in step with rapidly advancing language models.
Google DeepMind recently released the File Search Tool, a fully managed RAG system built directly into the Gemini API. File Search abstracts away the retrieval pipeline, allowing developers to upload documents, code, and other text data, automatically generate embeddings, and query their knowledge base. We wanted to understand how the DeepMind team designed a general-purpose RAG system that maintains high retrieval quality.
Animesh Chatterji is a Software Engineer at Google DeepMind and Ivan Solovyev is a Product Manager at DeepMind, and they worked on File Search Tool. They joined the podcast with Sean Falconer to discuss the evolution of RAG, why simplicity and pricing transparency matter, how embedding models have improved retrieval quality, the tradeoffs between configurability and ease of use, and what’s next for multimodal retrieval across text, images, and beyond.
Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn.
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Interactive notebooks were popularized by the Jupyter project and have since become a core tool for data science, research, and data exploration. However, traditional, imperative notebooks often break down as projects grow more complex. Hidden state, non-reproducible execution, poor version control ergonomics, and difficulty reusing notebook code in real software systems make it hard to move from exploration to production. At the same time, sharing results often requires collaborators to recreate entire environments, limiting interactivity and slowing feedback.
Marimo is an open-source, next-generation Python notebook designed to address these problems directly. Akshay Agrawal is the creator of Marimo and he previously worked at Google Brain. He joins the show with Kevin Ball to discuss the limitations of traditional notebooks, the design of reactive notebooks in Python, how marimo bridges research and production, and where notebooks fit in an increasingly agentic, AI-assisted development world.
Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space.
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AI agents have taken on a growing share of software development work, so much so that the hardest problems are shifting away from code generation towards something new, context. The challenge is now contextualizing why systems work the way they do, how architectural decisions were made, and the sources of truth that exist outside of the code base. As teams adopt agentic tools, gaps or inconsistencies in context have emerged as a primary reason why software fails to meet production standards.
Unblocked is a startup focused on solving this context gap. Their context engine aggregates and reasons over organizational knowledge spread across source code, pull requests, documentation, chat systems, and production telemetry. By acting as a context engine for both developers and AI agents, Unblocked aims to improve AI code quality and review, reduce interruptions, accelerate onboarding, and enable safer, more effective agentic workflows.
Dennis Pilarinos is the Founder and CEO of Unblocked. Previously, he helped build Azure at Microsoft, worked at AWS, and co-founded BuddyBuild, which is a mobile CI platform acquired by Apple. Dennis joins Kevin Ball to discuss context engineering, reconciling conflicting sources nof truth, permission to wear AI systems, the shifting bottlenecks in the software development lifecycle, and what it means to be a software engineer in an increasingly agentic world.
Full Disclosure: This episode is sponsored by Unblocked.
Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space.
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Sponsorship inquiries: sponsor@softwareengineeringdaily.com
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SED News is a monthly podcast from Software Engineering Daily where hosts Gregor Vand and Sean Falconer unpack the biggest stories shaping software engineering, Silicon Valley, and the broader tech industry.
In this episode, they cover the viral rise of OpenClaw and its founder’s move to OpenAI, OpenAI’s exploration of ads inside ChatGPT, and Alibaba’s push into agent-powered commerce during Lunar New Year. They also discuss Mistral’s acquisition of Koyeb to deepen its compute stack, the growing competition between ChatGPT, Claude, and Gemini, and what these moves signal about monetization, infrastructure, and control in the AI arms race.
Gregor and Sean then dive deep into the rapid acceleration of agentic engineering. They examine how tools like Claude Code and Codex are compressing the idea-to-production cycle, what multi-agent orchestration means for software teams, whether the era of the “10x engineer” is ending, and how organizational structures may need to evolve as coding shifts from manual craft to supervised automation.
Finally, they highlight standout threads from Hacker News, including reverse engineering a 1990 DOS classic, a 3D reimagining of flight tracking data, old-school practical film effects using cloud tanks, and the privacy-focused GrapheneOS mobile operating system.
Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn.
Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn.
Please click here to see the transcript of this episode.
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The post SED News: OpenClaw Goes Viral, Mistral’s Compute Play, and the Agent Arms Race appeared first on Software Engineering Daily.
AI-assisted coding tools have made it easier than ever to spin up prototypes, but turning those prototypes into reliable, production-grade systems remains a major challenge. Large language models are non-deterministic, prone to drift, and often lose track of intent over long development sessions.
Kiro is an AI-powered IDE that’s built around a spec-driven development workflow. It’s focused on helping developers capture intent up front, translate it into concrete requirements and designs, and systematically validate implementations through tasks, testing, and guardrails. It aims to preserve the creativity of AI-assisted development while producing software that is ready for real-world use.
David Yanacek is a Senior Principal Engineer and a lead advisor on the Agentic AI team at AWS. Today, his work focuses on Kiro, frontier agents, Amazon Bedrock AgentCore, and AWS’s operational agents. He joins the show with Kevin Ball to discuss the design of Kiro, how spec-driven development changes the way teams work with AI coding agents, and what the next generation of agentic software development might look like.
Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space.
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Enterprise IT systems have grown into sprawling, highly distributed environments spanning cloud infrastructure, applications, data platforms, and increasingly AI-driven workloads. Observability tools have made it easier to collect metrics, logs, and traces, but understanding why systems fail and responding quickly remains a persistent challenge. As complexity continues to rise, the industry is looking beyond dashboards and alerts toward agentic AI systems that can reason about operational data, reduce toil, and take action when things go wrong.
SolarWinds offers solutions to monitor, understand, and remediate issues across complex, distributed systems. The company began as a leader in network and infrastructure monitoring, and has evolved to support modern applications, cloud environments, containers, and AI workloads, with a growing focus on reducing operational toil.
Krishna Sai is the Chief Technology Officer at SolarWinds. He joins the show with Sean Falconer to discuss how SolarWinds is rethinking observability in the age of AI, what it means to design agentic systems for mission-critical environments, how AI-assisted programming is reshaping engineering workflows, and why the future of operations depends on building platforms where humans and autonomous agents work together.
Full Disclosure: This episode is sponsored by SolarWinds.
Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn.
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China’s Great Firewall is often spoken about but is rarely understood. It is one of the most sophisticated and opaque censorship systems on the planet, and it shapes how over a billion people interact with the global internet, influences the design of privacy and proxy tools worldwide, and continues to evolve in ways that challenge researchers, developers, and policymakers alike.
Jackson Sippe is a PhD researcher at the University of Colorado Boulder whose work focuses on uncovering how national-scale censorship systems operate. Jackson recently helped lead a groundbreaking study analyzing a previously undocumented GFW technique that quietly broke fully encrypted proxy protocols across China for more than a year.
In this episode, Jackson joins Gregor Vand to discuss how the Great Firewall works at a technical level, the 2021–2023 blocking event, the popcount-based detection algorithm his team reverse-engineered, the cat-and-mouse ecosystem of censorship circumvention, and what these findings mean for the future of the open internet.
Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn.
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LLM -powered systems continue to move steadily into production, but this process is presenting teams with challenges that traditional software practices don’t commonly encounter. Models and agents are non-deterministic systems, which makes it difficult to test changes, reason about failures, and confidently ship updates. This has created the need for new evaluation tooling designed specifically around the properties of LLMs.
Comet is a platform with Roots and MLOps, to the rapidly evolving world of agent-based systems by treating prompts, tools, and workflows as optimizable components that can be evaluated and improved over time.
Gideon Mendels is the co -founder and CEO of Comet. He previously worked at Google on hate speech and deception detection, and he founded GroupWise, which trained and deployed NLP models processing billions of chats. In this episode, Gideon joins Kevin Ball to discuss how agent development sits between software engineering and ML, why eVals are the missing foundation for most AI teams, prompt optimization as a search problem, and the future for continuously improving agents in production.
Full Disclosure: This episode is sponsored by Comet.
Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space.
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AI-assisted programming has moved far beyond autocomplete. Large language models are now capable of editing entire codebases, coordinating long-running tasks, and collaborating across multiple systems. As these capabilities mature, the core challenge in software development is shifting away from writing code and toward orchestrating work, managing context, and maintaining shared understanding across fleets of agents.
Steve Yegge is a software engineer, writer, and industry veteran whose essays have shaped how many developers think about their work. Over the past year, Steve has been exploring the frontier of agentic software development, building tools like Beads and Gas Town to experiment with multi-agent coordination, shared memory, and AI-driven software workflows.
In this episode, Steve joins Kevin Ball to discuss the evolution of AI coding from chat-based assistance to full agent orchestration, the technical and cognitive challenges of managing fleets of agents, how concepts like task graphs and Git-backed ledgers change the nature of work, and what these shifts mean for software teams, tooling, and the future of the industry.
Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space.
Please click here to see the transcript of this episode.
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Python 3.14 is here and continues Python’s evolution toward greater performance, scalability, and usability. The new release formally supports free-threaded, no-GIL mode, introduces template string literals, and implements deferred evaluation of type annotations. It also includes new debugging and profiling tools, along with many other features.
Łukasz Langa is the CPython Developer in Residence at the Python Software Foundation, and he joins Sean Falconer to discuss the 3.14 release, the future of free threading, type system improvements, Python’s growing role in AI, and how the language continues to evolve while maintaining its commitment to backward compatibility.
Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn.
Please click here to see the transcript of this episode.
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Engineering teams often build microservices as their systems grow, but over time this can lead to a fragmented ecosystem with scattered data access patterns, duplicated business logic, and an uneven developer experience. A unified data graph with a consistent execution layer helps address these challenges by centralizing schema, simplifying how teams compose functionality, and reducing operational overhead while preserving performance and reliability.
Viaduct is Airbnb’s open-source, data-oriented service mesh and GraphQL platform built around a single, highly connected central schema. It has played a major role in scaling Airbnb’s engineering organization.
Adam Miskiewicz is a Principal Software Engineer at Airbnb and he worked on Viaduct. He joins the podcast with Gregor Vand to talk about how Viaduct originated inside Airbnb, the architectural principles that shaped it, the challenges of scaling GraphQL to millions of queries per second, and why the team decided to open-source the platform. They also discuss the future of backend development in an AI-driven world and how unified data layers may influence the next generation of engineering systems.
Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn.
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SED News is a monthly podcast from Software Engineering Daily where hosts Gregor Vand and Sean Falconer unpack the biggest stories shaping software engineering, Silicon Valley, and the broader tech industry.
In this episode, they cover Starlink’s rapid rollout of free, high-speed in-flight internet, Tesla’s move to deprecate Autopilot in favor of full self-driving, and Apple’s reported decision to power Siri with Google’s Gemini models. They also discuss Meta’s $2B acquisition of Manus, Waymo’s growing pains as autonomous vehicles scale, and the competitive shockwaves triggered by Google’s advances in custom AI hardware.
Gregor and Sean then dive deep into the state of the tech job market, examining OpenAI’s decision to eliminate vesting cliffs, the escalating war for elite AI talent, and what recent layoffs really say about the future of software engineering. They explore how AI coding tools are reshaping the balance between junior and senior engineers, why fundamentals still matter, and what developers should focus on heading into 2026.
Finally, they highlight standout threads from Hacker News, including Doom running on wireless earbuds, the enduring appeal of wildly over-engineered side projects, and why hacking for fun still matters in an age of industrial-scale AI.
Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn.
Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn.
Please click here to see the transcript of this episode.
Sponsorship inquiries: sponsor@softwareengineeringdaily.com
The post SED News: Apple Bets on Gemini, Google’s AI Advantage, and the Talent Arms Race appeared first on Software Engineering Daily.
AI coding agents are rapidly reshaping how software is built, reviewed, and maintained. As large language model capabilities continue to increase, the bottleneck in software development is shifting away from code generation toward planning, review, deployment, and coordination. This shift is driving a new class of agentic systems that operate inside constrained environments, reason over long time horizons, and integrate across tools like IDEs, version control systems, and issue trackers.
OpenAI is at the forefront of AI research and product development. In 2025, the company released Codex, which is an agentic coding system designed to work safely inside sandboxed environments while collaborating across the modern software development stack.
Thibault Sottiaux is the Codex engineering lead and Ed Bayes is the Codex product designer. In this episode, they join Kevin Ball to discuss how Codex is built, the co-evolution of models and harnesses, multi-agent futures, Codex’s open-source CLI, model specialization, latency and performance considerations, and much more.
Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space.
Please click here to see the transcript of this episode.
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Engineering teams around the world are building AI-focused applications or integrating AI features into existing products. The AI development ecosystem is maturing, which is accelerating how quickly these applications can be prototyped. However, taking AI applications to production remains a notoriously complex process. Modern AI stacks demand LLMs, embeddings, vector search, observability, new caching layers, and constant adaptation as the landscape shifts week to week. Increasingly, the data layer has become both the foundation and the bottleneck to AI app productionization.
MongoDB has been expanding beyond its core document database into a full AI-ready database platform with integrated capabilities for operational data, search, real-time analytics, and AI-powered data retrieval. The company also recently acquired Voyage AI to provide accurate and cost-effective embedding models and rerankers to its users.
Fred Roma is a veteran engineer and is currently the SVP of Product and Engineering at MongoDB. He joins the show with Kevin Ball to talk about the state of AI application development, the role of vector search and reranking, schema evolution in the LLM era, the Voyage AI acquisition, how data platforms must evolve to keep up with AI’s breakneck pace, and more.
Full Disclosure: This episode is sponsored by MongoDB.
Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space.
Please click here to see the transcript of this episode.
Sponsorship inquiries: sponsor@softwareengineeringdaily.com
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Package management sits at the foundation of modern software development, quietly powering nearly every software project in the world. Tools like npm and Yarn have long been the core of the JavaScript ecosystem, enabling developers to install, update, and share code with ease. But as projects grow larger and the ecosystem more complex, this older infrastructure is beginning to show its limits with performance bottlenecks, dependency conflicts, and growing concerns around supply chain security.
Darcy Clarke and Ruy Adorno are veterans of this ecosystem. Both spent years maintaining the npm CLI and helping guide the Node.js project, where they saw firsthand the technical debt and design tradeoffs that define modern JavaScript tooling. Now they’re building vlt, a new package manager and registry that rethinks performance, security, and developer experience from the ground up.
In this episode, Darcy and Ruy join Josh Goldberg to discuss how vlt works, why they believe package management needs a server-side reboot, what lessons they’ve drawn from npm’s evolution, and how features like declarative querying, self-hosted registries, and real-time security scanning could reshape how developers build and share JavaScript in the years ahead.
Josh Goldberg is an independent full time open source developer in the TypeScript ecosystem. He works on projects that help developers write better TypeScript more easily, most notably on typescript-eslint: the tooling that enables ESLint and Prettier to run on TypeScript code. Josh regularly contributes to open source projects in the ecosystem such as ESLint and TypeScript. Josh is a Microsoft MVP for developer technologies and the author of the acclaimed Learning TypeScript (O’Reilly), a cherished resource for any developer seeking to learn TypeScript without any prior experience outside of JavaScript. Josh regularly presents talks and workshops at bootcamps, conferences, and meetups to share knowledge on TypeScript, static analysis, open source, and general frontend and web development.
Please click here to see the transcript of this episode.
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WebAssembly, or WASM, has grown from a low-level compilation target for C and C++ into one of the most influential technologies in modern computing. It now powers browser applications, edge compute platforms, embedded systems, and a growing ecosystem of languages targeting a portable and secure execution model.
Andreas Rossberg is a programming languages researcher and former member of the V8 team at Google. Andreas helped architect WebAssembly from its earliest concepts through its most recent milestone releases, including the groundbreaking 3.0 spec that introduces garbage collection, richer reference types, and major steps toward multi-language interoperability.
In this episode, Andreas joins Kevin Ball to explore the history of WebAssembly, the constraints that shaped its earliest design, the major turning points in versions 1.0, 2.0, and 3.0, and what’s coming next for WebAssembly.
Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space.
Please click here to see the transcript of this episode.
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Surveillance technology is advancing faster than the laws meant to govern it. Across the United States, police departments are deploying automated license plate readers, facial recognition tools, and predictive systems that quietly log the daily movements of millions of people. These tools promise efficiency and safety, but critics argue that they represent a form of warrantless mass surveillance, and raise deep constitutional questions about privacy, accountability, and the limits of government power in the digital age.
Michael Soyfer is an attorney at the Institute for Justice, a nonprofit public interest law firm focused on defending individual rights. His work centers on the Fourth Amendment and the growing use of surveillance technologies by local governments. Michael joins the show with Kevin Ball to discuss the rise of Flock Safety cameras, the Institute for Justice’s lawsuit against the City of Norfolk, how decades-old legal precedents struggle to keep up with modern technology, and what citizens, technologists, and policymakers can do to protect privacy in an era of pervasive data collection.
Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space.
Please click here to see the transcript of this episode.
Sponsorship inquiries: sponsor@softwareengineeringdaily.com
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Modern software development is evolving rapidly. New tools, processes, and AI-powered systems are reshaping how teams collaborate and how engineers find satisfaction in their craft. At the same time, developer experience has become a critical function for helping organizations balance agility, security, and scale while maintaining the creativity and flow that make top tier engineering possible.
Capital One is continuously transforming its developer culture, with a focus on faster development cycles, reducing operational overhead, and boosting productivity across the organization.
Catherine McGarvey is the SVP of Developer Experience at Capital One. She joins the podcast with Sean Falconer to talk about what developer enablement means at enterprise scale, measuring developer productivity, being agile in a regulated environment, AI in enterprise development, the future for developers, and much more.
Full Disclosure: This episode is sponsored by Capital One.
Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn.
Please click here to see the transcript of this episode.
Sponsorship inquiries: sponsor@softwareengineeringdaily.com
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Modern software development is more complex than ever. Teams work across different operating systems, chip architectures, and cloud environments, each with its own dependency quirks and version mismatches. Ensuring that code runs reproducibly across these environments has become a major challenge that’s made even harder by growing concerns around software supply chain security.
Nix is a powerful open-source package manager that builds software in controlled, declarative environments where dependencies are explicitly defined and reproducible. Its functional approach has made it a gold standard for reproducible builds, but it can also be difficult to learn and adopt.
Flox is a company that builds on top of Nix, with increased supply chain security and abstractions that streamline the developer experience.
Michael Stahnke is the VP of Engineering at Flox and formerly worked at companies including Caterpillar, Puppet, and CircleCI. He joins the podcast with Kevin Ball to talk about Flox, building on top of Nix, how reproducibility underpins software security, the concept of “secure by construction, how deterministic environments are reshaping both human and AI-driven development, and much more.
Full Disclosure: This episode is sponsored by Flox.
Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space.
Please click here to see the transcript of this episode.
Sponsorship inquiries: sponsor@softwareengineeringdaily.com
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Visual Studio Code has become one of the most influential tools in modern software development. The open-source code editor has evolved into a platform used by millions of developers around the world, and it has reshaped expectations for what a modern development environment can be through its intuitive UX, rich extension marketplace, and deep integration with today’s tooling landscape. Now, in an era defined by rapid advances in AI-assisted programming, VS Code is at the center of a profound shift in how software is written.
Kai Maetzel is the Engineering Manager leading the VS Code team at Microsoft. He joins the show with Kevin Ball to talk about the origins of VS Code, how AI has reshaped the editor’s design philosophy, the rise of agentic programming models, and what the future of development might look like.
Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space.
Please click here to see the transcript of this episode.
Sponsorship inquiries: sponsor@softwareengineeringdaily.com
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Blender Studio is the creative arm of the Blender Foundation and it’s dedicated to producing films, games, and other projects that showcase the full potential of Blender. The studio functions as both an art and technology lab and pushes the boundaries of 3D animation through open productions. All of their assets, production files, and workflows are shared publicly, which gives artists and developers valuable resources to learn from and build upon.
Most recently, Blender Studio released its second game, DOGWALK, where the playable character is a dog exploring snowy winter woods with a child. The project was built entirely with open-source tools including Blender, the Godot engine, Krita for concept art, Kitsu for project management, and Linux.
Simon Thommes is a Lead Technical Artist at Blender Studio and a developer on DOGWALK. He joins the podcast with Joe Nash to talk about Blender Studio, the process behind building DOGWALK, and developing a pipeline between Blender and Godot.
Joe Nash is a developer, educator, and award-winning community builder, who has worked at companies including GitHub, Twilio, Unity, and PayPal. Joe got his start in software development by creating mods and running servers for Garry’s Mod, and game development remains his favorite way to experience and explore new technologies and concepts.
Please click here to see the transcript of this episode.
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The post Blender and Godot in Game Development with Simon Thommes appeared first on Software Engineering Daily.
JavaScript has grown far beyond the browser. It now powers millions of backend systems, APIs, and cloud services through Node.js, which is one of the most widely deployed runtimes on the planet. Keeping such a critical piece of infrastructure fast, secure, and stable is a massive engineering challenge, and the work behind it is often invisible.
Rafael Gonzaga is a Principal Open Source Engineer at NodeSource and a member of the Node.js Technical Steering Committee. He’s spent years digging into the performance and security layers of Node’s core, helping shape the direction of the runtime itself. Rafael joins the show to talk about the state of Node.js performance, how benchmarking really works, the balance between speed and stability, and what it means to contribute to one of the world’s most important open-source projects.
