The Pragmatic Engineer

Brought 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 [email protected]. Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe

Brought 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 [email protected]. Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe

Brought 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 [email protected]. Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe

Brought 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 [email protected]. Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe

Brought 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 [email protected]. Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe

Brought 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 [email protected]. Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe