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
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• Antithesis – verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages.
• turbopuffer – a vector and full-text search engine built on object storage. It’s fast, cheap, and extremely scalable.
• WorkOS – everything you need to make your app enterprise ready.
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There’s a popular theory that AI will finally make formal verification mainstream because mathematical proof of correctness will be needed when machines write most or all of the code. But will this happen? Today, I’m talking with one of the best people to tackle the prediction. Hillel Wayne is a formal methods consultant, educator, and author, who’s deeply interested in software history.
In this episode of Pragmatic Engineer podcast, I sit down with Hillel to compare software engineering with traditional engineering, discuss where formal methods fit into modern software development, and we explore why they are essential for some of the world's most complex systems. We cover the formal specification language, TLA+, walk through several formal verification tools, examine why distributed systems are so difficult to reason about, and look into whether AI will make formal methods accessible to more engineering teams.
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Timestamps
00:00 Intro
03:21 The Crossover Project
10:26 What software engineering does better
14:19 What traditional engineering does better
17:06 Formal methods
28:21 TLA+: what it is and demo
35:47 TLA+ at Amazon
36:59 Ways distributed systems break
39:52 Formal methods and systems thinking
45:09 The value of learning math
49:12 What TLA+ is good for and isn’t
51:39 Alloy: a declarative language for software modeling
57:42 Other formal methods tools
1:00:13 Property-based testing
1:04:20 AI and the need for formal verification
1:11:18 Logic for programmers
1:13:24 Hillel’s 2025 prediction on AI’s impact
1:20:19 Book recommendation
—
The Pragmatic Engineer deepdives relevant for this episode:
• How to debug large, distributed systems: Antithesis
• How AWS S3 is built
• Paying down tech debt
• How Big Tech does quality assurance (QA)
• Bug management that works
• Resiliency in distributed systems
—
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
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.
—
Knowing how LLM contexts work and how to work around context limitations – aka “context engineering” – is becoming more important for software engineers working with LLMs. Let’s look into what works and what doesn’t, today.
In this episode of The Pragmatic Engineer podcast, I sit down with the CEO and cofounder of HumanLayer, Dex Horthy, who coined the term “context engineering”. We discuss the ideas behind this context engineering, harness engineering, loop engineering, software factories, why his approach to AI-assisted software development has evolved, and how HumanLayer is helping engineering teams automate more of the software development lifecycle without sacrificing code quality.
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Timestamps
00:00 Intro
03:35 Dex’s path into tech
05:36 Early work in platform engineering
07:30 Replicated
13:26 Metalytics
14:38 12-factor agents
20:29 Context engineering
25:40 Harness engineering
28:13 Context overload
32:47 Loop engineering
46:36 Software factories before and after AI
52:35 Automation limits
57:20 Three options for automating
1:01:02 RPI framework
1:06:18 Intentional compaction
1:13:50 Token harder vs. token smarter
1:18:46 AI slop
1:21:17 HumanLayer
1:31:11 Book recommendation
—
The Pragmatic Engineer deepdives relevant for this episode:
• How Uber uses AI for development: inside look
• Are AI agents actually slowing us down?
• AI Tooling for Software Engineers in 2026
• Vibe Coding as a software engineer
• How Claude Code is built
• AI Engineering in the real world
• The AI Engineering Stack
• How AI-assisted coding will change software engineering: hard truths
• The creator of OpenClaw: "I ship code I don't read"
—
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
Brought to You By:
• Antithesis – verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages.
—
In this special “ask me anything” episode of Pragmatic Engineer podcast, I am in the hot seat facing questions sent in by subscribers that are read out by guest Volodymyr Giginiak, CTO and cofounder of Wordsmith AI, a legal tech startup (note: I’m an investor).
I tackle your questions on the software industry, AI, hiring, engineering organizations, career growth, the business model of the Pragmatic Engineer, and more. We also discuss where software engineering is headed, and I offer advice on some specific situations. Thanks to everyone who sent questions!
