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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...

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Gergely Orosz

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TypeScript was inspired by a strange ask from the outlook team. Anders Hejlsberg(Anders Hejlsberg) - creator of C#, TypeScript, Turbo Pascal & Delphi - tells us how TypeScript was born: “The world started building larger and larger applications in JavaScript. We saw that externally, but also internally. One of the trigger events was when the outlook team came to see the C# team and asked us whether we would pretty please productize this thing called Script#. And we go, well, what is Script#? It's this cross compiler that allows you to cross compile C# into JavaScript such that you can basically treat JavaScript as an instruction language and run your C# apps in a browser. Why would anyone want to do that? Well, because then you can get a grownup programming language with grownup tooling. You can use Visual Studio, you can have projects. You can do all of these wonderful things that you can't do with JavaScript because JavaScript is just the scripting language with shitty tooling. And we thought, well, perhaps a better approach would be to fix JavaScript. I mean, surely you're not going to be best of breed in the JavaScript ecosystem by telling people to write in a different programming language”
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TypeScript was inspired by a strange ask from the outlook team. Anders Hejlsberg(Anders Hejlsberg) - creator of C#, TypeScript, Turbo Pascal & Delphi - tells us how TypeScript was born: “The world started building larger and larger applications in JavaScript. We saw that externally, but also internally. One of the trigger events was when the outlook team came to see the C# team and asked us whether we would pretty please productize this thing called Script#. And we go, well, what is Script#? It's this cross compiler that allows you to cross compile C# into JavaScript such that you can basically treat JavaScript as an instruction language and run your C# apps in a browser. Why would anyone want to do that? Well, because then you can get a grownup programming language with grownup tooling. You can use Visual Studio, you can have projects. You can do all of these wonderful things that you can't do with JavaScript because JavaScript is just the scripting language with shitty tooling. And we thought, well, perhaps a better approach would be to fix JavaScript. I mean, surely you're not going to be best of breed in the JavaScript ecosystem by telling people to write in a different programming language”

The Pragmatic Engineer

116,193 Aufrufe • vor 3 Monaten

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
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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

59,982 Aufrufe • vor 8 Tagen

Creator of C#, Anders Hejlsberg(Anders Hejlsberg), on the design goals that shaped the language and why standardisation was a goal from the beginning: “The overarching thing was this power and productivity of C++ with the ease of use of Visual Basic. We knew we wanted to build an object oriented language. We wanted managed code or byte code so we could target different runtime environments. We wanted garbage collection and exception handling, but also things like a unified object system where, and that's true in C#, anything can be assigned to an object. If it's a value type, we box it. And it's a self-describing object. So, reflection: you can ask an object, ‘what are you’? And you can get all of the facts about it at runtime, and you can dynamically manipulate it in ways that just don't exist in a lot of other environments. We wanted a language that made this new model of properties, methods and events first class because that was how components were built as opposed to just sort of functions and procedures and even objects. And we wanted to create a language that was standardised. We wanted to give this language to a standardisation committee and try to level the playing field there. And all of those things were sort of like what was rolled up in C#”
1:43

Creator of C#, Anders Hejlsberg(Anders Hejlsberg), on the design goals that shaped the language and why standardisation was a goal from the beginning: “The overarching thing was this power and productivity of C++ with the ease of use of Visual Basic. We knew we wanted to build an object oriented language. We wanted managed code or byte code so we could target different runtime environments. We wanted garbage collection and exception handling, but also things like a unified object system where, and that's true in C#, anything can be assigned to an object. If it's a value type, we box it. And it's a self-describing object. So, reflection: you can ask an object, ‘what are you’? And you can get all of the facts about it at runtime, and you can dynamically manipulate it in ways that just don't exist in a lot of other environments. We wanted a language that made this new model of properties, methods and events first class because that was how components were built as opposed to just sort of functions and procedures and even objects. And we wanted to create a language that was standardised. We wanted to give this language to a standardisation committee and try to level the playing field there. And all of those things were sort of like what was rolled up in C#”