Josh Goldberg is an independent full time open source developer in the TypeScript ecosystem. He works on projects that help developers write better TypeScript more easily, most notably on typescript-eslint: the tooling that enables ESLint and Prettier to run on TypeScript code. Josh regularly contributes to open source projects in the ecosystem such as ESLint and TypeScript. Josh is a Microsoft MVP for developer technologies and the author of the acclaimed Learning TypeScript (O’Reilly), a cherished resource for any developer seeking to learn TypeScript without any prior experience outside of JavaScript. Josh regularly presents talks and workshops at bootcamps, conferences, and meetups to share knowledge on TypeScript, static analysis, open source, and general frontend and web development.
Please click here to see the transcript of this episode.
Sponsorship inquiries: sponsor@softwareengineeringdaily.com
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Zachtronics is a legendary independent game studio known for creating intricate, engineering-focused puzzle games that merge logic, creativity, and code. The studio was founded by Zach Barth in 2011, and it has become a cult favorite among programmers and tinkerers alike with titles such as SpaceChem, Infinifactory, TIS-100, and Shenzhen I/O. Most recently, Zachtronics released Kaizen: A Factory Story, in which players take on the role of an American engineer hired by a Japanese manufacturing company in the 1980s to design assembly processes for various products.
Zach Barth joins the podcast with Joe Nash to talk about the games he makes.
Joe Nash is a developer, educator, and award-winning community builder, who has worked at companies including GitHub, Twilio, Unity, and PayPal. Joe got his start in software development by creating mods and running servers for Garry’s Mod, and game development remains his favorite way to experience and explore new technologies and concepts.
Please click here to see the transcript of this episode.
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Rivals of Aether and Rivals of Aether II are indie fighting games that combine fast-paced platform combat with elemental-themed characters. The game takes inspiration from Super Smash Bros. and emphasizes skillful movement, tight controls, and competitive balance, making it popular in the fighting game community.
Dan Fornace is a game director and designer at Aether Studios, the developer of Rivals of Aether. He joins the show with Joe Nash to talk about developing platform fighting games.
Joe Nash is a developer, educator, and award-winning community builder, who has worked at companies including GitHub, Twilio, Unity, and PayPal. Joe got his start in software development by creating mods and running servers for Garry’s Mod, and game development remains his favorite way to experience and explore new technologies and concepts.
Please click here to see the transcript of this episode.
Sponsorship inquiries: sponsor@softwareengineeringdaily.com
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Aviation cybersecurity is becoming an urgent priority as modern aircraft increasingly rely on complex digital systems for navigation, communication, and engine performance. These systems were once isolated but are now interconnected and vulnerable to cyber threats ranging from GPS spoofing to ransomware attacks on airline infrastructure. As nation-state actors and criminal groups grow more sophisticated, the aviation sector faces a rapidly expanding attack surface, with life-or-death consequences. Understanding and addressing these risks is essential not only for passenger safety but for the resilience of global transportation networks.
Serge Christiaans is a former Dutch Air Force pilot with a background in electronic and hybrid warfare. He later flew commercially for Singapore Airlines and is now the Lead Instructor and Program Director at the Aviation Cyber Academy. He joins the podcast with Gregor Vand to discuss the convergence of aviation and cybersecurity, the aircraft as a digital attack surface, hybrid warfare, the urgent need for aviation cyber resilience, and much more.
Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn.
Sponsorship inquiries: sponsor@softwareengineeringdaily.com
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Modern software relies heavily on open source dependencies, often pulling in thousands of packages maintained by developers all over the world. This accelerates innovation but also creates serious supply chain risks as attackers increasingly compromise popular libraries to spread malware at scale.
Feross Aboukhadijeh is the founder and CEO of Socket which is a security platform designed to protect software projects from open source supply chain attacks. In this episode he joins Josh Goldberg to talk about his career in open source, open source supply chain attacks, practical security lessons, the expanding attack surface in software development, and more.
Josh Goldberg is an independent full time open source developer in the TypeScript ecosystem. He works on projects that help developers write better TypeScript more easily, most notably on typescript-eslint: the tooling that enables ESLint and Prettier to run on TypeScript code. Josh regularly contributes to open source projects in the ecosystem such as ESLint and TypeScript. Josh is a Microsoft MVP for developer technologies and the author of the acclaimed Learning TypeScript (O’Reilly), a cherished resource for any developer seeking to learn TypeScript without any prior experience outside of JavaScript. Josh regularly presents talks and workshops at bootcamps, conferences, and meetups to share knowledge on TypeScript, static analysis, open source, and general frontend and web development.
Please click here to see the transcript of this episode.
Sponsorship inquiries: sponsor@softwareengineeringdaily.com
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Python’s popularity in data science and backend engineering has made it the default language for building AI infrastructure. However, with the rapid growth of AI applications, developers are increasingly looking for tools that combine Python’s flexibility with the rigor of production-ready systems.
Pydantic began as a library for type-safe data validation in Python and has become one of the language’s most widely adopted projects. More recently, the Pydantic team created Pydantic AI, a type-safe agent framework for building reliable AI systems in Python.
Samuel Colvin is the creator of Pydantic and Pydantic AI. In this episode, he joins the podcast with Gregor Vand to discuss the origins of Pydantic, the design principles behind type safety in AI applications, the evolution of Pydantic AI, the LogFire observability platform, and how open-source sustainability and engineering discipline are shaping the next generation of AI tooling.
Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn.
Please click here to see the transcript of this episode.
Sponsorship inquiries: sponsor@softwareengineeringdaily.com
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SED News is a monthly podcast from Software Engineering Daily where hosts Gregor Vand and Sean Falconer unpack the biggest stories shaping software engineering, Silicon Valley, and the broader tech industry.
In this episode, they cover Jeff Bezos’s unexpected return to the CEO seat with Project Prometheus, the growing debate over whether AI investments are sustainable, and the ecosystem forming around OpenAI. They also dig into the surge of Nordic startups, and what it signals about innovation hotspots outside the Bay Area.
Gregor and Sean then dive deep into the idea of technology tipping points, when breakthrough products finally cross the threshold from fringe curiosity to mainstream adoption.
Finally, they highlight standout threads from Hacker News, including moss surviving in outer space, a typographer’s re-creation of San Francisco’s light-rail signage, CERN’s guiding principles for responsible AI adoption, and why classic Pixar films looked better on 35mm than they do in 4K.
Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn.
Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn.
Please click here to see the transcript of this episode.
Sponsorship inquiries: sponsor@softwareengineeringdaily.com
The post SED News: Bezos Returns to Building, AI’s Reality Check, and Europe’s Cloud Ambitions appeared first on Software Engineering Daily.
PICO-8 is a software-based gaming console for making, sharing, and playing small games with a retro aesthetic. It emulates the look and feel of 8-bit consoles, providing limited color palettes, screen resolutions, and memory constraints.
The PICO-8 dev environment uses Lua and is focused on being accessible to developers while offering depth for complex projects.
Johan Peitz is a games industry veteran and developer extraordinaire, having created dozens of games across many platforms. He’s an expert in PICO-8 development, and joins the podcast to talk about creating games for the console.
Joe Nash is a developer, educator, and award-winning community builder, who has worked at companies including GitHub, Twilio, Unity, and PayPal. Joe got his start in software development by creating mods and running servers for Garry’s Mod, and game development remains his favorite way to experience and explore new technologies and concepts.
Please click here to see the transcript of this episode.
Sponsorship inquiries: sponsor@softwareengineeringdaily.com
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Doom has seemingly been ported to every electronic device imaginable, including picture frames, lamps, and coffee machines. The meme of “it runs Doom” has become so widespread that it spawned the r/itrunsdoom sub-Reddit.
Recently, Doom made headlines again for being ported to TypeScript. The project involved representing Doom entirely in TypeScript, three and a half trillion lines of types, 90 GB of RAM to run, and a full year to complete.
Dimitri Mitropoulos is the engineer who carried out this heroic feat. He’s also a developer at Vercel, the founder of Michigan Typescript, and a co-founder of SquiggleConf. Dimitri joins the podcast with Josh Goldberg to talk about what it took to pull off one of the most mind-bending TypeScript projects to date.
Josh Goldberg is an independent full time open source developer in the TypeScript ecosystem. He works on projects that help developers write better TypeScript more easily, most notably on typescript-eslint: the tooling that enables ESLint and Prettier to run on TypeScript code. Josh regularly contributes to open source projects in the ecosystem such as ESLint and TypeScript. Josh is a Microsoft MVP for developer technologies and the author of the acclaimed Learning TypeScript (O’Reilly), a cherished resource for any developer seeking to learn TypeScript without any prior experience outside of JavaScript. Josh regularly presents talks and workshops at bootcamps, conferences, and meetups to share knowledge on TypeScript, static analysis, open source, and general frontend and web development.
Please click here to see the transcript of this episode.
Sponsorship inquiries: sponsor@softwareengineeringdaily.com
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Simon Shuster is a journalist who has reported on Russia and Ukraine for over 15 years, most of that time as a staff correspondent for TIME Magazine. He was born in Moscow, and he and his family came to the United States as refugees from the Soviet Union when he was six years old.
After graduating from Stanford University in 2005, Simon returned to Moscow to work as a reporter for The Moscow Times, Reuters, the Associated Press and other publications. His political coverage of Russia’s descent into authoritarianism included numerous profiles of Vladimir Putin and interviews with top Russian officials. He has also interviewed and profiled the last three presidents of Ukraine.
Simon has spent years covering the war in Ukraine from both sides of the front lines. The year after the annexation of Crimea, Russian authorities deemed Simon a security threat and banned him from entering the country.
Simon is the author of the 2024 book The Showman: Inside the Invasion That Shook the World and Made a Leader of Volodymyr Zelensky, and he recently wrote the TIME Magazine article “How Ukraine Gamified Drone Warfare.”
He is currently at work on a new book that examines the future of warfare and how the lessons and technologies that emerged from the war in Ukraine are changing warfare and security around the world.
Simon joins the podcast with Kevin Ball to discuss drone warfare, AI-assisted targeting, the gamification of drone combat, the rapid iteration cycle of drone innovation, new ethical dilemmas in warfare, the coming proliferation of war drones, and the shifting balance of global power.
Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space.
Please click here to see the transcript of this episode.
Sponsorship inquiries: sponsor@softwareengineeringdaily.com
The post Drone Warfare in Ukraine with Simon Shuster appeared first on Software Engineering Daily.
Radix UI is an open-source library of React components. Its “headless” primitives handle the complex logic and accessibility concerns—like dialogs, dropdowns, and tabs—while leaving styling completely up to the developer. The project emphasizes usability, accessibility, and composability and has become a vital part of modern web dev, in part because it forms the foundation of shad/cn UI.
Chance Strickland is a software engineer at WorkOS and a maintainer of Radix UI. Chase joins the show with Nick Nisi to talk about Radix, its primitives, Radix’s relationship with shad/cn UI, the evolution of web primitives, and much more.
Nick Nisi is a conference organizer, speaker, and developer focused on tools across the web ecosystem. He has organized and emceed several conferences and has led NebraskaJS for more than a decade. Nick currently works as a developer experience engineer at WorkOS.
Please click here to see the transcript of this episode.
Sponsorship inquiries: sponsor@softwareengineeringdaily.com
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The Stack Overflow Developer Survey is an annual survey conducted by Stack Overflow that gathers comprehensive insights from developers around the world. It offers a valuable snapshot of the global developer community, covering a wide range of topics such as preferred programming languages, tools, and technologies.
Jody Bailey is the Chief Product and Technology Officer at Stack Overflow and Erin Yepis is a Research Manager at Stack Overflow. They join the show with Sean Falconer to talk about the results of the 2025 Developer Survey, which was recently released.
Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn.
Please click here to see the transcript of this episode.
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The post The 2025 Stack Overflow Developer Survey with Jody Bailey and Erin Yepis appeared first on Software Engineering Daily.
Byran Huang is a full stack developer who recently made headlines in the hacker space when he created the anyon_e, which is a highly integrated, open source laptop. The effort was a massive undertaking and showcased great design, hardware, and software.
In this episode, Byran joins the show with Gregor Vand to talk about his work on the anyon_e laptop.
Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn.
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The post Building an Open-Source Laptop with Byran Huang appeared first on Software Engineering Daily.
The modern internet is a vast web of independent networks bound together by billions of routing decisions made every second. It’s an architecture so reliable we mostly take it for granted, but behind the scenes it represents one of humanity’s greatest engineering achievements. Today’s internet is also dramatically more complex and capable than in its early years.
Erik Seidel is a Network Engineer at Cloudflare, where he focuses on automating global network infrastructure. He joins the show to discuss his unique journey into tech, the fundamentals of how the internet works, the Border Gateway Protocol, peering versus transit, Cloudflare’s architecture, networking in China, and much more.
Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn.
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The post The Architecture of the Internet with Erik Seidel appeared first on Software Engineering Daily.
SED News is a monthly podcast from Software Engineering Daily where hosts Gregor Vand and Sean Falconer unpack the biggest stories shaping software engineering, Silicon Valley, and the broader tech industry.
In this episode, they cover the $1.7B acquisition of Security AI, LangChain’s massive valuation, and the surprise $300M funding” round for Periodic Labs. They also break down the massive AWS outage, Apple’s rare reversal on its glass UI design, and the emerging web of trillion-dollar AI infrastructure deals centered around OpenAI.
Gregor and Sean then dive deep into the world of chipmakers, exploring how Intel is fighting to survive, AMD’s new partnership and 10% investment from OpenAI, Apple’s long-term bet on on-device AI with its M5 chips, and NVIDIA’s push to defend its dominance amid growing custom-chip competition.
Finally, they highlight standout threads from Hacker News, including developers rediscovering the joy of curl, a hacker’s clever teardown of Kindle’s DRM protections, and more.
Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn.
Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn.
Please click here to see the transcript of this episode.
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The post SED News: AMD’s Big OpenAI Deal, Intel’s Struggles, and Apple’s AI Long Game appeared first on Software Engineering Daily.
Most AI agent frameworks are backend-focused and written in Python, which introduces complexity when building full-stack AI applications with JavaScript or TypeScript frontends. This gap makes it harder for frontend developers to prototype, integrate, and iterate on AI-powered features.
Mastra is an open-source TypeScript framework focused on building AI agents and has primitives such as agents, tools, workflows, and RAG.
Sam Bhagwat and Abhi Aiyer are co-founders at Mastra. They join the podcast with Nick Nisi to talk about this state of frontend tooling for AI agents, AI agent primitives, MCP integration, and more.
Nick Nisi is a conference organizer, speaker, and developer focused on tools across the web ecosystem. He has organized and emceed several conferences and has led NebraskaJS for more than a decade. Nick currently works as a developer experience engineer at WorkOS.
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The post Building AI Agents on the Frontend with Sam Bhagwat and Abhi Aiyer appeared first on Software Engineering Daily.
X-Plane is a popular flight simulator developed by Laminar Research. It features a first-principles physics engine, realistic aircraft systems, and a wide variety of aircraft.
We wanted to understand the engineering that goes into creating a flight simulator so we invited Ben Supnik on the show. Ben is a software engineer at Laminar and he’s been working on X-Plane for the past 20 years. He joins the show with Kevin Ball to talk about X-Plane and his career working on the simulator.
Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space.
Please click here to see the transcript of this episode.
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The post The X-Plane Flight Simulator with Ben Supnik appeared first on Software Engineering Daily.
A common challenge in software development is creating and maintaining robust development environments. The rise of AI agents has amplified this complexity by adding new demands around permission controls, environment isolation, and resource management.
Ona is a platform for AI-native software development and engineering agents. The platform combines autonomous agents with secure, standardized environments, with a focus on giving enterprises control, security, and productivity so they can scale AI-native engineering without scaling risk.
Chris Weichel has more than two decades of experience spanning software engineering and human–computer interaction. He is currently the Chief Technology Officer at Ona – formerly Gitpod – where he leads the engineering team behind the company’s cloud-native development platform. Chris joins the podcast with Kevin Ball to talk about Ona, the impact of coding with parallel agents, the future of IDEs, choosing agent-friendly languages, code review as a new bottleneck in the software development lifecycle, and much more.
Full Disclosure: This episode is sponsored by Ona formerly Gitpod.
Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space.
Please click here to see the transcript of this episode.
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The post Turning Agent Autonomy into Productivity with Chris Weichel appeared first on Software Engineering Daily.
Homebrew is a widely used package manager that simplifies the installation of open-source software on macOS. It was created in response to the growing demand for a lightweight, developer-friendly tool suited to an increasingly Mac-centric development ecosystem. Today, Homebrew is a near-essential part of the macOS software development toolkit.
Mike McQuaid joined the project early on and collaborated closely with its creator, Max Howell. He joins the podcast with Kevin Ball to discuss Homebrew’s origins, architecture, its emphasis on automation and CI/CD, long-term sustainability, controversial trade-offs, and much more.
Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space.
Please click here to see the transcript of this episode.
Sponsorship inquiries: sponsor@softwareengineeringdaily.com
The post Homebrew and macOS Package Management with Mike McQuaid appeared first on Software Engineering Daily.
Modern software platforms are increasingly composed of diverse microservices, third-party APIs, and cloud resources. The distributed nature of these systems makes it difficult for engineers to gain a clear view of how their systems behave, which can slow down troubleshooting and increase operational risk.
groundcover is an observability platform that uses eBPF sensors to capture logs, metrics, and traces directly from the kernel. Critically, groundcover runs on a bring-your-own-cloud model so all data remains within the user’s own environment, which gives increased privacy, security, and cost efficiency.
The company is also focused on adapting to how AI-generated code is changing observability. Code can now be produced at superhuman speed, which increases the challenges for reviewing code before it enters production. This means that observability is likely to play a growing role in code validation and providing guardrails.
Yechezkel Rabinovich, or Chez, is the CTO and Co-founder of groundcover. He joins the podcast with Kevin Ball to discuss his journey from kernel engineering to building an eBPF-powered observability company. The conversation explores the power of eBPF, the realities of observability in modern systems, the impact of AI on software development and security, and where the future of root-cause analysis is headed.
Full Disclosure: This episode is sponsored by groundcover.
Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space.
Please click here to see the transcript of this episode.
Sponsorship inquiries: sponsor@softwareengineeringdaily.com
The post Engineering in the Age of Agents with Yechezkel Rabinovich appeared first on Software Engineering Daily.
Dynamic languages like Ruby, Python, and JavaScript determine the types of variables at runtime rather than at compile time. This flexibility allows for rapid development and concise code, but it also makes it harder to catch certain classes of bugs before execution. Type checkers for dynamic languages add structure and safety without compromising their expressive power.
Sorbet is a static type checker developed by the Stripe team and designed specifically for Ruby. The motivation behind Sorbet stemmed from the growing complexity of production Ruby applications, where developers needed stronger guarantees and more scalable code quality tools than dynamic typing alone could offer.
Jake Zimmerman is a software engineer at Stripe and leads development on Sorbet. He joins the podcast with Josh Goldberg to discuss his background, the challenges of typing in Ruby, the motivation behind Sorbet, its architecture, performance optimizations, and more.
Josh Goldberg is an independent full time open source developer in the TypeScript ecosystem. He works on projects that help developers write better TypeScript more easily, most notably on typescript-eslint: the tooling that enables ESLint and Prettier to run on TypeScript code. Josh regularly contributes to open source projects in the ecosystem such as ESLint and TypeScript. Josh is a Microsoft MVP for developer technologies and the author of the acclaimed Learning TypeScript (O’Reilly), a cherished resource for any developer seeking to learn TypeScript without any prior experience outside of JavaScript. Josh regularly presents talks and workshops at bootcamps, conferences, and meetups to share knowledge on TypeScript, static analysis, open source, and general frontend and web development.
Please click here to see the transcript of this episode.
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The post Static Analysis for Ruby with Jake Zimmerman appeared first on Software Engineering Daily.
The rise of language-model coding assistants has led to the creation of the vibe coding paradigm. In this mode of software development, AI agents take a plain language prompt and generate entire applications, which dramatically lowers the barriers to entry and democratizes access to software creation.
However, many enterprise environments have large, legacy codebases and these sprawling systems are complex, interdependent, and far less amenable to the greenfield style of vibe coding. Working effectively within them requires deep context awareness, something language models commonly struggle to maintain.
Augment Code is an AI coding assistant that focuses on contextual understanding of large codebases in enterprise settings. It emphasizes tooling to manage large development surface areas while automating PRs and code review.
Guy Gur-Ari is a Co-Founder at Augment. He has a PhD in physics and was previously a Research Scientist at Google where he worked on AI reasoning in math and science. Guy joins the podcast with Kevin Ball to talk about Augment Code, its focus on full context for large enterprise codebases, code review as the new bottleneck in AI-driven development, and much more.
Full Disclosure: This episode is sponsored by Augment Code.
Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space.
Please click here to see the transcript of this episode.
Sponsorship inquiries: sponsor@softwareengineeringdaily.com
The post Scaling AI in Enterprise Codebases with Guy Gur-Ari appeared first on Software Engineering Daily.
SED News is a monthly podcast from Software Engineering Daily where hosts Gregor Vand and Sean Falconer unpack the biggest stories shaping software engineering, Silicon Valley, and the broader tech industry.
In this episode, they cover NVIDIA‘s $5B investment in Intel and $100M stake in OpenAI, Meta’s stumble with its AR glasses demo, and the surprise $50B private equity acquisition of Electronic Arts. They also break down Anthropic’s record-setting Series F round and what Google’s Genie 3 “world model” reveals about the next frontier of AI.
Gregor and Sean then zoom things out to debate the future of devices and hardware. They cover Apple’s underwhelming Vision Pro to Snap’s all-in bet on AR specs, and what form factors might actually matter for developers.