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Timestamps
00:00 Intro
01:56 From Uber to writing
09:22 AI-native SDLC
14:00 AI and hiring
19:06 Engineers currently thriving
22:18 Junior roles
24:44 Meta’s war mode
27:54 AI at Big Tech vs. startups
36:46 Tech debt
41:36 Types of engineering managers
44:40 Measuring AI productivity
48:30 The value of CS degrees
50:53 AI at Pragmatic Engineer
56:09 Future-proofing your career
1:01:36 The EU job market
1:03:55 Making money as a creator
1:08:20 What’s next for The Pragmatic Engineer
1:09:27 Bunq and Pollen
1:13:38 Spotting trends
1:14:33 Book updates
1:15:20 Favorite books & tech products
1:17:13 What won’t change in engineering
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The Pragmatic Engineer deepdives relevant for this episode:
• State of the software engineering job market in 2026
• The impact of AI on software engineers in 2026: key trends.
• How 10 tech companies choose the next generation of dev tools
• The reality of tech interviews
—
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
Brought to You By:
• Antithesis – verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages.
• turbopuffer – a vector and full-text search engine built on object storage. It’s fast, cheap, and extremely scalable.
• WorkOS – everything you need to make your app enterprise ready.
—
Few have made as big an impact on software engineering as this week’s guest on the Pragmatic Engineer podcast, Kent Beck. He created Extreme Programming, pioneered test-driven development (TDD), co-created JUnit, and is one of the authors of the famous ‘Agile Manifesto’. But these days, he's re-examining many ideas for the age of AI, and says we’re failing to accumulate trust during this new era at the same high rate as new code is being accumulated.
In this episode of the Pragmatic Engineer podcast, Kent and I dig into his journey from discovering Smalltalk in the early days of personal computing, to helping define modern software engineering practices. We explore the origins of TDD, design patterns, Extreme Programming, and Agile – along with some lessons learned at Apple and Facebook.
Kent explains why he believes software engineering is about far more than writing code, why no one yet knows exactly how engineers should work alongside AI agents, and how his "explore, expand, extract" framework can help engineers navigate major technology shifts.
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Timestamps
00:00 Intro
03:47 Human engineers aren’t going away
08:00 Kent's path into tech
13:50 Undergraduate and graduate studies
17:21 Kent’s first programming job
18:54 The rise and fall of Smalltalk
27:04 Working with Ward Cunningham
37:36 Design patterns
44:05 Working at Apple
51:08 CRC Cards
59:29 Testing tools in the language
1:04:22 The C3 project with Martin Fowler
1:09:54 Extreme Programming
1:16:25 Developing TDD
1:25:07 Writing the Agile Manifesto
1:30:00 Agile’s impact
1:32:40 Agile’s downside
1:37:32 The Dotcom Bust
1:44:30 Lessons from working at Facebook
1:59:44 Kent’s ‘Good to Great’ program at Facebook
2:06:07 Soft skills engineers need to learn
2:09:30 AI and the challenges of acceleration
2:15:53 Explore, expand, extract
2:22:33 What Kent is excited about
—
The Pragmatic Engineer deepdives relevant for this episode:
• Measuring developer productivity? A response to McKinsey – co-written with Kent Beck
• TDD, AI agents and coding with Kent Beck
• Paying down tech debt
• The past and future of modern backend practices
—
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
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
• Google Cloud Run – run your code and host LLMs directly on top of Google’s scalable infrastructure, without having to worry about managing infra.
—
Navdeep Singh – oftentimes better known as NeetCode – is the creator of NeetCode.io, one of the most popular coding interview preparation platforms and YouTube channels for software engineers. Before building NeetCode full-time, he worked as a software engineer at Amazon and Google.
In this episode of The Pragmatic Engineer, I sit down with Neet to discuss his path from Amazon and Google to building his own startup, why he left Amazon after just two months, what he learned at Google, and the decision to leave a stable engineering career to bet on himself. We also discuss what coding interview preparation teaches beyond passing interviews, the value of going deep on difficult problems, and why systems thinking and domain expertise remain essential engineering skills in the age of AI.
Throughout the conversation, NeetCode makes the case that learning hard things is one of the single best investments an engineer can make, helping build the judgment and expertise that remain valuable no matter how the tools change.
—
Timestamps
00:00 Intro
02:57 Neet’s take on coding interviews
06:41 Getting into tech
08:56 Why Neet isn't a fan of the CAP theorem
13:12 Quitting Amazon after two months
18:22 Google vs Amazon
22:26 The origins of NeetCode
25:27 Leaving Google to go all in on NeetCode
32:02 Why Neet doesn't fix every bug
39:26 The value of coding interview prep
42:57 Systems thinking and domain expertise
47:28 Hiring at Big Tech
52:15 Tech stack at Neetcode
57:57 The NeetCode redesign contest
1:01:46 The future of software engineers
1:09:04 Hot takes: AGI, AI skill erosion, personality traits
1:22:49 “Maybe some people should just give up”
1:24:39 How to be a standout engineer
1:27:55 Book recommendation
—
The Pragmatic Engineer deepdives relevant for this episode:
• Learnings from conducting ~1,000 interviews at Amazon
• How experienced engineers get unstuck in coding interviews
• The Reality of Tech Interviews in 2025
• Tech hiring: is this an inflection point?