The Pragmatic Engineer

18,332 Aufrufe • vor 3 Monaten

Why did so many languages copy async/await from C#? Anders Hejlsberg(Anders Hejlsberg) - creator of TypeScript, C# & Turbo Pascal - on what they got right with the design: #1 - async/await was designed to solve a common problem in the event-loop model: "A lot of languages are built around cooperative multitasking in the sense that they have an event loop that sits and dispatches events. Then you handle the event and then you yield back to the event handler loop. And it all runs in a single thread cooperatively. The problem with that is if you then want to do some long running work: how do I stop in the middle of this piece of long running work and yield back to the event loop cooperatively? And then when my result is ready, I can come back and continue executing here." #2 - state machines are the solution, but hard to build: "Well, in order to do that in an inverted architecture like that, you have to build a state machine. State machines are notoriously hard for people to implement because you've got to move all of your state off of the stack into objects. And then you have this big case statement that envelopes your entire logic. It's a nightmare to figure out. But, the transformation from serially executing code into a state machine, its continuation-passing-style translation is actually one that you can do in a machine-based fashion." #3 - compilers are good at writing state machines: "You can have the compiler write the state machine if you introduce syntax that allows you to indicate where you want to yield. And that's what await is. Await is basically saying, I want to yield here, and I want to yield this promise, and then when the promise completes, I want you to come back here and continue executing. Then the compiler writes a state machine around it and it actually turns it into this big switch statement and moves all of the state that survives across the await into something that's heap allocated. So it can be brought back. And doing all of that work is something that compilers are great at. And so that was sort of the idea that we have this new style of programming where we're using promises or the equivalent of promises and the ability to yield and then we have callbacks. But trying to write your program in that style, that's also what JavaScript suffered from a lot. It's like all this callback style stuff. With Async and Await, you get the illusion that you're just writing normal sequential code and then the compiler does the painful transformation for you. That turns out to be really useful."
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Why did so many languages copy async/await from C#? Anders Hejlsberg(Anders Hejlsberg) - creator of TypeScript, C# & Turbo Pascal - on what they got right with the design: #1 - async/await was designed to solve a common problem in the event-loop model: "A lot of languages are built around cooperative multitasking in the sense that they have an event loop that sits and dispatches events. Then you handle the event and then you yield back to the event handler loop. And it all runs in a single thread cooperatively. The problem with that is if you then want to do some long running work: how do I stop in the middle of this piece of long running work and yield back to the event loop cooperatively? And then when my result is ready, I can come back and continue executing here." #2 - state machines are the solution, but hard to build: "Well, in order to do that in an inverted architecture like that, you have to build a state machine. State machines are notoriously hard for people to implement because you've got to move all of your state off of the stack into objects. And then you have this big case statement that envelopes your entire logic. It's a nightmare to figure out. But, the transformation from serially executing code into a state machine, its continuation-passing-style translation is actually one that you can do in a machine-based fashion." #3 - compilers are good at writing state machines: "You can have the compiler write the state machine if you introduce syntax that allows you to indicate where you want to yield. And that's what await is. Await is basically saying, I want to yield here, and I want to yield this promise, and then when the promise completes, I want you to come back here and continue executing. Then the compiler writes a state machine around it and it actually turns it into this big switch statement and moves all of the state that survives across the await into something that's heap allocated. So it can be brought back. And doing all of that work is something that compilers are great at. And so that was sort of the idea that we have this new style of programming where we're using promises or the equivalent of promises and the ability to yield and then we have callbacks. But trying to write your program in that style, that's also what JavaScript suffered from a lot. It's like all this callback style stuff. With Async and Await, you get the illusion that you're just writing normal sequential code and then the compiler does the painful transformation for you. That turns out to be really useful."

The Pragmatic Engineer

13,258 Aufrufe • vor 2 Monaten

Bjarne Stroustrup is the creator of C++ and a former researcher at Bell Labs at its peak. I interviewed him about: • What made Bell Labs different • Programming language design: types, memory safety, bootstrapping • When abstraction improves performance • Anecdotes from building C++ • Thoughts on AI writing C++ • Mistakes he'd change while building C++ Where to watch: • YouTube: • Spotify: • Apple Podcasts: • Transcript: Thank you to this episode's sponsors for supporting my work: • Cursor 3: a unified workspace for building software with agents, check it out at • 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 Timestamps: 0:00 - Intro 0:50 - The origin of C++ 8:46 - What Bell Labs was like 17:24 - Dennis Ritchie 24:00 - When to build a programming language 31:59 - Bootstrapping a language 33:58 - C++ is not object-oriented 37:32 - Discussing type systems 46:20 - Memory safety 49:26 - Standards committee anecdotes 1:09:40 - Adding automatic garbage collection to C++ 1:18:25 - Template instantiation is Turing complete 1:21:57 - Abstraction and performance 1:28:51 - AI writing code 1:35:54 - His motivation 1:39:18 - Famous quotes 1:46:48 - Reflecting on building C++ 1:49:12 - Top C++ book recommendation 1:50:59 - Advice for his younger self 1:58:06 - Outro
1:59:03

