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The Pragmatic Engineer

The Pragmatic Engineer

Auteur(s): Gergely Orosz
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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.

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  • Netflix’s Engineering Culture

    Netflix’s Engineering Culture
    Nov 12 2025
    Brought to You By:•⁠ Statsig ⁠ — ⁠ 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.•⁠ Linear ⁠ — ⁠ The system for modern product development. When most companies hit real scale, they start to slow down, and are faced with “process debt.” This often hits software engineers the most. Companies switch to Linear to hit a hard reset on this process debt – ones like Scale cut their bug resolution in half after the switch. Check out Linear’s migration guide for details.—What’s it like to work as a software engineer inside one of the world’s biggest streaming companies?In this special episode recorded at Netflix’s headquarters in Los Gatos, I sit down with Elizabeth Stone, Netflix’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 “unusually responsible” 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 doesn’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.—Timestamps(00:00) Intro(01:44) The scale of Netflix (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 play (11:31) How Netflix enables engineers to make decisions (13:26) Building Netflix Live for global sports(16:25) Learnings from Paul vs. Tyson for NFL Live(17:47) Inside the control room (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 Test (41:27) Why engineers leave or stay (44:27) How AI tools are used at Netflix(47:54) AI’s highest-impact use cases(50:20) What new grads add and why senior talent still matters(53:25) Open source at Netflix (57:07) Elizabeth’s parting advice for new engineers to succeed at Netflix —The Pragmatic Engineer deepdives relevant for this episode:• The end of the senior-only level at Netflix• Netflix revamps its compensation philosophy• Live streaming at world-record scale with Ashutosh Agrawal• Shipping to production• What is good software architecture?—Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email podcast@pragmaticengineer.com. Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe
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    1 h
  • From Swift to Mojo and high-performance AI Engineering with Chris Lattner

    From Swift to Mojo and high-performance AI Engineering with Chris Lattner
    Nov 5 2025
    Brought to You By:•⁠ Statsig ⁠ — ⁠ 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.•⁠ Linear – 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 Apple’s Liquid Glass design language. The new app is about speed and performance – just like Linear is. Check it out.—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 – and Swift opened iOS development to a broader group of engineers. With Mojo, he’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 – of course! – compilers. It’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.—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 Apple (19:25) How Chris got support to go open source at Apple(20:28) The story of Swift (24:32) The process for designing a language (31:00) Learnings from launching Swift (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 Tesla (51:23) SiFive: learning about hardware engineering(52:24) Mojo’s origin story(57:15) Modular’s bet on a two-level stack(1:01:49) Compiler shortcomings(1:09:11) Getting started with Mojo (1:15:44) How big is Modular, as a company?(1:19:00) AI coding tools the Modular team uses (1:22:59) What kind of software engineers Modular hires (1:25:22) A programming language for LLMs? No thanks(1:29:06) Why you should study and understand compilers—The Pragmatic Engineer deepdives relevant for this episode:•⁠ AI Engineering in the real world• The AI Engineering stack• Uber's crazy YOLO app rewrite, from the front seat• Python, Go, Rust, TypeScript and AI with Armin Ronacher• Microsoft’s developer tools roots—Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email podcast@pragmaticengineer.com. Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe
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    1 h et 32 min
  • Beyond Vibe Coding with Addy Osmani

    Beyond Vibe Coding with Addy Osmani
    Oct 29 2025

    Brought to You By:

    •⁠ Statsig ⁠ — ⁠ The unified platform for flags, analytics, experiments, and more.

    •⁠ Linear – The system for modern product development.

    —

    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 you’ve ever opened Chrome’s Developer Tools bar, you’ve definitely used features Addy has built. He’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 quality—and why this last 30% is tackled easily by software engineers who understand how the system actually works.

    —

    Timestamps

    (00:00) Intro

    (02:17) Vibe coding vs. AI-assisted engineering

    (06:07) How Addy uses AI tools

    (13:10) Addy’s learnings about applying AI for development

    (18:47) Addy’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 questions

    —

    The Pragmatic Engineer deepdives relevant for this episode:

    •⁠ Vibe Coding as a software engineer

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

    •⁠ AI Engineering in the real world

    •⁠ The AI Engineering stack

    •⁠ How Claude Code is built

    —

    Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email podcast@pragmaticengineer.com.



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    1 h et 8 min
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