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The Pragmatic Engineer

Gergely Orosz
The Pragmatic Engineer
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  • Code security for software engineers
    Brought to You By:โ€ขโ  Statsig โ  โ€” โ  The unified platform for flags, analytics, experiments, and more. Statsig are helping make the first-ever Pragmatic Summit a reality. Join me and 400 other top engineers and leaders on 11 February, in San Francisco for a special one-day event. Reserve your spot here.โ€ขโ  Linear โ  โ€” โ  The system for modern product development. Engineering teams today move much faster, thanks to AI. Because of this, coordination increasingly becomes a problem. This is where Linear helps fast-moving teams stay focused. Check out Linear.โ€”As software engineers, what should we know about writing secure code?Johannes Dahse is the VP of Code Security at Sonar and a security expert with 20 years of industry experience. In todayโ€™s episode of The Pragmatic Engineer, he joins me to talk about what security teams actually do, what developers should own, and where real-world risk enters modern codebases.We cover dependency risk, software composition analysis, CVEs, dynamic testing, and how everyday development practices affect security outcomes. Johannes also explains where AI meaningfully helps, where it introduces new failure modes, and why understanding the code you write and ship remains the most reliable defense.If you build and ship software, this episode is a practical guide to thinking about code security under real-world engineering constraints.โ€”Timestamps(00:00) Intro(02:31) What is penetration testing?(06:23) Who owns code security: devs or security teams?(14:42) What is code security?ย (17:10) Code security basics for devs(21:35) Advanced security challenges(24:36) SCA testingย (25:26) The CVE Programย (29:39) The State of Code Security reportย (32:02) Code quality vs security(35:20) Dev machines as a security vulnerability(37:29) Common security tools(42:50) Dynamic security tools(45:01) AI security reviews: what are the limits?(47:51) AI-generated code risks(49:21) More code: more vulnerabilities(51:44) AIโ€™s impact on code security(58:32) Common misconceptions of the security industry(1:03:05) When is security โ€œgood enough?โ€(1:05:40) Johannesโ€™s favorite programming languageโ€”The Pragmatic Engineer deepdives relevant for this episode:โ€ข What is Security Engineering?โ€ขโ  Mishandled security vulnerability in Next.jsโ€ขโ  Okta Schooled on Its Security Practicesโ€”Production and marketing by โ โ โ โ โ โ โ โ https://penname.co/โ โ โ โ โ โ โ โ . 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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  • How AI will change software engineering โ€“ with Martin Fowler
    Brought to You By:โ€ขโ  Statsig โ  โ€” โ  The unified platform for flags, analytics, experiments, and more. AI-accelerated development isnโ€™t just about shipping faster: itโ€™s about measuring whether, what you ship, actually delivers value. This is where modern experimentation with Statsig comes in. Check it out.โ€ขโ  Linear โ  โ€” โ  The system for modern product development. I had a jaw-dropping experience when I dropped in for the weekly โ€œQuality Wednesdaysโ€ meeting at Linear. Every week, every dev fixes at least one quality isse, large or small. Even if itโ€™s one pixel misalignment, like this one. Iโ€™ve yet to see a team obsess this much about quality. Read more about how Linear does Quality Wednesdays โ€“ itโ€™s fascinating!โ€”Martin Fowler is one of the most influential people within software architecture, and the broader tech industry. He is the Chief Scientist at Thoughtworks and the author of Refactoring and Patterns of Enterprise Application Architecture, and several other books. He has spent decades shaping how engineers think about design, architecture, and process, and regularly publishes on his blog, MartinFowler.com.In this episode, we discuss how AI is changing software development: the shift from deterministic to non-deterministic coding; where generative models help with legacy code; and the narrow but useful cases for vibe coding. Martin explains why LLM output must be tested rigorously, why refactoring is more important than ever, and how combining AI tools with deterministic techniques may be what engineering teams need.We also revisit the origins of the Agile Manifesto and talk about why, despite rapid changes in tooling and workflows, the skills that make a great engineer remain largely unchanged.