• AI fakers exposed in tech dev recruitment: postmortem
—
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
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.
—
Robert Erez is a principal engineer at Octopus Deploy, and a longtime expert in CI/CD, deployment systems, and software delivery. Rob and I were also once colleagues on the Skype web team, working on large-scale deployments and release processes.
In this episode of The Pragmatic Engineer, I sit down with Rob to discuss how teams deploy software safely and efficiently at scale. We cover Kubernetes, GitOps, platform engineering, progressive delivery, feature flags, cloud development environments, and the growing role of AI in CI/CD workflows. We also get into the tradeoffs in different deployment approaches, why self-hosted software still matters for some organizations, and the recent evolution of software delivery practices.
—
Timestamps
00:00 Intro
02:09 Canary deployments at Skype
05:01 Joining at Octopus Deploy
06:15 Continuous deployment
10:26 Why Kubernetes won
15:51 Kubernetes on-prem
18:50 How GitOps works
25:00 The uses and limitations of GitOps
31:04 The rise of platform teams
35:51 How AI is changing CI/CD
39:49 Progressive delivery explained
47:31 Rollbacks and roll-forwards
50:14 Feature flags
54:32 How development environments are evolving
57:40 Cloud development environments (CDEs)
1:03:45 Self-hosting CI/CD
1:09:25 Getting started with progressive delivery
1:11:15 Book recommendations
—
The Pragmatic Engineer deepdives relevant for this episode:
• Kubernetes and retiring at the top with Kelsey Hightower
• The past and future of modern backend practices
• Microsoft is dogfooding AI dev tools’ future
• How Kubernetes is built with Kat Cosgrove
• 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
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.
—
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
—
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
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.
—
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
—
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
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
(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
—
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
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
(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
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
(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
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
(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
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 podcast@pragmaticengineer.com.
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 podcast@pragmaticengineer.com.
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 podcast@pragmaticengineer.com.
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 podcast@pragmaticengineer.com.
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 podcast@pragmaticengineer.com.
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 podcast@pragmaticengineer.com.
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.
—
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
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.
—
Every few decades, software engineering is declared “dead” or on the verge of being automated away. We’ve heard versions of this story before. But what if it’s just the start of a new “golden age” of a different type of software engineering, like it has been many times before?
In this episode of The Pragmatic Engineer, I’m joined once again by Grady Booch, one of the most influential figures in the history of software engineering, to put today’s claims about AI and automation into historical context.
Grady is the co-creator of the Unified Modeling Language, author of several books and papers that have shaped modern software development, and Chief Scientist for Software Engineering at IBM, where he focuses on embodied cognition.
Grady shares his perspective on three golden ages of computing since the 1940s, and how each emerged in response to the constraints of its time. He explains how technical limits and human factors have always shaped the systems we build, and why periods of rapid change tend to produce both real progress and inflated expectations.
He also responds to current claims that software engineering will soon be fully automated, explaining why systems thinking, human judgment, and responsibility remain central to the work, even as tools continue to evolve.
—
Timestamps
(00:00) Intro
(01:04) The first golden age of software engineering
(18:05) The software crisis
(32:07) The second golden age of software engineering
(41:27) Y2K and the Dotcom crash
(44:53) Early AI
(46:40) The third golden age of software engineering
(50:54) Why software engineers will very much be needed
(57:52) Grady responds to Dario Amodei
(1:06:00) New skills engineers will need to succeed
(1:09:10) Resources for studying complex systems
(1:13:39) How to thrive during periods of change
—
The Pragmatic Engineer deepdives relevant for this episode:
• When AI writes almost all code, what happens to software engineering?
• Inside a five-year-old startup’s rapid AI makeover
• Software architecture with Grady Booch
• What is old is new again
—
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
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.
—
Peter Steinberger ships more code than I’ve seen a single person do: in January, he was at more than 6,600 commits alone. As he puts it: “From the commits, it might appear like it's a company. But it’s not. This is one dude sitting at home having fun."
How does he do it?
