The Pragmatic Engineer

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• Antithesis – verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages.

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What’s changed in “the unwritten rules” of engineering management since the 2010s, what drove the shift to today, and what has it done to the engineer-to-CTO career path? Today’s guest is one of the best people I know who can tackle these questions.

Will Larson is CTO of Imprint, was an engineering executive for 10+ years, and is the author of several popular books on engineering leadership, including ‘The Engineering Executive's Primer,’ ‘Staff Engineer, An Elegant Puzzle,’ and ‘Crafting Engineering Strategy.’ Previously, he was also CTO at Carta and Calm, and an engineering leader at Stripe and Uber.

In this episode, we explore how engineering leadership has evolved from management practices shaped in the long post-financial-crisis ZIRP era to the altered expectations of engineers and managers today.

Is middle management an increasingly precarious career path, and might managers need to reskill and return to hands-on technical work – or even consider retirement? Will makes the case, and we hear why the traditional path to engineering executive roles is changing, as well as why taking one step backward might sometimes be the best way forward.

We discuss Will's early career, and get his first-hand account of Digg's disastrous relaunch, his own path to CTO, and he tells us why career ladders still matter. We also explore how AI is changing engineering roles and bringing technical decision-making back to leadership.

Timestamps

00:00 Intro

08:20 Stories from Yahoo!

12:47 Digg’s v4 rewrite: the insider story

26:02 Will’s path to eng leadership

30:03 Uber, Stripe, Carta, and Calm

35:11 What does a CTO do?

38:09 Will’s role at Imprint

42:48 How engineering management has evolved

51:08 ZIRP’s impact on tech

57:21 Career ladders

1:01:03 Eng 1 vs Eng 2

1:06:58 How software engineering roles are changing

1:16:25 Revised rules of engineering leadership

1:21:57 The risks of becoming a bureaucrat as an EM

1:25:57 How to become a CTO?

1:31:33 Middle management vs. line management

1:41:36 Will’s writing

1:52:13 Book recommendations

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The Pragmatic Engineer deepdives relevant for this episode:

• The end of ZIRP and what it means for startups and the tech industry

• Getting an Engineering Executive Job – an excerpt from Will’s book, The Engineering Executive's Primer

• What is a Staff+ Engineer?

• Inside Stripe’s engineering culture

• Engineering career paths at Big Tech and startups

• Staying technical as an engineering manager

• Performance calibrations at tech companies

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

• Hypergrowth startups: Uber and CloudKitchens

—

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Brought to You By:

• turbopuffer – a vector and full-text search engine built on object storage. It’s fast, cheap, and extremely scalable

• Linear – the product development system for teams and agents

• WorkOS – everything you need to make your app enterprise-ready.

—

What do developer tools look like inside of Uber, today? They are very different from what you would see at most places. For example, can you guess what service a system called DevPods, Code Inbox, Slate or Wisdom would be providing to engineers?

Today’s episode is a deepdive into the day-to-day tools of devs at Uber, our tour guide being on of Uber’s longest-tenured engineers: Uday Kiran Medisetty. Uday is one of the few Distinguished Engineers at the company, and leads much of the work on agentic AI, today.

With Uday, we talk through how Uber’s developer infrastructure prepared it for AI agents. We look at the systems Uber built before generative AI and the new tools that connect agents to its code, internal services, and engineering knowledge. Uday explains how engineers use background agents, why tools like Slate make development (and bugfixing!) more efficient, and how code review is changing. We also cover Uber’s approach to token costs and how it measures productivity as the engineer’s role changes.

Timestamps

00:00 Intro

02:48 Uday’s early days at Uber

05:40 Uber’s move to microservices

07:55 Uber’s engineering team, then and now

09:05 Developer tooling before AI

16:03 DevPods

19:28 Building for AI agents

29:27 MCP gateway

34:37 Agent Skills repo

38:46 The context graph and Cortana

47:54 PRD and ERD tool

50:45 Minion

54:46 Testing and code review with Bazel, Slate, and Capri

1:12:26 Edge Gateway, Shepherd, and Wisdom

1:21:33 The changing role of engineer and measuring productivity

1:30:40 Uday’s favorite new products

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The Pragmatic Engineer deepdives relevant for this episode:

• How Uber uses AI for development: inside look

• Scaling Uber with Thuan Pham (Uber's first CTO)

• Inside Uber's move to the Cloud

• The Platform and Program Split at Uber

• Hypergrowth startups: Uber and CloudKitchens with Charles-Axel Dein

• How Uber Built its Observability Platform

• How Uber is Measuring Engineering Productivity

• Developer Experience at Uber with Gautam Korlam

—

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:

• turbopuffer – a vector and full-text search engine built on object storage. It’s fast, cheap, and extremely scalable.

