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Why is Rust different than many/most programming languages? Alice Ryhl works on Google's Android Rust team, is a Rust language team advisor, and is a core maintainer of Tokio (the most widely-used async runtime in Rust) Timestamps: 00:00 Intro 04:09 Tokio: an overview 05:11 What Alice likes about Rust...

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Gergely Orosz

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52,183 views • 4 months ago •via X (Twitter)

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In Rust, error handling is opt-out, not opt-in. Alice Ryhl (Rust for Android at Google, Rust language advisor & Tokio maintainer) explains: “The other thing I think is quite good is error handling. So on one hand, Rust doesn't really use exceptions, so it actually returns the error as a value. So you return a value that's, using an enum, either the result or the error. And the way this is done is that there's an operator `?` - so you write: my_function and then a question mark at the end - which means if this function fails, return the error. So it's really easy to handle errors but it's not serial characters like it is with exceptions, so it's explicit. On the other hand, if you forget to put the question mark, that's a compilation error. So you have to check it, and of course you can also have it manually, but the point is it's this idea of there are these things where you write some code and there's some implicit error condition you didn't think of and now you just took down your server or something.”
1:13

In Rust, error handling is opt-out, not opt-in. Alice Ryhl (Rust for Android at Google, Rust language advisor & Tokio maintainer) explains: “The other thing I think is quite good is error handling. So on one hand, Rust doesn't really use exceptions, so it actually returns the error as a value. So you return a value that's, using an enum, either the result or the error. And the way this is done is that there's an operator `?` - so you write: my_function and then a question mark at the end - which means if this function fails, return the error. So it's really easy to handle errors but it's not serial characters like it is with exceptions, so it's explicit. On the other hand, if you forget to put the question mark, that's a compilation error. So you have to check it, and of course you can also have it manually, but the point is it's this idea of there are these things where you write some code and there's some implicit error condition you didn't think of and now you just took down your server or something.”

The Pragmatic Engineer

52,059 views • 4 months ago

Martin Odersky (Martin Odersky) is the creator of Scala and I interviewed him to compare different languages designs (Rust vs Zig vs Python vs Scala) and how AI will impact programming languages. In this episode: • Comparing Rust, Zig, Python, and Scala designs • Why its hard to write a compiler • Predictions about how AI will impact software • Why functional programming matters Where to watch: • YouTube - • Spotify - • Apple Podcasts - • Transcript - Thank you to the sponsors of this episode for supporting my work: • WorkOS: makes your app Enterprise Ready with easy to use APIs to add SSO, SCIM, RBAC, and more in just a few lines of code, check them out at • Jira by Atlassian: Get more work done with your favorite agents and models all in one place, check them out at Chapters: 00:00 Intro 00:44 Why care about functional programming 06:35 Why should people learn Scala 09:26 Rust vs Scala 12:42 Rust vs Zig 15:45 Scala vs Python 18:31 The programming languages that influenced him 22:16 How running on the JVM works 26:33 Why writing a compiler is hard 29:19 Why Twitter adopted Scala early on 31:00 How he believes AI will impact programming languages 43:40 Will there be less engineers in ten years 44:34 Top programming languages to learn to grow 46:18 Top technical book recommendation 46:51 Why he chose academia instead of industry 48:28 Reflecting on Scala 55:42 Advice for his younger self 56:33 Outro
57:30

Martin Odersky (Martin Odersky) is the creator of Scala and I interviewed him to compare different languages designs (Rust vs Zig vs Python vs Scala) and how AI will impact programming languages. In this episode: • Comparing Rust, Zig, Python, and Scala designs • Why its hard to write a compiler • Predictions about how AI will impact software • Why functional programming matters Where to watch: • YouTube - • Spotify - • Apple Podcasts - • Transcript - Thank you to the sponsors of this episode for supporting my work: • WorkOS: makes your app Enterprise Ready with easy to use APIs to add SSO, SCIM, RBAC, and more in just a few lines of code, check them out at • Jira by Atlassian: Get more work done with your favorite agents and models all in one place, check them out at Chapters: 00:00 Intro 00:44 Why care about functional programming 06:35 Why should people learn Scala 09:26 Rust vs Scala 12:42 Rust vs Zig 15:45 Scala vs Python 18:31 The programming languages that influenced him 22:16 How running on the JVM works 26:33 Why writing a compiler is hard 29:19 Why Twitter adopted Scala early on 31:00 How he believes AI will impact programming languages 43:40 Will there be less engineers in ten years 44:34 Top programming languages to learn to grow 46:18 Top technical book recommendation 46:51 Why he chose academia instead of industry 48:28 Reflecting on Scala 55:42 Advice for his younger self 56:33 Outro

Ryan Peterman

103,578 views • 25 days ago

Simon Peyton Jones is the co-creator of Haskell (pure functional programming language) and I interviewed him about functional programming, why it matters, and his thoughts on other programming languages. In this episode: • Useful and useless programming languages • Rust vs C • Haskell vs OCaml • Why functional programming matters • Static languages and their value for LLMs • Why Excel is his 2nd favorite programming language Where to watch: • YouTube - • Spotify - • Apple Podcasts - • Transcript - Thank you to the sponsor of this episode for supporting my work: • WorkOS: makes your app Enterprise Ready with easy to use APIs to add SSO, SCIM, RBAC, and more in just a few lines of code, check them out at Chapters: 00:00 - Intro 00:39 - What functional programming is 09:18 - Downsides of functional programming 10:53 - Specialized hardware for functional programming 21:47 - Haskell is useless 25:59 - Rust vs C 28:26 - Haskell vs OCaml 35:26 - Side effects in Haskell 44:26 - Type systems 57:30 - How the Haskell compiler works 01:04:35 - Why Haskell is talked about more than used 01:09:07 - Avoiding success at all costs 01:11:12 - LLMs and programming languages 01:13:57 - New programming language design 01:15:59 - Should students continue to learn programming 01:22:33 - Why Excel is is 2nd favorite programming language 01:25:04 - Advice for his younger self
1:27:52