Peter Steinberger is the creator of Clawdbot (as of yesterday: renamed to Moltbot) and founder of PSPDFKit. Moltbot – a work-in-progress AI agent that shows what the future of Siri could be like – is currently the hottest AI project in the tech industry, with more searches on Google than Claude Code or Codex. I sat down with Peter in London to talk about what building software looks like when you go all-in with AI tools like Claude and Codex.
Peter’s background is fascinating. He built and scaled PSPDFKit into a global developer tools business. Then, after a three-year break, he returned to building. This time, LLMs and AI agents sit at the center of his workflow. We discuss what changes when one person can operate like a team and why closing the loop between code, tests, and feedback becomes a prerequisite for working effectively with AI.
We also go into how engineering judgment shifts with AI, how testing and planning evolve when agents are involved, and which skills and habits are needed to work effectively. This is a grounded conversation about real workflows and real tradeoffs, and about designing systems that can test and improve themselves.
—
Timestamps
(00:00) Intro
(01:07) How Peter got into tech
(08:27) PSPDFKit
(19:14) PSPDFKit’s tech stack and culture
(22:33) Enterprise pricing
(29:42) Burnout
(34:54) Peter finding his spark again
(43:02) Peter’s workflow
(49:10) Managing agents
(54:08) Agentic engineering
(59:01) Testing and debugging
(1:03:49) Why devs struggle with LLM coding
(1:07:20) How PSPDFkit would look if built today
(1:11:10) How planning has changed with AI
(1:21:14) Building Clawdbot (now: Moltbot)
(1:34:22) AI’s impact on large companies
(1:38:38) “I don’t care about CI”
(1:40:01) Peter’s process for new features
(1:44:48) Advice for new grads
(1:50:18) Rapid fire round
—
The Pragmatic Engineer deepdives relevant for this episode:
• Inside a five-year-old startup’s rapid AI makeover
• When AI writes almost all code, what happens to software engineering?
• Why it’s so dramatic that “writing code by hand is dead”
• AI Engineering in the real world
• 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
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.
—
Amazon S3 is one of the largest distributed systems ever built, storing and serving data for a significant portion of the internet. Behind its simple interfaces hides an enormous amount of engineering work, careful tradeoffs, and long-term thinking.
In this episode, I sit down with Mai-Lan Tomsen Bukovec, VP of Data and Analytics at AWS, who has been running Amazon S3 for more than a decade. Mai-Lan shares how S3 operates at extreme scale, what it takes to design for durability and availability across millions of servers, and why building for failure is a core principle.
We also go deep into how AWS approaches correctness using formal methods, how storage tiers and limits shape system design, and why simplicity remains one of the hardest and most important goals at S3’s scale.
—
Timestamps
(00:00) Intro
(01:03) S3’s scale
(03:58) How S3 started
(07:25) Parquet, Iceberg, and S3 tables
(09:46) S3 for developers
(13:37) Why AWS keeps S3 prices low
(17:10) AWS pricing tiers
(19:38) Availability and durability
(26:21) The cost of S3's consistency
(31:22) Automated reasoning and proof of correctness
(35:14) Durability at AWS scale
(39:58) Correlated failure and crash consistency
(43:22) Failure allowances
(46:04) Two opposing principles in S3 design
(49:09) S3’s evolution
(52:21) S3 Vectors
(1:01:16) The 50 TB limit on AWS
(1:07:54) The simplicity principle
(1:10:10) Types of engineers working on S3
(1:14:15) Closing recommendations
—
The Pragmatic Engineer deepdives relevant for this episode:
• Inside Amazon’s engineering culture
• How AWS deals with a major outage
• A Day in the Life of a Senior Manager at Amazon
• What is a Principal Engineer at Amazon? – with Steve Huynh
• Working at Amazon as a software engineer – with Dave Anderson
Amazon papers recommended by Mai-Lan:
• Using lightweight formal methods to validate a key-value storage node in Amazon S3
• Formally verified cloud-scale authorization
• Analyzing metastable failures
• Amazon’s engineering tenets
—
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
Brought to You By:
• Statsig — The unified platform for flags, analytics, experiments, and more.
• Linear — The system for modern product development.
—
How have servers and the cloud evolved in the last 30 years, and what might be next? Bryan Cantrill was a distinguished engineer at Sun Microsystems during both the Dotcom Boom and the Dotcom Bust. Today, he is the co-founder and CTO of Oxide Computer, where he works on modern server infrastructure.
