The Pragmatic Engineer

• TDD, AI agents and coding with Kent Beck

  Supported by Our Partners• Sonar —  Code quality and code security for ALL code. •⁠ Statsig ⁠ — ⁠ The unified platform for flags, analytics, experiments, and more.• Augment Code — AI coding assistant that pro engineering teams love.—Kent Beck is one of the most influential figures in modern software development. Creator of Extreme Programming (XP), co-author of The Agile Manifesto, and a pioneer of Test-Driven Development (TDD), he’s shaped how teams write, test, and think about code.Now, with over five decades of programming experience, Kent is still pushing boundaries—this time with AI coding tools. In this episode of Pragmatic Engineer, I sit down with him to talk about what’s changed, what hasn’t, and why he’s more excited than ever to code.In our conversation, we cover:• Why Kent calls AI tools an “unpredictable genie”—and how he’s using them• Why Kent no longer has an emotional attachment to any specific programming language• The backstory of The Agile Manifesto—and why Kent resisted the word “agile”• An overview of XP (Extreme Programming) and how Grady Booch played a role in the name • Tape-to-tape experiments in Kent’s childhood that laid the groundwork for TDD• Kent’s time at Facebook and how he adapted to its culture and use of feature flags• And much more!—Timestamps(00:00) Intro(02:27) What Kent has been up to since writing Tidy First(06:05) Why AI tools are making coding more fun for Kent and why he compares it to a genie(13:41) Why Kent says languages don’t matter anymore(16:56) Kent’s current project building a small talk server(17:51) How Kent got involved with The Agile Manifesto(23:46) Gergely’s time at JP Morgan, and why Kent didn’t like the word ‘agile’(26:25) An overview of “extreme programming” (XP) (35:41) Kent’s childhood tape-to-tape experiments that inspired TDD(42:11) Kent’s response to Ousterhout’s criticism of TDD(50:05) Why Kent still uses TDD with his AI stack (54:26) How Facebook operated in 2011(1:04:10) Facebook in 2011 vs. 2017(1:12:24) Rapid fire round—The Pragmatic Engineer deepdives relevant for this episode:• —See the transcript and other references from the episode at ⁠⁠https://newsletter.pragmaticengineer.com/podcast⁠⁠—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

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  1:15:31
• 50 Years of Microsoft and Developer Tools with Scott Guthrie

  Supported by Our Partners•⁠ Statsig ⁠ — ⁠ The unified platform for flags, analytics, experiments, and more.•⁠ Sinch⁠ — Connect with customers at every step of their journey.•⁠ Modal⁠ — The cloud platform for building AI applications.—How has Microsoft changed since its founding in 1975, especially in how it builds tools for developers?In this episode of The Pragmatic Engineer, I sit down with Scott Guthrie, Executive Vice President of Cloud and AI at Microsoft. Scott has been with the company for 28 years. He built the first prototype of ASP.NET, led the Windows Phone team, led up Azure, and helped shape many of Microsoft’s most important developer platforms.We talk about Microsoft’s journey from building early dev tools to becoming a top cloud provider—and how it actively worked to win back and grow its developer base.In this episode, we cover:• Microsoft’s early years building developer tools • Why Visual Basic faced resistance from devs back in the day: even though it simplified development at the time• How .NET helped bring a new generation of server-side developers into Microsoft’s ecosystem• Why Windows Phone didn’t succeed • The 90s Microsoft dev stack: docs, debuggers, and more• How Microsoft Azure went from being the #7 cloud provider to the #2 spot today• Why Microsoft created VS Code• How VS Code and open source led to the acquisition of GitHub• What Scott’s excited about in the future of developer tools and AI• And much more!—Timestamps(00:00) Intro(02:25) Microsoft’s early years building developer tools(06:15) How Microsoft’s developer tools helped Windows succeed(08:00) Microsoft’s first tools were built to allow less technically savvy people to build things(11:00) A case for embracing the technology that’s coming(14:11) Why Microsoft built Visual Studio and .NET(19:54) Steve Ballmer’s speech about .NET(22:04) The origins of C# and Anders Hejlsberg’s impact on Microsoft (25:29) The 90’s Microsoft stack, including documentation, debuggers, and more(30:17) How productivity has changed over the past 10 years (32:50) Why Gergely was a fan of Windows Phone—and Scott’s thoughts on why it didn’t last(36:43) Lessons from working on (and fixing)  Azure under Satya Nadella (42:50) Codeplex and the acquisition of GitHub(48:52) 2014: Three bold projects to win the hearts of developers(55:40) What Scott’s excited about in new developer tools and cloud computing (59:50) Why Scott thinks AI will enhance productivity but create more engineering jobs—The Pragmatic Engineer deepdives relevant for this episode:• Microsoft is dogfooding AI dev tools’ future• Microsoft’s developer tools roots• Why are Cloud Development Environments spiking in popularity, now?• Engineering career paths at Big Tech and scaleups• How Linux is built with Greg Kroah-Hartman—See the transcript and other references from the episode at ⁠⁠https://newsletter.pragmaticengineer.com/podcast⁠⁠—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