• Entire – Git hosting, rebuilt for the agentic era. Every agent session, prompt and tool calls: stored in your repo.

• Antithesis – verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages.

—

Sam Newman wrote one of the most-read books about microservices (“Building Microservices”), but he calls them an architecture of “last resort.”

In this episode of the Pragmatic Engineer podcast, Sam explains his thinking on this, and he’s certainly well placed to do so; he was in the room when the term “microservices” was coined. We discuss what teams get wrong when adopting microservices, why independent deployment matters, and how microservices can help teams work more autonomously.

We also delve into his new book, ‘Building Resilient Distributed Systems’, and Sam explains his three rules for distributed systems, why observability is essential, and also why it’s vital to take business context into account when deciding whether to fail open or fail closed on errors.

We also explore how AI is changing software development, from specs versus code as a source of truth to cognitive debt and cognitive surrender. Sam shares how modular architecture can help teams experiment with AI while maintaining understanding of the systems being built.

Timestamps

00:00 Intro

03:16 Sam’s path into tech

09:32 Thoughtworks

20:55 The rise of microservices

33:37 Are specs becoming more important than code?

45:22 Building Resilient Distributed Systems (Sam’s new book)

52:35 Three rules of distributed systems

56:16 Observability

1:02:20 Resilience tradeoffs

1:07:54 Idempotency

1:15:58 Thundering herds

1:21:03 Business context and resilience decisions

1:25:51 Resilience engineering: four concepts

1:32:42 AI and resilience

1:36:26 AI’s limitations and where to use it

1:40:06 Cognitive debt and cognitive surrender

1:45:02 Modular architecture and AI software factories

1:51:53 Resources for learning software architecture

1:55:30 Where to find Sam

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The Pragmatic Engineer deepdives relevant for this episode:

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

• What is good software architecture at Netflix?

• The past and future of modern backend practices

• What is reliability engineering, and the history of SRE

• How to debug large, distributed systems: Antithesis

• Designing Data-intensive Applications with Martin Kleppmann

• Building Bluesky: a Distributed Social Network

—

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:

• turbopuffer – a vector and full-text search engine built on object storage. It’s fast, cheap, and extremely scalable

• Linear – the product development system for teams and agents

• WorkOS – everything you need to make your app enterprise ready.

—

How is it that a software veteran who regularly shipped ~100K of database-grade code to production each year, pre-AI, feels like he’s even more productive today, with no drop in quality? Peter Mattis is co-founder and CTO of Cockroach Labs, and an original creator of GIMP. He also worked on Gmail and distributed storage at Google.

In this episode, Peter reflects on his journey from open source to Google to founding a database company, and we explore how to keep systems fast, reliable, and correct at scale, from Gmail’s early storage challenges to the tradeoffs in building distributed databases.

Peter tells us how AI has brought him back to writing code after his work shifted toward management, and why he believes AI can improve quality and multiply the impact of domain experts. We also consider the future of code review, and Peter has some advice about how to level up our engineering skills.

Timestamps

00:00 Intro

02:42 Peter’s path into tech

04:00 Building GIMP

09:30 Working on Gmail at Google

14:51 Google’s infra: google3, build files, Bazel, and Colossus

21:30 Distributed storage bottlenecks

23:59 Latency, throughput, and availability

30:04 Contributing to libraries

41:52 Google Spanner

46:10 CockroachDB

52:00 Manual vs. automatic sharding

55:28 Consistency models and strong consistency

1:00:03 Raft consensus

1:06:15 How AI brought Peter back to coding

1:19:12 Peter’s tools and agentic workflows

1:23:08 How AI can improve quality

1:26:39 Code reviews: are they done?

1:29:17 100x engineers

1:35:33 Peter’s advice for leveling up your engineering skills

—

The Pragmatic Engineer deepdives relevant for this episode:

• Inside Google’s Engineering Culture

• Resiliency in distributed systems

• How to debug large, distributed systems: Antithesis

• Pushing software engineering limits with “napkin math”

• Designing Data-intensive Applications with Martin Kleppmann

• Formal methods with Hillel Wayne

—

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:

• turbopuffer – a vector and full-text search engine built on object storage. It’s fast, cheap, and extremely scalable.

• O'Reilly Early Release: Scaling AI Adoption in Engineering – a free book on how to adopt and scale AI in a pragmatic way inside of engineering orgs. Complimentary, thanks to Antithesis.

• Entire – every agent prompt, tool call, stored in your repo, and mirrored.

