The Pragmatic Engineer

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• 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.


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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.
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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.


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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.
• 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:
• 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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Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email podcast@pragmaticengineer.com.


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

Brought to You By:
• 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
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Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email podcast@pragmaticengineer.com.


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

Brought to You By:
• 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 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
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Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email podcast@pragmaticengineer.com.


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