The Pragmatic Engineer

Gergely Orosz

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

  1. The programming language after Kotlin – with the creator of Kotlin

    1 GÜN ÖNCE

    The programming language after Kotlin – with the creator of Kotlin

    Brought to You By: • Statsig — ⁠ The unified platform for flags, analytics, experiments, and more. • Sonar – The makers of SonarQube, the industry standard for automated code review • WorkOS – Everything you need to make your app enterprise ready. — Andrey Breslav is the creator of Kotlin and the founder of CodeSpeak, a new programming language that aims to reduce boilerplate by replacing trivial code with concise, plain-English descriptions. He led Kotlin’s design at JetBrains through its early releases, shaping both the language and its compiler as Kotlin grew into a core part of the Android ecosystem. In this episode, we talk about what it takes to design and evolve a programming language in production. We discuss the influences behind Kotlin, the tradeoffs that shaped it, and why interoperability with Java became so central to its success.  Andrey also explains why he is building CodeSpeak as a response to growing code complexity in an era of LLM agents, and why he believes keeping humans in control of the software development lifecycle will matter even more as AI becomes more capable. — Timestamps (00:00) Intro (01:02) Why Kotlin was created (06:26) Dynamic vs. static languages (09:27) Andrey joins the Kotlin project (14:26) Designing a new language  (19:40) Frontend vs. Backend in language design (21:05) Why is it named Kotlin? (24:37) Kotlin vs. Java tradeoffs (28:32) Null safety  (31:24) Kotlin’s influences  (39:12) Smartcasts  (40:42) Features Kotlin left out (44:54) Bidirectional Java interoperability (55:01) The Kotlin timeline  (58:00) Kotlin’s development process (1:07:20) From Java to Android developers (1:12:12) How Android became Kotlin-first  (1:18:20) CodeSpeak: a language for LLMs (1:24:07) LLMs and new languages (1:28:20) How software engineering is changing with AI (1:36:12) Developer tools of the future  (1:39:00) Andrey’s advice for junior engineers and students  (1:42:32) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: • Cross-platform mobile development • How Swift was built – with Chris Lattner, the creator of the language • Building Reddit’s iOS and Android app • Notion: going native on iOS and Android • Is there a drop in native iOS and Android hiring at startups? — 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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  2. The third golden age of software engineering – thanks to AI, with Grady Booch

    4 ŞUB

    The third golden age of software engineering – thanks to AI, with Grady Booch

    Brought to You By: • Statsig — ⁠ The unified platform for flags, analytics, experiments, and more. • Sonar – The makers of SonarQube, the industry standard for automated code review • WorkOS – Everything you need to make your app enterprise ready. — Every few decades, software engineering is declared “dead” or on the verge of being automated away. We’ve heard versions of this story before. But what if it’s just the start of a new “golden age” of a different type of software engineering, like it has been many times before? In this episode of The Pragmatic Engineer, I’m joined once again by Grady Booch, one of the most influential figures in the history of software engineering, to put today’s claims about AI and automation into historical context. Grady is the co-creator of the Unified Modeling Language, author of several books and papers that have shaped modern software development, and Chief Scientist for Software Engineering at IBM, where he focuses on embodied cognition. Grady shares his perspective on three golden ages of computing since the 1940s, and how each emerged in response to the constraints of its time. He explains how technical limits and human factors have always shaped the systems we build, and why periods of rapid change tend to produce both real progress and inflated expectations. He also responds to current claims that software engineering will soon be fully automated, explaining why systems thinking, human judgment, and responsibility remain central to the work, even as tools continue to evolve. — Timestamps (00:00) Intro (01:04) The first golden age of software engineering (18:05) The software crisis (32:07) The second golden age of software engineering  (41:27) Y2K and the Dotcom crash  (44:53) Early AI  (46:40) The third golden age of software engineering  (50:54) Why software engineers will very much be needed (57:52) Grady responds to Dario Amodei (1:06:00) New skills engineers will need to succeed (1:09:10) Resources for studying complex systems  (1:13:39) How to thrive during periods of change — The Pragmatic Engineer deepdives relevant for this episode: • When AI writes almost all code, what happens to software engineering?  • Inside a five-year-old startup’s rapid AI makeover • Software architecture with Grady Booch • What is old is new again — 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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  3. 28 OCA