Simon Peyton Jones is the co-creator of Haskell (pure functional programming language) and I interviewed him about functional programming, why it matters, and his thoughts on other programming languages. In this episode: • Useful and useless programming languages • Rust vs C • Haskell vs OCaml • Why functional programming matters • Static languages and their value for LLMs • Why Excel is his 2nd favorite programming language Where to watch: • YouTube - • Spotify - • Apple Podcasts - • Transcript - Thank you to the sponsor of this episode for supporting my work: • WorkOS: makes your app Enterprise Ready with easy to use APIs to add SSO, SCIM, RBAC, and more in just a few lines of code, check them out at Chapters: 00:00 - Intro 00:39 - What functional programming is 09:18 - Downsides of functional programming 10:53 - Specialized hardware for functional programming 21:47 - Haskell is useless 25:59 - Rust vs C 28:26 - Haskell vs OCaml 35:26 - Side effects in Haskell 44:26 - Type systems 57:30 - How the Haskell compiler works 01:04:35 - Why Haskell is talked about more than used 01:09:07 - Avoiding success at all costs 01:11:12 - LLMs and programming languages 01:13:57 - New programming language design 01:15:59 - Should students continue to learn programming 01:22:33 - Why Excel is is 2nd favorite programming language 01:25:04 - Advice for his younger self

Ryan Peterman

128,845 views • 3 months ago

Xavier Leroy (creator of OCaml) is an expert in compilers, formal verification of software and functional programming. This interview should be an approachable resource if you're curious about formal verification of software since I was learning that on the fly during it. In this episode: • OCaml compared with Rust and JavaScript • What is formal verification and how does it work • How languages call each other across boundaries • How to address "almost-correct" LLM code • How type inference works in programming languages Where to watch: • YouTube - • Spotify - • Apple Podcasts - • Transcript - Thank you to the sponsor of this episode for supporting my work: • WorkOS: makes your app Enterprise Ready with easy to use APIs to add SSO, SCIM, RBAC, and more in just a few lines of code, check them out at Chapters: 00:00 - Intro 00:43 - What sets OCaml apart 04:39 - OCaml vs Rust 07:57 - Why is manual memory management more performant 11:21 - Javascript vs OCaml 14:00 - Famous Rob Pike quote 16:05 - Type inference and how it works 22:12 - What is formal verification and how does it work 40:07 - What made multicore support difficult for OCaml 50:17 - How programming languages interface and call each other 57:41 - The danger of almost-correct LLM code 01:05:39 - How LLMs will change programming languages 01:10:26 - Industry vs academia 01:15:05 - Most interesting unsolved problems 01:18:30 - Top book recommendations for engineers 01:21:17 - Advice for his younger self 01:23:31 - Outro
1:24:27

Xavier Leroy (creator of OCaml) is an expert in compilers, formal verification of software and functional programming. This interview should be an approachable resource if you're curious about formal verification of software since I was learning that on the fly during it. In this episode: • OCaml compared with Rust and JavaScript • What is formal verification and how does it work • How languages call each other across boundaries • How to address "almost-correct" LLM code • How type inference works in programming languages Where to watch: • YouTube - • Spotify - • Apple Podcasts - • Transcript - Thank you to the sponsor of this episode for supporting my work: • WorkOS: makes your app Enterprise Ready with easy to use APIs to add SSO, SCIM, RBAC, and more in just a few lines of code, check them out at Chapters: 00:00 - Intro 00:43 - What sets OCaml apart 04:39 - OCaml vs Rust 07:57 - Why is manual memory management more performant 11:21 - Javascript vs OCaml 14:00 - Famous Rob Pike quote 16:05 - Type inference and how it works 22:12 - What is formal verification and how does it work 40:07 - What made multicore support difficult for OCaml 50:17 - How programming languages interface and call each other 57:41 - The danger of almost-correct LLM code 01:05:39 - How LLMs will change programming languages 01:10:26 - Industry vs academia 01:15:05 - Most interesting unsolved problems 01:18:30 - Top book recommendations for engineers 01:21:17 - Advice for his younger self 01:23:31 - Outro

Ryan Peterman

23,942 views • 2 months ago

Well that was an adventure. Toad's fuzzy file search boosts first characters, smallest number of groups, and it highlights the best scoring match. So you can locate a file with the bare minimum of keypresses. It's an expensive algorithm as it scores every possible combination of the query characters. For any given path there could be many indices which match, and each of them is scored separately. Still, it was fast enough to search as you type for my largest repository. Alas, it was slow for very large repos. Like Microsoft's Typescript, which has more than 84,000 files. I figured I'd reach the limit of what I could do with Python. So my options were to use something like ripgrep or re-implement the fuzzy searching in a faster language. I didn't want to add the ripgrep dependency, so I built a Rust version (with the help of AI) which eventually yielded a ~14X speedup. Nice. Better. But it could still become painful to type under certain pathological conditions. An inefficient algorithm can always be slow if you add more items. So I scrapped the Rust solution and built an index in Python—so the fuzzy search could throw away most paths that wouldn't match or have low scores. And its faster than the Rust solution was. The only downside is that there is a little additional work upfront (done in the background). I think this will do for a while. Until I get an issue that it is slow with 10million files. Rust is still an option, as is doing the work in parallel. This will be in the next version of Toad. Here I am searching the Typescript repo... #Python
0:43

Well that was an adventure. Toad's fuzzy file search boosts first characters, smallest number of groups, and it highlights the best scoring match. So you can locate a file with the bare minimum of keypresses. It's an expensive algorithm as it scores every possible combination of the query characters. For any given path there could be many indices which match, and each of them is scored separately. Still, it was fast enough to search as you type for my largest repository. Alas, it was slow for very large repos. Like Microsoft's Typescript, which has more than 84,000 files. I figured I'd reach the limit of what I could do with Python. So my options were to use something like ripgrep or re-implement the fuzzy searching in a faster language. I didn't want to add the ripgrep dependency, so I built a Rust version (with the help of AI) which eventually yielded a ~14X speedup. Nice. Better. But it could still become painful to type under certain pathological conditions. An inefficient algorithm can always be slow if you add more items. So I scrapped the Rust solution and built an index in Python—so the fuzzy search could throw away most paths that wouldn't match or have low scores. And its faster than the Rust solution was. The only downside is that there is a little additional work upfront (done in the background). I think this will do for a while. Until I get an issue that it is slow with 10million files. Rust is still an option, as is doing the work in parallel. This will be in the next version of Toad. Here I am searching the Typescript repo... #Python