Finally, they highlight standout projects from Hacker News, including hosting a website on a disposable vape, playing Snake in your browser’s address bar, and Slack’s six-figure billing fiasco that the community helped reverse.
Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn.
Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn.
Please click here to see the transcript of this episode.
Sponsorship inquiries: sponsor@softwareengineeringdaily.com
The post SED News: NVIDIA Bets on Intel, Meta’s Demo Crash, and Anthropic’s Explosive Growth appeared first on Software Engineering Daily.
Modern software systems are composed of many independent microservices spanning frontends, backends, APIs, and AI models, and coordinating and scaling them reliably is a constant challenge. A workflow orchestration platform addresses this by providing a structured framework to define, execute, and monitor complex workflows with resilience and clarity.
Orkes is an enterprise-scale agentic orchestration platform that builds on the open-source Conductor project, which was pioneered at Netflix. The platform coordinates AI agents, humans and APIs, with a focus on scalability, compliance, and trust. It further expands on the Conductor core by adding features like security, governance, and long-running workflows.
Viren Baraiya is the Founder and CTO at Orkes, and he’s the creator of Netflix Conductor. Viren joins the show with Gregor Vand to talk about his building Conductor at Netflix, the challenge of orchestrating microservices, rule-based versus programmatic workflow orchestration, agentic orchestration, MCP integration, and much more.
Full Disclosure: This episode is sponsored by Orkes.
Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn.
Please click here to see the transcript of this episode.
Sponsorship inquiries: sponsor@softwareengineeringdaily.com
The post Orkes and Agentic Workflow Orchestration with Viren Baraiya appeared first on Software Engineering Daily.
Vector search has become a foundational technology for AI applications, enabling everything from semantic code search to contextual retrieval for large language models. However, a major challenge with vector databases has been the cost as data storage scales.
Turbopuffer is a vector database that focuses on speed, cost and scalability. It was created by Simon Hørup Eskildsen and Justin Li in 2023 and has seen adoption from high-profile companies such as Cursor and Notion.
Simon joins the podcast with Gregor Vand to discuss the origin of turbopuffer, its unique technical design, the economics of vector storage, and more.
Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn.
Please click here to see the transcript of this episode.
Sponsorship inquiries: sponsor@softwareengineeringdaily.com
The post Turbopuffer with Simon Hørup Eskildsen appeared first on Software Engineering Daily.
Cassette Beasts is a turn-based monster-battling RPG that lets players record creatures onto cassette tapes and transform into them during battle. The game was an indie hit, and is also one of the most successful games built with the open source Godot Engine.
Jay Baylis and Tom Coxon are the creators of Cassette Beasts at Bytten Studio. They join the show with Joe Nash to talk about the development of their game.
Joe Nash is a developer, educator, and award-winning community builder, who has worked at companies including GitHub, Twilio, Unity, and PayPal. Joe got his start in software development by creating mods and running servers for Garry’s Mod, and game development remains his favorite way to experience and explore new technologies and concepts.
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The post Building an Indie Hit in Godot with Jay Baylis and Tom Coxon appeared first on Software Engineering Daily.
A challenge in modern frontend application design is efficiently fetching and managing GraphQL data while keeping UI components responsive and maintainable. Developers often face issues like over-fetching, under-fetching, and handling complex query dependencies, which can lead to performance bottlenecks and increased development effort.
Relay is a JavaScript framework developed by Meta for managing GraphQL data in React applications. It’s designed to optimize data fetching by colocating queries with components, ensuring that each part of the UI declares its own data dependencies.
Robert Balicki was on the Relay team at Meta and is now a Staff Software Engineer at Pinterest. He is currently developing Isograph, which provides a declarative and type-safe approach to data fetching.
Robert joins the show to talk about challenges and solutions for managing data in frontend applications.
Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn.
Please click here to see the transcript of this episode.
Sponsorship inquiries: sponsor@softwareengineeringdaily.com
The post Rethinking GraphQL Frontends with Robert Balicki appeared first on Software Engineering Daily.
Traditional package management systems for JavaScript have faced several inefficiencies related to dependency storage, resolution, and project performance.
pnpm is a fast, disk-efficient package manager for JavaScript and TypeScript projects, serving as an alternative to npm and Yarn. Due to its efficiency and reliability, pnpm is increasingly popular for managing monorepos and large-scale applications.
Zoltan Kochan is a full stack web developer and the creator of pnpm. He joins the show with Josh Goldberg to talk about his background and package management in the web.
Josh Goldberg is an independent full time open source developer in the TypeScript ecosystem. He works on projects that help developers write better TypeScript more easily, most notably on typescript-eslint: the tooling that enables ESLint and Prettier to run on TypeScript code. Josh regularly contributes to open source projects in the ecosystem such as ESLint and TypeScript. Josh is a Microsoft MVP for developer technologies and the author of the acclaimed Learning TypeScript (O’Reilly), a cherished resource for any developer seeking to learn TypeScript without any prior experience outside of JavaScript. Josh regularly presents talks and workshops at bootcamps, conferences, and meetups to share knowledge on TypeScript, static analysis, open source, and general frontend and web development.
Please click here to see the transcript of this episode.
Sponsorship inquiries: sponsor@softwareengineeringdaily.com
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Modern application development often involves juggling multiple types of databases to handle diverse data models. The lack of unification can lead to complex architectures with attendant security concerns and fragmented development workflows.
SurrealDB is an open-source, multi-model database developed in Rust and integrates functionalities of many databases including relational, document, graph, time series, search and vector databases. It supports both schema-less and schema-full data models and has a SQL-like query language.
The project has rapidly grown in popularity, and version 3.0 was just released with a focus on enabling AI-powered analysis of unstructured data directly within the database, along with tooling for building event-driven applications.
Tobie Morgan Hitchcock is the CEO and co-founder of SurrealDB. He joins the podcast with Kevin Ball to talk about SurrealDB, handling multi-model data, unstructured data processing, building event driven AI applications, coupling databases with AI models, and more.
Full Disclosure: This episode is sponsored by SurrealDB.
Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space.
Please click here to see the transcript of this episode.
Sponsorship inquiries: sponsor@softwareengineeringdaily.com
The post SurrealDB 3.0 and Building Event-Driven AI Applications with Tobie Morgan Hitchcock appeared first on Software Engineering Daily.
Modern web development faces several challenges, particularly when building scalable, maintainable, and high-performance applications. As applications grow, managing complex user interfaces, and ensuring efficient data handling and modular code structures, becomes increasingly difficult.
Angular is a TypeScript-based web framework developed by Google. It’s component-driven and designed for building single-page applications with a strong emphasis on modular architecture and performance optimization. Angular’s scalability, maintainability, and built-in features like modular architecture, TypeScript support, and robust tooling, have made it popular for enterprise applications.
Jessica Janiuk is a Staff Software Engineer at Google where she works on Angular, which just hit version 19 late last year. In this episode, Jessica joins the show with Josh Goldberg to talk about the Angular project.
Josh Goldberg is an independent full time open source developer in the TypeScript ecosystem. He works on projects that help developers write better TypeScript more easily, most notably on typescript-eslint: the tooling that enables ESLint and Prettier to run on TypeScript code. Josh regularly contributes to open source projects in the ecosystem such as ESLint and TypeScript. Josh is a Microsoft MVP for developer technologies and the author of the acclaimed Learning TypeScript (O’Reilly), a cherished resource for any developer seeking to learn TypeScript without any prior experience outside of JavaScript. Josh regularly presents talks and workshops at bootcamps, conferences, and meetups to share knowledge on TypeScript, static analysis, open source, and general frontend and web development.
Please click here to see the transcript of this episode.
Sponsorship inquiries: sponsor@softwareengineeringdaily.com
The post Angular with Jessica Janiuk appeared first on Software Engineering Daily.
SED News is a monthly podcast from Software Engineering Daily where hosts Gregor Vand and Sean Falconer unpack the biggest stories shaping software engineering, Silicon Valley, and the broader tech industry.
In this episode, they discuss Perplexity’s headline-grabbing offer to buy Google Chrome, the U.S. government’s large stake in Intel, Meta’s abrupt pause on AI hiring, and a reality check on what “agentic” systems can actually deliver today versus the hype.
They also dive into standout discussions from Hacker News, including a proposal to curb “ghost job” postings with stricter transparency rules, an interactive Big-O explainer, and more.
Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn.
Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn.
Please click here to see the transcript of this episode.
Sponsorship inquiries: sponsor@softwareengineeringdaily.com
The post SED News: Perplexity’s Chrome Play, Meta’s AI Freeze, and Intel Becomes Too Big to Fail appeared first on Software Engineering Daily.
A common challenge in data-rich organizations is that critical context about the data is often hard to capture and even harder to keep up to date. As more people across the organization use data and data models get more complex, simply finding the right dataset can be slow and create bottlenecks.
Select Star is a data discovery and metadata platform that builds a continuously updated knowledge graph of an organization’s data by analyzing both its structure and how it’s actually used. It enriches data with context such as popularity, lineage, and semantic models, making it easier for AI and teams to discover, trust, and use the right data. These enriched metadata layers are also highly valuable for large language models, significantly improving the accuracy of generated SQL queries.
Shinji Kim is the founder and CEO of Select Star, and she joins Sean Falconer to discuss solving metadata curation challenges, managing data context at scale, using LLMs for SQL generation, emerging trends in metadata management, and more.
Full Disclosure: This episode is sponsored by Select Star.
Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn.
Please click here to see the transcript of this episode.
Sponsorship inquiries: sponsor@softwareengineeringdaily.com
The post Context-Aware SQL and Metadata with Shinji Kim appeared first on Software Engineering Daily.
Data visualization is increasingly important as organizations prioritize data-driven decision-making. Tools that transform complex datasets into intuitive, interpretable visualizations are arguably just as critical as the data itself.
Robert Kosara is a Data Visualization Developer at Observable which is a platform for creating interactive data visualizations, and which makes extensive use of the popular D3 JavaScript library. Robert previously worked at companies including Salesforce and Tableau, and has deep experience in data visualization and data visualization tools. He joins the show to talk about modern data visualization and his work at Observable.
Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn.
Please click here to see the transcript of this episode.
Sponsorship inquiries: sponsor@softwareengineeringdaily.com
The post Modern Data Visualization with Robert Kosara appeared first on Software Engineering Daily.
Werner Vogels is the Chief Technology Officer at Amazon, where he has played a pivotal role in shaping the company’s technology vision for over two decades. Before joining Amazon in 2004, Werner was a research scientist at Cornell University where he focused on distributed systems and scalability, both of which are concepts that would later influence the design of AWS. He holds a PhD in computer science and has authored numerous academic papers on the reliability and performance of large-scale systems.
As CTO, Werner has been instrumental in guiding Amazon’s transition from an online retailer to a global cloud infrastructure provider. He is one of the key architects behind Amazon’s push into cloud computing, helping to define the new model for delivering infrastructure. He is known for his pragmatic, customer-focused approach to technology and for championing ideas such as “you build it, you run it,” “APIs are forever,” and more recently, Frugal Architecting, which emphasizes cost-effective and sustainable software design.
In this episode, Kevin Ball sits down with Werner for a wide-ranging conversation. They discuss the early days of Amazon, the birth of AWS, the principles of the Frugal Architect, aligning cost to the business, engineering-business collaboration, technical debt, and much more.
Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space.
Please click here to see the transcript of this episode.
Sponsorship inquiries: sponsor@softwareengineeringdaily.com
The post A Conversation with Amazon CTO Werner Vogels appeared first on Software Engineering Daily.
A key challenge with designing AI agents is that large language models are stateless and have limited context windows. This requires careful engineering to maintain continuity and reliability across sequential LLM interactions. To perform well, agents need fast systems for storing and retrieving short-term conversations, summaries, and long-term facts.
Redis is an open‑source, in‑memory data store widely used for high‑performance caching, analytics, and message brokering. Recent advances have extended Redis’ capabilities to vector search and semantic caching, which has made it an increasingly popular part of the agentic application stack.
Andrew Brookins is a Principal Applied AI Engineer at Redis. He joins the show with Sean Falconer to discuss the challenges of building AI agents, the role of memory in agents, hybrid search versus vector-only search, the concept of world models, and more.
Full Disclosure: This episode is sponsored by Redis.
Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn.
Please click here to see the transcript of this episode.
Sponsorship inquiries: sponsor@softwareengineeringdaily.com
The post Redis and AI Agent Memory with Andrew Brookins appeared first on Software Engineering Daily.
Deploying and managing cloud workloads is a complex task that requires developers to handle infrastructure, scaling, CI/CD pipelines, and database hosting. Configuring and maintaining Kubernetes, ensuring smooth deployments, and integrating various services efficiently is a common challenge.
Will Stewart is the co-founder and CEO of Northflank, which is a platform focused on streamlining application deployment and management.
In this episode, he joins the show to talk about the contemporary challenges and solutions around workload deployment.
Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn.
Please click here to see the transcript of this episode.
Sponsorship inquiries: sponsor@softwareengineeringdaily.com
The post Complex Workload Deployment with Will Stewart appeared first on Software Engineering Daily.
Modern software teams typically rely on a patchwork of tools to manage planning, development, feature rollout, and post-release analysis. This fragmentation is a known challenge that can create friction and slow down software development iteration. It’s especially problematic for cross-functional teams, where differences in roles, expertise, and work culture can further complicate collaboration.
There is growing consensus that successful software product development requires continuous collaboration across functions, including design, engineering and operations.
Tobias Dunn-Krahn is the CTO and Doug Peete is the Chief Product Officer of Atono, which is a software development lifecycle platform focused on cross-functional teams. They join the podcast with Kevin Ball to talk about the challenges of modern product development, the importance of low-friction UX, the role of AI in product tooling, and how to unify product, design, engineering, and operations in a single workflow.
Full Disclosure: This episode is sponsored by Atono.
Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space.
Please click here to see the transcript of this episode.
Sponsorship inquiries: sponsor@softwareengineeringdaily.com
The post Empowering Cross-Functional Product Teams with Tobias Dunn-Krahn and Doug Peete appeared first on Software Engineering Daily.
Carbon is a programming language developed by Google as a successor to C++, and it aims to provide modern safety features while maintaining high performance. It’s designed to offer seamless interoperability with C++ while addressing shortcomings of C++ such as slow compilation times and lack of memory safety. Carbon also introduces features like a more readable syntax, improved generics, and automatic memory management while still allowing low-level control.
Chandler Carruth is the creator of Carbon, and he leads the C++, C Lang, and LLVM teams at Google, and he also worked on several pieces of Google’s distributed build system. In this episode, he joins Kevin Ball to talk about Carbon and the future of the language.
Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space.
Please click here to see the transcript of this episode.
Sponsorship inquiries: sponsor@softwareengineeringdaily.com
The post Carbon and Modernizing C++ with Chandler Carruth appeared first on Software Engineering Daily.
Anaconda is a popular platform for data science, machine learning, and AI. It provides trusted repositories of Python and R packages and has over 35 million users worldwide.
Rob Futrick is the CTO at Anaconda, and he joins the show to talk about the platform, the concept of an OS for AI, and more.
This episode is hosted by Lee Atchison. Lee Atchison is a software architect, author, and thought leader on cloud computing and application modernization. His best-selling book, Architecting for Scale (O’Reilly Media), is an essential resource for technical teams looking to maintain high availability and manage risk in their cloud environments.
Lee is the host of his podcast, Modern Digital Business, an engaging and informative podcast produced for people looking to build and grow their digital business with the help of modern applications and processes developed for today’s fast-moving business environment. Listen at mdb.fm. Follow Lee at softwarearchitectureinsights.com, and see all his content at leeatchison.com.
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Java is one of the most widely used programming languages, and a key contributor to its success is VMware Tanzu’s Spring, the most common framework for Java development. The Spring Framework is built on top of the Java Virtual Machine and provides a consistent programming and configuration model for application developers. From inception, it was designed with developer experience and modularity in mind.
The open-source application framework has been accelerating Java development times since its inception in 2004 (Happy 20th birthday). Since then, the platform has been expanding: growing 50% year over year during the last five years. In this blog we’re exploring what makes Spring important to Java, how the framework influenced the developer experience, and a look at the latest version of Spring, which introduces features to support AI integration.
Spring SuccessTwo decades ago, there were dozens of different ways to connect to a database, something just about every application has to do at some point. “At the time, the various approaches were very cumbersome: developers wrote a lot of code and gained very little functionality,” explained Mark Pollack, a Senior Staff Engineer, Tanzu Division, Broadcom. “Spring provided a lot of value by simplifying the process. Just getting a single app to talk to a database, present a web form, and do transactions correctly was a huge win. In that era, developers could spend weeks trying to create that function.”
Another reason for its success is its enterprise-focus. Most open source projects concentrate on the consumer market. However, large companies invest a lot of money building applications to run their businesses. “At the end of the day, large corporations’ largest expense is probably their developers,” explained Ryan Morgan, Senior Director of Engineering, Tanzu Division, Broadcom. Spring makes developer teams more efficient, which greatly enhances the bottom line.
Through the years, the ecosystem has grown. “There’s a large and vibrant community behind Spring,” noted Tanzu’s Morgan. Now, it has more than 200 different technology starters. These software building blocks make it simpler for software engineers to integrate their code with different third-party systems.
Get a Great StartDevelopment problems evolve over time, so various elements were added. Spring Initializer is a bootstrapping tool, a way for developers to create a new project. “Normally, software engineers started from a blank piece of paper and had to figure out what type of project it was and what type of libraries were needed,” said Tanzu’s Morgan. Then, they searched the web to find some place in the documentation that told them what library dependency was needed to add for different pieces of functionality. Then invariably, you cut and pasted from something that wasn’t consistent. You ended up with a mess.”
With Spring Initializer, software engineers go to a website which has clear instructions about what the options are and presents them in a typical web form. Then, they enter the Generate command and out comes a shell that they can use to start building their program. The solution does not generate any code but solves the problem of finding the right application dependencies. Developers start faster and are less frustrated than previous methods.
Under development is Spring CLI, which not only creates the shell of the app but also includes code. The advances have a significant impact because 1 million new projects are created each month.
Meet the Need for Development SpeedThe last 10 years have seen a major move to container deployment and Spring has aligned with this paradigm shift. “Really, when you think about all those cloud native patterns, a lot of those container functions are really baked into our projects already,” stated Tanzu’s Morgan. “If you want to do distributed configuration, we have a solution for that. You want to do service discovery; Spring has patterns and tools for that.”
Recently, a major change to Spring occurred. Rather than release new functions autonomously, they are gathered and bundled into Spring Boot. Version 3.0, which is based on Spring Framework 6.0, requires Java 17 or above. Previously, Spring supported Java 8, so the change is significant for some companies.
Better performance is one benefit from the change. “We’ve seen customers realize 15% performance improvements, just from doing the upgrade,” said Tanzu’s Morgan.
Java Supports Generative AIAI is being woven into many applications, especially with the emergence of Generative AI solutions. They represent a quantum leap in capabilities and overall intelligence compared to previous iterations of AI. One reason why today’s generative models are gaining so much attention is that they work with much larger volumes of information (hundreds of billions of words) and larger data models (hundreds of billions of parameters) compared to previous AI systems. They possess impressive and unprecedented power. Consequently, they can perform very sophisticated functions.
However as developers try to take advantage of the functionality, platform diversity again presents development challenges. “OpenAI has their API, Amazon Bedrock offers a different one, and so do other companies,” noted Tanzu’s Pollack.
A guiding focus and design principle in the Spring framework is simplifying such work by providing common abstractions over similar technologies and interfaces. Spring AI is quickly becoming the starting point when Java developers write AI applications. “Spring AI has the common patterns that Spring developers are used to,” noted Tanzu’s Pollack. It can abstract out models, clients, etc. in ways that are familiar to Spring users.”
Another crucial part of AI applications is using a vector database. Spring supports multiple vector databases, and its portable API simplifies changing implementations. So, Spring streamlines AI application development.
Java has been a popular programming language for enterprises for decades. Spring provides software engineers with tools that help them enhance the development process. The framework has reached its 20th year of empowering developers, and its years, engaged community is laying the groundwork for continued expansion in the coming decades. “Maybe one reason why Spring continues to do well is it constantly tries to improve itself and doesn’t just rest on its laurels,” concluded Tanzu’s Pollack.
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Vercel provides a cloud platform to rapidly deploy web projects, and they develop the highly successful Next.js framework. The company recently made headlines when they announced v0 which is a generative AI tool to create React code from text prompts. The generated code uses open-source tools like Tailwind CSS and shadcn/ui.
Lee Robinson is the VP of Product at Vercel. He helps lead the product teams and focuses on developer experience on the platform. He joins the show to talk about Vercel, their AI SDK to easily connect frontend code with LLMs, the v0 AI tool, and more.
Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from information visualization to quantum computing. Currently, Sean is Head of Marketing and Developer Relations at Skyflow and host of the podcast Partially Redacted, a podcast about privacy and security engineering. You can connect with Sean on Twitter @seanfalconer .
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Algolia is a platform that provides search as a service. The company was founded in 2012, was part of Y Combinator’s Winter 2014 class, and has become highly popular for integrating modern search functionality into web-facing services.Sean Mullaney is the CTO of Algolia and has worked at Google X, Stripe, and Zolando. He joins the show today to talk about Algolia, neural search, vector compression, search optimization, and more.
Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from information visualization to quantum computing. Currently, Sean is Head of Marketing and Developer Relations at Skyflow and host of the podcast Partially Redacted, a podcast about privacy and security engineering. You can connect with Sean on Twitter @seanfalconer.
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Jodie Burchell is the Data Science Developer Advocate at JetBrains, which makes integrated development environments or, IDEs, for many major languages. After observing the rapid growth of the AI coding assistant landscape, the company recently announced integration of an AI assistant into their IDEs.Jodie joins the show today to talk about why the company decided to take this step, the design challenges of adding AI tools to software products, and the team’s particular interest in auto-generating code documentation. Jodie also talks about the different types of language AIs, how AI tools will impact software development, and more.Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from information visualization to quantum computing. Currently, Sean is Head of Marketing and Developer Relations at Skyflow and host of the podcast Partially Redacted, a podcast about privacy and security engineering. You can connect with Sean on Twitter @seanfalconer .
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This episode of Software Engineering Daily is part of our on-site coverage of AWS re:Invent 2023, which took place from November 27th through December 1st in Las Vegas.
In today’s interview, host Jordi Mon Companys speaks with Ankur Mehrotra who is the Director and GM of Amazon SageMaker.
Jordi Mon Companys is a product manager and marketer that specializes in software delivery, developer experience, cloud native and open source. He has developed his career at companies like GitLab, Weaveworks, Harness and other platform and devtool providers. His interests range from software supply chain security to open source innovation. You can reach out to him on Twitter at @jordimonpmm.
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An embedding is a concept in machine learning that refers to a particular representation of text, images, audio, or other information. Embeddings are designed to make data consumable by ML models.
However, storing embeddings presents a challenge to traditional databases. Vector databases are designed to solve this problem.
Pinecone has developed one of the most prominent vector databases that is widely used for ML and AI applications.Marek Galovic is a software engineer at Pinecone and works on the core database team. He joins the podcast today to talk about how vector embeddings are created, engineering a vector database, unsolved challenges in the space, and more.
Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from information visualization to quantum computing. Currently, Sean is Head of Marketing and Developer Relations at Skyflow and host of the podcast Partially Redacted, a podcast about privacy and security engineering. You can connect with Sean on Twitter @seanfalconer.
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Vespa is a fully featured search engine and vector database, and it has integrated ML model inference. The project open sourced in 2017, and since then has grown to become a prominent platform for applying AI to big data sets at serving time.
Vespa began as a project to solve Yahoo’s use cases in search, recommendation, and ad serving. The company made headlines in October when they announced they’re spinning Vespa.ai out of Yahoo as a separate company.
Jon Bratseth is the CEO at Vespa and he joins the show to talk about large language models, retrieval augmented generation, or RAG, vector database engineering, and more.Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from information visualization to quantum computing. Currently, Sean is Head of Marketing and Developer Relations at Skyflow and host of the podcast Partially Redacted, a podcast about privacy and security engineering. You can connect with Sean on Twitter @seanfalconer .
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GitHub Copilot is an AI tool developed by GitHub and OpenAI to assist software developers by autocompleting code. Copilot kicked off a revolution in software engineering, and AI assistants are now considered essential tools to many developers.
Joseph Katsioloudes is a cyber security specialist and works at the GitHub Security Lab. He joins the show today to talk about Copilot, the future of software development in an AI world, using AI to improve security, and more.
Check out Joseph’s bio and the Secure Code Game which is an in-repo learning experience that Joseph created to teach how to secure vulnerable code.
Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from information covisualization to quantum computing. Currently, Sean is Head of Marketing and Developer Relations at Skyflow and host of the podcast Partially Redacted, a podcast about privacy and security engineering. You ca connect with Sean on Twitter @seanfalconer .
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On a recent trip to my hometown in Eastern Canada, my father picked me up at the airport. One of the first things he asked me was, “Is AI going to take everyone’s jobs?”.
When AI, generative AI, and large language models (LLM) have become topics of conversation within the senior citizen community of rural Canada, you know it’s on everyone’s minds. Generative AI, and especially the use of LLMs, is the “new new thing”. It dominates my X (i.e. Twitter) feed and nearly every conversation I have about technology.
There’s justifiably a ton of excitement about the power of generative AI, reminiscent of the introduction of the Internet or the first smartphone. Generative AI is poised to transform how we build products, design drugs, write content, and interact with technology. But as the utilization of AI grows, many governments and companies have raised concerns about the privacy and compliance issues that adopters of these technologies face.
The core challenge posed by generative AI right now is that unlike conventional applications, LLMs have no “delete” button. There’s no straightforward mechanism to “unlearn” specific information, no equivalent to deleting a row in your database’s user table. In a world where the “right to be forgotten” is central to many privacy regulations, using LLMs presents some difficult challenges.
So what does all this mean for businesses that are building new AI-powered applications or AI models?
In this post, we’ll explore this question and attempt to provide answers. We’ll examine the potential impact of generative AI, ongoing compliance hurdles, and a variety of privacy strategies. Finally, we’ll examine a novel approach grounded in the IEEE’s recommended architecture for securely storing, managing, and utilizing sensitive customer PII (Personally Identifiable Information)—the data privacy vault.
Generative AI’s privacy and compliance challengesImagine the following scenario: You’ve just copied and pasted sensitive contract details into an LLM to get some quick assistance with routine contract due diligence. The LLM serves its purpose, but here’s the catch: depending on how it’s configured, that confidential contract data might linger within the LLM, accessible to other users. Deleting it isn’t an option, predicting its future use—or misuse—becomes a daunting task, and retraining the LLM to “roll it back” to its state before you shared those sensitive contract details can be prohibitively expensive.
The only foolproof solution?
Keep sensitive data far away from LLMs.
Sensitive information, including internal company project names, core intellectual property, or personal data like birthdates, social security numbers, and healthcare records, can inadvertently find its way into LLMs in several ways:
- Training data: LLMs are trained and honed on expansive datasets that often contain PII. Without robust anonymization or redaction measures in place, sensitive data becomes part of the model’s training dataset, meaning that this data can potentially resurface later.
- Inference: LLMs generate text based on user inputs or prompts. Much like training data, a prompt containing sensitive data seeps into the model and can influence the generated content, potentially exposing this data.
Privacy lawsAI data privacy is a formidable challenge for any company interested in investing in generative AI technology. Recent temporary bans of ChatGPT in Italy and by companies like Samsung have pushed these concerns to the forefront for businesses looking to invest in generative AI.
Even outside of generative AI, there are increasing concerns about protecting data privacy. Meta was recently fined $1.3 billion by the European Union (EU) for its non-compliant transfers of sensitive data to the U.S. And this isn’t just an issue for companies doing business in the EU.
There are now more than 100 countries with some form of privacy regulation in place. Each country’s privacy regulations include unique and nuanced requirements that place a variety of restrictions on the use and handling of sensitive data. The most common restrictions relate to cross-border data transfers, where sensitive data can be stored, and to individual data subject rights such as the “right to be forgotten.”
One of the biggest shortcomings of LLMs is their inability to selectively delete or “unlearn” specific data points, such as an individual’s name or date of birth. This limitation presents significant risks for businesses leveraging these systems.
For example, privacy regulations in Europe, Argentina, and the Philippines (just to name a few) all support an individual’s “right to be forgotten.” This grants individuals the right to have their personal information removed or erased from a system. Without an LLM delete button, there’s no way for a business to address such a request without retraining their LLM from scratch.
Consider the European Union’s General Data Protection Regulation (GDPR), which grants individuals the right to access, rectify, and erase their personal data—a task that becomes daunting if that data is embedded within an LLM. GDPR also empowers individuals with the right to object to automated decision-making, further complicating compliance for companies that use LLMs.
Data localization requirements pose another challenge for users of LLMs. These requirements pertain to the physical location where customer data is stored. Different countries and regions have precise laws dictating how customer data should be handled, processed, stored, and safeguarded. This poses a significant challenge when using an LLM used for a company’s global customer base.
Data Subject Access Requests (DSARs) under GDPR and other laws add another layer of complexity. In the EU and California, individuals (i.e., “data subjects”) have the right to request access to their personal data, but complying with such requests proves challenging if that data has been processed by LLMs.
Considering the intricate privacy and compliance landscape and the complexity of LLMs, the most practical approach to maintaining compliance is to prevent sensitive data from entering the model altogether. By implementing stringent data handling practices, businesses can mitigate the privacy risks associated with LLMs, while also maintaining the utility of the model. Many companies have already decided that the risks are too high, so they’ve banned the use of ChatGPT, but this approach is shortsighted. Properly managed, these models can create a lot of value.
Privacy approaches for generative AITo address the privacy challenges associated with generative AI models, there have been a few proposals such as banning or controlling access, using synthetic data instead of real data, and running private LLMs.
Banning ChatGPT and other generative AI systems isn’t an effective long-term strategy, and these other “band aid” approaches are bound to fail as people can find easy workarounds. Using synthetic data replaces sensitive information with similar-looking but non-sensitive data and keeps PII out of the model, but at the cost of losing the value that motivated you to share sensitive data with the LLM in the first place. The model loses context, and there’s no referential integrity between the synthetically generated data and the original sensitive information.
The most popular approach to addressing AI data privacy, and the one that’s being promoted by cloud providers like Google, Microsoft, AWS, and Snowflake, is to run your LLM privately on their infrastructure.
For example, with Snowflake’s Snowpark Model Registry, you can take an open source LLM and run it within a container service in your Snowflake account. They state that this allows you to train the LLM using your proprietary data.
Snowpark Model Registry and Container Service (Source: Snowflake Blog)However, there are several drawbacks to using this approach.
Outside of privacy concerns, if you’re choosing to run an LLM privately rather than take advantage of an existing managed service, then you’re stuck with managing the updates, and possibly the infrastructure, yourself. It’s also going to be much more expensive to run an LLM privately. Taken together, these drawbacks mean running a private LLM likely doesn’t make sense for most companies.
But the bigger issue is that, from a privacy standpoint, private LLMs simply don’t provide effective data privacy. Private LLMs give you model isolation, but they don’t provide data governance in the form of fine-grained access controls: any user who can access the private LLM can access all of the data that it contains. Data privacy is about giving a user control over their data, but private LLMs still suffer from all of the intrinsic limitations around data deletion that are blocking the adoption of public LLMs.
What matters to a business—and individual data subjects—is who sees what, when, where, and for how long. Using a private LLM doesn’t give you the ability to make sure that Susie in accounting sees one type of LLM response based on her job title while Bob in customer support sees something else.
So how can we prevent PII and other sensitive data from entering an LLM, but also support data governance so we can control who can see what and support the need to delete sensitive data?
A new approach to PII managementIn the world of traditional data management, an increasingly popular approach to protecting the privacy of sensitive data is through the use of a data privacy vault. A data privacy vault isolates, protects, and governs sensitive customer data while facilitating region-specific compliance with laws like GDPR through data localization.
With a vault architecture, sensitive data is stored in your vault, isolated outside of your existing systems. Isolation helps ensure the integrity and security of sensitive data, and simplifies the regionalization of this data. De-identified data that serve as references to the sensitive data are stored in traditional cloud storage and downstream services.
De-identification happens through a tokenization process. This is not the same as LLM tokenization, that has to do with splitting texts into smaller units. With data de-identification, tokenization is a non-algorithmic approach to data obfuscation that swaps sensitive data for tokens. A token is a pointer that lets you reference something somewhere else while providing obfuscation.Traditional data management versus a data privacy vault architec
Traditional data management versus a data privacy vault architectureLet’s look at a simple example. In the workflow below a phone number is collected by a front end application. The phone number, along with any other PII, is stored securely in the vault, which is isolated outside of your company’s existing infrastructure. In exchange, the vault generates a de-identified representation of the phone number (e.g. ABC123). The de-identified (or tokenized) data has no mathematical connection with the original data, so it can’t be reverse engineered.
Any downstream services—application databases, data warehouse, analytics, any logs, etc.—store only a token representation of the data, and are removed from the scope of compliance:
Example of a data privacy vault in actionAdditionally, a data privacy vault can store sensitive data in a specific geographic location, and tightly control access to this data. Other systems, including LLMs, only have access to non-sensitive de-identified data.
The vault not only stores and generates de-identified data, but it tightly controls access to sensitive data through a zero trust model where no user account or process has access to data unless it’s granted by explicit access control policies. These policies are built from the bottom, granting access to specific columns and rows of PII. This allows you to control who sees what, when, where, for how long, and in what format.
For example, let’s say we have a vault containing customer records with columns defined for a customer’s name, social security number (SSN), date of birth (DOB), and email. In our application we want to support two types of users: support and marketing.
Support doesn’t need to know the exact details about a customer, they only need masked data so they can speak to the customer by name and verify their identity using the last four digits of the customer’s SSN. We can create a policy for the role support that grants access to only the limited view of the data.
ALLOW READ ON users.full_name, users.ssn, users.email WITH REDACTION = MASKEDALLOW READ ON users.dob WITH REDACTION = REDACTED
Similarly, a marketing person needs someone’s name and email, but they don’t need the customer’s SSN or need to know how old someone is.
ALLOW READ ON users.full_name, users.email WITH REDACTION = PLAIN_TEXTALLOW READ ON users.dob WITH REDACTION = MASKEDALLOW READ ON users.ssn WITH REDACTION = REDACTED
With roles and policies similar to ones above in place, the same de-identified data is exchanged with the vault. Based on the role and associated access control policies for the caller, different views of the same sensitive data can be supported.
Different views of sensitive data based on role.A privacy firewall for LLMsCompanies can address privacy and compliance concerns with LLMs with a similar application of the data privacy vault architectural pattern. A data privacy vault prevents the leakage of sensitive data into LLMs, addressing privacy concerns around LLM training and inference.
Because data privacy vaults use modern privacy-enhancing technologies like polymorphic encryption and tokenization, sensitive data can be de-identified in a way that preserves referential integrity. This means that responses from an LLM containing de-identified data can be re-identified based on zero trust policies defined in the vault that let you make sure that only the right information is shared with the LLM user. This lets you make sure Susie in accounting only sees what she should have access to (i.e., account numbers and invoice amounts) while Bob in customer support sees only what he needs to do his job.
Preserving privacy during model trainingTo preserve privacy during model training, the data privacy vault sits at the head of your training pipeline. Training data that might include sensitive and non-sensitive data goes to the data privacy vault first. The vault detects the sensitive data, stores it within the vault, and replaces it with de-identified data. The resulting dataset is de-identified and safe to share with an LLM.
Model Training Pipeline with a Data Privacy VaultAn LLM doesn’t care whether my name, Sean Falconer, is part of the training data or some consistently generated representation of my name (such as “dak5lhf9w”) is part of the training data. Eventually, it’s just a vector.
Preserving privacy during inferenceSensitive data may also enter a model during inference. In the example below, a prompt is created asking for a summary of a will. The vault detects the sensitive information, de-identifies it, and shares a non-sensitive version of the prompt with the LLM.
Since the LLM was trained on non-sensitive and de-identified data, inference can be carried out as normal.
Example of de-identifying inference data with an LLM and a data privacy vaultOn egress from the LLM, the response is passed through the data privacy vault for re-identification. Any de-identified data will be re-identified assuming the end-user has the right to see the information, according to explicit access control policies configured in the vault.
Privacy and complianceFrom a privacy and compliance standpoint, using a data privacy vault means that no sensitive data is ever shared with an LLM, so it remains outside of the scope of compliance. Data residency, DSARs, and delete requests are now the responsibility of a data privacy vault that’s designed to handle these requirements and workflows.
Incorporating the vault into the model training and inference pipelines allows you to combine the best of modern sensitive data management with any LLM stack, private, public, or proprietary.
Final thoughtsAs every company gradually morphs into an AI company, it’s critically important to face data privacy challenges head-on. Without a concrete solution to data privacy requirements, businesses risk remaining stuck indefinitely in the “demo” or “proof-of-concept” phase. The fusion of data privacy vaults and generative AI offers a promising path forward, freeing businesses to harness the power of AI without compromising on privacy.
Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from information visualization to quantum computing. Currently, Sean is Head of Marketing and Developer Relations at Skyflow and host of the podcast Partially Redacted, a podcast about privacy and security engineering. You can connect with Sean on Twitter @seanfalconer.The post Privacy in the Age of Generative AI appeared first on Software Engineering Daily.
Machine learning model research requires running expensive, long-running experiments where even a slight mis-calibration can cost millions of dollars in underutilized compute resources. Once trained, model deployment, production monitoring, and observability requirements all present unique operational challenges.
Chris Van Pelt is the Chief Information Officer of Weights and Biases, which is the industry standard in experiment monitoring and visualization, and has expanded that expertise into a comprehensive suite of ML Ops tooling including model management, deployment, and monitoring.
Chris joins us today to discuss the state of the machine learning ecosystem at large, as well as some of their more recent work around production LLM tracing and monitoring.Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from information visualization to quantum computing. Currently, Sean is Head of Marketing and Developer Relations at Skyflow and host of the podcast Partially Redacted, a podcast about privacy and security engineering. You can connect with Sean on Twitter @seanfalconer .
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From the widely recognized GDPR in Europe to Brazil’s LGPD regulations, and the more recent introduction of India’s DPDP law, over 100 countries now have some form of privacy regulation in place. What’s common among many of these regulations is the concept of data residency – the physical location of your data. However, each region’s requirements bring their own unique nuances, encompassing restrictions on data transfer, data storage locations, and individual data rights.
Navigating this complex sphere of privacy regulations is a huge burden for many companies born in the cloud. Their data simply ends up everywhere, and tracking down the locations, adhering to local laws, and even storing and using it locally is enormously complex and expensive.
Over the past year, I’ve engaged with numerous companies eager to expand their businesses into new markets, such as Europe and Australia. However, they’ve encountered a significant roadblock – the absence of a robust technology solution to address the data residency requirements of these regions. As a result, they face the expensive and nightmarish scenario of duplicating their cloud infrastructure for each new region, which not only hampers operational efficiency but also limits their data analyst and scientists from running analytics globally.
In this blog post, I offer a solution to this pressing technology and business challenge by introducing a PII data privacy vault. This architectural approach to data privacy effectively removes the burden of data residency, compliance, and data security responsibilities from your infrastructure, providing a seamless path for global expansion and data management.
Let’s dive in.
Data Residency and Barriers to ExpansionTo grasp the intricacies of regulatory compliance in the context of global expansion, it’s important to understand a few key concepts.
ComplianceCompliance denotes a business’s adherence to the laws and regulations governing data privacy and protection. These regulations are contingent on the geographic location of the customer whose data is being collected. Ensuring compliance is imperative for legal reasons as it shields businesses from financial penalties, license revocations, and the erosion of customer trust.
Data ResidencyData residency pertains to the physical location where customer data is stored. For instance, a website may serve customers in the EU, but their data could be hosted on a server located in Chicago. Different countries and regions have precise laws dictating how customer data should be handled, processed, stored, and safeguarded, making data residency a critical consideration.
Varying RegulationsThe complexity surrounding data residency and compliance obligations primarily arises from the diversity of regulations worldwide. For instance, the European Union (EU) has GDPR, Brazil follows LGPD, and the United States enforces a patchwork of state-specific laws like CCPA in California and CTDPA in Connecticut. These regulations diverge significantly in terms of their stipulations and penalties.
Barriers to Global ExpansionThe disparities in regulations and compliance requirements often pose formidable obstacles for companies striving to attain a global presence. Navigating diverse regulatory frameworks demands significant time, resources, and expertise. The resulting complexity frequently dissuades businesses from venturing into new markets, thereby constraining opportunities for global expansion.
We’ve looked at the problem, now, let’s explore an approach to addressing these challenges.
What is a Data Privacy Vault?A data privacy vault isolates, protects, and governs access to sensitive customer data. Within the vault, confidential information is securely stored, while abstract and non-sensitive tokens, serving as references, are retained in conventional cloud storage. This means that only non-sensitive tokenized data is accessible to other systems, ensuring the utmost protection and compliance.
In a recent IEEE article, the authors made a case that this architectural approach to data privacy is the future of privacy engineering. Just as any modern system likely contains back end services, a database, and a warehouse, all modern systems need a data privacy vault to safely store, handle, and use of sensitive customer PII.
Traditional PII management versus a data privacy vault (source: IEEE).Let’s take a look at a specific example for a simple web application. In the image below, a phone number is being collected by a front-end application. For effective de-scoping, it’s ideal to initiate the de-identification process at the earliest stage in the data lifecycle. In this scenario, the phone number is stored directly within the vault during collection at the front end.
Example of vault architecture for collecting sensitive customer PII.Within the vault, the phone number, alongside any other personally identifiable information (PII), is stored within a robust and isolated environment, segregated from your organization’s existing infrastructure. All downstream services, ranging from application databases to data warehouses, analytics platforms, and logging systems, interact solely with tokenized (de-identified) representations of the data. Queries against the PII for specialized operations or algorithmic operations against PII execute directly within the vault.
Access to de-tokenize or re-identify data is controlled through a zero trust model. Policy-based rules control who sees what, when, where, and for how long on a row and column level.
Controlling access to vault data based on who is requesting the data.The vault combines the principle of isolation, zero trust, privacy-enhancing technologies, and governance controls to insulate your systems from ever having to touch PII directly. This places your AWS components beyond the scope of regulatory compliance, assuring a higher level of data protection and adherence to data residency requirements.
Your AWS Services Handle Only De-identified DataLet’s assume we have a simple application infrastructure as shown below with AWS Amplify providing the web server infrastructure, DynamoDB for application storage, and Redshift for warehousing.