    The creator of Clawd: "I ship code I don't read"

    Brought to You By: • Statsig — ⁠ The unified platform for flags, analytics, experiments, and more. • Sonar – The makers of SonarQube, the industry standard for automated code review • WorkOS – Everything you need to make your app enterprise ready. — Peter Steinberger ships more code than I’ve seen a single person do: in January, he was at more than 6,600 commits alone. As he puts it: “From the commits, it might appear like it's a company. But it’s not. This is one dude sitting at home having fun." How does he do it? Peter Steinberger is the creator of Clawdbot (as of yesterday: renamed to Moltbot) and founder of PSPDFKit. Moltbot – a work-in-progress AI agent that shows what the future of Siri could be like – is currently the hottest AI project in the tech industry, with more searches on Google than Claude Code or Codex. I sat down with Peter in London to talk about what building software looks like when you go all-in with AI tools like Claude and Codex. Peter’s background is fascinating. He built and scaled PSPDFKit into a global developer tools business. Then, after a three-year break, he returned to building. This time, LLMs and AI agents sit at the center of his workflow. We discuss what changes when one person can operate like a team and why closing the loop between code, tests, and feedback becomes a prerequisite for working effectively with AI. We also go into how engineering judgment shifts with AI, how testing and planning evolve when agents are involved, and which skills and habits are needed to work effectively. This is a grounded conversation about real workflows and real tradeoffs, and about designing systems that can test and improve themselves. — Timestamps (00:00) Intro (01:07) How Peter got into tech  (08:27) PSPDFKit (19:14) PSPDFKit’s tech stack and culture (22:33) Enterprise pricing (29:42) Burnout  (34:54) Peter finding his spark again (43:02) Peter’s workflow  (49:10) Managing agents  (54:08) Agentic engineering (59:01) Testing and debugging  (1:03:49) Why devs struggle with LLM coding (1:07:20) How PSPDFkit would look if built today  (1:11:10) How planning has changed with AI  (1:21:14) Building Clawdbot (now: Moltbot) (1:34:22) AI’s impact on large companies (1:38:38) “I don’t care about CI” (1:40:01) Peter’s process for new features  (1:44:48) Advice for new grads (1:50:18) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: • Inside a five-year-old startup’s rapid AI makeover • When AI writes almost all code, what happens to software engineering? • Why it’s so dramatic that “writing code by hand is dead” • AI Engineering in the real world • The AI Engineering stack — 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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  4. How AWS S3 is built

    21 OCA

    How AWS S3 is built

    Brought to You By: • Statsig — ⁠ The unified platform for flags, analytics, experiments, and more. • Sonar – The makers of SonarQube, the industry standard for automated code review • WorkOS – Everything you need to make your app enterprise ready. — Amazon S3 is one of the largest distributed systems ever built, storing and serving data for a significant portion of the internet. Behind its simple interfaces hides an enormous amount of engineering work, careful tradeoffs, and long-term thinking. In this episode, I sit down with Mai-Lan Tomsen Bukovec, VP of Data and Analytics at AWS, who has been running Amazon S3 for more than a decade. Mai-Lan shares how S3 operates at extreme scale, what it takes to design for durability and availability across millions of servers, and why building for failure is a core principle. We also go deep into how AWS approaches correctness using formal methods, how storage tiers and limits shape system design, and why simplicity remains one of the hardest and most important goals at S3’s scale. — Timestamps (00:00) Intro (01:03) S3’s scale  (03:58) How S3 started  (07:25) Parquet, Iceberg, and S3 tables (09:46) S3 for developers  (13:37) Why AWS keeps S3 prices low  (17:10) AWS pricing tiers (19:38) Availability and durability  (26:21) The cost of S3's consistency (31:22) Automated reasoning and proof of correctness  (35:14) Durability at AWS scale (39:58) Correlated failure and crash consistency  (43:22) Failure allowances  (46:04) Two opposing principles in S3 design (49:09) S3’s evolution  (52:21) S3 Vectors  (1:01:16) The 50 TB limit on AWS (1:07:54) The simplicity principle (1:10:10) Types of engineers working on S3 (1:14:15) Closing recommendations  — The Pragmatic Engineer deepdives relevant for this episode: • Inside Amazon’s engineering culture • How AWS deals with a major outage • A Day in the Life of a Senior Manager at Amazon • What is a Principal Engineer at Amazon? – with Steve Huynh • Working at Amazon as a software engineer – with Dave Anderson Amazon papers recommended by Mai-Lan: • Using lightweight formal methods to validate a key-value storage node in Amazon S3 • Formally verified cloud-scale authorization • Analyzing metastable failures • Amazon’s engineering tenets — 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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  5. 17.12.2025