Bjarne Stroustrup is the creator of C++ and a former researcher at Bell Labs at its peak. I interviewed him about: • What made Bell Labs different • Programming language design: types, memory safety, bootstrapping • When abstraction improves performance • Anecdotes from building C++ • Thoughts on AI writing C++ • Mistakes he'd change while building C++ Where to watch: • YouTube: • Spotify: • Apple Podcasts: • Transcript: Thank you to this episode's sponsors for supporting my work: • Cursor 3: a unified workspace for building software with agents, check it out at • 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 Timestamps: 0:00 - Intro 0:50 - The origin of C++ 8:46 - What Bell Labs was like 17:24 - Dennis Ritchie 24:00 - When to build a programming language 31:59 - Bootstrapping a language 33:58 - C++ is not object-oriented 37:32 - Discussing type systems 46:20 - Memory safety 49:26 - Standards committee anecdotes 1:09:40 - Adding automatic garbage collection to C++ 1:18:25 - Template instantiation is Turing complete 1:21:57 - Abstraction and performance 1:28:51 - AI writing code 1:35:54 - His motivation 1:39:18 - Famous quotes 1:46:48 - Reflecting on building C++ 1:49:12 - Top C++ book recommendation 1:50:59 - Advice for his younger self 1:58:06 - Outro

Ryan Peterman

322,418 Aufrufe • vor 3 Monaten

AI is changing the software engineering craft. Anders Hejlsberg (Anders Hejlsberg) - creator of C#, TypeScript and industry legend - on why code review needs to get more enjoyable in response: #1 - AI is shifting the craft from writing code, to reviewing code: "In a sense, we're all turning into project managers. We can have an army of junior programmers, called agents, that will just spit out reams of code but someone's got to have the big picture and review all of that. And so, increasingly, our craft is going from one of writing the code, to one of reviewing the code and building the architecture of the code and overseeing the work. It's a different kind of craft. It's a different kind of enjoyment. I've always liked writing the code. To me that was the fulfilling part, seeing it work. In a way, AI robs a little bit of that, because I am less interested in reviewing code." #2 - The code review experience should be improved: "I think we could also make the process of reviewing code much more interesting than it is today. I mean, today, you see a list of diffs in alphabetical order and now it's up to you to make heads or tails of it. There are more pedagogical ways of presenting that. And you could have commentary generated by the AI that tells you what the changes are and whatever, and then tries to guide you along. So that symbiotic relationship, I think we need to work on that more and to keep the enjoyment in there."
2:04

AI is changing the software engineering craft. Anders Hejlsberg (Anders Hejlsberg) - creator of C#, TypeScript and industry legend - on why code review needs to get more enjoyable in response: #1 - AI is shifting the craft from writing code, to reviewing code: "In a sense, we're all turning into project managers. We can have an army of junior programmers, called agents, that will just spit out reams of code but someone's got to have the big picture and review all of that. And so, increasingly, our craft is going from one of writing the code, to one of reviewing the code and building the architecture of the code and overseeing the work. It's a different kind of craft. It's a different kind of enjoyment. I've always liked writing the code. To me that was the fulfilling part, seeing it work. In a way, AI robs a little bit of that, because I am less interested in reviewing code." #2 - The code review experience should be improved: "I think we could also make the process of reviewing code much more interesting than it is today. I mean, today, you see a list of diffs in alphabetical order and now it's up to you to make heads or tails of it. There are more pedagogical ways of presenting that. And you could have commentary generated by the AI that tells you what the changes are and whatever, and then tries to guide you along. So that symbiotic relationship, I think we need to work on that more and to keep the enjoyment in there."

The Pragmatic Engineer

39,073 Aufrufe • vor 3 Monaten

Choose a model (any model) and build your application with it. Do not spend time swapping models early on. Do not try to optimize before you have a working system. This is one of the first recommendations I make to every new team I consult with. Eventually, it will be time to optimize the model. • You may need a cheaper model • You may need a faster model • You might need a smarter model Good luck if you stitched together 12 different APIs and SDKs from 7 different vendors. Over half of the companies I consult for run on Microsoft software and have access to Microsoft Foundry. Microsoft Foundry is a complete agentic ecosystem. If you're in that world and building AI applications, Microsoft Foundry is where everything lives: • Models (largest selection in the market) • Agentic SDK (Python, C#, JavaScript/TypeScript) • Tools • Evaluations • Monitoring They are fully integrated with GitHub and Visual Studio Code. The best part: Their agentic platform is fully agnostic of the models you use. You can integrate with any model using the same OpenAI-style API. Swapping one model for another takes 1 second.
3:49

