Stay ConnectedSoftware 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.
Copyright: © Gergely Orosz
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.
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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
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Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@pragmaticengineer.com.
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.
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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
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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
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Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@pragmaticengineer.com.
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.
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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:
• Design-first software engineering: Craft, with Balint Orosz
• Are AI agents actually slowing us down?
• Vibe Coding as a software engineer
• From Chrome DevTools to AI Engineering, with Addy Osmani
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Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@pragmaticengineer.com.
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.
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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:
• The Philosophy of Software Design – with John Ousterhout
• Context engineering with Dex Horthy
• Are AI agents actually slowing us down?
• How Uber uses AI for development: inside look
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Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@pragmaticengineer.com.
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.
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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
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The Pragmatic Engineer deepdives relevant for this episode:
• 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 podcast@pragmaticengineer.com.
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
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The Pragmatic Engineer deepdives relevant for this episode:
•Pushing software engineering limits with “napkin math”withSimon 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
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Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@pragmaticengineer.com.
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
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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.
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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
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The Pragmatic Engineer deepdives relevant for this episode:
• Inside Google’s engineering culture
• How AI-assisted coding will change software engineering: hard truths
• Are AI agents actually slowing us down?
• From IDEs to AI Agents with Steve Yegge
• Google’s engineering culture: the podcast
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Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@pragmaticengineer.com.
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
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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.
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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
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The Pragmatic Engineer deepdives relevant for this episode:
• 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
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Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@pragmaticengineer.com.
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.
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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
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The Pragmatic Engineer deepdives relevant for this episode:
• How to debug large, distributed systems: Antithesis
• How Big Tech does quality assurance (QA)
• Resiliency in distributed systems
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@pragmaticengineer.com.
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
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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
• AI Engineering in the real world
• 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.
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