In this episode of The Pragmatic Engineer, Bryan joins me to break down how modern computing infrastructure evolved. We discuss why the Dotcom Bust produced deeper innovation than the Boom, how constraints shape better systems, and what the rise of the cloud changed and did not change about building reliable infrastructure.
Our conversation covers early web infrastructure at Sun, the emergence of AWS, Kubernetes and cloud neutrality, and the tradeoffs between renting cloud space and building your own. We also touch on the complexity of server-side software updates, experimenting with AI, the limits of large language models, and how engineering organizations scale without losing their values.
If you want a systems-level perspective on computing that connects past cycles to today’s engineering decisions, this episode offers a rare long-range view.
—
Timestamps
(00:00) Intro
(01:26) Computer science in the 1990s
(03:01) Sun and Cisco’s web dominance
(05:41) The Dotcom Boom
(10:26) From Boom to Bust
(15:32) The innovations of the Bust
(17:50) The open source shift
(22:00) Oracle moves into Sun’s orbit
(24:54) AWS dominance (2010–2014)
(28:15) How Kubernetes and cloud neutrality
(30:58) Custom infrastructure
(36:10) Renting the cloud vs. buying hardware
(45:28) Designing a computer from first principles
(50:02) Why everyone is paid the same salary at Oxide
(54:14) Oxide’s software stack
(58:33) The evolution of software updates
(1:02:55) How Oxide uses AI
(1:06:05) The limitations of LLMs
(1:11:44) AI use and experimentation at Oxide
(1:17:45) Oxide’s diverse teams
(1:22:44) Remote work at Oxide
(1:24:11) Scaling company values
(1:27:36) AI’s impact on the future of engineering
(1:31:04) Bryan’s advice for junior engineers
(1:34:01) Book recommendations
—
The Pragmatic Engineer deepdives relevant for this episode:
• Startups on hard mode: Oxide. Part 1: Hardware
• Startups on hard mode: Oxide, Part 2: Software & Culture
• Three cloud providers, three outages: three different responses
• Inside Uber’s move to the Cloud
• Inside Agoda’s private Cloud
—
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
Brought to You By:
• Statsig — The unified platform for flags, analytics, experiments, and more.
• Linear — The system for modern product development.
—
Michelle Lim joined Warp as engineer number one and is now building her own startup, Flint. She brings a strong product-first mindset shaped by her time at Facebook, Slack, Robinhood, and Warp. Michelle shares why she chose Warp over safer offers, how she evaluates early-stage opportunities, and what she believes distinguishes great founding engineers.
Together, we cover how product-first engineers create value, why negotiating equity at early-stage startups requires a different approach, and why asking founders for references is a smart move. Michelle also shares lessons from building consumer and infrastructure products, how she thinks about tech stack choices, and how engineers can increase their impact by taking on work outside their job descriptions.
If you want to understand what founders look for in early engineers or how to grow into a founding-engineer role, this episode is full of practical advice backed by real examples
—
Timestamps
(00:00) Intro
(01:32) How Michelle got into software engineering
(03:30) Michelle’s internships
(06:19) Learnings from Slack
(08:48) Product learnings at Robinhood
(12:47) Joining Warp as engineer #1
(22:01) Negotiating equity
(26:04) Asking founders for references
(27:36) The top reference questions to ask
(32:53) The evolution of Warp’s tech stack
(35:38) Product-first engineering vs. code-first
(38:27) Hiring product-first engineers
(41:49) Different types of founding engineers
(44:42) How Flint uses AI tools
(45:31) Avoiding getting burned in founder exits
(49:26) Hiring top talent
(50:15) An overview of Flint
(56:08) Advice for aspiring founding engineers
(1:01:05) Rapid fire round
—
The Pragmatic Engineer deepdives relevant for this episode:
• Thriving as a founding engineer: lessons from the trenches
• From software engineer to AI engineer
• AI Engineering in the real world
• 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
Brought to You By:
• Statsig — The unified platform for flags, analytics, experiments, and more. Statsig are helping make the first-ever Pragmatic Summit a reality. Join me and 400 other top engineers and leaders on 11 February, in San Francisco for a special one-day event. Reserve your spot here.
• Linear — The system for modern product development. Engineering teams today move much faster, thanks to AI. Because of this, coordination increasingly becomes a problem. This is where Linear helps fast-moving teams stay focused. Check out Linear.
—
As software engineers, what should we know about writing secure code?
