The Pragmatic Engineer

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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.
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Timestamps
00:00 Intro
03:34 Kelsey’s first job at McDonald’s
05:04 His non-traditional path into tech
11:45 Landing his first tech job with an A+ certification
15:33 His entrepreneurial years
19:45 Joining Google as a data center technician
27:48 Learning automation at a Rackspace spinoff
33:26 Moving into financial services
50:00 Building a reputation through open source
53:55 From configuration management to containers
1:08:20 The rise of Kubernetes
1:25:05 Why he almost joined NASA instead of Google
1:29:20 Defining DevRel at Google
1:38:20 Demonstrating impact at Google
1:41:20 Microsoft's offer
1:55:20 Learning how to slow down
2:06:39 Advising and investing
2:15:03 A people-first view of GenAI
2:24:27 Using AI with guardrails
2:28:26 Matching AI to the task
2:36:06 Staying relevant in the AI era
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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!)
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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.
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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.
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Timestamps
00:00 Intro
07:03 Dax’s path into tech
09:04 Early startup experience
13:16 Getting involved with open source
16:13 OpenCode
23:17 Anthropic banning OpenCode
30:34 From terminal to GUI
32:34 OpenCode’s business model
36:33 Why inference is profitable
39:11 GPU bottlenecks
40:54 AI hype
45:50 AI spending
48:47 Dax’s memo
55:41 Dax’s skepticism of predictions
58:58 Engineering culture at OpenCode
1:02:38 How building works at OpenCode
1:05:36 Taste and quality
1:11:32 Dax’s work setup
1:12:35 The role of engineers and EMs
1:15:50 Advice for engineers
1:18:12 Book recommendation
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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
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Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email [email protected].


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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.
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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.
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Timestamps
(00:00) Intro
(04:09) Tokio: an overview
(05:11) What Alice likes about Rust
(12:48) Rust for TypeScript engineers
(13:51) Moving from C++ to Rust
(14:34) Memory safety
(18:12) Garbage collection tradeoffs
(21:46) Ownership, references, and borrowing
(26:59) Unsafe in Rust
(31:21) Crates and Cargo
(35:55) Language design and RFCs
(43:02) Building new features
(46:30) Editions vs. versions
(49:47) Getting paid to work on Rust
(51:27) Contributing to Rust
(53:03) Rust in the Linux kernel
(55:45) AI use cases for Rust
(1:01:35) Learning Rust
(1:03:54) Book recommendation
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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
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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:
• 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.
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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.
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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
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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
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Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email [email protected].


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• 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.
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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.
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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
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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
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Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email [email protected].


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