Choose a model (any model) and build your application with it. Do not spend time swapping models early on. Do not try to optimize before you have a working system. This is one of the first recommendations I make to every new team I consult with. Eventually, it will be time to optimize the model. • You may need a cheaper model • You may need a faster model • You might need a smarter model Good luck if you stitched together 12 different APIs and SDKs from 7 different vendors. Over half of the companies I consult for run on Microsoft software and have access to Microsoft Foundry. Microsoft Foundry is a complete agentic ecosystem. If you're in that world and building AI applications, Microsoft Foundry is where everything lives: • Models (largest selection in the market) • Agentic SDK (Python, C#, JavaScript/TypeScript) • Tools • Evaluations • Monitoring They are fully integrated with GitHub and Visual Studio Code. The best part: Their agentic platform is fully agnostic of the models you use. You can integrate with any model using the same OpenAI-style API. Swapping one model for another takes 1 second.

Santiago

12,014 Aufrufe • vor 6 Monaten

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 12:48 Rust for TypeScript engineers 13:51 Moving from C++ to Rust 14:34 Memory safety 18:12 Garbage collection tradeoffs 21:46 Ownership, references, and borrowing 26:59 Unsafe in Rust 31:21 Crates and Cargo 35:55 Language design and RFCs 43:02 Building new features 46:30 Editions vs. versions 49:47 Getting paid to work on Rust 51:27 Contributing to Rust 53:03 Rust in the Linux kernel 55:45 AI use cases for Rust 1:01:35 Learning Rust 1:03:54 Book recommendation 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 Three things worth knowing about Rust: 1. Rust was designed to turn implicit failures into compile errors. Where other languages allow you to forget something, Rust makes an omission into a compilation error for things like null checks, uninitialized variables, or error propagation with the ‘?’ character. If you mess something up, it’s almost certain your program will not compile. If it does, at the very least you should see a lint warning. 2. Refactoring in Rust is safe and easy, thanks to the compiler. Alice: “I change a return type or struct field, then just fix the compiler errors until the compiler stops shouting. And then once I’ve done that, I’ve updated every place I need to update.” Rust’s focus on correctness makes refactoring it more straightforward than dynamically-typed languages and Java-style typed ones are to refactor. 3. “Editions” allow Rust to make breaking changes without ‘breaking’ anyone’s code. Rust editions (2015, 2018, 2021, 2024) can be mixed freely across crates. A library on the 2021 edition works seamlessly with a binary on the 2024 edition. This is how Rust evolves syntax (like adding async/await as keywords) without forcing an ecosystem-wide migration. Thanks a lot, Alice for this great discussion! And for your work on Rust.
1:05:52

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 12:48 Rust for TypeScript engineers 13:51 Moving from C++ to Rust 14:34 Memory safety 18:12 Garbage collection tradeoffs 21:46 Ownership, references, and borrowing 26:59 Unsafe in Rust 31:21 Crates and Cargo 35:55 Language design and RFCs 43:02 Building new features 46:30 Editions vs. versions 49:47 Getting paid to work on Rust 51:27 Contributing to Rust 53:03 Rust in the Linux kernel 55:45 AI use cases for Rust 1:01:35 Learning Rust 1:03:54 Book recommendation 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 Three things worth knowing about Rust: 1. Rust was designed to turn implicit failures into compile errors. Where other languages allow you to forget something, Rust makes an omission into a compilation error for things like null checks, uninitialized variables, or error propagation with the ‘?’ character. If you mess something up, it’s almost certain your program will not compile. If it does, at the very least you should see a lint warning. 2. Refactoring in Rust is safe and easy, thanks to the compiler. Alice: “I change a return type or struct field, then just fix the compiler errors until the compiler stops shouting. And then once I’ve done that, I’ve updated every place I need to update.” Rust’s focus on correctness makes refactoring it more straightforward than dynamically-typed languages and Java-style typed ones are to refactor. 3. “Editions” allow Rust to make breaking changes without ‘breaking’ anyone’s code. Rust editions (2015, 2018, 2021, 2024) can be mixed freely across crates. A library on the 2021 edition works seamlessly with a binary on the 2024 edition. This is how Rust evolves syntax (like adding async/await as keywords) without forcing an ecosystem-wide migration. Thanks a lot, Alice for this great discussion! And for your work on Rust.