—

What can everyone else learn from designers and design engineers? As it turns out, there’s plenty, as I discovered when one of the best design engineers in the industry, Maggie Appleton, came onto the Pragmatic Engineer Podcast. She’s a staff research engineer at GitHub Next, where she builds prototypes to explore how software engineers might collaborate with AI in new ways. Maggie is at the intersection of design, anthropology, and web development, and was the first designer hired by AI startup Elicit, and Lead Design engineer at AI startup, Normally.

Today’s episode is more visual than usual because Maggie brought her notebook along, so there are peeks inside its pages of prototypes and more:

We got into designers’ work and how their design processes are adapting to and changing with AI. We explore why Maggie starts projects with pens and notebooks, what distinguishes design engineers from other designers, and why understanding engineering constraints leads to better collaboration with engineers.

We also discuss how Maggie uses jigs to gain more control over AI agents, why human judgment and style still matter when models can generate designs, and how inconsistent AI capabilities can mislead us.

Timestamps

00:00 Intro

03:24 From anthropology to tech

10:18 What does a designer do?

18:23 How Maggie works

24:55 The case for planning with physical tools

31:53 Why Maggie is learning woodworking

33:13 Design engineers and engineering constraints

38:49 How Maggie uses Figma

40:30 Design at GitHub Next

45:12 How has AI changed design

50:37 When models design and why humans are still needed

53:30 UX and UI

58:29 Capability gaslighting

1:00:33 One Developer, Two Dozen Agents, Zero Alignment

1:07:21 Craft and AI tells

1:14:17 Visual gardens, home-cooked software, and barefoot developers

1:21:02 Advice for engineers and lessons from anthropology

1:25:34 Book recommendation

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The Pragmatic Engineer deepdives relevant for this episode:

• What is “loop engineering?”

• Design-first software engineering: Craft, with Balint Orosz

• Are AI agents actually slowing us down?

• Vibe Coding as a software engineer

• How Codex is built

• How Claude Code is built

• From Chrome DevTools to AI Engineering, with Addy Osmani

—

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Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe

Brought to You By:

• turbopuffer – a vector and full-text search engine built on object storage. It’s fast, cheap, and extremely scalable

• Linear – the product development system for teams and agents

• WorkOS – everything you need to make your app enterprise ready.

—

Why is the “grill-me” skill so popular, and why does its creator swear by the importance of software fundamentals? Matt Pocock created this widely-used skill – and many others – alongside being an educator, content creator, and engineer. His latest course is AI Hero, and he previously created the Total TypeScript course that generated more than $2.5 million in sales.

In this episode, Matt and I discuss his unconventional path from working as a voice teacher to becoming a developer and going all-in on technical education. He reveals how communication skills helped him break into tech, why he took an unusual three-days-a-week contract at Vercel, and how he built Total TypeScript through workshops, courses, and a lot of free content.

We also explore “strategic coding,” and how he uses skills like “grill me” and “wayfinder” to plan, delegate, and course-correct with AI agents. Matt explains his “day shift” and “night shift” approach, why splitting context up can keep agents in their “smart zone,” and how concepts from classic software engineering books can guide agents to do better. In this episode, there’s also local versus cloud workflows, whether agents need TDD, how AI is changing the ways that engineers learn the fundamentals, and why humans are still essential in teaching.

Timestamps

00:00 Intro

05:48 How Matt got into tech

10:14 How Matt got into open source

12:58 Joining Vercel

18:39 Total TypeScript

23:21 AI’s impact on technical education

30:32 Building reusable skills for AI coding agents

40:46 The “smart zone” vs the “dumb zone”

45:02 The wayfinder skill

47:52 Why agents excel at software engineering

50:54 “Leading words”

1:01:10 Learning the fundamentals

1:09:17 Local vs. cloud agents

1:12:36 Planning vs. course-correcting

1:18:13 TDD and agents

1:23:06 Living in the UK

1:24:21 Teaching: the human part

1:28:36 Advice for junior engineers

1:31:07 Gardeners and great engineers

1:34:01 Book recommendation

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The Pragmatic Engineer deepdives relevant for this episode:

• What is "loop engineering?"

• The Philosophy of Software Design – with John Ousterhout

• Context engineering with Dex Horthy

• Are AI agents actually slowing us down?

• The AI Engineering Stack

• How Codex is built

• How Claude Code is built

• How Uber uses AI for development: inside look

—

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:

• turbopuffer – a vector and full-text search engine built on object storage. It’s fast, cheap, and extremely scalable

• Antithesis – verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages.