Will McGugan

12,261 views • 7 months ago

Anders Hejlsberg ( Anders Hejlsberg ) is the creator of TypeScript and C#, and I asked him about how the TypeScript compiler got 10x faster through a rewrite in Go and his thoughts on how AI has impacted software engineering. In this episode: • How rewriting the compiler makes it 10x faster • Why they picked Go instead of Rust • Why they didn't use LLMs for the migration • Predictions on AI's impact on software engineering Where to watch: • YouTube - • Spotify - • Apple Podcasts - • Transcript - Thank you to the sponsors of this episode for supporting my work: • WorkOS: makes your app Enterprise Ready with easy to use APIs to add SSO, SCIM, RBAC, and more in just a few lines of code, check them out at • Jira by Atlassian: Get more work done with your favorite agents and models all in one place, check them out at Chapters: 00:00 Intro 00:48 Why write a compiler in JavaScript 07:29 Why rewrite the compiler in Go 14:49 LLMs for large migrations 20:12 Why Javascript is so popular 26:32 Why ever use Javascript on the backend 32:59 What it takes to build a programming language 37:06 Will there be fewer languages in 10 years 42:57 Hands on engineering vs delegation 49:14 Why fast tooling matters more now 51:16 AI software engineering predictions 58:52 The most technically challenging work 01:02:04 Top book recommendation 01:03:50 Advice for his younger self 01:05:00 Outro
1:05:57

Anders Hejlsberg ( Anders Hejlsberg ) is the creator of TypeScript and C#, and I asked him about how the TypeScript compiler got 10x faster through a rewrite in Go and his thoughts on how AI has impacted software engineering. In this episode: • How rewriting the compiler makes it 10x faster • Why they picked Go instead of Rust • Why they didn't use LLMs for the migration • Predictions on AI's impact on software engineering Where to watch: • YouTube - • Spotify - • Apple Podcasts - • Transcript - Thank you to the sponsors of this episode for supporting my work: • WorkOS: makes your app Enterprise Ready with easy to use APIs to add SSO, SCIM, RBAC, and more in just a few lines of code, check them out at • Jira by Atlassian: Get more work done with your favorite agents and models all in one place, check them out at Chapters: 00:00 Intro 00:48 Why write a compiler in JavaScript 07:29 Why rewrite the compiler in Go 14:49 LLMs for large migrations 20:12 Why Javascript is so popular 26:32 Why ever use Javascript on the backend 32:59 What it takes to build a programming language 37:06 Will there be fewer languages in 10 years 42:57 Hands on engineering vs delegation 49:14 Why fast tooling matters more now 51:16 AI software engineering predictions 58:52 The most technically challenging work 01:02:04 Top book recommendation 01:03:50 Advice for his younger self 01:05:00 Outro

Ryan Peterman

61,265 views • 1 month ago

If you use Codex, Tibo needs no introduction. We talked 100% about Codex (fun fact: he started building it!), and 0% about resets. 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 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. Entire hosts your code in-region, and is up to 89x faster than any other competitor. Oh, and to those still reading and wondering about resets: there was one yesterday already 😅
1:14:22

If you use Codex, Tibo needs no introduction. We talked 100% about Codex (fun fact: he started building it!), and 0% about resets. 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 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. Entire hosts your code in-region, and is up to 89x faster than any other competitor. Oh, and to those still reading and wondering about resets: there was one yesterday already 😅

Gergely Orosz

80,608 views • 16 days ago

Can you restore a BlackStone sent to the wasteland...? Some solid tips here on "how-to." You can absolutely restore a rusty curb-find Blackstone griddle from the wasteland and make it last for years with proper care. Start by firing up all the burners on high for about 20 minutes to loosen old seasoning and surface rust, then let it cool slightly. Once it’s manageable, scrape off loose debris with a metal spatula or scraper. Apply a thin layer of cooking oil and scrub vigorously in circular motions using a pumice grill stone (the most effective tool), steel wool, or medium-grit sandpaper. For stubborn rust spots, a vinegar-and-water mix can sit for a few minutes to help dissolve it before wiping everything clean. Rinse lightly with water if needed, but dry the surface completely—using low heat helps evaporate any moisture to prevent new rust. The key to longevity is re-seasoning: spread a very thin layer of high-smoke-point oil like flaxseed (ideal), avocado, or canola across the entire cooktop with a paper towel. Heat it on medium-high until it smokes, usually 15–20 minutes, then repeat this process 3–5 times to build a dark, durable non-stick patina. Over time, food will stick less and the surface will perform like new. To keep it lasting “forever,” always clean it while still hot after every use by scraping residue, wiping with warm water, and applying a light oil coat. Never use soap unless doing a deep clean, avoid leaving it exposed to rain, and store it covered or indoors. Re-season monthly or as needed, and it will reward you with many great cooks ahead. Grab a grill stone, scraper, and some oil, and you’ll have it back in action in just a couple of hours plus seasoning time. 🔥
2:01

Can you restore a BlackStone sent to the wasteland...? Some solid tips here on "how-to." You can absolutely restore a rusty curb-find Blackstone griddle from the wasteland and make it last for years with proper care. Start by firing up all the burners on high for about 20 minutes to loosen old seasoning and surface rust, then let it cool slightly. Once it’s manageable, scrape off loose debris with a metal spatula or scraper. Apply a thin layer of cooking oil and scrub vigorously in circular motions using a pumice grill stone (the most effective tool), steel wool, or medium-grit sandpaper. For stubborn rust spots, a vinegar-and-water mix can sit for a few minutes to help dissolve it before wiping everything clean. Rinse lightly with water if needed, but dry the surface completely—using low heat helps evaporate any moisture to prevent new rust. The key to longevity is re-seasoning: spread a very thin layer of high-smoke-point oil like flaxseed (ideal), avocado, or canola across the entire cooktop with a paper towel. Heat it on medium-high until it smokes, usually 15–20 minutes, then repeat this process 3–5 times to build a dark, durable non-stick patina. Over time, food will stick less and the surface will perform like new. To keep it lasting “forever,” always clean it while still hot after every use by scraping residue, wiping with warm water, and applying a light oil coat. Never use soap unless doing a deep clean, avoid leaving it exposed to rain, and store it covered or indoors. Re-season monthly or as needed, and it will reward you with many great cooks ahead. Grab a grill stone, scraper, and some oil, and you’ll have it back in action in just a couple of hours plus seasoning time. 🔥