Gergely Orosz

51,961 Aufrufe • vor 3 Monaten

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

34,525 Aufrufe • vor 27 Tagen

There are few people who have impacted the software engineering industry like Kent Beck 🌻 has. He'd never before told his career story from start to today in one sitting - until now. What a treat. Timestamps: 00:00 Intro 03:47 Human engineers aren’t going away 08:00 Kent's path into tech 13:50 Undergraduate and graduate studies 17:21 Kent’s first programming job 18:54 The rise and fall of Smalltalk 27:04 Working with Ward Cunningham 37:36 Design patterns 44:05 Working at Apple 51:08 CRC Cards 59:29 Testing tools in the language 1:04:22 The C3 project with Martin Fowler 1:09:54 Extreme Programming 1:16:25 Developing TDD 1:25:07 Writing the Agile Manifesto 1:30:00 Agile’s impact 1:32:40 Agile’s downside 1:37:32 The Dotcom Bust 1:44:30 Lessons from working at Facebook 1:59:44 Kent’s ‘Good to Great’ program at Facebook 2:06:07 Soft skills engineers need to learn 2:09:30 AI and the challenges of acceleration 2:15:53 Explore, expand, extract 2:22:33 What Kent is excited about 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 Kent shared so many previously untold stories - like how he was fired from Apple (!!), how he and Ward Cunningham used a thesaurus to find the right words, how the Agile Manifesto came together. My favorite reflection from is this though: The human part is the most important one in software engineering. As Kent explained: “This is the biggest cosmic, practical joke ever. As young people, we were promised: “Okay, here’s this computer and once you’ve completely understand this computer, you’ll be fine. That’s all you need to do.” So I set out the first part of my career just to become the best programmer that I could be because that’s what it would take to be successful. And then you realize: sorry, there’s this whole human side. Your ability to affect change in the world is gated by your ability to communicate with, to soothe, to understand other human beings. And those are exactly the skills that I thought I didn’t need to learn! So I was promised: just understand the computer and you’ll be successful. And then someone went “just kidding, understand people!” And now I was in a position of being ten years behind.”
2:28:43

There are few people who have impacted the software engineering industry like Kent Beck 🌻 has. He'd never before told his career story from start to today in one sitting - until now. What a treat. Timestamps: 00:00 Intro 03:47 Human engineers aren’t going away 08:00 Kent's path into tech 13:50 Undergraduate and graduate studies 17:21 Kent’s first programming job 18:54 The rise and fall of Smalltalk 27:04 Working with Ward Cunningham 37:36 Design patterns 44:05 Working at Apple 51:08 CRC Cards 59:29 Testing tools in the language 1:04:22 The C3 project with Martin Fowler 1:09:54 Extreme Programming 1:16:25 Developing TDD 1:25:07 Writing the Agile Manifesto 1:30:00 Agile’s impact 1:32:40 Agile’s downside 1:37:32 The Dotcom Bust 1:44:30 Lessons from working at Facebook 1:59:44 Kent’s ‘Good to Great’ program at Facebook 2:06:07 Soft skills engineers need to learn 2:09:30 AI and the challenges of acceleration 2:15:53 Explore, expand, extract 2:22:33 What Kent is excited about 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 Kent shared so many previously untold stories - like how he was fired from Apple (!!), how he and Ward Cunningham used a thesaurus to find the right words, how the Agile Manifesto came together. My favorite reflection from is this though: The human part is the most important one in software engineering. As Kent explained: “This is the biggest cosmic, practical joke ever. As young people, we were promised: “Okay, here’s this computer and once you’ve completely understand this computer, you’ll be fine. That’s all you need to do.” So I set out the first part of my career just to become the best programmer that I could be because that’s what it would take to be successful. And then you realize: sorry, there’s this whole human side. Your ability to affect change in the world is gated by your ability to communicate with, to soothe, to understand other human beings. And those are exactly the skills that I thought I didn’t need to learn! So I was promised: just understand the computer and you’ll be successful. And then someone went “just kidding, understand people!” And now I was in a position of being ten years behind.”