• Entire – Git hosting, rebuilt for the agentic era. Every agent session, prompt and tool calls: stored in your repo.

—

Tibo Sottiaux is one of the engineers who created Codex, and today, he heads up the Core Products & Platform org at OpenAI which also includes Codex. He’s also one of the most public faces of Codex due to his frequent – and generous – usage reset announcements, like this one yesterday.

In this episode of the Pragmatic Engineer Podcast, Tibo and I discuss how Codex was built and continues to be iterated upon. We explore why the Codex CLI is written in Rust and was released as open source, how the harness and models have evolved, and why Codex supports models from multiple providers.

Tibo also shares details about how the OpenAI team uses Codex throughout the software development lifecycle, including code reviews, maintenance, and system rearchitecture. We look into how AI is lowering the cost of changing code – and some interesting side effects of this – the merger of ChatGPT and Codex, and also how Tibo uses the tools in his own work.

—

Timestamps

00:00 Intro

07:21 Working at Google

12:41 What drew Tibo to OpenAI

15:19 The early days of Codex

18:20 Why Codex was built in Rust

21:15 Why Codex is open source

25:50 Codex plays nice with other models: why?

32:09 How the harness works

36:44 Harness and model improvements

41:19 The SDLC behind Codex

46:39 Code reviews at Codex

52:09 Maintenance and architecture

56:43 How AI tools expand what engineers can do

1:02:30 The Merge: ChatGPT + Codex

1:07:16 How Tibo uses Codex and ChatGPT

1:10:44 Advice for engineers who want to work in AI

—

The Pragmatic Engineer deepdives relevant for this episode:

• How Codex is built

• How Claude Code is built

• How Cursor was built

• What is "loop engineering?”

• How Uber uses AI for development: inside look

• Why Ramp built its own in-house coding agent, Inspect

• “I ship code I don’t read”: with Peter Steinberger, the creator of OpenClaw

—

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.

• Sentry – application monitoring software considered “not bad” by millions of developers.

• turbopuffer – a vector and full-text search engine built on object storage. It’s fast, cheap, and extremely scalable.

—

There can be few people around who care about software performance more than today’s pod guest, Casey Muratori. He’s a programmer and videogame developer, founder of Molly Rocket, and creator of Handmade Hero – a long-running series about building a game from scratch. He also evangelizes about performance on his Substack, Computer, Enhance.

We got to know each other about three years ago, first via messages, including this one from Casey:

“Why does the industry zeitgeist place so little emphasis on software performance when there seems to be overwhelming evidence that performance is critical to their bottom line?

Like you, I run a Substack for professional programmers, but I focus exclusively on software performance. Although we are quite large by Substack standards, so a certain subset of programmers must believe performance is important, I nonetheless hear lots of dismissive excuses when I post on social media. This happens so frequently, I devoted an entire article to cataloging the extensive pro-performance evidence we already have from the world's leading software companies: Performance Excuses Debunked.

Strangely, nobody has a rebuttal to why performance is important. When I point people to this, they actually tend to agree. But the prevailing attitude nonetheless stays the same.”

I’m delighted we finally have Casey on the podcast because it’s overdue! In this episode, we discuss why software performance matters, why it’s overlooked, and how developers can get better at writing performant code. We explore why performance should be considered during design, the value of learning to read assembly & understanding how CPUs work, Casey’s critique of ‘clean code’, and why he believes testing shouldn't drive software design.

We touch on how videogame development has changed, and influential game engines. Casey also tells us why he prefers to write code by hand, not with AI, and more.

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Timestamps

00:00 Intro

05:17 Games at Microsoft

12:52 Building games

16:00 Why performance matters

27:12 Why you should learn to read assembly

30:36 Designing for optimization

42:51 How to get better at writing performant software

49:04 Understanding how the CPU works

55:53 Building games then and now

1:05:56 How game engines changed building games

1:10:48 Why new games compete with old games

1:13:25 GTA 6: why is it taking so long?

1:16:59 Casey’s critique of clean code

1:21:48 Casey’s take on TDD

1:24:30 What is good code?

1:27:32 What makes a good software engineer?

1:33:56 Why Casey doesn’t code with AI

1:39:01 AI’s impact on the game industry

1:44:43 AI and burnout

1:50:21 Why you should read papers

—

The Pragmatic Engineer deepdives relevant for this episode:

•Pushing software engineering limits with “napkin math” with Simon Eskildsen

•How Games Typically Get Built: prototyping, game engines, and a different type of QA

•Game Development Basics: deepdive on how game studios differ from standard software teams

•Inside Linear's Engineering Culture: building a performant product with a tiny team

•Building a best-selling game with a tiny team – with Jonas Tyroller. A two-person team built a game that sold 1M+ copies

More on premature optimization: read or watch Casey’s extended take on “premature optimization is the root of all evil”: https://www.computerenhance.com/p/theroot

—

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.

