Podcast
The Pragmatic Engineer
Hosted by Gergely Orosz · EN
Software engineering at Big Tech and startups, from the inside. Deepdives with experienced engineers and tech professionals who share their hard-earned lessons, interesting stories and advice they have on building software.
Especially relevant for software engineers and engineering leaders: useful for those working in tech.
newsletter.pragmaticengineer.com
newsletter.pragmaticengineer.com
37episodes
Episodes
Newest firstAll episodes
TypeScript, C# and Turbo Pascal with Anders Hejlsberg
2d ago01:15:09Summary readyBrought to You By:• Antithesis – verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages.• WorkOS – Everything you need to make your app enterprise ready.• turbopuffer – a vector and full-text search engine built on object storage. It’s fast, cheap, and extremely scalable.—Anders Hejlsberg is a living legend and one of the most influential programming language designers of all time. He created Turbo Pascal, Delphi, C#, and also TypeScript. As well as that, he spent nearly a decade at the pioneering dev tools company, Borland, and is now in his 30th year of working at Microsoft, where he’s a Technical Fellow.In this episode, we discuss what it takes to build programming languages that developers love to use, and trace his career from writing his first compiler to creating Turbo Pascal and Delphi, and helping to pioneer modern software development through C# and TypeScript.Anders details how C# was designed by a small group of experienced language designers who met a few hours each week, and he explains why tooling was just as important as the language for TypeScript’s success, and what he has learned from building languages which stay relevant for decades.We also look into how Anders uses AI today, which language features suit AI-assisted development, and what he thinks is changing in the craft of software engineering as developers move further away from writing code line by line.—Timestamps(00:00) Intro(02:48) How Anders got into programming (05:40) Building his first compiler (07:44) Turbo Pascal(12:25) Delphi (14:53) Joining Microsoft(19:41) Building C# (29:11) Async/await(34:01) The rise of JavaScript(37:52) Building TypeScript(42:58) How the TypeScript compiler works (48:30) JavaScript’s strengths and weaknesses(52:18) How Anders uses AI (56:03) What language features work well with AI (1:02:49) How software craftsmanship is changing(1:07:49) Performance and efficiency (1:09:29) Anders’ tool stack (1:11:30) A 30-year career at Microsoft(1:13:40) Book recommendation—The Pragmatic Engineer deepdives relevant for this episode:• Microsoft’s developer tools roots• 50 Years of Microsoft and developer tools with Scott Guthrie• How Linux is built with Greg Kroah-Hartman• How will AI change operating systems? Part 1: Ubuntu and Linux• How Uber uses AI for development: inside look—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@pragmaticengineer.com. Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe
Summary
Building Pi, and what makes self-modifying software so fascinating
2w ago01:33:07Summary readyBrought to You By:• Statsig — The unified platform for flags, analytics, experiments, and more.• Sonar – The makers of SonarQube, the industry standard for automated code review• WorkOS – Everything you need to make your app enterprise ready.—Mario Zechner is the creator of Pi, a minimalist, self-modifying AI coding agent, that is the foundation upon which OpenClaw (created by Peter Steinberger) is built. Meanwhile, Armin Ronacher is the creator of Flask, and a longtime user of Pi. The pair are also friends.I sat down with Mario and Armin for the latest episode of the Pragmatic Engineer Podcast for an interesting conversation about AI and their reservations about it – even though both are heavily invested in building AI-powered tools.Mario explains why he built Pi, and gives his take on why it has become so popular. Armin walks us through how he uses AI tools, including building a game with Pi, and why he always puts human judgment firmly at the heart of his approach.We cover the risks of over-automation, the limits of agentic workflows, and why strong engineers with informed judgment still matter. We also get into the challenges of working with code written by non-engineers, and whether open source can withstand a tidal wave of agent-generated code.—Timestamps(00:00) Intro(07:30) How Mario, Armin, and Peter Steinberger met(15:15) How 30 dev teams use AI agents: learnings(21:50) The importance of judgment(24:26) Challenges when non-engineers write code(28:30) Downsides of over-automation(32:18) Pi(48:09) OpenClaw + Pi(50:54) “Clankers”(57:32) Open source and AI(1:00:22) Complexity as the enemy(1:02:50) Building an AI-native startup(1:11:52) “Slow the F down”(1:16:40) MCPs vs. CLI(1:25:03) Predictions and staying up to date—The Pragmatic Engineer deepdives relevant for this episode:• The impact of AI on software engineers in 2026: key trends• Cycles of disruption in the tech industry• The AI engineering stack• The creator of OpenClaw: "I ship code that I don't read"• What is inference engineering? Deepdive—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@pragmaticengineer.com. Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe
Summary
Designing Data-intensive Applications with Martin Kleppmann
3w ago01:25:00Summary readyBrought to You By:• Statsig — The unified platform for flags, analytics, experiments, and more.• Sonar – The makers of SonarQube, the industry standard for automated code review• WorkOS – Everything you need to make your app enterprise ready.—Martin Kleppmann is a researcher and the author of Designing Data-Intensive Applications, one of the most influential books on modern distributed systems. As of this month, the second, heavily updated edition of the book is out.In this episode of Pragmatic Engineer, we discuss Martin’s career in tech building startups, how he ended up writing this iconic book, and what he’s focused on now after moving into academia.We talk about the tradeoffs behind modern infrastructure, how the cloud has changed what it means to scale, and the thinking behind Designing Data-Intensive Applications, including what’s changing in the second edition.Martin reflects on lessons from building startups like Rapportive, which he sold to LinkedIn, and shares how his experience in both academia and industry shaped his perspective.We also explore what’s ahead: why formal verification may become more important in an AI-assisted world, the challenges of building local-first software, and his recent research into using cryptography to improve transparency in supply chains without exposing sensitive data.—Timestamps(00:00) Early career(05:46) Building Rapportive(10:47) Working at LinkedIn(14:09) Writing Designing Data-Intensive Applications(23:00) Reliability, scalability, and repeatability (26:24) DDIA: the second edition(30:50) Tradeoffs of using cloud services (39:02) How the cloud changed scaling (42:53) The trouble with distributed systems(49:02) Ethics for software engineers (52:45) Formal verification(1:00:12) Academia vs. industry (1:03:50) Local-first software (1:09:50) Computer science education(1:18:32) Martin’s current research and advice—The Pragmatic Engineer deepdives relevant for this episode:• Building Bluesky: a distributed social network• Inside Uber’s move to the cloud• The history of servers, the cloud, and what’s next• The past and future of modern backend practices• How Kubernetes is built—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@pragmaticengineer.com. Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe
Summary
DHH’s new way of writing code
Apr 801:46:14Summary readyBrought to You By:• Statsig — The unified platform for flags, analytics, experiments, and more.• Sonar – The makers of SonarQube, the industry standard for automated code review• WorkOS – Everything you need to make your app enterprise ready.—David Heinemeier Hansson (DHH) is the creator of Ruby on Rails and Omarchy, co-founder and CTO of 37signals (maker of Basecamp and HEY), and the author of several books including the best-seller, Remote: Office Not Required, co-written with Jason Fried.Six months ago, in an episode of the Lex Fridman podcast, David shared how he doesn’t use AI tools to write code: he types out all his code. But things have changed a lot since then. In this episode, we discuss his approach to building software, how it’s changed in the last six months, and why he now takes an agent-first approach, and how he barely writes any code by hand. We go into how he uses AI agents: which alter how he builds and explores ideas, but also how his standards of quality and craft remain the same.We also discuss how 37signals thinks about product development, from the role of designers to the importance of aesthetics and taste. David gets into how he sees beauty and functionality as closely linked, and why strong opinions about design lead to better software.Finally, we look into the uneven impact of AI which amplifies senior engineers while creating challenges for junior developers, and what this may mean for the role of the software engineer.—Timestamps(00:00) Intro(02:11) Omarchy and Ruby on Rails(08:25) 37signals overview(10:12) Launching HEY(18:38) Building HEY(22:47) Designers at 37signals(28:08) The craft of design(31:52) Why DHH now embraces AI workflows(39:45) The AI inflection point(44:23) DHH’s agent-first workflow(55:09) AI’s impact on junior developers(1:03:08) Developer experience with AI(1:16:43) What does AI mean for developers?(1:23:33) 37signals teams and hiring(1:38:20) Work-life balance with AI(1:41:41) Why DHH keeps building(1:45:24) Closing—The Pragmatic Engineer deepdives relevant for this episode:• Are AI agents actually slowing us down?• How Claude Code is built• The future of software engineering with AI: six predictions• The AI Engineering Stack• Mitchell Hashimoto’s new way of writing code• How Linux is built with Greg Kroah-Hartman—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@pragmaticengineer.com. Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe
Summary
Scaling Uber with Thuan Pham (Uber’s first CTO)
Apr 101:38:55Summary readyBrought to You By:• Statsig — The unified platform for flags, analytics, experiments, and more.• Sonar – The makers of SonarQube, the industry standard for automated code review• WorkOS – Everything you need to make your app enterprise ready.—Thuan Pham was Uber's first and longest-serving CTO, and today he’s the CTO of Faire, a B2B wholesale platform. Back when Thuan joined Uber, it had around 40 engineers and 30,000 rides per day, and the system crashed multiple times a week. Over seven years, he helped rebuild the system, move it from a monolith to microservices, and scaled the engineering organization behind it. I had the privilege of working with Thuan for four of those seven years. Later, the very first issue of The Pragmatic Engineer newsletter was a deepdive into Uber’s Program and Platform split. This episode of the podcast contains a nice “full circle” moment, where Thuan shares even more details about why Uber chose to embrace that structure.We discuss what it takes to operate and build in that kind of environment. Thuan explains how he divided his time at Uber into three “tours of duty,” from stabilizing a fragile system, to re-architecting it, and scaling the org.We go deep into the platform-and-program split, the Helix app rewrite, and what it took to launch Uber in China in just five months (the original estimate was 18 months). We also cover Uber’s in-house tools and explain why they were necessary to support rapid growth.Finally, we discuss his role today as CTO of Faire, how the company is using AI, and how he sees AI changing software engineering.—Timestamps(00:00) Intro(05:32) Getting into tech(16:09) The dot-com bust(20:42) VMware(26:29) Getting hired by Travis at Uber(33:22) Early days at Uber and scaling challenges(40:57) Uber’s China launch(47:12) The platform and program split(50:26) From monolith to microservices (53:38) Internal tools at Uber (57:05) Helix: Uber’s mobile app rewrite(59:55) Thuan’s email about naming(1:02:03) Org structure changes under(1:06:34) Thuan’s work philosophy (1:12:23) The “three tours of duty” at Uber(1:15:37) Why Thuan left Uber (1:17:34) Coupang and Nubank(1:21:59) Faire(1:25:31) How Faire uses AI(1:28:24) AI’s impact on software engineering (1:31:09) The role of the CTO (1:35:13) Career advice—The Pragmatic Engineer deepdives relevant for this episode:• How Uber uses AI for development: inside look• The Platform and Program split at Uber• How Uber is measuring engineering productivity• Inside Uber’s move to the cloud• Uber's crazy YOLO app rewrite, from the front seat• How Uber built its observability platform• Developer experience at Uber with Gautam Korlam• Uber’s engineering level changes—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@pragmaticengineer.com. Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe
Summary
Building WhatsApp with Jean Lee
Mar 1801:10:37Summary readyBrought to You By:• Statsig — The unified platform for flags, analytics, experiments, and more.• Sonar – The makers of SonarQube, the industry standard for automated code review• WorkOS – Everything you need to make your app enterprise ready.—How did a tiny team of 30 engineers build the world-famous messaging app more than a decade ago, and what can dev teams learn from that feat today? Jean Lee was engineer #19 at WhatsApp, joining when the company was still small, with almost no formal processes. She helped it scale to hundreds of millions of users, went through the $19B acquisition by Facebook, and later worked at Meta.In this episode of Pragmatic Engineer, I talk with Jean about what it was like building WhatsApp. When Facebook bought WhatsApp in 2014, only around 30 engineers supported hundreds of millions of users across eight platforms.We discuss how the founders kept things simple, saying “no” to most feature requests for years. Jean explains why WhatsApp chose Erlang for the backend, why the team avoided cross-platform abstractions, and how charging users $1 per year paid everyone’s salaries, while keeping growth intentionally slow.Jean also shares what the Facebook acquisition was like on the inside, how she dealt with sudden personal wealth, and what it was like transitioning from an IC to a manager at Facebook – including the reality of calibration meetings and performance reviews.We also discuss how AI enables smaller engineering teams, and why WhatsApp’s experience suggests ownership and trust might matter more than tools.—Timestamps(00:00) Intro(01:39) Early years in tech(06:18) Becoming engineer #19 at WhatsApp(13:53) WhatsApp’s tech stack(18:09) WhatsApp’s unique ways of working(25:27) Countdown displays and outages(27:07) Why WhatsApp won(28:53) The Facebook acquisition(33:13) Life after acquisition(39:27) Working at Facebook in London(44:07) Transitioning to management(47:27) Performance reviews as a manager(53:29) After Facebook(58:53) AI’s impact on engineering(1:02:34) Jean’s advice to new grads and startups(1:06:45) Empowering employees(1:08:17) Book recommendations—The Pragmatic Engineer deepdives relevant for this episode:• How Meta built Threads• How Big Tech runs tech projects and the curious absence of Scrum• Performance calibrations at tech companies• Software engineers leading projects—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@pragmaticengineer.com. Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe
Summary
From IDEs to AI Agents with Steve Yegge
Mar 1101:31:17Summary readyBrought to You By:• Statsig — The unified platform for flags, analytics, experiments, and more.• Sonar – The makers of SonarQube, the industry standard for automated code review• WorkOS – Everything you need to make your app enterprise ready.—Steve Yegge has spent decades writing software and thinking about how the craft evolves. From his early years at Amazon and Google, to his influential blog posts, he has often been early at spotting shifts in how software gets built. In this episode of Pragmatic Engineer, I talk with Steve about how AI is changing engineering work, why he believes coding by hand may gradually disappear, and what developers should focus on, instead. We discuss his latest book, Vibe Coding, and the open-source AI agent orchestrator he built called Gas Town, which he said most devs should avoid using.Steve shares his framework for levels of AI adoption by engineers, ranging from avoiding AI tools entirely, to running multiple agents in parallel. We discuss why he believes the knowledge that engineers need to know keeps changing, and why understanding how systems evolve may matter more than mastering any particular tool.We also explore broader implications. Steve argues that AI’s role is not primarily to replace engineers, but to amplify them. At the same time, he warns that the pace of change will create new kinds of technical debt, new productivity pressures, and fresh challenges for how teams operate.—Timestamps(00:00) Intro(01:43) Steve’s latest projects(02:27) Important blog posts(04:48) Shifts in what engineers need to know(10:46) Steve’s current AI stance(13:23) Steve’s book Vibe Coding(18:25) Layoffs and disruption in tech(31:13) Gas Town(40:10) New ways of working(51:08) The problem of too many people(54:45) Why AI results lag in business(59:57) Gamification and product stickiness(1:04:54) The ‘Bitter Lesson’ explained(1:07:14) The future of software development(1:23:06) Where languages stand(1:24:47) Adapting to change(1:27:32) Steve’s predictions —The Pragmatic Engineer deepdives relevant for this episode:• Vibe coding as a software engineer• The full circle of developer productivity with Steve Yegge• AI Tooling for Software Engineers in 2026• The AI Engineering Stack—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@pragmaticengineer.com. Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe
Summary
Building Claude Code with Boris Cherny
Mar 401:37:01Summary readyBrought to You By:• Statsig — The unified platform for flags, analytics, experiments, and more.• Sonar – The makers of SonarQube, the industry standard for automated code review• WorkOS – Everything you need to make your app enterprise ready.—Boris Cherny is the creator and Head of Claude Code at Anthropic. He previously spent five years at Meta as a Principal Engineer and is the author of the book Programming TypeScript.In this episode of Pragmatic Engineer, we went through how Claude Code was built and what it means when engineers no longer write most of the code themselves.We discuss how Claude Code evolved from a side project into a core internal tool at Anthropic and how Boris uses it day-to-day. We go deep into workflow details, including parallel agents, PR structure, deterministic review patterns, and how the system retrieves context from large codebases. We also get into how Claude Cowork was built.As coding becomes more accessible, the role of engineers shifts rather than shrinks. We examine what that shift means in practice, which skills become more important, and why the lines between product, engineering, and design are blurring.—Timestamps(00:00) Intro(11:15) Lessons from Meta(19:46) Joining Anthropic(23:08) The origins of Claude Code(32:55) Boris's Claude Code workflow(36:27) Parallel agents(40:25) Code reviews(47:18) Claude Code's architecture(52:38) Permissions and sandboxing(55:05) Engineering culture at Anthropic(1:05:15) Claude Cowork(1:12:48) Observability and privacy(1:14:45) Agent swarms(1:21:16) LLMs and the printing press analogy(1:30:16) Standout engineer archetypes(1:32:12) What skills still matter for engineers(1:35:24) Book recommendations—The Pragmatic Engineer deepdives relevant for this episode:• How Claude Code is built• How Anthropic built Artifacts• How Codex is built• Real-world engineering challenges: building Cursor—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@pragmaticengineer.com. Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe
Summary
Mitchell Hashimoto’s new way of writing code