    The history of servers, the cloud, and what’s next – with Oxide

    Brought to You By: •⁠ Statsig ⁠ — ⁠ The unified platform for flags, analytics, experiments, and more. •⁠ Linear ⁠ — ⁠ The system for modern product development. — How have servers and the cloud evolved in the last 30 years, and what might be next? Bryan Cantrill was a distinguished engineer at Sun Microsystems during both the Dotcom Boom and the Dotcom Bust. Today, he is the co-founder and CTO of Oxide Computer, where he works on modern server infrastructure. In this episode of The Pragmatic Engineer, Bryan joins me to break down how modern computing infrastructure evolved. We discuss why the Dotcom Bust produced deeper innovation than the Boom, how constraints shape better systems, and what the rise of the cloud changed and did not change about building reliable infrastructure. Our conversation covers early web infrastructure at Sun, the emergence of AWS, Kubernetes and cloud neutrality, and the tradeoffs between renting cloud space and building your own. We also touch on the complexity of server-side software updates, experimenting with AI, the limits of large language models, and how engineering organizations scale without losing their values. If you want a systems-level perspective on computing that connects past cycles to today’s engineering decisions, this episode offers a rare long-range view. — Timestamps (00:00) Intro (01:26) Computer science in the 1990s (03:01) Sun and Cisco’s web dominance (05:41) The Dotcom Boom (10:26) From Boom to Bust  (15:32) The innovations of the Bust (17:50) The open source shift (22:00) Oracle moves into Sun’s orbit (24:54) AWS dominance (2010–2014) (28:15) How Kubernetes and cloud neutrality (30:58) Custom infrastructure  (36:10) Renting the cloud vs. buying hardware (45:28) Designing a computer from first principles  (50:02) Why everyone is paid the same salary at Oxide (54:14) Oxide’s software stack  (58:33) The evolution of software updates (1:02:55) How Oxide uses AI  (1:06:05) The limitations of LLMs (1:11:44) AI use and experimentation at Oxide  (1:17:45) Oxide’s diverse teams (1:22:44) Remote work at Oxide (1:24:11) Scaling company values (1:27:36) AI’s impact on the future of engineering  (1:31:04) Bryan’s advice for junior engineers (1:34:01) Book recommendations — The Pragmatic Engineer deepdives relevant for this episode: • Startups on hard mode: Oxide. Part 1: Hardware • Startups on hard mode: Oxide, Part 2: Software & Culture • Three cloud providers, three outages: three different responses • Inside Uber’s move to the Cloud • Inside Agoda’s private Cloud — 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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  6. 03.12.2025