• Google Cloud Run – run your code and host LLMs directly on top of Google’s scalable infrastructure, without having to worry about managing infra.

• Sentry – application monitoring software considered “not bad” by millions of developers

—

Addy Osmani spent more than 14 years at Google, working on Chrome, DevTools, Core Web Vitals, and most recently, AI developer experience.

If you've ever opened Chrome DevTools, or optimized a page for Core Web Vitals, you’ve used software built by Addy Osmani. In this episode, I sit down with Addy and we talk about his path from building a web browser aged just 16 to becoming a director at Google. We discuss what he learned from building tools for millions of developers, Google’s engineering culture, and why he continued doing hands-on coding work as a manager. We also get into how he works with AI agents today, the risks of ‘cognitive surrender,’ his approach to ‘loop engineering,’ and why it’s good to develop skills in product management, go-to-market, and other areas.

—

Timestamps

00:00 Intro

02:50 Addy’s current workflow

05:11 Addy’s path into tech

15:04 Addy’s work on jQuery

16:44 TodoMVC

21:44 Getting hired at Google and working on Chrome

27:17 Building dev tools

40:15 Core Web Vitals

45:42 Google’s engineering culture

51:03 Addy’s career trajectory at Google

57:55 The director role at Google

1:01:40 Cognitive debt and cognitive surrender

1:03:03 Working with agents

1:05:52 Loop engineering

1:12:55 The changing role of the software engineer

1:18:15 How Addy uses AI in writing

1:27:40 What’s next for Addy

1:28:47 Career advice

—

The Pragmatic Engineer deepdives relevant for this episode:

• What is loop engineering?

• Inside Google’s engineering culture

• How AI-assisted coding will change software engineering: hard truths

• Are AI agents actually slowing us down?

• How Claude Code is built

• How Codex is built

• From IDEs to AI Agents with Steve Yegge

• Google’s engineering culture: the podcast

—

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.

• Buildkite – CI software built to absorb whatever your coding agents throw at the build queue

—

In 2025, it was rational to be skeptical about AI, but in 2026 it’s clear that AI is changing all of the industry, and there’s less and less place for skepticism. This take is from one of my favorite voices in software reliability and observability: Charity Majors, CTO and cofounder of Honeycomb, co-author of Observability Engineering. (Note: the second edition of Observability Engineering is out, and it’s pretty much a full rewrite of the book, I recommend grabbing it if you’re building reliable systems)

In this episode, I sat down with Charity to discuss how her thinking on AI has evolved, why she believes it is becoming a foundational part of software engineering, and what that means for how teams build, review, and ship software.

We explore how AI is changing the economics of code generation, why reliability and verification are increasingly the bottlenecks, and why the rise of non-deterministic systems requires more engineering discipline. Charity shares her views on code reviews, observability, DevOps, leadership, and why both AI skeptics and enthusiasts are getting important things right.

—

Timestamps

00:00 Intro

02:56 How Parse led to Honeycomb

06:00 The limits of individual productivity metrics

09:08 How Charity’s perspective on AI has evolved

13:50 Rewriting code vs. editing code

19:20 Production as a stage of development

22:14 Code reviews

26:56 Non-deterministic systems

31:11 Sensible uses of AI

37:41 The two AI camps

44:40 Why AI works so well for building software

49:42 DevOps

55:13 Modern observability

1:00:40 Handling context overload

1:01:56 What’s new in Observability Engineering’s 2nd edition

1:07:45 What effective leadership looks like

1:10:25 Engineering management: what is changing?

1:16:31 Junior engineers

1:18:01 AI fatigue

1:21:39 Book recommendations

—

The Pragmatic Engineer deepdives relevant for this episode:

• Shipping to production

• Deepdive: How 10 tech companies choose the next generation of dev tools

• Why is Meta destroying its engineering organization?

• When AI writes almost all code, what happens to software engineering?

• Are AI agents actually slowing us down?

• Observability: the present and future, with Charity Majors

• The third golden age of software engineering – thanks to AI, with Grady Booch

—

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.

• 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.

—

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.

—

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

—

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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.

• 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.

—

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 [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.

—

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!

—

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

—

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 [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.

• 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.

—

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 [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

• 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 [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.

• 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 [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.

• 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 [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.

• 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 [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.

—

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 [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.

• 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 [email protected].

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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 [email protected].

Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe

Brought to You By:

• Statsig — ⁠ The unified platform for flags, analytics, experiments, and more.

• Sonar – The makers of SonarQube, the industry standard for automated code review

• WorkOS – Everything you need to make your app enterprise ready.

—

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 [email protected].

Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe

Brought to You By:

• Statsig — ⁠ The unified platform for flags, analytics, experiments, and more.

• Sonar – The makers of SonarQube, the industry standard for automated code review

• WorkOS – Everything you need to make your app enterprise ready.

—

David Heinemeier Hansson (DHH) is the creator of Ruby on Rails and Omarchy, co-founder and CTO of 37signals (maker of Basecamp and HEY), and the author of several books including the best-seller, Remote: Office Not Required, co-written with Jason Fried.

Six months ago, in an episode of the Lex Fridman podcast, David shared how he doesn’t use AI tools to write code: he types out all his code. But things have changed a lot since then.

In this episode, we discuss his approach to building software, how it’s changed in the last six months, and why he now takes an agent-first approach, and how he barely writes any code by hand. We go into how he uses AI agents: which alter how he builds and explores ideas, but also how his standards of quality and craft remain the same.

We also discuss how 37signals thinks about product development, from the role of designers to the importance of aesthetics and taste. David gets into how he sees beauty and functionality as closely linked, and why strong opinions about design lead to better software.

Finally, we look into the uneven impact of AI which amplifies senior engineers while creating challenges for junior developers, and what this may mean for the role of the software engineer.

—

Timestamps

(00:00) Intro

(02:11) Omarchy and Ruby on Rails

(08:25) 37signals overview

(10:12) Launching HEY

(18:38) Building HEY

(22:47) Designers at 37signals

(28:08) The craft of design

(31:52) Why DHH now embraces AI workflows

(39:45) The AI inflection point

(44:23) DHH’s agent-first workflow

(55:09) AI’s impact on junior developers

(1:03:08) Developer experience with AI

(1:16:43) What does AI mean for developers?

(1:23:33) 37signals teams and hiring

(1:38:20) Work-life balance with AI

(1:41:41) Why DHH keeps building

(1:45:24) Closing

—

The Pragmatic Engineer deepdives relevant for this episode:

• Are AI agents actually slowing us down?

• How Claude Code is built

• The future of software engineering with AI: six predictions

• The AI Engineering Stack

• Mitchell Hashimoto’s new way of writing code

• How Linux is built with Greg Kroah-Hartman

—

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

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—

Thuan Pham was Uber's first and longest-serving CTO, and today he’s the CTO of Faire, a B2B wholesale platform. Back when Thuan joined Uber, it had around 40 engineers and 30,000 rides per day, and the system crashed multiple times a week. Over seven years, he helped rebuild the system, move it from a monolith to microservices, and scaled the engineering organization behind it. I had the privilege of working with Thuan for four of those seven years. Later, the very first issue of The Pragmatic Engineer newsletter was a deepdive into Uber’s Program and Platform split. This episode of the podcast contains a nice “full circle” moment, where Thuan shares even more details about why Uber chose to embrace that structure.

We discuss what it takes to operate and build in that kind of environment. Thuan explains how he divided his time at Uber into three “tours of duty,” from stabilizing a fragile system, to re-architecting it, and scaling the org.

We go deep into the platform-and-program split, the Helix app rewrite, and what it took to launch Uber in China in just five months (the original estimate was 18 months). We also cover Uber’s in-house tools and explain why they were necessary to support rapid growth.

Finally, we discuss his role today as CTO of Faire, how the company is using AI, and how he sees AI changing software engineering.

—

Timestamps

(00:00) Intro

(05:32) Getting into tech

(16:09) The dot-com bust

(20:42) VMware

(26:29) Getting hired by Travis at Uber

(33:22) Early days at Uber and scaling challenges

(40:57) Uber’s China launch

(47:12) The platform and program split

(50:26) From monolith to microservices

(53:38) Internal tools at Uber

(57:05) Helix: Uber’s mobile app rewrite

(59:55) Thuan’s email about naming

(1:02:03) Org structure changes under

(1:06:34) Thuan’s work philosophy

(1:12:23) The “three tours of duty” at Uber

(1:15:37) Why Thuan left Uber

(1:17:34) Coupang and Nubank

(1:21:59) Faire

(1:25:31) How Faire uses AI

(1:28:24) AI’s impact on software engineering

(1:31:09) The role of the CTO

(1:35:13) Career advice

—

The Pragmatic Engineer deepdives relevant for this episode:

• How Uber uses AI for development: inside look

• The Platform and Program split at Uber

• How Uber is measuring engineering productivity

• Inside Uber’s move to the cloud

• Uber's crazy YOLO app rewrite, from the front seat

• How Uber built its observability platform

• Developer experience at Uber with Gautam Korlam

• Uber’s engineering level changes

—

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

Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe

Brought to You By:

• Statsig — ⁠ The unified platform for flags, analytics, experiments, and more.