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  1:04:06
• From Software Engineer to AI Engineer – with Janvi Kalra

  Supported by Our Partners•⁠ Statsig ⁠ — ⁠ The unified platform for flags, analytics, experiments, and more.•⁠ Sinch⁠ — Connect with customers at every step of their journey.•⁠ Cortex⁠ — Your Portal to Engineering Excellence.—What does it take to land a job as an AI Engineer—and thrive in the role?In this episode of Pragmatic Engineer, I’m joined by Janvi Kalra, currently an AI Engineer at OpenAI. Janvi shares how she broke into tech with internships at top companies, landed a full-time software engineering role at Coda, and later taught herself the skills to move into AI Engineering: by things like building projects in her free time, joining hackathons, and ultimately proving herself and earning a spot on Coda’s first AI Engineering team.In our conversation, we dive into the world of AI Engineering and discuss three types of AI companies, how to assess them based on profitability and growth, and practical advice for landing your dream job in the field.We also discuss the following: • How Janvi landed internships at Google and Microsoft, and her tips for interview prepping• A framework for evaluating AI startups• An overview of what an AI Engineer does• A mini curriculum for self-learning AI: practical tools that worked for Janvi• The Coda project that impressed CEO Shishir Mehrotra and sparked Coda Brain• Janvi’s role at OpenAI and how the safety team shapes responsible AI• How OpenAI blends startup speed with big tech scale• Why AI Engineers must be ready to scrap their work and start over• Why today’s engineers need to be product-minded, design-aware, full-stack, and focused on driving business outcomes• And much more!—Timestamps(00:00) Intro(02:31) How Janvi got her internships at Google and Microsoft(03:35) How Janvi prepared for her coding interviews (07:11) Janvi’s experience interning at Google(08:59) What Janvi worked on at Microsoft (11:35) Why Janvi chose to work for a startup after college(15:00) How Janvi picked Coda (16:58) Janvi’s criteria for picking a startup now (18:20) How Janvi evaluates ‘customer obsession’ (19:12) Fast—an example of the downside of not doing due diligence(21:38) How Janvi made the jump to Coda’s AI team(25:48) What an AI Engineer does (27:30) How Janvi developed her AI Engineering skills through hackathons(30:34) Janvi’s favorite AI project at Coda: Workspace Q&A (37:40) Learnings from interviewing at 46 companies(40:44) Why Janvi decided to get experience working for a model company (43:17) Questions Janvi asks to determine growth and profitability(45:28) How Janvi got an offer at OpenAI, and an overview of the interview process(49:08) What Janvi does at OpenAI (51:01) What makes OpenAI unique (52:30) The shipping process at OpenAI(55:41) Surprising learnings from AI Engineering (57:50) How AI might impact new graduates (1:02:19) The impact of AI tools on coding—what is changing, and what remains the same(1:07:51) Rapid fire round—The Pragmatic Engineer deepdives relevant for this episode:•⁠ AI Engineering in the real world•⁠ The AI Engineering stack•⁠ Building, launching, and scaling ChatGPT Images—See the transcript and other references from the episode at ⁠⁠https://newsletter.pragmaticengineer.com/podcast⁠⁠—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

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  1:09:30
• How Kubernetes is Built with Kat Cosgrove