    Being a founding engineer at an AI startup

    Brought to You By: •⁠ Statsig ⁠ — ⁠ The unified platform for flags, analytics, experiments, and more. •⁠ Linear ⁠ — ⁠ The system for modern product development. — Michelle Lim joined Warp as engineer number one and is now building her own startup, Flint. She brings a strong product-first mindset shaped by her time at Facebook, Slack, Robinhood, and Warp. Michelle shares why she chose Warp over safer offers, how she evaluates early-stage opportunities, and what she believes distinguishes great founding engineers. Together, we cover how product-first engineers create value, why negotiating equity at early-stage startups requires a different approach, and why asking founders for references is a smart move. Michelle also shares lessons from building consumer and infrastructure products, how she thinks about tech stack choices, and how engineers can increase their impact by taking on work outside their job descriptions. If you want to understand what founders look for in early engineers or how to grow into a founding-engineer role, this episode is full of practical advice backed by real examples — Timestamps (00:00) Intro (01:32) How Michelle got into software engineering  (03:30) Michelle’s internships  (06:19) Learnings from Slack  (08:48) Product learnings at Robinhood (12:47) Joining Warp as engineer #1 (22:01) Negotiating equity (26:04) Asking founders for references (27:36) The top reference questions to ask (32:53) The evolution of Warp’s tech stack  (35:38) Product-first engineering vs. code-first (38:27) Hiring product-first engineers  (41:49) Different types of founding engineers  (44:42) How Flint uses AI tools  (45:31) Avoiding getting burned in founder exits (49:26) Hiring top talent (50:15) An overview of Flint (56:08) Advice for aspiring founding engineers (1:01:05) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: • Thriving as a founding engineer: lessons from the trenches • From software engineer to AI engineer • AI Engineering in the real world • The AI Engineering stack — 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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  7. 26.11.2025

    Code security for software engineers

    Brought to You By: •⁠ Statsig ⁠ — ⁠ 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. •⁠ Linear ⁠ — ⁠ 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. — 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 today’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. — 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?  (17:10) Code security basics for devs (21:35) Advanced security challenges (24:36) SCA testing  (25:26) The CVE Program  (29:39) The State of Code Security report  (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) AI’s impact on code security (58:32) Common misconceptions of the security industry (1:03:05) When is security “good enough?” (1:05:40) Johannes’s favorite programming language — The Pragmatic Engineer deepdives relevant for this episode: • What is Security Engineering? •⁠ Mishandled security vulnerability in Next.js •⁠ Okta Schooled on Its Security Practices — 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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  8. 19.11.2025

    How AI will change software engineering – with Martin Fowler

    Brought to You By: •⁠ Statsig ⁠ — ⁠ The unified platform for flags, analytics, experiments, and more. AI-accelerated development isn’t just about shipping faster: it’s about measuring whether, what you ship, actually delivers value. This is where modern experimentation with Statsig comes in. Check it out. •⁠ Linear ⁠ — ⁠ The system for modern product development. I had a jaw-dropping experience when I dropped in for the weekly “Quality Wednesdays” meeting at Linear. Every week, every dev fixes at least one quality isse, large or small. Even if it’s one pixel misalignment, like this one. I’ve yet to see a team obsess this much about quality. Read more about how Linear does Quality Wednesdays – it’s fascinating! — 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. — Timestamps (00:00) Intro (01:50) How Martin got into software engineering  (07:48) Joining Thoughtworks  (10:07) The Thoughtworks Technology Radar (16:45) From Assembly to high-level languages (25:08) Non-determinism  (33:38) Vibe coding (39:22) StackOverflow vs. coding with AI (43:25) Importance of testing with LLMs  (50:45) LLMs for enterprise software (56:38) Why Martin wrote Refactoring  (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 Manifesto  (1:28:35) How Martin learns about AI  (1:34:58) Advice for junior engineers  (1:37:44) The state of the tech industry today (1:42:40) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: • Vibe coding as a software engineer • The AI Engineering stack • AI Engineering in the real world • What changed in 50 years of computing — 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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Hakkında

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

Bilgi

  • Yaratıcı
    Gergely Orosz
  • Aktif Yıllar
    2024 - 2026
  • Bölümler
    52
  • Sınıflandırma
    Uygun
  • Telif Hakkı
    © Gergely Orosz
  • Şovun Web Sitesi
    The Pragmatic Engineer

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