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 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.

• 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 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.

• 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 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

• ⁠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 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.

• 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 podcast@pragmaticengineer.com.

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 podcast@pragmaticengineer.com.