Johannes Dahse is the VP of Code Security at Sonar and a security expert with 20 years of industry experience. In today’s episode of The Pragmatic Engineer, he joins me to talk about what security teams actually do, what developers should own, and where real-world risk enters modern codebases.
We cover dependency risk, software composition analysis, CVEs, dynamic testing, and how everyday development practices affect security outcomes. Johannes also explains where AI meaningfully helps, where it introduces new failure modes, and why understanding the code you write and ship remains the most reliable defense.
If you build and ship software, this episode is a practical guide to thinking about code security under real-world engineering constraints.
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Timestamps
(00:00) Intro
(02:31) What is penetration testing?
(06:23) Who owns code security: devs or security teams?
(14:42) What is code security?
(17:10) Code security basics for devs
(21:35) Advanced security challenges
(24:36) SCA testing
(25:26) The CVE Program
(29:39) The State of Code Security report
(32:02) Code quality vs security
(35:20) Dev machines as a security vulnerability
(37:29) Common security tools
(42:50) Dynamic security tools
(45:01) AI security reviews: what are the limits?
(47:51) AI-generated code risks
(49:21) More code: more vulnerabilities
(51:44) AI’s impact on code security
(58:32) Common misconceptions of the security industry
(1:03:05) When is security “good enough?”
(1:05:40) Johannes’s favorite programming language
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The Pragmatic Engineer deepdives relevant for this episode:
• What is Security Engineering?
• Mishandled security vulnerability in Next.js
• Okta Schooled on Its Security Practices
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Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@pragmaticengineer.com.
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Brought to You By:
• Statsig — The unified platform for flags, analytics, experiments, and more. AI-accelerated development isn’t just about shipping faster: it’s about measuring whether, what you ship, actually delivers value. This is where modern experimentation with Statsig comes in. Check it out.
• Linear — The system for modern product development. I had a jaw-dropping experience when I dropped in for the weekly “Quality Wednesdays” meeting at Linear. Every week, every dev fixes at least one quality isse, large or small. Even if it’s one pixel misalignment, like this one. I’ve yet to see a team obsess this much about quality. Read more about how Linear does Quality Wednesdays – it’s fascinating!
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Martin Fowler is one of the most influential people within software architecture, and the broader tech industry. He is the Chief Scientist at Thoughtworks and the author of Refactoring and Patterns of Enterprise Application Architecture, and several other books. He has spent decades shaping how engineers think about design, architecture, and process, and regularly publishes on his blog, MartinFowler.com.
In this episode, we discuss how AI is changing software development: the shift from deterministic to non-deterministic coding; where generative models help with legacy code; and the narrow but useful cases for vibe coding. Martin explains why LLM output must be tested rigorously, why refactoring is more important than ever, and how combining AI tools with deterministic techniques may be what engineering teams need.
We also revisit the origins of the Agile Manifesto and talk about why, despite rapid changes in tooling and workflows, the skills that make a great engineer remain largely unchanged.
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Timestamps
(00:00) Intro
(01:50) How Martin got into software engineering
(07:48) Joining Thoughtworks
(10:07) The Thoughtworks Technology Radar
(16:45) From Assembly to high-level languages
(25:08) Non-determinism
(33:38) Vibe coding
(39:22) StackOverflow vs. coding with AI
(43:25) Importance of testing with LLMs
(50:45) LLMs for enterprise software
(56:38) Why Martin wrote Refactoring
(1:02:15) Why refactoring is so relevant today
(1:06:10) Using LLMs with deterministic tools
(1:07:36) Patterns of Enterprise Application Architecture
(1:18:26) The Agile Manifesto
(1:28:35) How Martin learns about AI
(1:34:58) Advice for junior engineers
(1:37:44) The state of the tech industry today
(1:42:40) Rapid fire round
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The Pragmatic Engineer deepdives relevant for this episode:
• Vibe coding as a software engineer
• The AI Engineering stack
• AI Engineering in the real world
• What changed in 50 years of computing
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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
Brought to You By:
• Statsig — The unified platform for flags, analytics, experiments, and more. Statsig enables two cultures at once: continuous shipping and experimentation. Companies like Notion went from single-digit experiments per quarter to over 300 experiments with Statsig. Start using Statsig with a generous free tier, and a $50K startup program.
• Linear — The system for modern product development. When most companies hit real scale, they start to slow down, and are faced with “process debt.” This often hits software engineers the most. Companies switch to Linear to hit a hard reset on this process debt – ones like Scale cut their bug resolution in half after the switch. Check out Linear’s migration guide for details.