🚫👁️Drinks on Saturday🇺🇸

116,886 views • 4 months ago

“If you can vertically center a div, you can probably learn assembly”. Casey Muratori, Casey Muratori, programmer and performance nerd, on why it’s essential for optimization: “People often ask: assembly language, what would I ever need that for? There's a very good reason for it: everything else that you might use doesn't tell you anything about what the CPU is actually receiving. If I look at a Java program, if I look at a C program, if I look at Haskell, OCaml, Rust, all I'm seeing is input to a compiler. I have no idea what the CPU is actually going to be asked to do. It's not that hard to be able to learn to read assembly language so that you can see very quickly, is the CPU being asked to do the things that I think it should be asked to do. For the vast majority of tasks you might do in optimization, writing it, no. Reading it, essential. In assembly language, maybe there's 20 or 30 instructions you might have to learn total, because most things in legacy assembly, like x64, are hardly ever output by the compiler. So you only need to learn a very small subset that you're going to be seeing in 90% of cases.”
1:21

“If you can vertically center a div, you can probably learn assembly”. Casey Muratori, Casey Muratori, programmer and performance nerd, on why it’s essential for optimization: “People often ask: assembly language, what would I ever need that for? There's a very good reason for it: everything else that you might use doesn't tell you anything about what the CPU is actually receiving. If I look at a Java program, if I look at a C program, if I look at Haskell, OCaml, Rust, all I'm seeing is input to a compiler. I have no idea what the CPU is actually going to be asked to do. It's not that hard to be able to learn to read assembly language so that you can see very quickly, is the CPU being asked to do the things that I think it should be asked to do. For the vast majority of tasks you might do in optimization, writing it, no. Reading it, essential. In assembly language, maybe there's 20 or 30 instructions you might have to learn total, because most things in legacy assembly, like x64, are hardly ever output by the compiler. So you only need to learn a very small subset that you're going to be seeing in 90% of cases.”

The Pragmatic Engineer

69,647 views • 3 days ago

Here's my conversation with ThePrimeagen, a programmer who has educated, entertained, and inspired millions of people to build software and have fun doing it. It's here on X in full, and is up everywhere else too (see comment). Timestamps: 0:00 - Introduction 0:42 - Love for programming 10:15 - Hardest part of programming 12:31 - Types of programming 20:08 - Life story 30:12 - Hardship 31:44 - High school 37:30 - Porn addiction 47:16 - God 1:02:59 - Perseverance 1:12:55 - Netflix 1:25:23 - Groovy 1:30:27 - Printf() debugging 1:36:49 - Falcor 1:46:19 - Breaking production 1:49:04 - Pieter Levels 1:53:34 - Netflix, Twitch, and YouTube infrastructure 2:05:36 - ThePrimeagen origin story 2:20:52 - Learning programming languages 2:29:55 - Best programming languages in 2025 2:34:50 - Python 2:35:30 - HTML & CSS 2:36:20 - Bash 2:37:00 - FFmpeg 2:43:42 - Performance 2:46:15 - Rust 2:51:03 - Epic projects 3:04:27 - Asserts 3:13:41 - ADHD 3:21:49 - Productivity 3:26:13 - Programming setup 4:01:43 - Coffee 4:08:47 - Programming with AI 4:51:31 - Advice for young programmers 5:03:03 - Reddit questions 5:10:35 - God
5:20:10

Here's my conversation with ThePrimeagen, a programmer who has educated, entertained, and inspired millions of people to build software and have fun doing it. It's here on X in full, and is up everywhere else too (see comment). Timestamps: 0:00 - Introduction 0:42 - Love for programming 10:15 - Hardest part of programming 12:31 - Types of programming 20:08 - Life story 30:12 - Hardship 31:44 - High school 37:30 - Porn addiction 47:16 - God 1:02:59 - Perseverance 1:12:55 - Netflix 1:25:23 - Groovy 1:30:27 - Printf() debugging 1:36:49 - Falcor 1:46:19 - Breaking production 1:49:04 - Pieter Levels 1:53:34 - Netflix, Twitch, and YouTube infrastructure 2:05:36 - ThePrimeagen origin story 2:20:52 - Learning programming languages 2:29:55 - Best programming languages in 2025 2:34:50 - Python 2:35:30 - HTML & CSS 2:36:20 - Bash 2:37:00 - FFmpeg 2:43:42 - Performance 2:46:15 - Rust 2:51:03 - Epic projects 3:04:27 - Asserts 3:13:41 - ADHD 3:21:49 - Productivity 3:26:13 - Programming setup 4:01:43 - Coffee 4:08:47 - Programming with AI 4:51:31 - Advice for young programmers 5:03:03 - Reddit questions 5:10:35 - God

