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

—

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.

—

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

—

The Pragmatic Engineer deepdives relevant for this episode:

• Shipping to production

• 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

—

Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email [email protected].

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.

—

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

—

The Pragmatic Engineer deepdives relevant for this episode:

• How to debug large, distributed systems: Antithesis

• How AWS S3 is built

• Paying down tech debt

• How Big Tech does quality assurance (QA)

• Bug management that works

• Resiliency in distributed systems

—

Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email [email protected].

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.

—

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

—

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

• How Claude Code is built

• AI Engineering in the real world

• The AI Engineering Stack

• 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 [email protected].

Brought to You By:

• Antithesis – verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages.

—

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!

—

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

1:03:55 Making money as a creator

1:08:20 What’s next for The Pragmatic Engineer

1:09:27 Bunq and Pollen

1:13:38 Spotting trends

1:14:33 Book updates

1:15:20 Favorite books & tech products

1:17:13 What won’t change in engineering

—

The Pragmatic Engineer deepdives relevant for this episode:

• State of the software engineering job market in 2026

• The impact of AI on software engineers in 2026: key trends. 

• How 10 tech companies choose the next generation of dev tools 

• The reality of tech interviews

—

Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email [email protected].

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.

—

Few have made as big an impact on software engineering as this week’s guest on the Pragmatic Engineer podcast, Kent Beck. He created Extreme Programming, pioneered test-driven development (TDD), co-created JUnit, and is one of the authors of the famous ‘Agile Manifesto’. But these days, he's re-examining many ideas for the age of AI, and says we’re failing to accumulate trust during this new era at the same high rate as new code is being accumulated.

In this episode of the Pragmatic Engineer podcast, Kent and I dig into his journey from discovering Smalltalk in the early days of personal computing, to helping define modern software engineering practices. We explore the origins of TDD, design patterns, Extreme Programming, and Agile – along with some lessons learned at Apple and Facebook.

Kent explains why he believes software engineering is about far more than writing code, why no one yet knows exactly how engineers should work alongside AI agents, and how his "explore, expand, extract" framework can help engineers navigate major technology shifts.

—

Timestamps

00:00 Intro

03:47 Human engineers aren’t going away

08:00 Kent's path into tech

13:50 Undergraduate and graduate studies

17:21 Kent’s first programming job

18:54 The rise and fall of Smalltalk

27:04 Working with Ward Cunningham

37:36 Design patterns

44:05 Working at Apple

51:08 CRC Cards

59:29 Testing tools in the language

1:04:22 The C3 project with Martin Fowler

1:09:54 Extreme Programming

1:16:25 Developing TDD

1:25:07 Writing the Agile Manifesto

1:30:00 Agile’s impact

1:32:40 Agile’s downside

1:37:32 The Dotcom Bust

1:44:30 Lessons from working at Facebook

1:59:44 Kent’s ‘Good to Great’ program at Facebook

2:06:07 Soft skills engineers need to learn

2:09:30 AI and the challenges of acceleration

2:15:53 Explore, expand, extract

2:22:33 What Kent is excited about

—

The Pragmatic Engineer deepdives relevant for this episode:

• Measuring developer productivity? A response to McKinsey – co-written with Kent Beck

• TDD, AI agents and coding with Kent Beck

• Paying down tech debt

• The past and future of modern backend practices

—

Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email [email protected].

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 [email protected].

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 [email protected].

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

—

Kelsey Hightower went from a self-taught technician installing DSL modems to becoming one of Google’s elite Distinguished Engineers, whom the CEO of Microsoft personally tried to recruit. Hightower’s career achievements are rooted in hard work and self-directed learning, and today he’s one of the most influential voices in modern infrastructure, through his talks, open source work, and writing.

In this episode of The Pragmatic Engineer podcast, Kelsey and I cover his unconventional path into tech and the lessons he’s learned during three decades in the industry. We discuss his entrepreneurial years, building a reputation through open source, the rise of containers and Kubernetes, and his time at Google during one of the most consequential periods in cloud computing. 

He recounts how a job offer from a big tech giant led to the biggest raise of his career, what prompted him to slow down after years of career acceleration, and we also discuss his perspective on AI. Throughout, Kelsey keeps a simple idea front of mind: that technology is ultimately about people. Whether it’s infrastructure, leadership, careers, or AI, he argues that the goal is not to build technology for its own sake; it’s to solve meaningful human problems.

—

Timestamps

00:00 Intro

03:34 Kelsey’s first job at McDonald’s

05:04 His non-traditional path into tech

11:45 Landing his first tech job with an A+ certification

15:33 His entrepreneurial years

19:45 Joining Google as a data center technician

27:48 Learning automation at a Rackspace spinoff

33:26 Moving into financial services

50:00 Building a reputation through open source

53:55 From configuration management to containers

1:08:20 The rise of Kubernetes

1:25:05 Why he almost joined NASA instead of Google

1:29:20 Defining DevRel at Google

1:38:20 Demonstrating impact at Google

1:41:20 Microsoft's offer

1:55:20 Learning how to slow down

2:06:39 Advising and investing

2:15:03 A people-first view of GenAI

2:24:27 Using AI with guardrails

2:28:26 Matching AI to the task

2:36:06 Staying relevant in the AI era

—

The Pragmatic Engineer deepdives relevant for this episode:

• Career paths for software engineers at large tech companies

• The past and future of modern backend practices

• How Kubernetes is built

• How Linux is built

• The Staff Engineer’s Path: You’re a role model now (sorry!)

—

Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email [email protected].

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 [email protected].

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 [email protected].

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 [email protected].