Example web application infrastructure running on AWS.Without a vault in place, everything within our AWS account is under compliance and security scope.
By introducing the vault as shown below (in this example, the collection of PII is handled directly from the vault), we de-scope all our AWS services. The services are only ever handling de-identified data, including the warehouse.
Many analytical operations can be performed with de-identified data provided the data is consistently generated. A warehouse doesn’t need to have access to someone’s name, it only needs a consistently generated representation of the name in order to execute counts, group bys, and joins.
Example of de-scoping AWS services with a data privacy vault.Storing PII to Different Regionalized VaultsWith Skyflow, a data privacy vault company, you can host vaults in various global regions and route sensitive data to a specific regional vault for storage and use. For instance, consider how the following application architecture meets data residency requirements across multiple regions:
Using regional multiple vaults to comply with data residency requirements.1. Your company’s site collects customer PII during account creation. 2. On the client side, the website detects the customer’s location. 3. Detecting that the customer is in the EU, the client-side code uses Skyflow’s SDK to collect the PII data and store it in your company’s data privacy vault in Frankfurt, Germany. Note: For customers based in the US, the PII data is instead routed to the data privacy vault in the US (in this case, Virginia). 4. The EU-based customer’s sensitive PII is stored in the EU-based data privacy vault, and Skyflow responds with de-identified data. 5. The client-side code sends the account request, now with de-identified data, to the server. 6. The server processes the request, storing the data (now de-identified and tokenized) in cloud storage in the “Oregon, US” region. 7. At the end of the week, your company’s Redshift instance in Tokyo, Japan, loads the data (already de-identified and tokenized) from cloud storage to perform analytics.
Deploying multiple vaults situated in different regions streamlines the management of your sensitive data, ensuring compliance with data residency requirements across all your markets.
The data privacy vault architecture significantly simplifies the complexities associated with data residency and compliance. Furthermore, by exempting Redshift (or any warehouse) from the compliance responsibilities tied to data residency, global analytics operations continue seamlessly within a single warehouse instance.
Final ThoughtsCompliance regulations, with their stringent data residency stipulations, necessitate businesses to maintain rigorous standards for data localization, protection, privacy, and security. Adhering to these regulations is essential to mitigating the risks associated with breaches, penalties, and potential damage to reputation. However, enterprises operating in various global regions, serving diverse customer bases, are left to deal with the complex task of navigating multiple regulatory landscapes.
Using data privacy vaults as your core infrastructure for customer PII offers a streamlined solution to simplify global compliance, particularly concerning AWS services and cloud storage.
With a data privacy vault, organizations gain the ability to centralize the security of all sensitive data, effectively removing AWS and cloud storage from their compliance scope. By deploying data privacy vaults in various regions, companies can ensure that sensitive data storage and transmission align with the specific laws and regulations of each operational jurisdiction, thereby enhancing their overall compliance and security posture.
If you have thoughts on this or questions about this approach, please reach out to me on LinkedIn.
Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from information visualization to quantum computing. Currently, Sean is Head of Marketing and Developer Relations at Skyflow and host of the podcast Partially Redacted, a podcast about privacy and security engineering. You can connect with Sean on Twitter @seanfalconer.The post De-scoping Your AWS Services from Data Residency Requirements appeared first on Software Engineering Daily.
Hugging Face was founded in 2016 and has grown to become one of the most prominent ML platforms. It’s commonly used to develop and disseminate state-of-the-art ML models and is a central hub for researchers and developers.
Sayak Paul is a Machine Learning Engineer at Hugging Face and a Google Developer Expert. He joins the show today to talk about how he entered the ML field, diffusion model training, the transformer-based architecture, and more.
Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from information visualization to quantum computing. Currently, Sean is Head of Marketing and Developer Relations at Skyflow and host of the podcast Partially Redacted, a podcast about privacy and security engineering. You can connect with Sean on Twitter @seanfalconer .
Please click here to see the transcript of this episode.
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The post Hugging Face with Sayak Paul appeared first on Software Engineering Daily.
In any given week, if you search the news for “data breach”, you’ll see headlines like the ones below.
Companies like MGM and Caesars spend millions of dollars on firewalls, SIEMs, HSMs, and a whole smorgasbord of cybersecurity tools and yet, they can’t protect your social security number.
From hotels and casinos to some of the most innovative technology companies in the world, why is it that companies with seemingly endless financial and talent resources can’t get a handle on their data security challenges?
I believe this is due to a fundamental misunderstanding about the nature of data that started over 40 years ago.
Back in the 1980s, as computers found their way more and more into businesses, we lived in a disconnected world. To steal someone’s data, you had to physically steal the box the data lived on. As a consequence, we assumed that all data is created equal, that all data is simply ones and zeros, but this is wrong. All data isn’t created equal, some data is special, and needs to be treated that way.
In this blog post, I share my thoughts on what I refer to as the “Cheese and Diamond Problem” and how this has led to the data security challenges companies face today. I also offer explore an alternative approach, a new way of thinking, a privacy by engineering approach that helps us move towards a world where security is the default, and not bolted on.
The Cheese and Diamond ProblemImagine that in my house I have cheese and I have diamonds. As a gracious host, I want guests of my home to be able to access my cheese. They should be able to freely go into the refrigerator and help themselves to some delicious cheese and perhaps a cracker.
However, I don’t want just anyone to touch my diamonds. Perhaps my diamonds even have sentimental value because it’s a diamond ring that’s been passed down through many generations in my family. Clearly the diamond is special.
Yet, if I store my diamonds in the refrigerator next to my cheese, it makes controlling access to the diamonds much more challenging. By co-locating these very different objects, my refrigerator alone isn’t enough to make sure my wife has access to the diamonds and cheese, but my guests only have access to my cheese.
The rules of engagement for something like diamonds are completely different than the rules of engagement for cheese. We all understand this distinction when it comes to physical objects.
This is exactly why my passport and my children’s birth certificates aren’t in the junk drawer in my kitchen with my batteries and my flashlights. If someone breaks into my home and steals my batteries, it’s not that big a deal, but if someone steals my daughter’s birth certificate, then I not only feel like I’ve failed as a parent, but the information on her birth certificate is also now compromised forever. I can’t simply replace her date of birth.
Despite all of us intuitively understanding that some physical objects are different, that they’re special, we somehow miss this point when we work with data. We don’t apply this thinking to Personally Identifiable Information (PII). We treat it like any other form of transactional or application data. We stuff it in a database, pass it around, make a million copies, and this leads to a whole host of problems.
The PII Replication ProblemLet’s consider a simple example.
In the diagram below, which represents an abstraction of a modern system, a phone number is being collected in the front end of the application, perhaps during account creation. That phone number ends up being passed downstream through each node and edge of the graph and at each node, we potentially end up with a copy of the phone number.
We store it in our database, in the warehouse, but we may also end up with a copy in our log files and the backups of all these systems. Instead of just having one copy of the phone number, we now have many copies and we need to protect all those locations and control access consistently wherever the data is stored.
Imagine that instead of having one copy of your passport that you keep in a secure location, you made 10,000 copies and then distributed them all over the world. Suddenly keeping your passport safe becomes a much harder problem in all 10,000 locations than if you have one copy secure in your home.
But this is exactly what we do with data.
We copy it everywhere and then attempt to lock down the hatches across all these systems and keep the policies and controls in sync about who can see what, when, and where. Additionally, because of the Cheese and Diamond Problem, we can’t adequately govern access to the data because the intermixing of our data conflates the rules of engagement about who has access. This quickly becomes an intractable problem because businesses don’t know what they’re storing or where it is, leading to the world we live in now where major corporations have data breaches on a regular basis.
Not All Data is EqualBusinesses are collecting and processing more data than ever. With the explosion of generative AI, as much as we are in an AI revolution, we are also in a data revolution. We can’t have powerful LLMs without access to massive data.
Companies leverage their data to drive business decisions, product direction, help serve customers better, and even create new types of consumer experiences. However, as discussed, not all data is created equal, some data, like PII, is special.
Over time, we’ve recognized that other forms of data like encryption keys, secrets, and identity are special and need to be treated that way. There was a time when we stored secrets in our application code or database. We eventually realized that was a bad idea and moved them into secret managers.
Approaches to managing different types of sensitive data.Despite this progress, we are still left without an accepted standard for the storage and management of sensitive PII data. PII deserves the same type of special handling. You shouldn’t be contaminating your database with customer PII.
Luckily there’s a solution to this problem originally pioneered by companies like Netflix, Google, Apple, and Goldman Sachs and now touted by the IEEE as the future of privacy engineering, the PII Data Privacy Vault.
The PII Data Privacy VaultA data privacy vault isolates, protects, and governs access to sensitive customer data (i.e. PII) while also keeping it usable. With a vault approach, you remove PII from your existing infrastructure, effectively de-scoping it from the responsibility of compliance and data security.
A vault is a first principles architectural approach to data privacy and security, facilitating workflows like:
- PII storage and management for regulated industries
- PCI storage and payment orchestration
- Data residency compliance
- Privacy-preserving analytics
- Privacy-preserving AI
Let’s go back to our example from earlier where we were collecting a phone number from the front end of an application.
In the vault world, the phone number is sent directly to the vault from the front end. From a security perspective, we ideally want to de-identify sensitive data as early in the life cycle as possible. The real phone number will only exist within the vault, it acts as a single source of truth that’s isolated and protected outside of the existing systems.
Example of using a data privacy vault to de-scope an application.The vault securely stores the phone number and generates a de-identified reference in the form of a token that gets passed back to the front end. The token has no mathematical connection to the original data, so it can’t be reverse engineered to reveal the original value.
This way, even if someone steals the data, as what happened with the Capital One data breach, the tokenized data carries no value. In fact, Capital One was fined only because they failed to tokenize all regulated data, some records were purely encrypted and those records were compromised.
Revealing Sensitive DataWhile it’s great to securely store sensitive data, if we simply lock it up and throw away the key, it’s not super useful. We store all this customer PII so we can use it.
For example, we may need to reveal some of the data to a customer support agent, an IT administrator, a data analyst, or to the owner of the data. In this case, if we absolutely need to reveal some of the data, we want to re-identify it as late as possible, for example during render. We also want to limit what a user has access to based on the operations they need to perform with the data. While I might be able to see my full phone number, a customer support agent likely only needs the last four digits of my phone number and an analyst maybe only needs the area code for executing geo-based analytics.
The vault facilitates all of these use cases through a zero trust model where no one and no thing has access to data without explicit policies in place. The policies are built bottoms up, granting access to specific columns and rows of PII. This allows you to control who sees what, when, where, for how long, and in what format.
Let’s consider the situation where we have a user logging into an application and navigating to their account page. On the account page, we want to show the user their name, email, phone number, and home address based on the information they registered with us.
In the application database, we’ll have a table similar to the one shown below where the actual PII has been replaced by de-identified tokens.
Example of users table within the application database.As in the non-vault world, the application will query the application database for the user record associated with the logged in user. The record will be passed to the front end application and the front end will exchange the tokens for a representation of the original values depending on the policies in place.
In the image below, the front end already has the tokenized data but needs to authenticate with the vault attaching the identity of the logged in user so that access is restricted based on the contextual information of the user’s identity. This is known as context-aware authorization.
Once authenticated and authorized, the front end can directly call the data privacy vault to reveal the true values of the user’s account information. But the front end only has access to this singular row of data and it’s limited to the few columns needed to render the information on the account page.
Example of revealing sensitive data for a single record.Sharing Sensitive DataNo modern application exists in a silo. Most applications need to share customer PII with third party services to send emails, SMS, issue a payment, or some other type of workflow. This is also supported by the vault architecture by using the vault as a proxy to the third party service.
In this case, instead of calling a third party API directly, you call the data privacy vault with the de-identified data. The vault knows how to re-identify the PII securely within its environment, and then securely share that with the third party service.
An example of this flow for sending HIPAA compliant forms of communication is shown below. The backend server calls the vault directly with tokenized data and the vault then shares the actual sensitive data with the third party communication service.
Example of using a vault to send HIPAA compliant communication.Final ThoughtsWe’ve come a long way since building business applications in the 1980s, but we’ve failed to evolve our thinking regarding how we secure and manage customer PII. Point solutions like firewalls, encryption, and tokenization alone aren’t enough to address the fundamental problem. We need a new approach to cut to the root of the Cheese and Diamond Problem.
Not all data is the same, PII belongs in a data privacy vault.The data privacy vault provides such an approach.
It’s an architectural approach to data privacy where security is the default. Multiple techniques like polymorphic encryption, confidential computing, tokenization, data governance, and others combine with the principle of isolation and zero trust to give you all the tools you need to store and use PII securely without exposing your systems to the underlying data.
If you have comments or questions about this approach, please connect with me on LinkedIn. Thanks for reading!
Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from information visualization to quantum computing. Currently, Sean is Head of Marketing and Developer Relations at Skyflow and host of the podcast Partially Redacted, a podcast about privacy and security engineering. You can connect with Sean on Twitter @seanfalconer.The post The Data Cloud’s Cheese and Diamond Problem appeared first on Software Engineering Daily.
As Large Language Models (LLMs) and generative AI continue to grow more sophisticated and available, many organizations are starting to build, fine-tune, and customize LLMs based on their internal data and documents. This can bring incredible efficiency and reliability to data-driven decision-making processes. However, this practice comes with its share of challenges, primarily around data privacy, protection, and governance.
Let’s consider the construction of the LLM itself, which is trained on a massive amount of data collected from public and private sources. Without careful anonymization and filtering, sensitive data — such as PII or intellectual property — may be inadvertently included in the training set, potentially leading to a privacy breach.
Furthermore, privacy concerns are introduced when interacting with LLMs, as users might input sensitive data, such as names, addresses, or even confidential business information. If these inputs aren’t handled properly, the misuse or exposure of this information is a genuine risk.
In this post, we’ll explore how to work with LLMs in a privacy-preserving way when building an LLM-based chatbot. As we walk through the technology from end-to-end, we’ll highlight the most acute data privacy concerns and we’ll show how using a data privacy vault addresses those concerns.
Let’s start by taking a closer look at the problem we need to solve.
The problem: Protecting sensitive information from exposure by a chatbotConsider a company that has uses an LLM-based chatbot for its internal operations. The LLM for the chatbot was built by modifying a pre-existing base model with embeddings created from internal company documents. The chatbot provides an easy-to-use interface that lets non-technical users within the company access information from internal data and documents.
The company has a sensitive internal project called “Project Titan.” Project Titan is so important and so sensitive that only people working on Project Titan know about it. In fact, the team often says: the first rule of Project Titan is don’t talk about Project Titan. Naturally, the team wants to take advantage of the internal chatbot and also include Project Titan specific information to speed up creation of design documents, documentation, and press releases. However, they need to control who can see details about this sensitive project.
What we have is a tangible and pressing privacy concern that sits at the intersection of AI and data. These challenges appear extremely difficult to solve in a scalable and production-ready way. Simply having a private version of the LLM doesn’t address the core issue of data access.
The proposed solution: Sensitive data de-identification and fine-grained access controlUltimately, we need to identify the key points where sensitive data must be de-identified during the process of building (or fine-tuning) the LLM and the end user’s interaction with the LLM-based chatbot. After careful analysis, we’ve identified that there are two key points in the process where we need to de-identify (and later re-identify) sensitive data:
- Before ingestion: When documents from Project Titan are used to create embeddings, the project name, any PII, and anything else sensitive to the project must be de-identified. This de-identification should occur as part of the ETL pipeline prior to data ingestion into the LLM.
- During use: When a user inputs data to the chatbot, any sensitive data included in that input must also be de-identified.
You can de-identify sensitive data using Skyflow’s polymorphic encryption and tokenization engine that’s included within Skyflow Data Privacy Vault. This includes detection of PII but also terms you define within a sensitive data dictionary, like intellectual property (i.e. Project Titan).
Of course, only Project Titan team members who use the chatbot should be able to access the sensitive project data. Therefore, when the chatbot forms a response, we’ll rely on Skyflow’s governance engine (which provides fine-grained access control) and detokenization API to retrieve the sensitive data from the data privacy vault, making it available only to authorized end users.
Before we dive into the technical implementation, let’s go through a brief overview of foundational LLM concepts. If you’re already familiar with these concepts, you can skip the next section.
A brief primer on LLMsLLMs are sophisticated artificial intelligence (AI) systems designed to analyze, generate, and work with human language. Built on advanced machine learning architectures, they are trained on vast quantities of text data, enabling them to generate text that is convincingly human-like in its coherence and relevance.
LLMs leverage a technology called transformers — one example is GPT, which stands for Generative Pre-Trained Transformer — to predict or generate a piece of text when given input or context. LLMs learn from patterns in the data they are trained on and then apply these learnings to understand newly given content or to generate new content.
Despite their benefits, LLMs pose potential challenges in terms of privacy, data security, and ethical considerations. This is because LLMs can inadvertently memorize sensitive information from their training data or generate inappropriate content if not properly regulated or supervised. Therefore, the use of LLMs necessitates effective strategies for data handling, governance, and preserving user privacy.
A technical overview of the solutionWhen embarking on any LLM project, we need to start with a model. Many open-source LLMs have been released in recent months, each with its specific area of focus. Instead of building an entire LLM model from scratch, many developers choose a pre-built model and then adjust the model with vector embeddings generated from domain-specific data.
Vector embeddings encapsulate the semantic relationship between words and help algorithms understand context. The embeddings act as an additional contextual knowledge base to help augment the facts known by the base model.
In our case, we’ll begin with an existing model from Hugging Face, and then customize it with embeddings. Hugging Face provides ML infrastructure services as well as open-source models and datasets.
In addition to the Hugging Face model, we’ll use the following additional tools to build out our privacy-preserving LLM-based ETL pipeline and chatbot:
- LangChain an open-source Python library that chains together components typically used for building applications (such as chatbots) powered by LLMs
- Snowflake, which we’ll use for internal document and data storage
- Snowpipe, which we’ll use with Snowflake for automated data loading
- Chroma, an AI-native, open-source database for vector embeddings
- Streamlit, an open-source framework for building AI/ML-related applications using Python
- RetrievalQA, a question-answering chain in LangChain which gets documents from a Retriever and then uses a QA chain to answer questions from those documents
The following diagram shows the high-level ETL and embeddings data flow:
Example of the ETL and embeddings data flow.The ETL and embeddings flows from end to end are:
ETL
- Start with source data, which may contain sensitive data.
- Send data to Skyflow Data Privacy Vault for de-identification.
- Use Snowpipe to load clean data into Snowflake.
Create vector embeddings
- Load documents from Snowflake into LangChain.
- Create vector embeddings with LangChain.
- Store embeddings in Chroma.
Once the model has been customized with the Project Titan information, the user interaction and inference flow is as follows:
User interaction and inference information flow
- Chat UI input
- Accept user input via Streamlit’s chat UI.
- Send user input to Skyflow for de-identification.
2. Retrieve embeddings
- Get the embeddings from Chroma and attach to RetrievalQA.
3. Inference
- Send clean data to RetrievalQA.
- Use QA chain in RetrievalQA to answer the user’s question.
4. Chat UI response
- Send RetrievalQA’s response to Skyflow for detokenization.
- Send re-identified data to Streamlit for display to the end user.
Now that we’re clear on the high-level process, let’s dive in and take a closer look at each step.
ETL: Cleaning the source dataCleaning the source data with Skyflow Data Privacy Vault is fairly straightforward and I’ve covered some of this in a prior post. In this case, we need to process all the source documents for Project Titan available in an AWS S3 bucket.
Skyflow will store the raw files, de-identify PII and IP, and save the clean files to another S3 bucket.
import boto3from skyflow.vault import ConnectionConfig, Configuration, RequestMethod# Authentication to Skyflow APIbearerToken = ''def tokenProvider(): global bearerToken if is\_expired(bearerToken): return bearerToken bearerToken, \_ = generate\_bearer\_token('<YOUR\_CREDENTIALS\_FILE\_PATH>') return bearerTokendef processTrainingData(trainingData): try: # Vault connection configuration config = Configuration('<YOUR\_VAULT\_ID>', '<YOUR\_VAULT\_URL>', tokenProvider) # Define the connection API endpoint connectionConfig = ConnectionConfig('<YOUR\_CONNECTION\_URL>', RequestMethod.POST, requestHeader = { 'Content-Type': 'application/json', 'Authorization': '<YOUR\_CONNECTION\_BASIC\_AUTH>' } requestBody = { 'trainingData': trainingData } # Connect to the vault client = Client(config) # Call the Skyflow API to de-identify the training data response = client.invoke\_connection(connectionConfig) # Define the S3 bucket name and key for the file bucketName = "clean-data-bucket" fileKey = "{timestamp}-{generated-uuid}" # Write the data to a file in memory fileContents = bytes(response.training\_data.encode("UTF-8")) # Upload the file to S3 s3 = boto3.client("s3") s3.put\_object(Bucket=bucketName, Key=fileKey, Body=fileContents) except SkyflowError as e: print('Error Occurred:', e)
Next, we’ll configure Snowpipe to detect new documents in our S3 bucket and load that data into Snowflake. To do this, we’ll need to create the following in Snowflake:
- A new table
- A file format
- A stage
- A new pipe
CREATE OR REPLACE TABLE custom\_training\_data ( training\_text BINARY );CREATE OR REPLACE FILE FORMAT training\_data\_json\_format TYPE = JSON;CREATE OR REPLACE TEMPORARY STAGE training\_data\_stage FILE\_FORMAT = training\_data\_json\_format;CREATE PIPE custom\_training\_data AUTO\_INGEST = TRUE AS COPY INTO custom\_training\_data FROM (SELECT $1:records.fields.training\_text FROM @ training\_data\_stage t) ON\_ERROR = 'continue';
With that, we have raw data that goes through a de-identification process, and then we store the plaintext sensitive data in Snowflake. Any sensitive data related to Project Titan is now obscured in the LLM, but because of Skyflow’s polymorphic encryption and tokenization, the de-identified data has referential integrity, meaning we can return the data to its original form when interacting with the chatbot.