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What’s it like to work as a software engineer inside one of the world’s biggest streaming companies?
In this special episode recorded at Netflix’s headquarters in Los Gatos, I sit down with Elizabeth Stone, Netflix’s Chief Technology Officer. Before becoming CTO, Elizabeth led data and insights at Netflix and was VP of Science at Lyft. She brings a rare mix of technical depth, product thinking, and people leadership.
We discuss what it means to be “unusually responsible” at Netflix, how engineers make decisions without layers of approval, and how the company balances autonomy with guardrails for high-stakes projects like Netflix Live. Elizabeth shares how teams self-reflect and learn from outages and failures, why Netflix doesn’t do formal performance reviews, and what new grads bring to a company known for hiring experienced engineers.
This episode offers a rare inside look at how Netflix engineers build, learn, and lead at a global scale.
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Timestamps
(00:00) Intro
(01:44) The scale of Netflix
(03:31) Production software stack
(05:20) Engineering challenges in production
(06:38) How the Open Connect delivery network works
(08:30) From pitch to play
(11:31) How Netflix enables engineers to make decisions
(13:26) Building Netflix Live for global sports
(16:25) Learnings from Paul vs. Tyson for NFL Live
(17:47) Inside the control room
(20:35) What being unusually responsible looks like
(24:15) Balancing team autonomy with guardrails for Live
(30:55) The high talent bar and introduction of levels at Netflix
(36:01) The Keeper Test
(41:27) Why engineers leave or stay
(44:27) How AI tools are used at Netflix
(47:54) AI’s highest-impact use cases
(50:20) What new grads add and why senior talent still matters
(53:25) Open source at Netflix
(57:07) Elizabeth’s parting advice for new engineers to succeed at Netflix
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The Pragmatic Engineer deepdives relevant for this episode:
• The end of the senior-only level at Netflix
• Netflix revamps its compensation philosophy
• Live streaming at world-record scale with Ashutosh Agrawal
• Shipping to production
• What is good software architecture?
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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
Brought to You By:
• Statsig — The unified platform for flags, analytics, experiments, and more. Companies like Graphite, Notion, and Brex rely on Statsig to measure the impact of the pace they ship. Get a 30-day enterprise trial here.
• Linear – The system for modern product development. Linear is a heavy user of Swift: they just redesigned their native iOS app using their own take on Apple’s Liquid Glass design language. The new app is about speed and performance – just like Linear is. Check it out.
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Chris Lattner is one of the most influential engineers of the past two decades. He created the LLVM compiler infrastructure and the Swift programming language – and Swift opened iOS development to a broader group of engineers. With Mojo, he’s now aiming to do the same for AI, by lowering the barrier to programming AI applications.
I sat down with Chris in San Francisco, to talk language design, lessons on designing Swift and Mojo, and – of course! – compilers. It’s hard to find someone who is as enthusiastic and knowledgeable about compilers as Chris is!
We also discussed why experts often resist change even when current tools slow them down, what he learned about AI and hardware from his time across both large and small engineering teams, and why compiler engineering remains one of the best ways to understand how software really works.
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Timestamps
(00:00) Intro
(02:35) Compilers in the early 2000s
(04:48) Why Chris built LLVM
(08:24) GCC vs. LLVM
(09:47) LLVM at Apple
(19:25) How Chris got support to go open source at Apple
(20:28) The story of Swift
(24:32) The process for designing a language
(31:00) Learnings from launching Swift
(35:48) Swift Playgrounds: making coding accessible
(40:23) What Swift solved and the technical debt it created
(47:28) AI learnings from Google and Tesla
(51:23) SiFive: learning about hardware engineering
(52:24) Mojo’s origin story
(57:15) Modular’s bet on a two-level stack
(1:01:49) Compiler shortcomings
(1:09:11) Getting started with Mojo
(1:15:44) How big is Modular, as a company?
(1:19:00) AI coding tools the Modular team uses
(1:22:59) What kind of software engineers Modular hires
(1:25:22) A programming language for LLMs? No thanks
(1:29:06) Why you should study and understand compilers
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The Pragmatic Engineer deepdives relevant for this episode:
• AI Engineering in the real world
• The AI Engineering stack
• Uber's crazy YOLO app rewrite, from the front seat
• Python, Go, Rust, TypeScript and AI with Armin Ronacher
• Microsoft’s developer tools roots
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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
Brought to You By:
• Statsig — The unified platform for flags, analytics, experiments, and more.