Lex Fridman

1,143,197 views • 1 year ago

Anders Hejlsberg (Anders Hejlsberg) is a living legend: he created Turbo Pascal, Delphi, C# and TypeScript (and today TypeScript is the most-used programming language, globally, as per GitHub.) Timestamps: 00:00 Intro 02:48 How Anders got into programming 05:40 Building his first compiler 07:44 Turbo Pascal 12:25 Delphi 14:53 Joining Microsoft 19:41 Building C# 29:11 Async/await 34:01 The rise of JavaScript 37:52 Building TypeScript 42:58 How the TypeScript compiler works 48:30 JavaScript’s strengths and weaknesses 52:18 How Anders uses AI 56:03 What language features work well with AI 1:02:49 How software craftsmanship is changing 1:07:49 Performance and efficiency 1:09:29 Anders’ tool stack 1:11:30 A 30-year career at Microsoft 1:13:40 Book recommendation 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. turbopuffer – a vector and full-text search engine built on object storage. It’s fast, cheap, and extremely scalable. Four things that stood out to me: 1. “10x better for 1/10th of the price” is a proven winner. This is what Turbo Pascal did: it sold for $49.95 when competing compilers cost $500, and it was faster and more interactive than competitors’ products. Conveniently, the low price tag also killed off piracy 2. C# might have not existed without a famous court case. Microsoft originally hired Anders to architect its Java tools (Visual J++), but the Sun versus Microsoft lawsuit (1997-2001) meant Microsoft could not build on top of Java, as the company that owned Java’s IP (Sun) sued MS for alleged unauthorized changes to the Java language. Microsoft realized it had to build a new language that combined VB’s productivity with C++’s power. This led to C# and .NET. 3. TypeScript exists because Anders refused to build Script# for the Outlook .com team. Microsoft’s Outlook .com team asked Anders’ C# team to productize “ScriptSharp,” a language to cross-compile C# to JavaScript. Anders and the C# team pushed back, suggesting that a better approach was to fix JavaScript. Anders felt strongly that to be attractive to the best-of-breed developers in the JavaScript ecosystem, you want people to write JavaScript, and not another language like C#. 4. Designing a programming language is a 10-year play. As Anders puts it: “Version one is great, but has all sorts of issues. You’ve got to do version two, but it’s not until version three that it really starts to be great. Then you’ve got to convince people to adopt it.”
1:16:01

Anders Hejlsberg (Anders Hejlsberg) is a living legend: he created Turbo Pascal, Delphi, C# and TypeScript (and today TypeScript is the most-used programming language, globally, as per GitHub.) Timestamps: 00:00 Intro 02:48 How Anders got into programming 05:40 Building his first compiler 07:44 Turbo Pascal 12:25 Delphi 14:53 Joining Microsoft 19:41 Building C# 29:11 Async/await 34:01 The rise of JavaScript 37:52 Building TypeScript 42:58 How the TypeScript compiler works 48:30 JavaScript’s strengths and weaknesses 52:18 How Anders uses AI 56:03 What language features work well with AI 1:02:49 How software craftsmanship is changing 1:07:49 Performance and efficiency 1:09:29 Anders’ tool stack 1:11:30 A 30-year career at Microsoft 1:13:40 Book recommendation 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. turbopuffer – a vector and full-text search engine built on object storage. It’s fast, cheap, and extremely scalable. Four things that stood out to me: 1. “10x better for 1/10th of the price” is a proven winner. This is what Turbo Pascal did: it sold for $49.95 when competing compilers cost $500, and it was faster and more interactive than competitors’ products. Conveniently, the low price tag also killed off piracy 2. C# might have not existed without a famous court case. Microsoft originally hired Anders to architect its Java tools (Visual J++), but the Sun versus Microsoft lawsuit (1997-2001) meant Microsoft could not build on top of Java, as the company that owned Java’s IP (Sun) sued MS for alleged unauthorized changes to the Java language. Microsoft realized it had to build a new language that combined VB’s productivity with C++’s power. This led to C# and .NET. 3. TypeScript exists because Anders refused to build Script# for the Outlook .com team. Microsoft’s Outlook .com team asked Anders’ C# team to productize “ScriptSharp,” a language to cross-compile C# to JavaScript. Anders and the C# team pushed back, suggesting that a better approach was to fix JavaScript. Anders felt strongly that to be attractive to the best-of-breed developers in the JavaScript ecosystem, you want people to write JavaScript, and not another language like C#. 4. Designing a programming language is a 10-year play. As Anders puts it: “Version one is great, but has all sorts of issues. You’ve got to do version two, but it’s not until version three that it really starts to be great. Then you’ve got to convince people to adopt it.”

Gergely Orosz

129,652 views • 4 months ago

This is a great Fermented Hot Sauce! It is loaded with gut-healthy probiotics, tangy flavor, and you control the heat level by choosing your peppers!! 🌶️ FERMENTED HOT SAUCE RECIPE: (Quart Jar Batch) Ingredients: •10–12 hot peppers (jalapeño, serrano, cayenne, or a mix) •1 medium tomato, chopped •½ medium onion, chopped •4–5 garlic cloves •2 cups (475 g) filtered water •2 ½ tsp (14 g) fine sea salt (~3% brine) Instructions: 1. Add peppers, tomatoes, onion, and garlic to a clean quart jar. 2. Dissolve the salt in water to make your brine. Pour brine over veggies until everything is fully submerged, removing air bubbles. 3. Place a Pickle Pebble (a glass weight used for home fermentation) on top to weigh down the veggies. Top the jar with a Pickle Pipe (a one-piece, waterless airlock that functions as a fermenting lid for mason jars) and secure with a rust-proof Tough Band (a brand of reusable, rust-proof mason jar bands made of durable plastic, designed to create a leak-proof seal for fermenting and food storage). 4. Let ferment at room temp (65–75°F) for 7-14 days. The longer it ferments, the tangier and more complex it becomes. 5. Once fermented to taste, strain the veggies and blend until smooth. Add a splash of brine if needed for consistency. Store in the fridge.
0:29

This is a great Fermented Hot Sauce! It is loaded with gut-healthy probiotics, tangy flavor, and you control the heat level by choosing your peppers!! 🌶️ FERMENTED HOT SAUCE RECIPE: (Quart Jar Batch) Ingredients: •10–12 hot peppers (jalapeño, serrano, cayenne, or a mix) •1 medium tomato, chopped •½ medium onion, chopped •4–5 garlic cloves •2 cups (475 g) filtered water •2 ½ tsp (14 g) fine sea salt (~3% brine) Instructions: 1. Add peppers, tomatoes, onion, and garlic to a clean quart jar. 2. Dissolve the salt in water to make your brine. Pour brine over veggies until everything is fully submerged, removing air bubbles. 3. Place a Pickle Pebble (a glass weight used for home fermentation) on top to weigh down the veggies. Top the jar with a Pickle Pipe (a one-piece, waterless airlock that functions as a fermenting lid for mason jars) and secure with a rust-proof Tough Band (a brand of reusable, rust-proof mason jar bands made of durable plastic, designed to create a leak-proof seal for fermenting and food storage). 4. Let ferment at room temp (65–75°F) for 7-14 days. The longer it ferments, the tangier and more complex it becomes. 5. Once fermented to taste, strain the veggies and blend until smooth. Add a splash of brine if needed for consistency. Store in the fridge.