Creating vector embeddings: Customizing our LLMNow that we have our de-identified text data stored in Snowflake, we’re confident that all information related to Project Titan has been properly concealed. The next step is to create embeddings of these documents.
We’ll use the Instructor model provided by Hugging Face as our embedding model. We store our embeddings in Chroma, a vector database built expressly for this purpose. This will allow for the downstream retrieval and search support of the textual data stored in our vector database.
The code below loads the base model, embedding model, and storage context.
from langchain.chat\_models import ChatOpenAIfrom langchain.embeddings import HuggingFaceEmbeddingsfrom langchain.embeddings.openai import OpenAIEmbeddingsmodel\_id = "hkunlp/instructor-large"embed\_model = HuggingFaceEmbeddings(model\_name=model\_id)vectorstore = Chroma("langchain\_store", embed\_model)
Next, we need to load all documents and add them to the vector store. For this, we use the Snowflake document loader in LangChain.
from snowflakeLoader import SnowflakeLoaderimport settings as sQUERY = "select training\_text as source from custom\_training\_data"snowflake\_loader = SnowflakeLoader( query=QUERY, user=s.SNOWFLAKE\_USER, password=s.SNOWFLAKE\_PASS, account=s.SNOWFLAKE\_ACCOUNT, warehouse=s.SNOWFLAKE\_WAREHOUSE, role=s.SNOWFLAKE\_ROLE, database=s.SNOWFLAKE\_DATABASE, schema=s.SNOWFLAKE\_SCHEMA, metadata\_columns=["source"],)training\_documents = snowflake\_loader.load()vector\_store.add\_documents(training\_documents)
With the training document and vector store created, we create the question-answering chain.
qa = RetrievalQA.from\_chain\_type(llm=ChatOpenAI(temperature=0.2,model\_name='gpt-3.5-turbo'), chain\_type="stuff", retriever=vector\_store.as\_retriever())result = qa.run("What is Project Titan?")
This question (“What is Project Titan?”) will fail because the model doesn’t actually know about Project Titan, it knows about a de-identified version of the string “Project Titan”.
To issue a query like this, the query needs to be first sent through Skyflow to de-identify the string and then the de-identified version is passed to the model. We’ll tackle this next as we start to put the pieces together for our chat UI.
Chat UI Input: Preserving privacy of user-supplied dataWe’re ready to focus on the chatbot UI aspect of our project, dealing with accepting and processing user input as well as returning results with Project Titan data detokenized when needed.
For this portion of the project, we will use Streamlit for our UI. The code below creates a simple chatbot UI with Streamlit.
import openaiimport streamlit as stst.title("Acme Corp Assistant")# Initialize the chat messages historyif "messages" not in st.session\_state.keys(): st.session\_state.messages = [ {"role": "assistant", "content": "Hello ! \nHow can I help?"} ]# Prompt for user input and saveif prompt := st.chat\_input(): st.session\_state.messages.append({"role": "user", "content": prompt})# display the prior chat messagesfor message in st.session\_state.messages: with st.chat\_message(message["role"]): st.write(message["content"])# If last message is not from assistant, we need to generate a new responseif st.session\_state.messages[-1]["role"] != "assistant": # Generate a response with st.chat\_message("assistant"): with st.spinner("Thinking..."): response = "TODO" message = {"role": "assistant", "content": response} st.session\_state.messages.append(message)
Our simple chat UI looks like this:
As you can see, the UI accepts a user input, but doesn’t currently integrate with our LLM. Next, we need to send the user input to Skyflow for de-identification before we use RetrievalQA to answer the user’s question. Let’s start with accepting and processing our input data.
To detect and de-identify plaintext sensitive data with Skyflow, we can use the detect API endpoint with code similar to the following:
def deIdentifyText(input): data = { "text": [ { "message": input } ], "deidentify\_option": "tokenize" } response = client.detect(data) return response[0].processed\_text
Now that we’ve de-identified the user input data, we can send the question to RetrievalQA, which will then use a QA chain to answer the question from our documents.
def performCompletion(input): clean\_input = deIdentifyText(input) qa = RetrievalQA.from\_chain\_type(llm=ChatOpenAI(temperature=0.2,model\_name='gpt-3.5-turbo'), chain\_type="stuff", retriever=vector\_store.as\_retriever()) return qa.run(clean\_input)
We now have our response from RetrievalQA. However, we need to take one additional step before we can send it back to our user: detokenize (re-identify) our response through Skyflow’s detokenization API. This is fairly straightforward, similar to previous API calls to Skyflow.
Everything we need is encapsulated by the function performInference, which calls a function to reIdentifyText after the completion is returned.
Who can see what and in which format is controlled by Skyflow’s governance engine. There’s too much to cover here, but if you want to learn more, see Introducing the Skyflow Data Governance Engine.
def performInference(input): response = performCompletion(input) return reIdentifyText(response)
These final steps connect our entire application from end-to-end. Now, we need to update our UI code from above so that the response is correctly set.
```
If last message is not from assistant, we need to generate a new responseif st.session_state.messages[-1]["role"] != "assistant": # Generate a response with st.chat_message("assistant"): with st.spinner("Thinking..."): response = performInference(m["content"])
``` With these pieces in place, here’s a quick demo of our privacy-preserving LLM-based chatbot in action:
Example of the privacy-preserving bot in action.Tying it all togetherIn this article, we walked through the general steps to construct a privacy-preserving LLM-based chatbot. With organizations increasingly using LLM-based applications in their businesses and operations, the need to preserve data privacy has become acute. Concerns about protecting the privacy and security of sensitive data are the biggest adoption blocker that prevents many companies from making full use of AI with their datasets.
Solving this problem requires identifying the key points where sensitive data might enter your system and need to be de-identified. When working with LLMs, those points occur during model training — both when building an LLM or customizing one — and at the user input stage. You can use Skyflow Data Privacy Vault to implement effective de-identification and data governance for LLM-based AI tools like chatbots.
Building an LLM-based chatbot requires the use of several tools to ensure that data is handled in a manner that preserves privacy. Taking privacy-preserving measures is critical to prevent the misuse or exposure of sensitive information. By using the tools and methods we’ve demonstrated here, companies can leverage AI’s benefits and promote efficient data-driven decision-making while prioritizing data privacy and protection.
Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from information visualization to quantum computing. Currently, Sean is Head of Marketing and Developer Relations at Skyflow and host of the podcast Partially Redacted, a podcast about privacy and security engineering. You can connect with Sean on Twitter @seanfalconer.The post Building a Privacy-Preserving LLM-Based Chatbot appeared first on Software Engineering Daily.
Cloud-based software development platforms such as GitHub Codespaces continue to grow in popularity. These platforms are attractive to enterprise organizations because they can be managed centrally with security controls. However, many, if not most, developers prefer a local IDE.Daytona is aiming to bridge that gap. It’s a layer between a local IDE and a backend server, so developers can work locally while interfacing invisibly with a remote environment. Ivan Burazin is the CEO and Co-Founder at Daytona, and he joins the show today to talk about how Daytona works, Spotify as an inspiration for his product, and more.Jordi Mon Companys is a product manager and marketer that specializes in software delivery, developer experience, cloud native and open source. He has developed his career at companies like GitLab, Weaveworks, Harness and other platform and devtool providers. His interests range from software supply chain security to open source innovation. You can reach out to him on Twitter at @jordimonpmm
Please click here to see the transcript of this episode.Sponsorship inquiries: sponsor@softwareengineeringdaily.comThe post Daytona with Ivan Burazin appeared first on Software Engineering Daily.
Knowledge graphs are an intuitive way to define relationships between objects, events, situations, and concepts. Their ability to encode this information makes them an attractive database paradigm.
Hume is a graph-based analysis solution developed by GraphAware. It represents data as a network of interconnected entities and provides analysis capabilities to extract insights from the data. Luanne Misquitta is VP of Engineering at GraphAware and she joins the show today to talk about graph databases, and the engineering of Hume.Starting her career as a software developer, Jocelyn Houle is now a Senior Director of Product Management at Securiti.ai, a unified data protection and governance platform. Before that, she was an Operating Partner at Capital One Ventures investing in data and AI startups. Jocelyn has been a founder of two startups and a full life cycle, technical product manager at large companies like Fannie Mae, Microsoft and Capital One. Follow Jocelyn on LinkedIn or Twitter @jocelynbyrne.
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Data residency requirements, which govern where sensitive data can be stored or processed in the cloud (or in an on-prem server) are a common feature of many modern data protection laws. Because of data residency requirements, the location of sensitive data has significant regulatory compliance implications in countries and regions around the world.
In this post, we’ll look at the challenges of managing data residency with Snowflake. We’ll start by examining how Snowflake Cloud Regions address data residency challenges, and consider the compliance implications of this approach — especially when loading data from cloud storage. Then, we’ll look at how to simplify data residency compliance using one or more regional data privacy vaults.
Let’s begin with a deeper dive into data residency, and how it impacts compliance.
The implications of data residency on complianceWhen you work with personally identifiable information (PII), where you store and process this information has a direct impact on your legal compliance requirements. Some jurisdictions have regulations that govern the protection and privacy of their residents’ PII, restricting how and where it’s used by businesses and other organizations.
For example, the personal data (i.e., PII) of European Union residents cannot be transferred outside the EU without appropriate safeguards.
The laws of each jurisdiction impact how you transmit, manage, process, and store sensitive data in that jurisdiction. Because data residency dictates where (geographically ) data is stored in the cloud, data residency becomes a critical concern in cloud environments that handle sensitive data.
Choose your cloud region carefullyCloud service providers have data centers located in multiple regions around the world. When businesses sign up for cloud services and configure storage regions and other tooling, they select specific regions where their data is stored.
For many businesses, the selection of regions and locations for data storage is an afterthought.
But, treating this decision as an afterthought is a costly mistake that can come back to haunt you if you’re handling sensitive data. That’s because choosing storage regions is a weighty decision that can have a long-term impact on compliance, and on your business operations.
Snowflake Cloud Regions: a data residency solution?Snowflake Cloud Regions let you choose the geographic location where your Snowflake data is stored across the data centers provided by the Snowflake-supported public cloud providers — AWS, GCP, and Azure. Each cloud provider offers a set of regions across the globe, with specific geographic data center locations in each cloud provider region.
Source: Snowflake Documentation Supported Cloud RegionsIf your company uses Snowflake Cloud Regions, you have your choice of providers, as well as regions where your data can be stored. When you create an account to deploy and set up Snowflake, whichever region you select becomes the primary location for data storage and for data processing resources.
At first glance, it might seem like Snowflake Cloud Regions provides a simple, effective solution to your data residency and compliance concerns. But for global companies who need global analytics, it isn’t that simple. That’s because, as noted in the Snowflake Cloud Regions documentation:
Each Snowflake account is hosted in a single region. If you wish to use Snowflake across multiple regions, you must maintain a Snowflake account in each of the desired regions.
This means that for each region where your business operates that has data residency requirements, you’ll need a different Snowflake account hosted in that region. Compliance becomes increasingly complex as you scale globally to more and more regions around the world. With this approach, running global analytics operations across different accounts to get a comprehensive view of your business can be a massive and ongoing challenge.
Instead of managing multiple Snowflake accounts with multiple Snowflake instances distributed in various regions around the world, you’d rather maintain a Snowflake instance in a single region to support global data operations. However, you still need to consider the need to honor data residency requirements for sensitive data so you can uphold your compliance obligations and safeguard customer trust.
For example, if you collect the personal data (PII) of customers located in the EU, but your Snowflake instance is located somewhere else, then you need to think through the privacy and compliance impact of storing and processing that data.
Loading data from cloud storage into SnowflakeSnowflake also lets businesses load data from cloud storage services like AWS S3, Google Cloud Storage, Microsoft Azure — regardless of which cloud platform hosts the businesses’ Snowflake account. This can present additional challenges when working to ensure data residency compliance.
For example, let’s say that your company collects PII from both US and EU customers using its website. And, let’s say that this sensitive data is then stored in a Google Cloud Storage bucket that’s located in the AUSTRALIA-SOUTHEAST1 (Sydney) region.
How does transmitting this PII data to Australia, and then storing it in Australia, affect your compliance with regulations like the EU’s GDPR?
The answer is: doing this likely puts you out of compliance with GDPR. This is just one example of how the location where sensitive data is stored — and where it’s processed and replicated — complicates the compliance requirements faced by businesses that handle sensitive PII.
Businesses that handle PII must ensure regulatory compliance by aligning their choice of cloud storage regions with the data residency requirements of markets where they operate.
And beyond compliance issues, businesses should also consider data transfer costs. Transferring data between cloud storage regions can incur significant additional costs, especially if your company is frequently transferring large volumes of data. So, we not only have compliance concerns with cross-border transfers of PII, we also have a cost concern.
So, to briefly recap our problem:
- Countries and regions have their own laws and regulations that govern how to handle their residents’ sensitive data (PII).
- The geographic location where your business stores and processes sensitive data impacts whether you’re compliant with the data residency requirements of the markets where you operate.
- If you use Snowflake to perform analytics on PII, then the complexity of meeting your compliance obligations will depend on the location of your Snowflake account.
- If you load PII data into Snowflake from cloud storage, then your compliance obligations are also impacted by the location of your cloud storage.
So, how can we meet data residency requirements, support global analytics operations, and remove the operational overhead of managing multiple Snowflake accounts and instances?
We can solve our data residency problems and protect sensitive data with one or more data privacy vaults.
How a data privacy vault simplifies data privacyA data privacy vault isolates, protects, and governs access to sensitive customer data. Sensitive data is stored in the vault, while opaque tokens that serve as references to this data are stored in traditional cloud storage or used in data warehouses. A data privacy vault can store sensitive data in a specific geographic location, and tightly controls access to this data. Other systems only have access to non-sensitive tokenized data.
In the example architecture shown below, a phone number is collected by a front end application. Ideally, we should de-identify (i.e., tokenize) this sensitive information as early in the data lifecycle as possible. A data privacy vault lets us do just that.
This phone number, along with any other PII, is stored securely in the vault, which is isolated outside of your company’s existing infrastructure. Any downstream services — the application database, data warehouse, analytics, any logs, etc. — store only a token representation of the data, and are removed from the scope of compliance:
Example of reducing compliance scope with a data privacy vaultSnowflake handles only de-identified dataBecause no sensitive data is stored outside the data privacy vault, your compliance scope is restricted to just the vault. This removes the compliance burden from your Snowflake instance.
Example pipeline where sensitive data is isolated and protected within a data privacy vaultTo satisfy data residency requirements, we can extend this approach by using multiple regional data privacy vaults placed near customers whose data is subject to these requirements. With sensitive data stored in these data privacy vaults, Snowflake contains only de-identified, tokenized data. It no longer matters if you operate a single global instance of Snowflake or multiple Snowflake accounts across different regions because data residency concerns no longer apply to your Snowflake instances.
Compliance with data residency requirements now depends solely on where your data privacy vaults are located. You no longer need to worry about data residency for all the different parts of your data tech stack, including cloud storage and Snowflake. All sensitive data goes into your data privacy vaults, and these vaults become the only component of your architecture subject to data residency requirements.
Store PII in a data privacy vault in a specific regionWith Skyflow Data Privacy Vault you can host your vaults in a wide variety of regions around the world. You can also route sensitive data to a data privacy vault located in a specific region for storage.
For example, consider how the application architecture shown below supports data residency requirements from multiple regions:
Using vaults to satisfy multiple data residency requirements for one Snowflake instance1. Your company’s e-commerce site collects customer PII whenever a customer places an order.
2. On the client side, the website detects the customers’ location.
3. Detecting that the customer is in the EU, the client-side code uses Skyflow’s API to send the PII data to your company’s data privacy vault in Frankfurt, Germany.
Note: For customers based in the US, the PII data is instead routed to the data privacy vault in the US (in this case, Virginia).
4. This EU-based customer’s sensitive PII is stored in the EU-based data privacy vault, and Skyflow’s API responds with tokenized data.
5. The client-side code sends the customer order request, now with tokenized data, to the server.
6. The server processes the order, storing the data (now de-identified and tokenized) in cloud storage in the “Oregon, US” region.
7. At the end of the week, your company’s Snowflake instance in Tokyo, Japan, loads the data (already de-identified and tokenized) from cloud storage to perform analytics.
By using multiple vaults located in different regions around the world, you can easily manage all of your sensitive data to meet various data residency compliance obligations across each of your global markets.
The data privacy vault architectural pattern vastly simplifies the challenges of data residency and compliance. Additionally, by de-scoping Snowflake from the compliance burden of data residency, global analytics executes as normal — within a single Snowflake instance.
Final thoughtsCompliance regulations and their data residency requirements require that businesses uphold stringent standards for data localization, protection, privacy, and security to reduce their risk of breaches, penalties, and reputational damage. However, businesses with customers (and data) located in a variety of global regions face the added challenge of managing multiple regulations across jurisdictions.
Using data privacy vaults lets businesses simplify their global compliance obligations around data residency as they relate to Snowflake and cloud storage.
Using a data privacy vault, companies can isolate and secure all sensitive data in one or more data privacy vaults, removing Snowflake and cloud storage from their compliance footprint. At the same time, by leveraging data privacy vaults in different regions, companies can help ensure that sensitive data is stored and transmitted according to the laws and regulations of each specific region where they operate.
Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from information visualization to quantum computing. Currently, Sean is Head of Marketing and Developer Relations at Skyflow and host of the podcast Partially Redacted, a podcast about privacy and security engineering. You can connect with Sean on Twitter @seanfalconer.The post One Snowflake, Multiple Vaults: A Solution to Data Residency appeared first on Software Engineering Daily.
Speech technology has been around for a long time, but in the last 12 months it’s undergone a quantum leap. New speech synthesis models are able to produce speech that’s often indistinguishable from real speech. I’m sure many listeners have heard deep fakes where computer speech perfectly mimics the voice of famous actors or public figures. A major factor in driving the ongoing advances is generative AI.
Speechlab is at the forefront of using new AI techniques for realtime dubbing, which is the process of converting speech from one language into another. For the interested listener, we recommend hearing the examples with President Obama speaking Spanish or Elon Musk speaking Japanese in this YouTube video. Ivan Galea is the Co-founder and President at Speechlab and he joins the show to talk about how we’re on the cusp of reaching the holy grail of speech technology – real time dubbing – and how this will erase barriers to communication and likely transform the world.This episode is hosted by Lee Atchison. Lee Atchison is a software architect, author, and thought leader on cloud computing and application modernization. His best-selling book, Architecting for Scale (O’Reilly Media), is an essential resource for technical teams looking to maintain high availability and manage risk in their cloud environments.Lee is the host of his podcast, Modern Digital Business, an engaging and informative podcast produced for people looking to build and grow their digital business with the help of modern applications and processes developed for today’s fast-moving business environment. Listen at mdb.fm. Follow Lee at softwarearchitectureinsights.com, and see all his content at leeatchison.com.Please click here to view this show’s transcript.
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If you’re a developer, there’s a good chance you’ve experimented with coding assistants like GitHub Copilot. Many developers have even fully integrated these tools into their workflows. One way these tools accelerate development is by autocompleting entire blocks of code. The AI achieves this by having awareness of the surrounding code. It understands context. However, in many cases the context available to an AI is limited. This restricts the AI’s ability to suggest more sweeping changes to a codebase, or even to refactor an entire application.
Quinn Slack is the CEO of Sourcegraph. He is now hard at work on the challenge of giving more context to AI – to make it aware of entire codebases, dependencies, error logs, and other data. Quinn joins the show today to talk about what it takes to move beyond code autocomplete, how to develop the next generation of coding AI, and what the future looks like for software engineers and programming languages.
Josh Goldberg is an independent full time open source developer in the TypeScript ecosystem. He works on projects that help developers write better TypeScript more easily, most notably on typescript-eslint: the tooling that enables ESLint and Prettier to run on TypeScript code. Josh regularly contributes to open source projects in the ecosystem such as ESLint and TypeScript.Josh is a Microsoft MVP for developer technologies and the author of the acclaimed Learning TypeScript (O’Reilly), a cherished resource for any developer seeking to learn TypeScript without any prior experience outside of JavaScript. Josh regularly presents talks and workshops at bootcamps, conferences, and meetups to share knowledge on TypeScript, static analysis, open source, and general frontend and web development.You can find Josh on: Bluesky, Fosstodon, Twitter, Twitch, YouTube, and joshuakgoldberg.com.Please click here to view this show’s transcript.
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When StackOverflow launched in 2008 it lowered the barrier to writing complex software. It solved the longstanding problem of accessing accurate and reliable programming knowledge by offering a collaborative space where programmers could ask questions, share insights, and receive high-quality answers from a community of experts.Generative AI has impacted the way programmers want to consume this knowledge. It has also opened new possibilities in terms of getting a personalized and real-time response.