• Sonar – The makers of SonarQube, the industry standard for automated code review

• WorkOS – Everything you need to make your app enterprise ready.

—

How did a tiny team of 30 engineers build the world-famous messaging app more than a decade ago, and what can dev teams learn from that feat today? Jean Lee was engineer #19 at WhatsApp, joining when the company was still small, with almost no formal processes. She helped it scale to hundreds of millions of users, went through the $19B acquisition by Facebook, and later worked at Meta.

In this episode of Pragmatic Engineer, I talk with Jean about what it was like building WhatsApp. When Facebook bought WhatsApp in 2014, only around 30 engineers supported hundreds of millions of users across eight platforms.

We discuss how the founders kept things simple, saying “no” to most feature requests for years. Jean explains why WhatsApp chose Erlang for the backend, why the team avoided cross-platform abstractions, and how charging users $1 per year paid everyone’s salaries, while keeping growth intentionally slow.

Jean also shares what the Facebook acquisition was like on the inside, how she dealt with sudden personal wealth, and what it was like transitioning from an IC to a manager at Facebook – including the reality of calibration meetings and performance reviews.

We also discuss how AI enables smaller engineering teams, and why WhatsApp’s experience suggests ownership and trust might matter more than tools.

—

Timestamps

(00:00) Intro

(01:39) Early years in tech

(06:18) Becoming engineer #19 at WhatsApp

(13:53) WhatsApp’s tech stack

(18:09) WhatsApp’s unique ways of working

(25:27) Countdown displays and outages

(27:07) Why WhatsApp won

(28:53) The Facebook acquisition

(33:13) Life after acquisition

(39:27) Working at Facebook in London

(44:07) Transitioning to management

(47:27) Performance reviews as a manager

(53:29) After Facebook

(58:53) AI’s impact on engineering

(1:02:34) Jean’s advice to new grads and startups

(1:06:45) Empowering employees

(1:08:17) Book recommendations

—

The Pragmatic Engineer deepdives relevant for this episode:

• How Meta built Threads

• How Big Tech runs tech projects and the curious absence of Scrum

• Performance calibrations at tech companies

• Software engineers leading projects

—

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

Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe

Brought to You By:

• Statsig — ⁠ The unified platform for flags, analytics, experiments, and more.

• Sonar – The makers of SonarQube, the industry standard for automated code review

• WorkOS – Everything you need to make your app enterprise ready.

—

Steve Yegge has spent decades writing software and thinking about how the craft evolves. From his early years at Amazon and Google, to his influential blog posts, he has often been early at spotting shifts in how software gets built.

In this episode of Pragmatic Engineer, I talk with Steve about how AI is changing engineering work, why he believes coding by hand may gradually disappear, and what developers should focus on, instead. We discuss his latest book, Vibe Coding, and the open-source AI agent orchestrator he built called Gas Town, which he said most devs should avoid using.

Steve shares his framework for levels of AI adoption by engineers, ranging from avoiding AI tools entirely, to running multiple agents in parallel. We discuss why he believes the knowledge that engineers need to know keeps changing, and why understanding how systems evolve may matter more than mastering any particular tool.

We also explore broader implications. Steve argues that AI’s role is not primarily to replace engineers, but to amplify them. At the same time, he warns that the pace of change will create new kinds of technical debt, new productivity pressures, and fresh challenges for how teams operate.

—

Timestamps

(00:00) Intro

(01:43) Steve’s latest projects

(02:27) Important blog posts

(04:48) Shifts in what engineers need to know

(10:46) Steve’s current AI stance

(13:23) Steve’s book Vibe Coding

(18:25) Layoffs and disruption in tech

(31:13) Gas Town

(40:10) New ways of working

(51:08) The problem of too many people

(54:45) Why AI results lag in business

(59:57) Gamification and product stickiness

(1:04:54) The ‘Bitter Lesson’ explained

(1:07:14) The future of software development

(1:23:06) Where languages stand

(1:24:47) Adapting to change

(1:27:32) Steve’s predictions

—

The Pragmatic Engineer deepdives relevant for this episode:

• Vibe coding as a software engineer

• The full circle of developer productivity with Steve Yegge

• AI Tooling for Software Engineers in 2026

• The AI Engineering Stack

—

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

Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe

Brought to You By:

• Statsig — ⁠ The unified platform for flags, analytics, experiments, and more.