Feb 2501:57:36Summary readyBrought to You By:• Statsig — The unified platform for flags, analytics, experiments, and more.• Sonar – The makers of SonarQube, the industry standard for automated code review• WorkOS – Everything you need to make your app enterprise ready.—How has the day-to-day workflow of Mitchell Hashimoto changed, thanks to AI tools?Mitchell Hashimoto is one of the most influential infrastructure engineers of our time, and is one of the most pragmatic builders I’ve met. He is the co-founder of HashiCorp and creator of Ghostty. In this episode, we talk about how he got into software engineering, the history of HashiCorp, and the challenges of turning widely used open-source tools into a durable business. We also go into what it’s really like to work with AWS, Azure and GCP as a startup.Mitchell shares how he uses AI these days, and how agents have completely changed how he works. We touch on Ghostty, open source, and what’s changing for software engineers and founders in an AI-native era.—Timestamps(00:00) Intro(02:03) Mitchell’s path into software engineering(07:19) The origins of HashiCorp(15:52) Early cloud computing(18:22) The 2010s startup scene in SF(23:11) Funding HashiCorp(25:23) The Hashi stack(32:33) Why HashiCorp’s business lagged behind its technology(35:28) An early failure in commercialization(38:28) The open-core pivot and path to enterprise profitability(48:08) Taking HashiCorp public(51:58) The near VMware acquisition(59:10) Mitchell’s take on all the cloud providers(1:06:02) AI’s impact on open source(1:07:00) Why Mitchell built Ghostty(1:09:11) Why Mitchell used Zig(1:10:38) How terminals work and Ghostty’s approach(1:17:31) AI’s impact on terminals and libghostty(1:19:13) How Mitchell uses AI(1:22:02) Ghostty’s evolving AI use policy(1:28:36) Why open source must change(1:31:46) The problem of Git in monorepos(1:36:22) What needs to change to work effectively with AI(1:39:57) Mitchell’s hiring practices(1:47:52) Mitchell’s AI adoption journey(1:50:41) Advice to would-be founders(1:52:21) Mitchell’s advising work(1:53:20) What’s changing for software engineers(1:55:03) How Mitchell recharges(1:55:50) Book recommendation—The Pragmatic Engineer deepdives relevant for this episode:• AI Engineering in the real world• The AI Engineering stack• Pressure on commercial open source to make more money – and HashiCorp changing its license• How Linux is built with Greg Kroah-Hartman—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@pragmaticengineer.com. Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe
Summary
The programming language after Kotlin – with the creator of Kotlin
Feb 1201:44:10Summary readyBrought to You By:• Statsig — The unified platform for flags, analytics, experiments, and more.• Sonar – The makers of SonarQube, the industry standard for automated code review• WorkOS – Everything you need to make your app enterprise ready.—Andrey Breslav is the creator of Kotlin and the founder of CodeSpeak, a new programming language that aims to reduce boilerplate by replacing trivial code with concise, plain-English descriptions. He led Kotlin’s design at JetBrains through its early releases, shaping both the language and its compiler as Kotlin grew into a core part of the Android ecosystem.In this episode, we talk about what it takes to design and evolve a programming language in production. We discuss the influences behind Kotlin, the tradeoffs that shaped it, and why interoperability with Java became so central to its success. Andrey also explains why he is building CodeSpeak as a response to growing code complexity in an era of LLM agents, and why he believes keeping humans in control of the software development lifecycle will matter even more as AI becomes more capable.—Timestamps(00:00) Intro(01:02) Why Kotlin was created(06:26) Dynamic vs. static languages(09:27) Andrey joins the Kotlin project(14:26) Designing a new language (19:40) Frontend vs. Backend in language design(21:05) Why is it named Kotlin?(24:37) Kotlin vs. Java tradeoffs(28:32) Null safety (31:24) Kotlin’s influences (39:12) Smartcasts (40:42) Features Kotlin left out(44:54) Bidirectional Java interoperability(55:01) The Kotlin timeline (58:00) Kotlin’s development process(1:07:20) From Java to Android developers(1:12:12) How Android became Kotlin-first (1:18:20) CodeSpeak: a language for LLMs(1:24:07) LLMs and new languages(1:28:20) How software engineering is changing with AI(1:36:12) Developer tools of the future (1:39:00) Andrey’s advice for junior engineers and students (1:42:32) Rapid fire round—The Pragmatic Engineer deepdives relevant for this episode:• Cross-platform mobile development• How Swift was built – with Chris Lattner, the creator of the language• Building Reddit’s iOS and Android app• Notion: going native on iOS and Android• Is there a drop in native iOS and Android hiring at startups?—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@pragmaticengineer.com. Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe
Summary