StackOverflow has decided to put a fifth of its organizational effort in Generative AI to improve the user experience of the website.Ellen Brandenberger leads the Product Innovation team at Stack Overflow and she joins us in this episode.Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from information visualization to quantum computing. Currently, Sean is Head of Marketing and Developer Relations at Skyflow and host of the podcast Partially Redacted, a podcast about privacy and security engineering. You can connect with Sean on Twitter @seanfalconer .
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Please click here to view this show’s transcript.
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AI-assisted software delivery refers to the utilization of artificial intelligence to assist, enhance, or automate various phases of the software development lifecycle. AI can be used in numerous aspects of software development, from requirements gathering to code generation to testing and monitoring. The overarching aim is to streamline software delivery, reduce errors and, ideally, reduce
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Generative pre-trained transformer models, or GPT models, have countless applications and are being rapidly deployed across a wide range of domains. However, using GPT models without appropriate safeguards can lead to leakage of sensitive data. This concern underscores the critical need for privacy and data protection. Skyflow GPT Privacy Vault prevents sensitive data from reaching
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There are many types of early stage funding available from friends and family to seed to series A. Some firms invest across a wide set of technologies and seek only to provide capital. Others are in it for the long haul – they focus on specific areas of technology and develop both long term relationships and deep expertise over time.
Today, we are interviewing Matt Turck of First Mark Capital, who is in it for the long haul and whose portfolio companies include Dataiku, Crossbeam, Ada, Cockroach Labs, Clickhouse and more. Today we will talk about Matt’s career, investment point of view, founding the Data-driven NYC community and the recent release of the 20234 MAD – an industry resource for understanding the Machine Learning, AI and Data Landscape
Be sure to check out the show notes for links to the MAD
This epsiode is hosted by Jocelyn Houle. Follow Jocelyn on Linked or on Twitter @jocelynbyrne.
Show notes –
In today’s show we referenced a couple things you may want to check out.
Matt’s blog and MAD Landscape
The interactive MAD Landscape
The picture in Matt’s Office was The Son of Man by Rene Magritte
Matt’s full bio
FirstMark Capital Site
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ChatGPT is an artificial intelligence language model developed by OpenAI. It is part of the GPT (Generative Pre-trained Transformer) family of models, which are designed to generate human-like text based on input prompts. ChatGPT is specifically trained to carry out conversational tasks, such as answering questions, completing sentences, and engaging in dialogue. It has been pre-trained on a large corpus of text data and fine-tuned on specific tasks to improve its performance. As a result, ChatGPT can generate responses that are often coherent, relevant, and natural-sounding.
Christian Hubicki is an Assistant Professor in the Robotics Department at Florida State University. He joins us today to discuss ChatGPT and its implications. We also discussed the future of Artificial Intelligence in general.
This show is hosted by Sean Falconer. Sean is the Head of Developer Relations and Marketing @Skyflow. Follow Sean at @seanfalconer
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Today, we spoke with Daniel Situnayake of Edge Impulse. We discussed AI, machine learning, edge devices, TinyML and AI tool chain. Sponsorship inquiries: sponsor@softwareengineeringdaily.com
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The default configuration in most databases is meant for broad compatibility rather than performance. Database tuning is a process in which the configurations of a database are modified to achieve optimal performance. Databases have hundreds of configuration knobs that control various factors, such as the amount of memory to use for caches or how often
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Originally published on January 1, 2022. Charlie Gerard is an incredibly productive developer. In addition to being the author of Practical Machine Learning in JavaScript, her website charliegerard.dev has a long list of really interesting side projects exploring the intersection of human computer interaction, computer vision, interactivity, and art. In this episode we touch on
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At Lyft, Ketan Umare worked on Flyte, an orchestration system for machine learning. Flyte provides reliability and APIs for machine learning workflows, and is used at companies outside of Lyft such as Spotify. Since leaving Lyft, Ketan founded Union.ai, a company focused on productionizing Flyte as a service. He joins the show to talk about
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Historically, search engines made money by showing sponsored ads alongside organic results. As the idiom goes, if you’re not paying for something, you are the product. Neeva is a new take on search engines. When you search at neeva.com, you get the type of result you’d expect from a search engine minus any advertising. In
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Charlie Gerard is an incredibly productive developer. In addition to being the author of Practical Machine Learning in JavaScript, her website charliegerard.dev has a long list of really interesting side projects exploring the intersection of human computer interaction, computer vision, interactivity, and art. In this episode we touch on some of these projects and broadly
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Once a machine learning model is trained and validated, it often feels like a major milestone has been achieved. In reality, it’s more like the first lap in a relay race. Deploying ML to production bears many similarities to a typical software release process, but brings several novel challenges like failing to generalize as expected
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Machine learning models must first be trained. That training results in a model which must be serialized or packaged up in some way as a deployment artifact. A popular deployment path is using Tensorflow.js to take advantage of the portability of JavaScript, allowing your model to be run on a web server or client. Gant
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Imagine a world where you own some sort of building whether that’s a grocery store, a restaurant, a factory… and you want to know how many people reside in each section of the store, or maybe how long did the average person wait to be seated or how long did it take the average factory
The post No Code AI for Video Analytics with Alex Thiele appeared first on Software Engineering Daily.
The dream of machines with artificial general intelligence is entirely plausible in the future, yet well beyond the reach of today’s cutting edge technology. However, a virtual agent need not win in Alan Turing’s Imitation Game to be useful. Modern technology can deliver on some of the promises of narrow intelligence for accomplishing specific tasks.
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Interest in autonomous vehicles dates back to the 1920s. It wasn’t until the 1980s that the first truly autonomous vehicle prototypes began to appear. The first DARPA Grand Challenge took place in 2004 offering competitors $1 million dollars to complete a 150-mile course through the Mojave desert. The prize was not claimed. Since then, rapid
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Governments, consumers, and companies across the world are becoming more aware and attentive to the risks and causes of climate change. From recycling to using solar power, people are looking for ways to reduce their carbon footprint. Markets like the financial sector, governments, and consulting are looking for ways to understand climate data to make
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Mark Saroufim is the author of an article entitled “Machine Learning: The Great Stagnation”. Mark is a PyTorch Partner Engineer with Facebook AI. He has spent his entire career developing machine learning and artificial intelligence products. Before joining Facebook to do PyTorch engineering with external partners, Mark was a Machine Learning Engineer at Graphcore. Before
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Arun Kumar is an Assistant Professor in the Department of Computer Science and Engineering and the Halicioglu Data Science Institute at the University of California, San Diego. His primary research interests are in data management and systems for machine learning/artificial intelligence-based data analytics. Systems and ideas based on his research have been released as part
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Application Programming Interfaces (APIs) are interfaces that enable multiple software applications to send and retrieve data from one another. They are commonly used for retrieving, saving, editing, or deleting data from databases, transmitting data between apps, and embedding third-party services into apps. The company BaseTen helps companies build and deploy machine learning APIs and applications.
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Natural Language Processing (NLP) is a branch of artificial intelligence concerned with giving computers the ability to understand text and spoken words. “Understanding” includes intent, sentiment, and what’s important in the message. NLP powers things like voice-operated software, digital assistants, customer service chat bots, and many other academic, consumer and enterprise tools. The company Botpress
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Using artificial intelligence and machine learning in a product or database is traditionally difficult because it involves a lot of manual setup, specialized training, and a clear understanding of the various ML models and algorithms. You need to develop the right ML model for your data, train the model, evaluate it, optimize it, analyze it
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Creation Labs is helping bring Europe 1 step closer to fully autonomous long haul trucking. They have developed an AI Driver Assistance System (AIDAS) that retrofits to any commercial vehicle, starting with VW Crafters and MAN TGE trucks. Their system uses camera hardware mounted to the vehicle to capture video data that is processed with
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Vectors are the foundational mathematical building blocks of Machine Learning. Machine Learning models must transform input data into vectors to perform their operations, creating what is known as a vector embedding. Since data is not stored in vector form, an ML application must perform significant work to transform data in different formats into a form
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The incredible advances in machine learning research in recent years often take time to propagate out into usage in the field. One reason for this is that such “state-of-the-art” results for machine learning performance rely on the use of handwritten, idiosyncratic optimizations for specific hardware models or operating contexts. When developers are building ML-powered systems
The post OctoML: Automated Deep Learning Engineering with Jason Knight and Luis Ceze appeared first on Software Engineering Daily.
Embedded Software Engineering is the practice of building software that controls embedded systems- that is, machines or devices other than standard computers. Embedded systems appear in a variety of applications, from small microcontrollers, to consumer electronics, to large-scale machines such as cars, airplanes, and machine tools. iRobot is a consumer robotics company that applies embedded
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Reinforcement learning is a paradigm in machine learning that uses incentives- or “reinforcement”- to drive learning. The learner is conceptualized as an intelligent agent working within a system of rewards and penalties in order to solve a novel problem. The agent is designed to maximize rewards while pursuing a solution by trial-and-error. Programming a system
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Companies can have a negative impact on the environment by outputting excess carbon. Many companies want to reduce their net carbon impact to zero, which can be done by investing in forests. Pachama is a marketplace for forest investments. Pachama uses satellites, imaging, machine learning, and other techniques to determine how much carbon is being
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TensorFlow Lite is an open source deep learning framework for on-device inference. TensorFlow Lite was designed to improve the viability of machine learning applications on phones, sensors, and other IoT devices. Pete Warden works on TensorFlow Lite at Google and joins the show to talk about the world of machine learning applications and the necessary
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Originally published July 30, 2019 “Internet of Things” is a term used to describe the increasing connectivity and intelligence of physical objects within our lives. IoT has manifested within enterprises under the term “Industrial IoT,” as wireless connectivity and machine learning have started to improve devices such as centrifuges, conveyor belts, and factory robotics. In
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Originally published April 17, 2019 Drishti is a company focused on improving manufacturing workflows using computer vision. A manufacturing environment consists of assembly lines. A line is composed of sequential stations along that manufacturing line. At each station on the assembly line, a worker performs an operation on the item that is being manufactured. This
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Originally published June 21, 2019 Niantic is the company behind Pokemon Go, an augmented reality game where users walk around in the real world and catch Pokemon which appear on their screen. The idea for augmented reality has existed for a long time. But the technology to bring augmented reality to the mass market has
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Originally published December 9, 2019 Machine learning algorithms have existed for decades. But in the last ten years, several advancements in software and hardware have caused dramatic growth in the viability of applications based on machine learning. Smartphones generate large quantities of data about how humans move through the world. Software-as-a-service companies generate data about
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Originally published January 25, 2019 When TensorFlow came out of Google, the machine learning community converged around it. TensorFlow is a framework for building machine learning models, but the lifecycle of a machine learning model has a scope that is bigger than just creating a model. Machine learning developers also need to have a testing
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Originally published April 3, 2017 A hedge fund is a collection of investors that make bets on the future. The “hedge” refers to the fact that the investors often try to diversify their strategies so that the direction of their bets are less correlated, and they can be successful in a variety of future scenarios.
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For several years, we have had the ability to create artificially generated text articles. More recently, audio and video synthesis have been feasible for artificial intelligence. Rosebud is a company that creates animated virtual characters that can speak. Users can generate real or fictional presenters easily with Rosebud. Dzmitry Pletnikau is an engineer with Rosebud
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Originally published November 7, 2018 An instruction set defines a low level programming language for moving information throughout a computer. In the early 1970’s, the prevalent instruction set language used a large vocabulary of different instructions. One justification for a large instruction set was that it would give a programmer more freedom to express the
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October 1, 2019 The development of self-driving cars is one of the biggest technological changes that is under way. Across the world, thousands of engineers are working on developing self-driving cars. Although it still seems far away, self-driving cars are starting to feel like an inevitability. This is especially true if you spend much time
The post Cruise: Self-Driving Engineering with Mo Elshenawy Holiday Repeat appeared first on Software Engineering Daily.
Newer machine learning tooling is often focused on streamlining the workflows and developer experience. One such tool is BentoML. BentoML is a workflow that allows data scientists and developers to ship models more effectively. Chaoyu Yang is the creator of BentoML and he joins the show to talk about why he created Bento and the
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Data labeling is a major bottleneck in training and deploying machine learning and especially NLP. But new tools for training models with humans in the loop can drastically reduce how much data is required. Humanloop is a platform for annotating text and training NLP models with much less labelled data. Raza Habib, founder of Humanloop,
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Federated learning is machine learning without a centralized data source. Federated Learning enables mobile phones or edge servers to collaboratively learn a shared prediction model while keeping all the training data on device. Mike Lee Williams is an expert in federated learning, and he joins the show to give an overview of the subject and
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Machine learning models require training data, and training data needs to be labeled. Raw images and text can be labeled using a training data platform like Labelbox. Labelbox is a system of labeling tools that enables a human workforce to create data that is ready to be consumed by machine learning training algorithms. The Labelbox
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Training a computer vision model is not easy. Bottlenecks in the development process make it even harder. Ad hoc code, inconsistent data sets, and other workflow issues hamper the ability to streamline models. Roboflow is a company built to simplify and streamline these model training workflows. Brad Dwyer is a founder of Roboflow and joins
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Machine learning models are only as good as the datasets they’re trained on. Aquarium is a system that helps machine learning teams make better models by improving their dataset quality. Model improvement is often made by curating high quality datasets, and Aquarium helps make that a reality. Peter Gao works on Aquarium, and he joins
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Factories require quality assurance work. That QA work can be accomplished by a robot with a camera together with computer vision. This allows for sophisticated inspection techniques that do not require as much manual effort on the part of a human. Arye Barnehama is a founder of Elementary Robotics, a company that makes these kinds
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Robotic process automation involves the scripting and automation of highly repeatable tasks. RPA tools such as UIPath paved the way for a newer wave of automation, including the Robot Framework, an open source system for RPA. Antti Karjalainen is the CEO of Robocorp, a company that provides an RPA tool suite for developers. Antti joins
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Hyperparameters define the strategy for exploring a space in which a machine learning model is being developed. Whereas the parameters of a machine learning model are the actual data coming into a system, the hyperparameters define how those data points are fed into the training process for building a model to be used by an
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CrowdFlower was a company started in 2007 by Lukas Biewald, an entrepreneur and computer scientist. CrowdFlower solved some of the data labeling problems that were not being solved by Amazon Mechanical Turk. A decade after starting CrowdFlower, the company was sold for several hundred million dollars. Today, data labeling has only grown in volume and
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Chatbots are useful for developing well-defined applications such as first-contact customer support, sales, and troubleshooting. But the potential for chatbots is so much greater. Over the last five years, there have been numerous platforms that have arisen to allow for better, more streamlined chatbot creation. Dialogue software enables the creation of sophisticated chatbots. ParlAI is
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Image annotation is necessary for building supervised learning models for computer vision. An image annotation platform streamlines the annotation of these images. Well-known annotation platforms include Scale AI, Amazon Mechanical Turk, and Crowdflower. There are also large consulting-like companies that will annotate images in bulk for you. If you have an application that requires lots
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Drug trials can lead to new therapeutics and preventative medications being discovered and placed on the market. Unfortunately, these drug trials typically require animal testing. This means animals are killed or harmed as a result of needing to verify that a drug will not kill humans. Animal testing is unavoidable, but the extent to which
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Netflix runs all of its infrastructure on Amazon Web Services. This includes business logic, data infrastructure, and machine learning. By tightly coupling itself to AWS, Netflix has been able to move faster and have strong defaults about engineering decisions. And today, AWS has such an expanse of services that it can be used as a
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Developing machine learning models is not easy. From the perspective of the machine learning researcher, there is the iterative process of tuning hyperparameters and selecting relevant features. From the perspective of the operations engineer, there is a handoff from development to production, and the management of GPU clusters to parallelize model training. In the last
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Deepgram is an end-to-end deep learning platform for speech recognition. Unlike the general purpose APIs from Google or Amazon, Deepgram models are custom-trained for each customer. Whether the customer is a call center, a podcasting company, or a sales department, Deepgram can work with them to build something specific to their use case. Sound data
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At a customer service center, thousands of hours of audio are generated. This audio provides a wealth of information to transcribe and analyze. With the additional data of the most successful customer service representatives, machine learning models can be trained to identify which speech patterns are associated with a successful worker. By identifying these speaking
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Originally published October 8, 2019. We are taking a few weeks off. We’ll be back soon with new episodes. Video surveillance impacts human lives every day. On most days, we do not feel the impact of video surveillance. But the effects of video surveillance have tremendous potential. It can be used to solve crimes and
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Originally published June 13, 2019. We are taking a few weeks off. We’ll be back soon with new episodes. Machine learning allows software to improve as that software consumes more data. Machine learning is a tool that every software engineer wants to be able to use. Because machine learning is so broadly applicable, software companies
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Originally published January 31, 2019. We are taking a few weeks off. We’ll be back soon with new episodes. Artificial intelligence is reshaping every aspect of our lives, from transportation to agriculture to dating. Someday, we may even create a superintelligence–a computer system that is demonstrably smarter than humans. But there is widespread disagreement on
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Cruise is an autonomous car company with a development cycle that is highly dependent on testing its cars–both in the wild and in simulation. The testing cycle typically requires cars to drive around gathering data, and that data to subsequently be integrated into a simulated system called Matrix. With COVID-19, the ability to run tests
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Machine learning workflows have had a problem for a long time: taking a model from the prototyping step and putting it into production is not an easy task. A data scientist who is developing a model is often working with different tools, or a smaller data set, or different hardware than the environment which that
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Devices on the edge are becoming more useful with improvements in the machine learning ecosystem. TensorFlow Lite allows machine learning models to run on microcontrollers and other devices with only kilobytes of memory. Microcontrollers are very low-cost, tiny computational devices. They are cheap, and they are everywhere. The low-energy embedded systems community and the machine
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Chatbots became widely popular around 2016 with the growth of chat platforms like Slack and voice interfaces such as Amazon Alexa. As chatbots came into use, so did the infrastructure that enabled chatbots. NLP APIs and complete chatbot frameworks came out to make it easier for people to build chatbots. The first suite of chatbot
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Machine learning models require the use of training data, and that data needs to be labeled. Today, we have high quality data infrastructure tools such as TensorFlow, but we don’t have large high quality data sets. For many applications, the state of the art is to manually label training examples and feed them into the
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Descript is a software product for editing podcasts and video. Descript is a deceptively powerful tool, and its software architecture includes novel usage of transcription APIs, text-to-speech, speech-to-text, and other domain-specific machine learning applications. Some of the most popular podcasts and YouTube channels use Descript as their editing tool because it provides a set of
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Machine learning applications are widely deployed across the software industry. Most of these applications used supervised learning, a process in which labeled data sets are used to find correlations between the labels and the trends in that underlying data. But supervised learning is only one application of machine learning. Another broad set of machine learning
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Machine learning algorithms have existed for decades. But in the last ten years, several advancements in software and hardware have caused dramatic growth in the viability of applications based on machine learning. Smartphones generate large quantities of data about how humans move through the world. Software-as-a-service companies generate data about how these humans interact with
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Originally published June 7, 2018 Moore’s Law states that the number of transistors in a dense integrated circuit doubles about every two years. Moore’s Law is less like a “law” and more like an observation or a prediction. Moore’s Law is ending. We can no longer fit an increasing amount of transistors in the same
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Software bugs cause unexpected problems at every company. Some problems are small. A website goes down in the middle of the night, and the outage triggers a phone call to an engineer who has to wake up and fix the problem. Other problems can be significantly larger. When a major problem occurs, it can cause
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Video surveillance impacts human lives every day. On most days, we do not feel the impact of video surveillance. But the effects of video surveillance have tremendous potential. It can be used to solve crimes and find missing children. It can be used to intimidate journalists and empower dictators. Like any piece of technology, video
The post Traces: Video Recognition with Veronica Yurchuk and Kostyantyn Shysh appeared first on Software Engineering Daily.
The development of self-driving cars is one of the biggest technological changes that is under way. Across the world, thousands of engineers are working on developing self-driving cars. Although it still seems far away, self-driving cars are starting to feel like an inevitability. This is especially true if you spend much time in downtown San
The post Cruise: Self-Driving Engineering with Mo Elshenawy appeared first on Software Engineering Daily.
A large sales organization has hundreds of sales people. Each of those sales people manages a set of accounts who they are trying to close sales deals on. Sales people are overseen by managers who ensure that the sales people are performing well. Directors and VPs ensure the scalability and health of the overall sales
The post People.ai: Machine Learning for Sales with Andrey Akselrod appeared first on Software Engineering Daily.
“Internet of Things” is a term used to describe the increasing connectivity and intelligence of physical objects within our lives. IoT has manifested within enterprises under the term “Industrial IoT,” as wireless connectivity and machine learning have started to improve devices such as centrifuges, conveyor belts, and factory robotics. In the consumer space, IoT has
The post WebAssembly on IoT with Jonathan Beri appeared first on Software Engineering Daily.
A grocery store contains fruit, vegetables, meat, bread, and other items that can expire. In order to keep these items in stock, the store must be aware of how much food has been sold and what has gone bad. When a food item is low in stock, the store needs to order more of that
The post Afresh: Grocery Store Software with Volodymyr Kuleshov appeared first on Software Engineering Daily.
