Software engineering at Big Tech and startups, from the inside. Deepdives with experienced engineers and tech professionals who share their hard-earned lessons, interesting stories and advice they have on building software.
Especially relevant for software engineers and engineering leaders: useful for those working in tech.
• 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:
• 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.
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AI Skills with Matt Pocock
Thursday, September 17, 2026 • Duration 01:35:31
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.
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Building Codex with Tibo Sottiaux
Wednesday, September 9, 2026 • Duration 01:13:21
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.
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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.
• 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.
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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: .
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From Chrome DevTools to AI Engineering, with Addy Osmani
Wednesday, August 19, 2026 • Duration 01:31:35
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
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Stop being skeptical about AI for development with Charity Majors
Wednesday, August 12, 2026 • Duration 01:25:32
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
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Formal methods with Hillel Wayne
Wednesday, July 29, 2026 • Duration 01:23:42
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
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Context engineering with Dex Horthy
Wednesday, July 15, 2026 • Duration 01:32:24
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.
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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
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The Pragmatic Engineer AMA
Wednesday, July 8, 2026 • Duration 01:18:21
Brought to You By:
• Antithesis – verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages.
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In this special “ask me anything” episode of Pragmatic Engineer podcast, I am in the hot seat facing questions sent in by subscribers that are read out by guest Volodymyr Giginiak, CTO and cofounder of Wordsmith AI, a legal tech startup (note: I’m an investor).
I tackle your questions on the software industry, AI, hiring, engineering organizations, career growth, the business model of the Pragmatic Engineer, and more. We also discuss where software engineering is headed, and I offer advice on some specific situations. Thanks to everyone who sent questions!
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Timestamps
00:00 Intro
01:56 From Uber to writing
09:22 AI-native SDLC
14:00 AI and hiring
19:06 Engineers currently thriving
22:18 Junior roles
24:44 Meta’s war mode
27:54 AI at Big Tech vs. startups
36:46 Tech debt
41:36 Types of engineering managers
44:40 Measuring AI productivity
48:30 The value of CS degrees
50:53 AI at Pragmatic Engineer
56:09 Future-proofing your career
1:01:36 The EU job market
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Related Shows Based on Content Similarities
Discover shows related to The Pragmatic Engineer, based on actual content similarities. Explore podcasts with similar topics, themes, and formats, backed by real data.
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:
