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.

—

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

—

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

—

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

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

—

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].

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.

—

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

—

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

—

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.

• turbopuffer – a vector and full-text search engine built on object storage. It’s fast, cheap, and extremely scalable.

—

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: Performance Excuses Debunked.

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.

—

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

—

The Pragmatic Engineer deepdives relevant for this episode:

•Pushing software engineering limits with “napkin math” with Simon Eskildsen 

•How Games Typically Get Built: prototyping, game engines, and a different type of QA

•Game Development Basics: deepdive on how game studios differ from standard software teams

•Inside Linear's Engineering Culture: building a performant product with a tiny team

•Building a best-selling game with a tiny team – with Jonas Tyroller. A two-person team built a game that sold 1M+ copies

More on premature optimization: read or watch Casey’s extended take on “premature optimization is the root of all evil”: https://www.computerenhance.com/p/theroot

—

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.

• 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

—

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.

—

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

27:17 Building dev tools

40:15 Core Web Vitals

45:42 Google’s engineering culture

51:03 Addy’s career trajectory at Google

57:55 The director role at Google

1:01:40 Cognitive debt and cognitive surrender

1:03:03 Working with agents

1:05:52 Loop engineering

1:12:55 The changing role of the software engineer

1:18:15 How Addy uses AI in writing

1:27:40 What’s next for Addy

1:28:47 Career advice

—

The Pragmatic Engineer deepdives relevant for this episode:

• What is loop engineering?

• Inside Google’s engineering culture

• How AI-assisted coding will change software engineering: hard truths

• Are AI agents actually slowing us down?

• How Claude Code is built

• How Codex is built

• From IDEs to AI Agents with Steve Yegge

• Google’s engineering culture: the podcast

—

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.

• 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].