Niantic is the company behind Pokemon Go, an augmented reality game where users walk around in the real world and catch Pokemon which appear on their screen. The idea for augmented reality has existed for a long time. But the technology to bring augmented reality to the mass market has appeared only recently. Improved mobile
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Machine learning allows software to improve as that software consumes more data. Machine learning is a tool that every software engineer wants to be able to use. Because machine learning is so broadly applicable, software companies want to make the tools more accessible to the developers across the organization. There are many steps that an
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Augmented reality applications can be used on smartphones and dedicated AR headsets. On smartphones, ARCore (Google) and ARKit (Apple) allow developers to build for the camera on a user’s smartphone. AR headsets such as Microsoft HoloLens and Magic Leap allow for a futuristic augmented reality headset experience. The most prominent use of augmented reality today
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RECENT UPDATES: Podsheets is our open source set of tools for managing podcasts and podcast businesses New version of Software Daily, our app and ad-free subscription service Software Daily is looking for help with Android engineering, QA, machine learning, and more FindCollabs Hackathon has ended–winners will probably be announced by the time this episode airs;
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RECENT UPDATES: Podsheets is our open source set of tools for managing podcasts and podcast businesses New version of Software Daily, our app and ad-free subscription service Software Daily is looking for help with Android engineering, QA, machine learning, and more FindCollabs Hackathon has ended–winners will probably be announced by the time this episode airs;
The post Protein Structure Deep Learning with Mohammed Al Quraishi appeared first on Software Engineering Daily.
RECENT UPDATES: FindCollabs $5000 Hackathon Ends Saturday April 15th, 2019 New version of Software Daily, our app and ad-free subscription service Software Daily is looking for help with Android engineering, QA, machine learning, and more Data sets can be modeled in a row-wise, relational format. When two data sets share a common field, those data
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The demand for electricity is based on the consumption of the electrical grid at a given time. The supply of electricity is based on how much energy is being produced or stored on the grid at a given time. Because these sources of supply and demand fluctuate rapidly but predictably, energy markets present profit opportunities
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Zoox is a full-stack self-driving car company. Zoox engineers work on everything a self-driving car company needs, from the physical car itself to the algorithms running on the car to the ride hailing system which the company plans to use to drive around riders. Since starting in 2014, Zoox has grown to over 500 employees.
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DoorDash is a food delivery company where users find restaurants to order from. When a user opens the DoorDash app, the user can search for types of food or specific restaurants from the search bar or they can scroll through the feed section and look at recommendations that the app gives them within their local
The post Store2Vec: DoorDash Recommendations with Mitchell Koch appeared first on Software Engineering Daily.
Artificial intelligence is reshaping every aspect of our lives, from transportation to agriculture to dating. Someday, we may even create a superintelligence–a computer system that is demonstrably smarter than humans. But there is widespread disagreement on how soon we could build a superintelligence. There is not even a broad consensus on how we can define
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When TensorFlow came out of Google, the machine learning community converged around it. TensorFlow is a framework for building machine learning models, but the lifecycle of a machine learning model has a scope that is bigger than just creating a model. Machine learning developers also need to have a testing and deployment process for continuous
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Robots are making their way into every area of our lives. Security robots roll around industrial parks at night, monitoring the area for intruders. Amazon robots tirelessly move packages around in warehouses, reducing the time and cost of logistics. Self-driving cars have become a ubiquitous presence in cities like San Francisco. For a hacker in
The post Human Sized Robots with Zach Allen appeared first on Software Engineering Daily.
Originally posted on 13 September 2017. Machines understand the world through mathematical representations. In order to train a machine learning model, we need to describe everything in terms of numbers. Images, words, and sounds are too abstract for a computer. But a series of numbers is a representation that we can all agree on, whether
The post Word2Vec with Adrian Colyer Holiday Repeat appeared first on Software Engineering Daily.
Originally posted on 28 July 2017. Self-driving cars are here. Fully autonomous systems like Waymo are being piloted in less complex circumstances. Human-in-the-loop systems like Tesla Autopilot navigate drivers when it is safe to do so, and lets the human take control in ambiguous circumstances. Computers are great at memorization, but not yet great at
The post Self-Driving Deep Learning with Lex Fridman Holiday Repeat appeared first on Software Engineering Daily.
Originally posted on May 12, 2015. Humans have now been defeated by computers at heads up no-limit holdem poker. Some people thought this wouldn’t be possible. Sure, we can teach a computer to beat a human at Go or Chess. Those games have a smaller decision space. There is no hidden information. There is no
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The volume of data in the world is always increasing. The costs of storing that data is always decreasing. And the means for processing that data is always evolving. Sensors, cameras, and other small computers gather large quantities of data from the physical world around us. User analytics tools gather information about how we are
The post Reflow: Distributed Incremental Processing with Marius Eriksen appeared first on Software Engineering Daily.
An instruction set defines a low level programming language for moving information throughout a computer. In the early 1970’s, the prevalent instruction set language used a large vocabulary of different instructions. One justification for a large instruction set was that it would give a programmer more freedom to express the logic of their programs. Many
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Google Search allows humans to find and access information across the web. A human enters an unstructured query into the search box, the search engine provides several links as a result, and the human clicks on one of those links. That link brings up a web page, which is a set of unstructured data. Humans
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David Cancel has started five companies, most recently Drift. Drift is a conversational marketing and sales platform. David has a depth of engineering skills and a breadth of business experience that make him an amazing source of knowledge. In today’s episode, David discusses topics ranging from the technical details of making a machine learning-driven sales
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Google Brain is an engineering team focused on deep learning research and applications. One growing area of interest within Google Brain is that of generative models. A generative model uses neural networks and a large data set to create new data similar to the ones that the network has seen before. One approach to making
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Stock traders have access to high volumes of information to help them make decisions on whether to buy an asset. A trader who is considering buying a share of Google stock can find charts, reports, and statistical tools to help with their decision. There are a variety of machine learning products to help a technical
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Self-driving transportation will be widely deployed at some point in the future. How far off is that future? There are widely varying estimations: maybe you will summon a self-driving Uber in a New York within 5 years, or maybe it will take 20 years to work out all of the challenges in legal and engineering.
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Stitch Fix is a company that recommends packages of clothing based on a set of preferences that the user defines and updates over time. Stitch Fix’s software platform includes the website, data engineering infrastructure, and warehouse software. Stitch Fix has over 5000 employees, including a large team of engineers. Cathy Polinsky is the CTO of
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DoorDash is a last mile logistics company that connects customers with their favorite national and local businesses. When a customer orders from a restaurant, DoorDash needs to identify the ideal driver for picking up the order from the restaurant and dropping it off with the customer. This process of matching an order to a driver
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In the smartphone market there are two dominant operating systems: one closed source (iPhone) and one open source (Android). The market for self-driving cars could play out the same way, with a company like Tesla becoming the closed source iPhone of cars, and a company like Comma.ai developing the open source Android of self-driving cars.
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“Bots” are becoming increasingly relevant to our everyday interactions with technology. A bot sometimes mediates the interactions of two people. Examples of bots include automated reply systems, intelligent chat bots, classification systems, and prediction machines. These systems are often powered by machine learning systems that are black boxes to the user. Today’s guest Rob May
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Machine learning models allow our applications to perform highly accurate inferences. A model can be used to classify a picture as a cat, or to predict what movie I might want to watch. But before a machine learning model can be used to make these inferences, the model must be trained and deployed. In the
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When a patient comes into the hospital with stroke symptoms, the hospital will give that patient a CAT scan, a 3-dimensional imaging of the patient’s brain. The CAT scan needs to be examined by a radiologist, and the radiologist will decide whether to refer the patient to an interventionist–a surgeon who can perform an operation
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Evolutionary algorithms can generate surprising, effective solutions to our problems. Evolutionary algorithms are often let loose within a simulated environment. The algorithm is given a function to optimize for, and the engineers expect that algorithm to evolve a solution that optimizes for the objective function given the constraints of the simulated environment. But sometimes these
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Moore’s Law states that the number of transistors in a dense integrated circuit double about every two years. Moore’s Law is less like a “law” and more like an observation or a prediction. Moore’s Law is ending. We can no longer fit an increasing amount of transistors in the same amount of space with a
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Applications of artificial intelligence are permeating our everyday lives. We notice it in small ways–improvements to speech recognition; better quality products being recommended to us; cheaper goods and services that have dropped in price because of more intelligent production. But what can we quantitatively say about the rate at which artificial intelligence is improving? How
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A sample of the human voice is a rich piece of unstructured data. Voice recordings can be turned into visualizations called spectrograms. Machine learning models can be trained to identify features of these spectrograms. Using this kind of analytic strategy, breakthroughs in voice analysis are happening at an amazing pace. Rita Singh researches voice at
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Data Skeptic is a podcast about machine learning, data science, and how software affects our lives. The first guest on today’s episode is Kyle Polich, the host of Data Skeptic. Kyle is one of the best explainers of machine learning concepts I have met, and for this episode, he presented some material that is perfect
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Algorithms for building neural networks have existed for decades. For a long time, neural networks were not widely used. Recent changes to the cost of compute and the size of our data have made neural networks extremely useful. Our smartphones generate terabytes of useful data. Lower storage costs make it economical to keep that data.
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Keybase is a platform for managing public key infrastructure. Keybase’s products simplify the complicated process of associating your identity with a public key. Keybase is the subject of the first half of today’s show. Michael Maxim, an engineer from Keybase gives an overview of how the technology works and what kinds of applications Keybase unlocks.
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Rajat Monga is a director of engineering at Google where he works on TensorFlow. TensorFlow is a framework for numerical computation developed at Google. The majority of TensorFlow users are building machine learning applications such as image recognition, recommendation systems, and natural language processing–but TensorFlow is actually applicable to a broader range of scientific computation
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The easiest way to train a computer to recognize a picture of a cat is to show the computer a million labeled images of cats. The easiest way to train a computer to recognize a stop sign is to show the computer a million labeled stop signs. Supervised machine learning systems require labeled data. Today,
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Pinterest is a visual feed of ideas, products, clothing, and recipes. Millions of users browse Pinterest to find images and text that are tailored to their interests. Like most companies, Pinterest started with a large monolithic application that served all requests. As Pinterest’s engineering resources expanded, some of the architecture was broken up into microservices
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Training a deep learning model involves operations over tensors. A tensor is a multi-dimensional array of numbers. For several years, GPUs were used for these linear algebra calculations. That’s because graphics chips are built to efficiently process matrix operations. Tensor processing consists of linear algebra operations that are similar in some ways to graphics processing–but
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A modern farm has hundreds of sensors to monitor the soil health, and robotic machinery to reap the vegetables. A modern shipping yard has hundreds of computers working together to orchestrate and analyze the freight that is coming in from overseas. A modern factory has temperature gauges and smart security cameras to ensure workplace safety.
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Originally published November 17, 2015 “Changing anything changes everything.” Technical debt, referring to the compounding cost of changes to software architecture, can be especially challenging in machine learning systems. D. Sculley is a software engineer at Google, focusing on machine learning, data mining, and information retrieval. He recently co-authored the paper Machine Learning: The High
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Automation is changing the labor market. To automate a task, someone needs to put in the work to describe the task correctly to a computer. For some tasks, the reward for automating a task is tremendous–for example, putting together mobile phones. In China, companies like FOXCONN are investing time and money into programming the instructions
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Machine learning models can be built by plotting points in space and optimizing a function based off of those points. For example, I can plot every person in the United States in a 3 dimensional space: age, geographic location, and yearly salary. Then I can draw a function that minimizes the distance between my function
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A basketball game gives off endless amounts of data. Cameras from all angles capture the players making their way around the court, dribbling, passing, and shooting. With computer vision, a computer can build a well-defined understanding for what a sport looks like. With other machine learning techniques, the computer can make predictions by combining historical
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The applications that demand deep learning range from self-driving cars to healthcare, but the way that models are developed and trained is similar. A model is trained in the cloud and deployed to a device. The device engages with the real world, gathering more data. That data is sent back to the cloud, where it
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If I have a picture of a dog, and I want to search the Internet for pictures that look like that dog, how can I do that? I need to make an algorithm to build an index of all the pictures on the Internet. That index can define the different features of my images. I
The post Visual Search with Neel Vadoothker appeared first on Software Engineering Daily.
Machines understand the world through mathematical representations. In order to train a machine learning model, we need to describe everything in terms of numbers. Images, words, and sounds are too abstract for a computer. But a series of numbers is a representation that we can all agree on, whether we are a computer or a
The post Word2Vec with Adrian Colyer appeared first on Software Engineering Daily.
Software companies that have been around for a decade have a ton of data. Modern machine learning techniques are able to turn that data into extremely useful models. Salesforce users have been entering petabytes of data into the company’s CRM tool since 1999. With its Einstein suite of products, Salesforce is using that data to
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Automation will make healthcare more efficient and less prone to error. Today, machine learning is already being used to diagnose diabetic retinopathy and improve radiology accuracy. Someday, an AI assistant will assist a doctor in working through a complicated differential diagnosis. Our hospitals look roughly the same today as they did ten years ago, because
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Querying a search index for objects similar to a given object is a common problem. A user who has just read a great news article might want to read articles similar to it. A user who has just taken a picture of a dog might want to search for dog photos similar to it. In
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Self-driving cars are here. Fully autonomous systems like Waymo are being piloted in less complex circumstances. Human-in-the-loop systems like Tesla Autopilot navigate drivers when it is safe to do so, and lets the human take control in ambiguous circumstances. Computers are great at memorization, but not yet great at reasoning. We cannot enumerate to a
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Instacart is a grocery delivery service. Customers log onto the website or mobile app and pick their groceries. Shoppers at the store get those groceries off the shelves. Drivers pick up the groceries and drive them to the customer. This is an infinitely complex set of logistics problems, paired with a rich data set given
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Deep learning allows engineers to build models that can make decisions based on training data. These models improve over time using stochastic gradient descent. When a model gets big enough, the training must be broken up across multiple machines. Two strategies for doing this are “model parallelism” which divides the model across machines and “data
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Video object segmentation allows computer vision to identify objects as they move through space in a video. The DAVIS challenge is a contest among machine learning researchers working off of a shared dataset of annotated videos. The organizers of the DAVIS challenge join the show today to explain how video object segmentation models are trained
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Humans have now been defeated by computers at heads up no-limit holdem poker. Some people thought this wouldn’t be possible. Sure, we can teach a computer to beat a human at Go or Chess. Those games have a smaller decision space. There is no hidden information. There is no bluffing. Poker must be different! It
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Convolutional neural networks are a machine learning tool that uses layers of convolution and pooling to process and classify inputs. CNNs are useful for identifying objects in images and video. In this episode, we focus on the application of convolutional neural networks to image and video recognition and classification. Matt Zeiler is the CEO of
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Most popular music today uses a computer as the central instrument. A single musician is often selecting the instruments, programming the drum loops, composing the melodies, and mixing the track to get the right overall atmosphere. With so much work to do on each song, popular musicians need to simplify–the result is that pop music
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A hedge fund is a collection of investors that make bets on the future. The “hedge” refers to the fact that the investors often try to diversify their strategies so that the direction of their bets are less correlated, and they can be successful in a variety of future scenarios. Engineering-focused hedge funds have used
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Multiagent systems involve the interaction of autonomous agents that may be acting independently or in collaboration with each other. Examples of these systems include financial markets, robot soccer matches, and automated warehouses. Today’s guest Peter Stone is a professor of computer science who specializies in multiagent systems and robotics. In this episode, we discuss some
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Biology research is complex. The sample size of a biological data set is often too small to make confident judgments about the biological system being studied. During Jason Knight’s PhD research, the RNA sequence data that he was studying was not significant enough to make strong conclusions about the gene regulatory networks he was trying
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Every company that deals with payments deals with fraud. The question is not whether fraud will occur on your system, but rather how much of it you can detect and prevent. If a payments company flags too many transactions as fraudulent, then real transactions might accidentally get flagged as well. But if you don’t reject
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Machine learning frameworks like Torch and TensorFlow have made the job of a machine learning engineer much easier. But machine learning is still hard. Debugging a machine learning model is a slow, messy process. A bug in a machine learning model does not always mean a complete failure. Your model could continue to deliver usable
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Deep learning uses neural networks to identify patterns. Neural networks allow us to sequence “layers” of computing, with each layer using learning algorithms such as unsupervised learning, supervised learning, and reinforcement learning. Deep learning has taken off in the last few years, but it has been around for much longer. Adam Gibson founded Skymind, the
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Data science is typically done by engineers writing code in Python, R, or another scripting language. Lots of engineers know these languages, and their ecosystems have great library support. But these languages have some issues around deployment, reproducibility, and other areas. The programming language Golang presents an appealing alternative for data scientists. Daniel Whitenack transitioned
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Translation is a classic problem in computer science. How do you translate a sentence from one human language into another? This seems like a problem that computers are well-suited to solve. Languages follow well-defined rules, we have lots of sample data to train our machine learning models. And yet, the problem has not been solved–largely
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Medical imaging is used to understand what is going on inside the human body and prescribe treatment. With new image processing and machine learning techniques, the traditional medical imaging techniques such as CT scans can be enriched to get a more sophisticated diagnosis. HeartFlow uses data from a standard CT scan to model a human
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Data visualization tools are required to translate the findings of data scientists into charts, graphs, and pictures. Understanding how to utilize these tools and display data is necessary for a data scientist to communicate with people in other domains. In this episode, Srini Kadamati hosts a discussion with Jake VanderPlas about the Python ecosystem for
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Data engineering is the software engineering that enables data scientists to work effectively. In today’s episode, we explore the different sides of data engineering–the data science algorithms that need to be processed and the implementation of software architectures that enable those algorithms to run smoothly. The PANCAKE STACK is a 12-letter acronym that Chris Fregly
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Scikit-learn is a set of machine learning tools in Python that provides easy-to-use interfaces for building predictive models. In a previous episode with Per Harald Borgen about Machine Learning For Sales, he illustrated how easy it is to get up and running and productive with scikit-learn, even if you are not a machine learning expert.
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Machine learning can be used to generate music. In the case of Feynman Liang’s research project BachBot, the machine learning model is seeded with the music of famous composer Bach. The music that BachBot creates sounds remarkably similar to Bach, although it has been generated by an algorithm, not by a human. BachBot is
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You have probably read a news article that was written by a machine. When earnings reports come out, or a series of sports events like the Olympics occurs, there are so many small stories that need to be written that a news organization like the Associated Press would have to use all of its resources
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Research in artificial intelligence takes place mostly at universities and large corporations, but both of these types of institutions have constraints that cause the research to proceed a certain way. In a university, basic research might be hindered by lack of funding. At a big corporation, the researcher might be encouraged to study a domain
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TensorFlow is Google’s open source machine learning library. Rajat Monga is the engineering director for TensorFlow. In this episode, we cover how to use TensorFlow, including an example of how to build a machine learning model to identify whether a picture contains a cat or not. TensorFlow was built with the mission of simplifying the
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Data Validation is the process of ensuring that data is accurate. In many software domains, an application is pulling in large quantities of data from external sources. That data will eventually be exposed to users, and it needs to be correct. Radius Intelligence is a company that aggregates data on small businesses. In order to
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Machine learning has become simplified. Similar to how Ruby on Rails made web development approachable, scikit-learn takes away much of the frustrating aspects of machine learning, and lets the developer focus on building functionality with high-level APIs. Per Harald Borgen is a developer at Xeneta. He started programming fairly recently, but has already built
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The war against spam has been going on for decades. Email spam blockers and ad blockers help protect us from unwanted messages in our communication and browsing experience. These spam prevention tools are powered by machine learning, which catches most of the emails and ads that we don’t want to see. TrueCaller is a company
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“Building a model to predict disease and deploying that in the wild – the bar for success is much higher there than, say, deciding what ad to show you.” Diagnosing illness today requires the trained eye of a doctor. With machine learning, we might someday be able to diagnose illness using only a data set.
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“Nothing’s cool unless you call it ‘as a service.’ ” Monsanto is a company that is known for its chemical and biological engineering. It is less well known for its data science and software engineering teams. Tim Williamson is a data scientist at Monsanto, and on today’s show he talked about how he and a
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“I definitely think we can try to abstract away the first principles of intelligence and then try to go from these principles to an intelligent machine that might look nothing like the brain.”
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“You’ve got software engineers who are interested in machine learning, and think what they need to do is just bring in another module and then that will solve their problem. It’s particularly important for those people to understand that this is a different type of beast.”
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“You don’t mind if failures slow things down, but its very important that failures do not stop forward progress.”
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“I normally try to sit together or very close to a product team or engineering team. And by doing so, I get very close to the source of all kinds of challenging problems.”
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“When I was a graduate student, I was sitting in the office of my advisor in electrical engineering and he said, ‘Look out that window – you see a Volkswagon, I see a realization of a random variable.’ ”
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“Changing anything changes everything.”
Technical debt, referring to the compounding cost of changes to software architecture, can be especially challenging in machine learning systems.
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Current infrastructure makes it difficult for data scientists to share analytical models with the software engineers who need to integrate them. Yhat is an enterprise software company tackling the challenge of how data science gets done. Their products enable companies and users to easily deploy data science environments and translate analytical models into production code.
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Data science competitions are an effective way to crowdsource the best solutions for challenging datasets. Kaggle is a platform for data scientists to collaborate and compete on machine learning problems with the opportunity to win money from the competitions' sponsors.
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There is a need for more data scientists to make sense of the vast amounts of data we produce and store. Dataquest is an in-browser platform for learning data science that is tackling this problem.
Vik Paruchuri is the founder of Dataquest. He was previously a machine learning engineer at EdX and before that a U.S. diplomat.
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