Conspiracy Gardener Homestead

30,810 views • 1 year ago

There's so much focus on "how can AI do my work for me?" I think the more important question is "what work can I now do with AI that I would have never attempted before?" Earlier this year I wrote freestiler, a vector tiling engine for R and Python, with the help of Claude and Codex. I knew what the ideal engine looked like and how it would work at a high level. I didn't know how to put it together, and I don't know Rust, the language I wanted under the hood. Previously I would never have attempted this project as the ROI wasn't there. It would have taken me a year or more to learn the internals of a vector tiling engine and enough Rust to implement one. With Opus-level models, I could take it on. freestiler now powers all my vector tiling pipelines, including the map below rendering 143 million jobs from LODES, and it has 114 GitHub stars. Building this way has required a different set of skills. I don't review the code line by line. I set up adversarial agents to do that and write the test suites. What I review is the architecture, the behavior, and the results. Agent teams surface findings and explain their reasoning; I evaluate and critique. My job isn't to stress over code formatting, but instead to focus on questions like whether the engine is designed right, whether the output is correct, and if the UX makes sense. This means that I haven't "replaced my work." I've taken on entirely new work, with the help of agents, that I would have never done otherwise. It has taken some getting used to shipping code I haven't personally typed. In the old way of working, I built understanding through writing that code. Now I build understanding through managing the project - writing a spec, reviewing structure, evaluating UX. And that's helped me think a whole lot bigger in terms of what I can now do.
1:00

There's so much focus on "how can AI do my work for me?" I think the more important question is "what work can I now do with AI that I would have never attempted before?" Earlier this year I wrote freestiler, a vector tiling engine for R and Python, with the help of Claude and Codex. I knew what the ideal engine looked like and how it would work at a high level. I didn't know how to put it together, and I don't know Rust, the language I wanted under the hood. Previously I would never have attempted this project as the ROI wasn't there. It would have taken me a year or more to learn the internals of a vector tiling engine and enough Rust to implement one. With Opus-level models, I could take it on. freestiler now powers all my vector tiling pipelines, including the map below rendering 143 million jobs from LODES, and it has 114 GitHub stars. Building this way has required a different set of skills. I don't review the code line by line. I set up adversarial agents to do that and write the test suites. What I review is the architecture, the behavior, and the results. Agent teams surface findings and explain their reasoning; I evaluate and critique. My job isn't to stress over code formatting, but instead to focus on questions like whether the engine is designed right, whether the output is correct, and if the UX makes sense. This means that I haven't "replaced my work." I've taken on entirely new work, with the help of agents, that I would have never done otherwise. It has taken some getting used to shipping code I haven't personally typed. In the old way of working, I built understanding through writing that code. Now I build understanding through managing the project - writing a spec, reviewing structure, evaluating UX. And that's helped me think a whole lot bigger in terms of what I can now do.

Kyle Walker

13,867 views • 2 months ago

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? Hillel Wayne is one of the best people to answer. Timestamps: 00:00 Intro 04:32 The Crossover Project 11:37 What software engineering does better 15:30 What traditional engineering does better 18:17 Formal methods 29:32 TLA+: what it is and demo 36:58 TLA+ at Amazon 38:10 Ways distributed systems break 41:03 Formal methods and systems thinking 46:20 The value of learning math 50:23 What TLA+ is good for and isn’t 52:50 Alloy: a declarative language for software modeling 58:53 Other formal methods tools 1:01:24 Property-based testing 1:05:31 AI and the need for formal verification 1:12:29 Logic for programmers 1:14:35 Hillel’s 2025 prediction on AI’s impact 1:21:30 Book recommendation 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. Two things I found especially interesting, talking with Hillel: 1. Amazon used TLA+ to find a bug almost impossible to locate without formal methods. In the paper How AWS uses formal methods, the AWS team shared that they’d found a complicated bug for which the shortest error trace to exhibit was 35 steps (!!). The bug passed unnoticed through extensive design review, code reviews, and testing. AWS concluded they wouldn’t have uncovered it if they’d stuck to conventional testing approaches. 2. Why not use formal verification for everything, then? It’s because specs in the real world are a nightmare to write. Even a simple problem like “find the file in a directory that has the most lines” gets complicated when modeled with formal methods. We would have to answer questions like: ‘do we look at ASCII or UTF-8 new line characters, what about unreadable files, and Symlinks?’ Without formal methods, we can write a simple verification that is right in 99%+ of cases. Formal methods require a lot of extra effort for the less than 1% of exotic use cases!
1:24:52

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? Hillel Wayne is one of the best people to answer. Timestamps: 00:00 Intro 04:32 The Crossover Project 11:37 What software engineering does better 15:30 What traditional engineering does better 18:17 Formal methods 29:32 TLA+: what it is and demo 36:58 TLA+ at Amazon 38:10 Ways distributed systems break 41:03 Formal methods and systems thinking 46:20 The value of learning math 50:23 What TLA+ is good for and isn’t 52:50 Alloy: a declarative language for software modeling 58:53 Other formal methods tools 1:01:24 Property-based testing 1:05:31 AI and the need for formal verification 1:12:29 Logic for programmers 1:14:35 Hillel’s 2025 prediction on AI’s impact 1:21:30 Book recommendation 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. Two things I found especially interesting, talking with Hillel: 1. Amazon used TLA+ to find a bug almost impossible to locate without formal methods. In the paper How AWS uses formal methods, the AWS team shared that they’d found a complicated bug for which the shortest error trace to exhibit was 35 steps (!!). The bug passed unnoticed through extensive design review, code reviews, and testing. AWS concluded they wouldn’t have uncovered it if they’d stuck to conventional testing approaches. 2. Why not use formal verification for everything, then? It’s because specs in the real world are a nightmare to write. Even a simple problem like “find the file in a directory that has the most lines” gets complicated when modeled with formal methods. We would have to answer questions like: ‘do we look at ASCII or UTF-8 new line characters, what about unreadable files, and Symlinks?’ Without formal methods, we can write a simple verification that is right in 99%+ of cases. Formal methods require a lot of extra effort for the less than 1% of exotic use cases!