• Sonar – The makers of SonarQube, the industry standard for automated code review

• WorkOS – Everything you need to make your app enterprise ready.

—

Boris Cherny is the creator and Head of Claude Code at Anthropic. He previously spent five years at Meta as a Principal Engineer and is the author of the book Programming TypeScript.

In this episode of Pragmatic Engineer, we went through how Claude Code was built and what it means when engineers no longer write most of the code themselves.

We discuss how Claude Code evolved from a side project into a core internal tool at Anthropic and how Boris uses it day-to-day. We go deep into workflow details, including parallel agents, PR structure, deterministic review patterns, and how the system retrieves context from large codebases. We also get into how Claude Cowork was built.

As coding becomes more accessible, the role of engineers shifts rather than shrinks. We examine what that shift means in practice, which skills become more important, and why the lines between product, engineering, and design are blurring.

—

Timestamps

(00:00) Intro

(11:15) Lessons from Meta

(19:46) Joining Anthropic

(23:08) The origins of Claude Code

(32:55) Boris's Claude Code workflow

(36:27) Parallel agents

(40:25) Code reviews

(47:18) Claude Code's architecture

(52:38) Permissions and sandboxing

(55:05) Engineering culture at Anthropic

(1:05:15) Claude Cowork

(1:12:48) Observability and privacy

(1:14:45) Agent swarms

(1:21:16) LLMs and the printing press analogy

(1:30:16) Standout engineer archetypes

(1:32:12) What skills still matter for engineers

(1:35:24) Book recommendations

—

The Pragmatic Engineer deepdives relevant for this episode:

• How Claude Code is built

• How Anthropic built Artifacts

• How Codex is built

• Real-world engineering challenges: building Cursor

—

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

Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe

Brought to You By:

• Statsig — ⁠ The unified platform for flags, analytics, experiments, and more.

• Sonar – The makers of SonarQube, the industry standard for automated code review

• WorkOS – Everything you need to make your app enterprise ready.

—

How has the day-to-day workflow of Mitchell Hashimoto changed, thanks to AI tools?

Mitchell Hashimoto is one of the most influential infrastructure engineers of our time, and is one of the most pragmatic builders I’ve met. He is the co-founder of HashiCorp and creator of Ghostty. In this episode, we talk about how he got into software engineering, the history of HashiCorp, and the challenges of turning widely used open-source tools into a durable business. We also go into what it’s really like to work with AWS, Azure and GCP as a startup.

Mitchell shares how he uses AI these days, and how agents have completely changed how he works. We touch on Ghostty, open source, and what’s changing for software engineers and founders in an AI-native era.

—

Timestamps

(00:00) Intro

(02:03) Mitchell’s path into software engineering

(07:19) The origins of HashiCorp

(15:52) Early cloud computing

(18:22) The 2010s startup scene in SF

(23:11) Funding HashiCorp

(25:23) The Hashi stack

(32:33) Why HashiCorp’s business lagged behind its technology

(35:28) An early failure in commercialization

(38:28) The open-core pivot and path to enterprise profitability

(48:08) Taking HashiCorp public

(51:58) The near VMware acquisition

(59:10) Mitchell’s take on all the cloud providers

(1:06:02) AI’s impact on open source

(1:07:00) Why Mitchell built Ghostty

(1:09:11) Why Mitchell used Zig

(1:10:38) How terminals work and Ghostty’s approach

(1:17:31) AI’s impact on terminals and libghostty

(1:19:13) How Mitchell uses AI

(1:22:02) Ghostty’s evolving AI use policy

(1:28:36) Why open source must change

(1:31:46) The problem of Git in monorepos

(1:36:22) What needs to change to work effectively with AI

(1:39:57) Mitchell’s hiring practices

(1:47:52) Mitchell’s AI adoption journey

(1:50:41) Advice to would-be founders

(1:52:21) Mitchell’s advising work

(1:53:20) What’s changing for software engineers

(1:55:03) How Mitchell recharges

(1:55:50) Book recommendation

—

The Pragmatic Engineer deepdives relevant for this episode:

• AI Engineering in the real world

• The AI Engineering stack

• Pressure on commercial open source to make more money – and HashiCorp changing its license

• How Linux is built with Greg Kroah-Hartman

—

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

Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe

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 [email protected].

Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe

Brought to You By:

• Statsig — ⁠ The unified platform for flags, analytics, experiments, and more.

• Sonar – The makers of SonarQube, the industry standard for automated code review

• WorkOS – Everything you need to make your app enterprise ready.

—

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 [email protected].

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