โ€”Timestamps(00:00) Intro(01:50) How Martin got into software engineeringย (07:48) Joining Thoughtworksย (10:07) The Thoughtworks Technology Radar(16:45) From Assembly to high-level languages(25:08) Non-determinismย (33:38) Vibe coding(39:22) StackOverflow vs. coding with AI(43:25) Importance of testing with LLMsย (50:45) LLMs for enterprise software(56:38) Why Martin wrote Refactoringย (1:02:15) Why refactoring is so relevant today(1:06:10) Using LLMs with deterministic tools(1:07:36) Patterns of Enterprise Application Architecture(1:18:26) The Agile Manifestoย (1:28:35) How Martin learns about AIย (1:34:58) Advice for junior engineersย (1:37:44) The state of the tech industry today(1:42:40) Rapid fire roundโ€”The Pragmatic Engineer deepdives relevant for this episode:โ€ข Vibe coding as a software engineerโ€ข The AI Engineering stackโ€ข AI Engineering in the real worldโ€ข What changed in 50 years of computingโ€”Production and marketing by โ โ โ โ โ โ โ โ https://penname.co/โ โ โ โ โ โ โ โ . 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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  • Netflixโ€™s Engineering Culture
    Brought to You By:โ€ขโ  Statsig โ  โ€” โ  The unified platform for flags, analytics, experiments, and more. Statsig enables two cultures at once: continuous shipping and experimentation. Companies like Notion went from single-digit experiments per quarter to over 300 experiments with Statsig. Start using Statsig with a generous free tier, and a $50K startup program.โ€ขโ  Linear โ  โ€” โ  The system for modern product development. When most companies hit real scale, they start to slow down, and are faced with โ€œprocess debt.โ€ This often hits software engineers the most. Companies switch to Linear to hit a hard reset on this process debt โ€“ ones like Scale cut their bug resolution in half after the switch. Check out Linearโ€™s migration guide for details.โ€”Whatโ€™s it like to work as a software engineer inside one of the worldโ€™s biggest streaming companies?In this special episode recorded at Netflixโ€™s headquarters in Los Gatos, I sit down with Elizabeth Stone, Netflixโ€™s Chief Technology Officer. Before becoming CTO, Elizabeth led data and insights at Netflix and was VP of Science at Lyft. She brings a rare mix of technical depth, product thinking, and people leadership.We discuss what it means to be โ€œunusually responsibleโ€ at Netflix, how engineers make decisions without layers of approval, and how the company balances autonomy with guardrails for high-stakes projects like Netflix Live. Elizabeth shares how teams self-reflect and learn from outages and failures, why Netflix doesnโ€™t do formal performance reviews, and what new grads bring to a company known for hiring experienced engineers.This episode offers a rare inside look at how Netflix engineers build, learn, and lead at a global scale.โ€”Timestamps(00:00) Intro(01:44) The scale of Netflixย (03:31) Production software stack(05:20) Engineering challenges in production(06:38) How the Open Connect delivery network works(08:30) From pitch to playย (11:31) How Netflix enables engineers to make decisionsย (13:26) Building Netflix Live for global sports(16:25) Learnings from Paul vs. Tyson for NFL Live(17:47) Inside the control roomย (20:35) What being unusually responsible looks like(24:15) Balancing team autonomy with guardrails for Live(30:55) The high talent bar and introduction of levels at Netflix(36:01) The Keeper Testย ย (41:27) Why engineers leave or stayย (44:27) How AI tools are used at Netflix(47:54) AIโ€™s highest-impact use cases(50:20) What new grads add and why senior talent still matters(53:25) Open source at Netflixย (57:07) Elizabethโ€™s parting advice for new engineers to succeed at Netflixย โ€”The Pragmatic Engineer deepdives relevant for this episode:โ€ข The end of the senior-only level at Netflixโ€ข Netflix revamps its compensation philosophyโ€ข Live streaming at world-record scale with Ashutosh Agrawalโ€ข Shipping to productionโ€ข What is good software architecture?โ€”Production and marketing by โ โ โ โ โ โ โ โ https://penname.co/โ โ โ โ โ โ โ โ . 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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  • From Swift to Mojo and high-performance AI Engineering with Chris Lattner