Gergely Orosz

34,759 views • 1 month ago

What can us, engineers else learn from designers and design engineers? Turns out there’s lots, based on talking with one of the best design engineers in the industry, Maggie Appleton. 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 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 – every agent prompt, tool call, stored in your repo, and mirrored. Set up with one click: Hope you enjoy this unusually visual episode - thanks again, Maggie!
1:28:31

What can us, engineers else learn from designers and design engineers? Turns out there’s lots, based on talking with one of the best design engineers in the industry, Maggie Appleton. 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 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 – every agent prompt, tool call, stored in your repo, and mirrored. Set up with one click: Hope you enjoy this unusually visual episode - thanks again, Maggie!

Gergely Orosz

64,127 views • 2 days ago

Why is nobody talking about Polymarket's official API? You open Polymarket Wait for it to load... Click on a market... Wait again... Try to check another one... More waiting... By the time you see the price, it's already stale The opportunity moved while you were clicking through pages Someone else got there first There's a better way // Found the gem hiding in plain sight - library py-clob-client Official from Polymarket. MIT license I spent a week working with multiple markets Checking prices across different positions Monitoring various categories Tracking market movements Then discovered this library Game changer for workflow efficiency // Real example from last week: UI approach: Checking markets one by one through the interface API approach: Monitoring all relevant markets simultaneously with live updates Having comprehensive real-time data makes a meaningful difference Speed and information clarity are valuable advantages // Want to go deeper? Build an arbitrage bot You'll need Rust for execution speed Deploy the server geographically close to Polymarket's infrastructure Every millisecond counts when opportunities last 1-2 seconds. I built mine in Go for monitoring and analysis. Works great for that. But if you're hunting arbitrage at scale against other bots, Rust + low latency setup is the only way. The infrastructure race is real // "But I can't code" With today's AI tools you actually can Cursor, ChatGPT, Claude, Gemini, Kimi, Grok - they write code for you You just describe what you want AI generates it You copy-paste and run Join Polymarket to create: Programming is no longer a barrier Anyone can build now
0:23

Why is nobody talking about Polymarket's official API? You open Polymarket Wait for it to load... Click on a market... Wait again... Try to check another one... More waiting... By the time you see the price, it's already stale The opportunity moved while you were clicking through pages Someone else got there first There's a better way // Found the gem hiding in plain sight - library py-clob-client Official from Polymarket. MIT license I spent a week working with multiple markets Checking prices across different positions Monitoring various categories Tracking market movements Then discovered this library Game changer for workflow efficiency // Real example from last week: UI approach: Checking markets one by one through the interface API approach: Monitoring all relevant markets simultaneously with live updates Having comprehensive real-time data makes a meaningful difference Speed and information clarity are valuable advantages // Want to go deeper? Build an arbitrage bot You'll need Rust for execution speed Deploy the server geographically close to Polymarket's infrastructure Every millisecond counts when opportunities last 1-2 seconds. I built mine in Go for monitoring and analysis. Works great for that. But if you're hunting arbitrage at scale against other bots, Rust + low latency setup is the only way. The infrastructure race is real // "But I can't code" With today's AI tools you actually can Cursor, ChatGPT, Claude, Gemini, Kimi, Grok - they write code for you You just describe what you want AI generates it You copy-paste and run Join Polymarket to create: Programming is no longer a barrier Anyone can build now

BuBBliK

44,204 views • 8 months ago

If you’ve ever opened Chrome DevTools, or optimized a page for Core Web Vitals, you’ve used software built by Addy Osmani. 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 Brought to you by: • Antithesis – verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages. Teams like Jane Street, and the etcd community use Antithesis to ship better code, faster. • Sentry – application monitoring software considered “not bad” by millions of developers. • Google Cloud Run – run untrusted agent code without the security anxiety. Cloud Run sandboxes deliver hyper-isolated, ephemeral execution environments that spin up in milliseconds. Check them out: Here's Addy's advice on where he believes engineers should invest efforts, in the coming years, in his words: “What we are very likely to see happen next with engineering careers (as well as product and other roles) is the unbundling of them, so that an engineer also has product sense, while a product person also has engineering sense, or UX sense. You should think about the non-engineering things if you don’t [usually] have the time to think about product or technical evangelism, or go-to-market approaches, or any other parts of how businesses are successful. If you can show employers that you are not just a builder, but someone that can help them as roles start to become a little bit fuzzier, then I think that you can be successful in these times. Don’t be just an engineer.”
1:32:29

If you’ve ever opened Chrome DevTools, or optimized a page for Core Web Vitals, you’ve used software built by Addy Osmani. 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 Brought to you by: • Antithesis – verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages. Teams like Jane Street, and the etcd community use Antithesis to ship better code, faster. • Sentry – application monitoring software considered “not bad” by millions of developers. • Google Cloud Run – run untrusted agent code without the security anxiety. Cloud Run sandboxes deliver hyper-isolated, ephemeral execution environments that spin up in milliseconds. Check them out: Here's Addy's advice on where he believes engineers should invest efforts, in the coming years, in his words: “What we are very likely to see happen next with engineering careers (as well as product and other roles) is the unbundling of them, so that an engineer also has product sense, while a product person also has engineering sense, or UX sense. You should think about the non-engineering things if you don’t [usually] have the time to think about product or technical evangelism, or go-to-market approaches, or any other parts of how businesses are successful. If you can show employers that you are not just a builder, but someone that can help them as roles start to become a little bit fuzzier, then I think that you can be successful in these times. Don’t be just an engineer.”