• Linear – The system for modern product development.
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Addy Osmani is Head of Chrome Developer Experience at Google, where he leads teams focused on improving performance, tooling, and the overall developer experience for building on the web. If you’ve ever opened Chrome’s Developer Tools bar, you’ve definitely used features Addy has built. He’s also the author of several books, including his latest, Beyond Vibe Coding, which explores how AI is changing software development.
In this episode of The Pragmatic Engineer, I sit down with Addy to discuss how AI is reshaping software engineering workflows, the tradeoffs between speed and quality, and why understanding generated code remains critical. We dive into his article The 70% Problem, which explains why AI tools accelerate development but struggle with the final 30% of software quality—and why this last 30% is tackled easily by software engineers who understand how the system actually works.
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Timestamps
(00:00) Intro
(02:17) Vibe coding vs. AI-assisted engineering
(06:07) How Addy uses AI tools
(13:10) Addy’s learnings about applying AI for development
(18:47) Addy’s favorite tools
(22:15) The 70% Problem
(28:15) Tactics for efficient LLM usage
(32:58) How AI tools evolved
(34:29) The case for keeping expectations low and control high
(38:05) Autonomous agents and working with them
(42:49) How the EM and PM role changes with AI
(47:14) The rise of new roles and shifts in developer education
(48:11) The importance of critical thinking when working with AI
(54:08) LLMs as a tool for learning
(1:03:50) Rapid questions
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The Pragmatic Engineer deepdives relevant for this episode:
• Vibe Coding as a software engineer
• How AI-assisted coding will change software engineering: hard truths
• AI Engineering in the real world
• The AI Engineering stack
• How Claude Code is built
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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
Brought to You By:
• Statsig — The unified platform for flags, analytics, experiments, and more. Something interesting is happening with the latest generation of tech giants. Rather than building advanced experimentation tools themselves, companies like Anthropic, Figma, Notion and a bunch of others… are just using Statsig. Statsig has rebuilt this entire suite of data tools that was available at maybe 10 or 15 giants until now. Check out Statsig.
• Linear – The system for modern product development. Linear is just so fast to use – and it enables velocity in product workflows. Companies like Perplexity and OpenAI have already switched over, because simplicity scales. Go ahead and check out Linear and see why it feels like a breeze to use.
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What is it really like to be an engineer at Google?
In this special deep dive episode, we unpack how engineering at Google actually works. We spent months researching the engineering culture of the search giant, and talked with 20+ current and former Googlers to bring you this deepdive with Elin Nilsson, tech industry researcher for The Pragmatic Engineer and a former Google intern.
Google has always been an engineering-driven organization. We talk about its custom stack and tools, the design-doc culture, and the performance and promotion systems that define career growth. We also explore the culture that feels built for engineers: generous perks, a surprisingly light on-call setup often considered the best in the industry, and a deep focus on solving technical problems at scale.
If you are thinking about applying to Google or are curious about how the company’s engineering culture has evolved, this episode takes a clear look at what it was like to work at Google in the past versus today, and who is a good fit for today’s Google.
Jump to interesting parts:
(13:50) Tech stack
(1:05:08) Performance reviews (GRAD)
(2:07:03) The culture of continuously rewriting things
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Timestamps
(00:00) Intro
(01:44) Stats about Google
(11:41) The shared culture across Google
(13:50) Tech stack
(34:33) Internal developer tools and monorepo
(43:17) The downsides of having so many internal tools at Google
(45:29) Perks
(55:37) Engineering roles
(1:02:32) Levels at Google
(1:05:08) Performance reviews (GRAD)
(1:13:05) Readability
(1:16:18) Promotions
(1:25:46) Design docs
(1:32:30) OKRs
(1:44:43) Googlers, Nooglers, ReGooglers
(1:57:27) Google Cloud
(2:03:49) Internal transfers
(2:07:03) Rewrites
(2:10:19) Open source
(2:14:57) Culture shift
(2:31:10) Making the most of Google, as an engineer
(2:39:25) Landing a job at Google
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The Pragmatic Engineer deepdives relevant for this episode:
• Inside Google’s engineering culture
• Oncall at Google
• Performance calibrations at tech companies
• Promotions and tooling at Google
• How Kubernetes is built
• The man behind the Big Tech comics: Google cartoonist Manu Cornet
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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
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