    Brought to You By:โ€ขโ  Statsig โ  โ€” โ  The unified platform for flags, analytics, experiments, and more. Companies like Graphite, Notion, and Brex rely on Statsig to measure the impact of the pace they ship. Get a 30-day enterprise trial here.โ€ขโ  Linear โ€“ The system for modern product development. Linear is a heavy user of Swift: they just redesigned their native iOS app using their own take on Appleโ€™s Liquid Glass design language. The new app is about speed and performance โ€“ just like Linear is. Check it out.โ€”Chris Lattner is one of the most influential engineers of the past two decades. He created the LLVM compiler infrastructure and the Swift programming language โ€“ and Swift opened iOS development to a broader group of engineers. With Mojo, heโ€™s now aiming to do the same for AI, by lowering the barrier to programming AI applications.I sat down with Chris in San Francisco, to talk language design, lessons on designing Swift and Mojo, and โ€“ of course! โ€“ compilers. Itโ€™s hard to find someone who is as enthusiastic and knowledgeable about compilers as Chris is!We also discussed why experts often resist change even when current tools slow them down, what he learned about AI and hardware from his time across both large and small engineering teams, and why compiler engineering remains one of the best ways to understand how software really works.โ€”Timestamps(00:00) Intro(02:35) Compilers in the early 2000s(04:48) Why Chris built LLVM(08:24) GCC vs. LLVM(09:47) LLVM at Appleย (19:25) How Chris got support to go open source at Apple(20:28) The story of Swiftย (24:32) The process for designing a languageย (31:00) Learnings from launching Swiftย (35:48) Swift Playgrounds: making coding accessible(40:23) What Swift solved and the technical debt it created(47:28) AI learnings from Google and Teslaย (51:23) SiFive: learning about hardware engineering(52:24) Mojoโ€™s origin story(57:15) Modularโ€™s bet on a two-level stack(1:01:49) Compiler shortcomings(1:09:11) Getting started with Mojoย (1:15:44) How big is Modular, as a company?(1:19:00) AI coding tools the Modular team usesย (1:22:59) What kind of software engineers Modular hiresย (1:25:22) A programming language for LLMs? No thanks(1:29:06) Why you should study and understand compilersโ€”The Pragmatic Engineer deepdives relevant for this episode:โ€ขโ  AI Engineering in the real worldโ€ข The AI Engineering stackโ€ข Uber's crazy YOLO app rewrite, from the front seatโ€ข Python, Go, Rust, TypeScript and AI with Armin Ronacherโ€ข Microsoftโ€™s developer tools rootsโ€”Production and marketing by โ โ โ โ โ โ โ โ https://penname.co/โ โ โ โ โ โ โ โ . 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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  • Beyond Vibe Coding with Addy Osmani
    Brought to You By:โ€ขโ  Statsig โ  โ€” โ  The unified platform for flags, analytics, experiments, and more. โ€ขโ  Linear โ€“ The system for modern product development. โ€”Addy Osmani is Head of Chrome Developer Experience at Google, where he leads teams focused on improving performance, tooling, and the overall developer experience for building on the web. If youโ€™ve ever opened Chromeโ€™s Developer Tools bar, youโ€™ve definitely used features Addy has built. Heโ€™s also the author of several books, including his latest, Beyond Vibe Coding, which explores how AI is changing software development.In this episode of The Pragmatic Engineer, I sit down with Addy to discuss how AI is reshaping software engineering workflows, the tradeoffs between speed and quality, and why understanding generated code remains critical. We dive into his article The 70% Problem, which explains why AI tools accelerate development but struggle with the final 30% of software qualityโ€”and why this last 30% is tackled easily by software engineers who understand how the system actually works.โ€”Timestamps(00:00) Intro(02:17) Vibe coding vs. AI-assisted engineering(06:07) How Addy uses AI tools(13:10) Addyโ€™s learnings about applying AI for development(18:47) Addyโ€™s favorite tools(22:15) The 70% Problem(28:15) Tactics for efficient LLM usage(32:58) How AI tools evolved(34:29) The case for keeping expectations low and control high(38:05) Autonomous agents and working with them(42:49) How the EM and PM role changes with AI(47:14) The rise of new roles and shifts in developer education(48:11) The importance of critical thinking when working with AI(54:08) LLMs as a tool for learning(1:03:50) Rapid questionsโ€”The Pragmatic Engineer deepdives relevant for this episode:โ€ขโ  Vibe Coding as a software engineerโ€ขโ  How AI-assisted coding will change software engineering: hard truthsโ€ขโ  AI Engineering in the real worldโ€ขโ  The AI Engineering stackโ€ขโ  How Claude Code is builtโ€”Production and marketing by โ โ โ โ โ โ โ โ https://penname.co/โ โ โ โ โ โ โ โ . 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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Software engineering at Big Tech and startups, from the inside. Deepdives with experienced engineers and tech professionals who share their hard-earned lessons, interesting stories and advice they have on building software. Especially relevant for software engineers and engineering leaders: useful for those working in tech. newsletter.pragmaticengineer.com
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