The Pragmatic Engineer

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.Timestamps00:00 Intro02:48 Uday’s early days at Uber05:40 Uber’s move to microservices07:55 Uber’s engineering team, then and now09:05 Developer tooling before AI16:03 DevPods19:28 Building for AI agents29:27 MCP gateway34:37 Agent Skills repo38:46 The context graph and Cortana47:54 PRD and ERD tool50:45 Minion54:46 Testing and code review with Bazel, Slate, and Capri1:12:26 Edge Gateway, Shepherd, and Wisdom1:21:33 The changing role of engineer and measuring productivity1:30:40 Uday’s favorite new products—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• 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.Timestamps00:00 Intro02:42 Peter’s path into tech04:00 Building GIMP09:30 Working on Gmail at Google14:51 Google’s infra: google3, build files, Bazel, and Colossus21:30 Distributed storage bottlenecks23:59 Latency, throughput, and availability30:04 Contributing to libraries41:52 Google Spanner46:10 CockroachDB52:00 Manual vs. automatic sharding55:28 Consistency models and strong consistency1:00:03 Raft consensus1:06:15 How AI brought Peter back to coding1:19:12 Peter’s tools and agentic workflows1:23:08 How AI can improve quality1:26:39 Code reviews: are they done?1:29:17 100x engineers1:35:33 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• 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.Timestamps00:00 Intro05:48 How Matt got into tech10:14 How Matt got into open source12:58 Joining Vercel18:39 Total TypeScript23:21 AI’s impact on technical education30:32 Building reusable skills for AI coding agents40:46 The “smart zone” vs the “dumb zone”45:02 The wayfinder skill47:52 Why agents excel at software engineering50:54 “Leading words”1:01:10 Learning the fundamentals1:09:17 Local vs. cloud agents1:12:36 Planning vs. course-correcting1:18:13 TDD and agents1:23:06 Living in the UK1:24:21 Teaching: the human part1:28:36 Advice for junior engineers1:31:07 Gardeners and great engineers1:34:01 Book recommendation—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