The Pragmatic Engineer: Daily Summaries Delivered

Kubernetes and retiring at the top with Kelsey Hightower

Brought to You By:• Antithesis – verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages.• Buildkite – CI software built to absorb whatever your coding agents throw at the build queue• Sentry – application monitoring software considered “not bad” by millions of developers—Kelsey Hightower went from a self-taught technician installing DSL modems to becoming one of Google’s elite Distinguished Engineers, whom the CEO of Microsoft personally tried to recruit. Hightower’s career achievements are rooted in hard work and self-directed learning, and today he’s one of the most influential voices in modern infrastructure, through his talks, open source work, and writing.In this episode of The Pragmatic Engineer podcast, Kelsey and I cover his unconventional path into tech and the lessons he’s learned during three decades in the industry. We discuss his entrepreneurial years, building a reputation through open source, the rise of containers and Kubernetes, and his time at Google during one of the most consequential periods in cloud computing. He recounts how a job offer from a big tech giant led to the biggest raise of his career, what prompted him to slow down after years of career acceleration, and we also discuss his perspective on AI. Throughout, Kelsey keeps a simple idea front of mind: that technology is ultimately about people. Whether it’s infrastructure, leadership, careers, or AI, he argues that the goal is not to build technology for its own sake; it’s to solve meaningful human problems.—Timestamps00:00 Intro03:34 Kelsey’s first job at McDonald’s05:04 His non-traditional path into tech11:45 Landing his first tech job with an A+ certification15:33 His entrepreneurial years19:45 Joining Google as a data center technician27:48 Learning automation at a Rackspace spinoff33:26 Moving into financial services50:00 Building a reputation through open source53:55 From configuration management to containers1:08:20 The rise of Kubernetes1:25:05 Why he almost joined NASA instead of Google1:29:20 Defining DevRel at Google1:38:20 Demonstrating impact at Google1:41:20 Microsoft's offer1:55:20 Learning how to slow down2:06:39 Advising and investing2:15:03 A people-first view of GenAI2:24:27 Using AI with guardrails2:28:26 Matching AI to the task2:36:06 Staying relevant in the AI era—The Pragmatic Engineer deepdives relevant for this episode:• Career paths for software engineers at large tech companies• The past and future of modern backend practices• How Kubernetes is built• How Linux is built• The Staff Engineer’s Path: You’re a role model now (sorry!)—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

Building OpenCode with Dax Raad

Brought 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.—OpenCode is one of the fastest-growing AI developer tools around, surging in just a few months from roughly 650,000 monthly active users to nearly 8 million, and almost 1M daily active users.In this episode of The Pragmatic Engineer Podcast, we meet Dax Raad, co-founder of OpenCode, for a discussion about the gaps in developer tooling that led him to build OpenCode, the advantages of open source, and why taste and engineering judgment matter even more as AI becomes a core part of software development.We also cover how OpenCode turned Anthropic’s blocking of integration with Claude Code into a massive growth lever by partnering with OpenAI and other model providers, why GPU demand is becoming a bottleneck everywhere, how come AI coding tools don’t automatically mean engineering teams move faster, and also why Dax is personally skeptical about predictions for the future of engineering and work, in general.I found this conversation especially interesting because Dax displays a healthy skepticism toward the benefits of AI, even while building one of the most popular AI coding harnesses.—Timestamps00:00 Intro07:03 Dax’s path into tech09:04 Early startup experience13:16 Getting involved with open source16:13 OpenCode23:17 Anthropic banning OpenCode30:34 From terminal to GUI32:34 OpenCode’s business model36:33 Why inference is profitable39:11 GPU bottlenecks40:54 AI hype45:50 AI spending48:47 Dax’s memo55:41 Dax’s skepticism of predictions58:58 Engineering culture at OpenCode1:02:38 How building works at OpenCode1:05:36 Taste and quality1:11:32 Dax’s work setup1:12:35 The role of engineers and EMs1:15:50 Advice for engineers1:18:12 Book recommendation—The Pragmatic Engineer deepdives relevant for this episode:• How Claude Code is built• How Codex is built• Real-world engineering challenges: building Cursor• The AI Engineering stack• 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

Why Rust is different, with Alice Ryhl

Brought to You By:• Antithesis – verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages.• Sentry⁠ – application monitoring software considered “not bad” by millions of developers• ⁠Craft Conference⁠: join Gergely, Kent Beck, Hillel Wayne and others at the conference dedicated to the art and science of software delivery craft.—Rust is one of the most admired programming languages around – and also one of the hardest to learn. What makes developers stick with it?In this episode of The Pragmatic Engineer Podcast, I sit down with Alice Ryhl, a software engineer on Google’s Android Rust team, and a core maintainer of Tokio, which is the most widely-used async runtime in Rust.We discuss what makes Rust different from other languages like TypeScript, Go, and C++, and why so many developers say that “once it compiles, it works.” We go deep into memory safety, ownership, borrowing, unsafe Rust, and Cargo.We also cover how Rust is governed by RFCs, feature flags, its six-week release cycle, how engineers get paid to work on the language, and also look into how Rust’s use inside the Linux kernel is progressing.—Timestamps Intro Tokio: an overview What Alice likes about Rust Rust for TypeScript engineers Moving from C++ to Rust Memory safety Garbage collection tradeoffs Ownership, references, and borrowing Unsafe in Rust Crates and Cargo Language design and RFCs Building new features Editions vs. versions Getting paid to work on Rust Contributing to Rust Rust in the Linux kernel AI use cases for Rust Learning Rust Book recommendation—The Pragmatic Engineer deepdives relevant for this episode:• The past and future of modern backend practices• How Kotlin was built with Andrey Breslav• How Swift was built with Chris Lattner• How Linux is built with Greg KH—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

TypeScript, C# and Turbo Pascal with Anders Hejlsberg

Brought 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 Intro How Anders got into programming Building his first compiler Turbo Pascal Delphi Joining Microsoft Building C# Async/await The rise of JavaScript Building TypeScript How the TypeScript compiler works JavaScript’s strengths and weaknesses How Anders uses AI What language features work well with AI How software craftsmanship is changing Performance and efficiency Anders’ tool stack A 30-year career at Microsoft 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

Building Pi, and what makes self-modifying software so fascinating

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.—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 Intro How Mario, Armin, and Peter Steinberger met How 30 dev teams use AI agents: learnings The importance of judgment Challenges when non-engineers write code Downsides of over-automation Pi OpenClaw + Pi “Clankers” Open source and AI Complexity as the enemy Building an AI-native startup “Slow the F down” MCPs vs. CLI 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

Designing Data-intensive Applications with Martin Kleppmann

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.—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 Early career Building Rapportive Working at LinkedIn Writing Designing Data-Intensive Applications Reliability, scalability, and repeatability DDIA: the second edition Tradeoffs of using cloud services How the cloud changed scaling The trouble with distributed systems Ethics for software engineers Formal verification Academia vs. industry Local-first software Computer science education 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

DHH’s new way of writing code

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 Intro Omarchy and Ruby on Rails 37signals overview Launching HEY Building HEY Designers at 37signals The craft of design Why DHH now embraces AI workflows The AI inflection point DHH’s agent-first workflow AI’s impact on junior developers Developer experience with AI What does AI mean for developers? 37signals teams and hiring Work-life balance with AI Why DHH keeps building 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

Scaling Uber with Thuan Pham (Uber’s first CTO)