  Supported by Our Partners•⁠ WorkOS — The modern identity platform for B2B SaaS.•⁠ Modal⁠ — The cloud platform for building AI applications.•⁠ Cortex⁠ — Your Portal to Engineering Excellence.—Kubernetes is the second-largest open-source project in the world. What does it actually do—and why is it so widely adopted?In this episode of The Pragmatic Engineer, I’m joined by Kat Cosgrove, who has led several Kubernetes releases. Kat has been contributing to Kubernetes for several years, and originally got involved with the project through K3s (the lightweight Kubernetes distribution).In our conversation, we discuss how Kubernetes is structured, how it scales, and how the project is managed to avoid contributor burnout.We also go deep into: • An overview of what Kubernetes is used for• A breakdown of Kubernetes architecture: components, pods, and kubelets• Why Google built Borg, and how it evolved into Kubernetes• The benefits of large-scale open source projects—for companies, contributors, and the broader ecosystem• The size and complexity of Kubernetes—and how it’s managed• How the project protects contributors with anti-burnout policies• The size and structure of the release team• What KEPs are and how they shape Kubernetes features• Kat’s views on GenAI, and why Kubernetes blocks using AI, at least for documentation• Where Kat would like to see AI tools improve developer workflows• Getting started as a contributor to Kubernetes—and the career and networking benefits that come with it• And much more!—Timestamps(00:00) Intro(02:02) An overview of Kubernetes and who it’s for (04:27) A quick glimpse at the architecture: Kubernetes components, pods, and cubelets(07:00) Containers vs. virtual machines (10:02) The origins of Kubernetes (12:30) Why Google built Borg, and why they made it an open source project(15:51) The benefits of open source projects (17:25) The size of Kubernetes(20:55) Cluster management solutions, including different Kubernetes services(21:48) Why people contribute to Kubernetes (25:47) The anti-burnout policies Kubernetes has in place (29:07) Why Kubernetes is so popular(33:34) Why documentation is a good place to get started contributing to an open-source project(35:15) The structure of the Kubernetes release team (40:55) How responsibilities shift as engineers grow into senior positions(44:37) Using a KEP to propose a new feature—and what’s next(48:20) Feature flags in Kubernetes (52:04) Why Kat thinks most GenAI tools are scams—and why Kubernetes blocks their use(55:04) The use cases Kat would like to have AI tools for(58:20) When to use Kubernetes (1:01:25) Getting started with Kubernetes (1:04:24) How contributing to an open source project is a good way to build your network(1:05:51) Rapid fire round—The Pragmatic Engineer deepdives relevant for this episode:•⁠ Backstage: an open source developer portal•⁠ How Linux is built with Greg Kroah-Hartman•⁠ Software engineers leading projects•⁠ What TPMs do and what software engineers can learn from them•⁠ Engineering career paths at Big Tech and scaleups—See the transcript and other references from the episode at ⁠⁠https://newsletter.pragmaticengineer.com/podcast⁠⁠—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

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  1:08:47
• Building Windsurf with Varun Mohan

  Supported by Our Partners•⁠ Modal⁠ — The cloud platform for building AI applications•⁠ CodeRabbit⁠⁠ — Cut code review time and bugs in half. Use the code PRAGMATIC to get one month free.—What happens when LLMs meet real-world codebases? In this episode of The Pragmatic Engineer,  I am joined by Varun Mohan, CEO and Co-Founder of Windsurf. Varun talks me through the technical challenges of building an AI-native IDE (Windsurf) —and how these tools are changing the way software gets built. We discuss: • What building self-driving cars taught the Windsurf team about evaluating LLMs• How LLMs for text are missing capabilities for coding like “fill in the middle”• How Windsurf optimizes for latency• Windsurf’s culture of taking bets and learning from failure• Breakthroughs that led to Cascade (agentic capabilities)• Why the Windsurf teams build their LLMs• How non-dev employees at Windsurf build custom SaaS apps – with Windsurf!• How Windsurf empowers engineers to focus on more interesting problems• The skills that will remain valuable as AI takes over more of the codebase• And much more!—Timestamps(00:00) Intro(01:37) How Windsurf tests new models(08:25) Windsurf’s origin story (13:03) The current size and scope of Windsurf(16:04) The missing capabilities Windsurf uncovered in LLMs when used for coding(20:40) Windsurf’s work with fine-tuning inside companies (24:00) Challenges developers face with Windsurf and similar tools as codebases scale(27:06) Windsurf’s stack and an explanation of FedRAMP compliance(29:22) How Windsurf protects latency and the problems with local data that remain unsolved(33:40) Windsurf’s processes for indexing code (37:50) How Windsurf manages data (40:00) The pros and cons of embedding databases (42:15) “The split brain situation”—how Windsurf balances present and long-term (44:10) Why Windsurf embraces failure and the learnings that come from it(46:30) Breakthroughs that fueled Cascade(48:43) The insider’s developer mode that allows Windsurf to dogfood easily (50:00) Windsurf’s non-developer power user who routinely builds apps in Windsurf(52:40) Which SaaS products won’t likely be replaced(56:20) How engineering processes have changed at Windsurf (1:00:01) The fatigue that goes along with being a software engineer, and how AI tools can help(1:02:58) Why Windsurf chose to fork VS Code and built a plugin for JetBrains (1:07:15) Windsurf’s language server (1:08:30) The current use of MCP and its shortcomings (1:12:50) How coding used to work in C#, and how MCP may evolve (1:14:05) Varun’s thoughts on vibe coding and the problems non-developers encounter(1:19:10) The types of engineers who will remain in demand (1:21:10) How AI will impact the future of software development jobs and the software industry(1:24:52) Rapid fire round—The Pragmatic Engineer deepdives relevant for this episode:• IDEs with GenAI features that Software Engineers love• AI tooling for Software Engineers in 2024: reality check• How AI-assisted coding will change software engineering: hard truths• AI tools for software engineers, but without the hype—See the transcript and other references from the episode at ⁠⁠https://newsletter.pragmaticengineer.com/podcast⁠⁠—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

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  1:27:54

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