Gergely Orosz

398,385 views • 1 month ago

Here's my conversation all about FFmpeg, the legendary open-source software powering most video on the Internet. In the episode, I talk with Jean-Baptiste Kempf and Kieran Kunhya. JB is lead developer of VLC and Kieran is FFmpeg contributor, codec engineer, and the person behind the now-infamous FFmpeg account on X. VLC (VideoLAN), by the way, is also a legendary piece of open-source software: it's a video player that can open basically anything & has been downloaded over 6 billion times. I think both FFmpeg and VLC are two of the most important and impactful software systems ever created, both open source, and both created & maintained by volunteers: brilliant engineers from all walks of life. Thank you to everyone who contributed to FFmpeg and VLC, and in general to all engineers giving their heart & soul to building systems used by millions (or billions) of people, and often doing so not for money, status, or fame, but purely for the love of building great software and doing good for the world. Thank you to the builders! 🙏❤️ Shoutouts in this chat to John Carmack Andrej Karpathy Elon Musk Tim Sweeney and everyone who is a contributor & fan of open source! It's here on X in full and is up everywhere else (see comment). Timestamps: 0:00 - Episode highlight 2:17 - Introduction 5:35 - Weirdest things VLC opens 9:59 - How video playback works 19:20 - Video codecs and containers 30:07 - FFmpeg explained 51:07 - Linus Torvalds 55:46 - Turning down millions to keep VLC ad-free 1:10:04 - FFmpeg & Google drama 1:29:18 - FFmpeg developers 1:35:55 - VLC and FFmpeg 1:40:29 - History of FFmpeg 1:43:46 - Reverse engineering codecs 1:57:01 - FFmpeg testing 2:01:08 - Assembly code (handwritten) 2:25:26 - Rust programming language 2:34:42 - FFmpeg and Libav fork 2:43:04 - Open source burnout 2:50:51 - x264 and internet video 3:04:07 - Video compression basics 3:11:04 - CIA and fake VLC 3:21:39 - Ultra low latency streaming 3:39:07 - AV2 codec and video patents 3:48:59 - VLC backdoors 3:59:14 - Video archiving 4:05:51 - Future of FFmpeg and VLC
4:18:21

Here's my conversation all about FFmpeg, the legendary open-source software powering most video on the Internet. In the episode, I talk with Jean-Baptiste Kempf and Kieran Kunhya. JB is lead developer of VLC and Kieran is FFmpeg contributor, codec engineer, and the person behind the now-infamous FFmpeg account on X. VLC (VideoLAN), by the way, is also a legendary piece of open-source software: it's a video player that can open basically anything & has been downloaded over 6 billion times. I think both FFmpeg and VLC are two of the most important and impactful software systems ever created, both open source, and both created & maintained by volunteers: brilliant engineers from all walks of life. Thank you to everyone who contributed to FFmpeg and VLC, and in general to all engineers giving their heart & soul to building systems used by millions (or billions) of people, and often doing so not for money, status, or fame, but purely for the love of building great software and doing good for the world. Thank you to the builders! 🙏❤️ Shoutouts in this chat to John Carmack Andrej Karpathy Elon Musk Tim Sweeney and everyone who is a contributor & fan of open source! It's here on X in full and is up everywhere else (see comment). Timestamps: 0:00 - Episode highlight 2:17 - Introduction 5:35 - Weirdest things VLC opens 9:59 - How video playback works 19:20 - Video codecs and containers 30:07 - FFmpeg explained 51:07 - Linus Torvalds 55:46 - Turning down millions to keep VLC ad-free 1:10:04 - FFmpeg & Google drama 1:29:18 - FFmpeg developers 1:35:55 - VLC and FFmpeg 1:40:29 - History of FFmpeg 1:43:46 - Reverse engineering codecs 1:57:01 - FFmpeg testing 2:01:08 - Assembly code (handwritten) 2:25:26 - Rust programming language 2:34:42 - FFmpeg and Libav fork 2:43:04 - Open source burnout 2:50:51 - x264 and internet video 3:04:07 - Video compression basics 3:11:04 - CIA and fake VLC 3:21:39 - Ultra low latency streaming 3:39:07 - AV2 codec and video patents 3:48:59 - VLC backdoors 3:59:14 - Video archiving 4:05:51 - Future of FFmpeg and VLC

Lex Fridman

519,250 views • 4 months ago

Few people care more about software performance than Casey Muratori. Give him a few minutes of your attention with this episode, and he'll convince you to learn to read Assembly (no, really, I finally started to read it, it's really not that scary, esp with an AI that can help explain the sequences). 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 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 Three interesting things we talked about: 1. Is performance starting to matter to businesses? Enterprise software buyers care mainly about cost, compliance, and capabilities – but not performance. Even so, there are some products gaining major popularity and market share due to their performance, such as File Pilot (next-gen file explorer) and the Blick video editor. Is the tide turning? 2. Profiler-driven performance optimization is the wrong way to optimize The standard way of optimizing is to profile the application, tweak hotspots, then check if the stats have improved. But this only finds a local minimum; Casey says every engineer he’s worked with who was a great “optimizer” began by establishing what the hardware could theoretically do, and then did not stop until they’d closed the gap to that performance level. 3. Take a grain of salt with conventional wisdom that premature optimization is the “root of all evil” Many devs use it as an excuse to delay performance optimization, but Casey says that not optimizing in time could mean that only performance hotspots can be fixed later, and not the architectural issues that create poor performance. Architect your system to be performant, or you’ll have trouble solving problems without a rewrite!
1:53:58

Few people care more about software performance than Casey Muratori. Give him a few minutes of your attention with this episode, and he'll convince you to learn to read Assembly (no, really, I finally started to read it, it's really not that scary, esp with an AI that can help explain the sequences). 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 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 Three interesting things we talked about: 1. Is performance starting to matter to businesses? Enterprise software buyers care mainly about cost, compliance, and capabilities – but not performance. Even so, there are some products gaining major popularity and market share due to their performance, such as File Pilot (next-gen file explorer) and the Blick video editor. Is the tide turning? 2. Profiler-driven performance optimization is the wrong way to optimize The standard way of optimizing is to profile the application, tweak hotspots, then check if the stats have improved. But this only finds a local minimum; Casey says every engineer he’s worked with who was a great “optimizer” began by establishing what the hardware could theoretically do, and then did not stop until they’d closed the gap to that performance level. 3. Take a grain of salt with conventional wisdom that premature optimization is the “root of all evil” Many devs use it as an excuse to delay performance optimization, but Casey says that not optimizing in time could mean that only performance hotspots can be fixed later, and not the architectural issues that create poor performance. Architect your system to be performant, or you’ll have trouble solving problems without a rewrite!

Gergely Orosz

97,977 views • 1 month ago