Gergely Orosz

26,602 Aufrufe • vor 1 Monat

Why Microsoft didn't have LLMs do their recent Typescript native rewrite in Go Anders Hejlsberg (Creator of Typescript): "If we just let AI loose on, what is it, half a million lines of code that we have in the old compiler? Well, I don't know that that would absolve us from then having go in and carefully examining every line that came out of it to make sure that there were no hallucinations. Right. Unless you have 100% perfect test coverage, you probably still gotta go check all of that. Now I think a better approach, quite honestly, would be enlist AI to write a program that helps you translate from TypeScript to go, because at that point you can park the stochastic ness in that program and then you can get deterministic behavior whenever you run the program, which means you get the same transformation every time you run it. Right. That's always the thing about AI that people forget it's not deterministic. Right. So you can't really trust that it's going to do the same thing twice. For the full conversation, you can search Anders Hejlsberg's name on my YouTube, Spotify or Apple Podcasts (link in bio)
0:48

Why Microsoft didn't have LLMs do their recent Typescript native rewrite in Go Anders Hejlsberg (Creator of Typescript): "If we just let AI loose on, what is it, half a million lines of code that we have in the old compiler? Well, I don't know that that would absolve us from then having go in and carefully examining every line that came out of it to make sure that there were no hallucinations. Right. Unless you have 100% perfect test coverage, you probably still gotta go check all of that. Now I think a better approach, quite honestly, would be enlist AI to write a program that helps you translate from TypeScript to go, because at that point you can park the stochastic ness in that program and then you can get deterministic behavior whenever you run the program, which means you get the same transformation every time you run it. Right. That's always the thing about AI that people forget it's not deterministic. Right. So you can't really trust that it's going to do the same thing twice. For the full conversation, you can search Anders Hejlsberg's name on my YouTube, Spotify or Apple Podcasts (link in bio)

Ryan Peterman

83,519 Aufrufe • vor 5 Tagen

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

128,294 Aufrufe • vor 2 Monaten

James Currier on how he grew a photo sharing site to 47 million users in 6 months “Most product people start by saying, ‘There’s a problem and we’re going to build a product that’s going to solve that problem. Then we’re going to market it,” NFX founder James Currier observes. Of the 45 companies James has spoken to in the last 18 months, at least 40 of them have said some version of this. “I was also taught this back in the 1990s,” James says. “But through experimentation, I’ve realized that this is actually the wrong way to look at it. The right way to look at it — if you want to grow your business — is to ask, ‘What is the language? How am I going to market this?’ . . . Figure out what the language is and then build the product to fit what the language is.” James gives the example of a photo sharing site he launched years ago: “It said ‘Store your Photos’ on the homepage, and we were not growing. So one day, we literally just changed the homepage to say ‘Share your Photos.’ The team that was working on it said, ‘Our site doesn’t actually share photos. It just stores the photos.’ I said, ‘Well, fix that.’ What ended up happening was they started building features that allowed people to share their photos, and we registered 47 million people in 6 months. And that was back when the Internet was 250 million people.” He continues: “What we realized was that by changing the language, you change how the users are interacting with your site. And you also change your thinking about the product so that the features and subsequent language in the product actually fits with the main value proposition.” James gives another example: “We had a matchmaking site, and the promise there was ‘Find a date.’ It wasn’t growing and we had to buy all this traffic from Google and elsewhere to keep the whole thing going. So what we did was we changed the language and the value proposition to say, ‘Help people find a date.’ When we did that, we registered 28 million people in about 9 months, virally with no cost to us. We changed the language, and then we changed the product subsequently.” Video source: Greylock Partners (2016)
2:32

James Currier on how he grew a photo sharing site to 47 million users in 6 months “Most product people start by saying, ‘There’s a problem and we’re going to build a product that’s going to solve that problem. Then we’re going to market it,” NFX founder James Currier observes. Of the 45 companies James has spoken to in the last 18 months, at least 40 of them have said some version of this. “I was also taught this back in the 1990s,” James says. “But through experimentation, I’ve realized that this is actually the wrong way to look at it. The right way to look at it — if you want to grow your business — is to ask, ‘What is the language? How am I going to market this?’ . . . Figure out what the language is and then build the product to fit what the language is.” James gives the example of a photo sharing site he launched years ago: “It said ‘Store your Photos’ on the homepage, and we were not growing. So one day, we literally just changed the homepage to say ‘Share your Photos.’ The team that was working on it said, ‘Our site doesn’t actually share photos. It just stores the photos.’ I said, ‘Well, fix that.’ What ended up happening was they started building features that allowed people to share their photos, and we registered 47 million people in 6 months. And that was back when the Internet was 250 million people.” He continues: “What we realized was that by changing the language, you change how the users are interacting with your site. And you also change your thinking about the product so that the features and subsequent language in the product actually fits with the main value proposition.” James gives another example: “We had a matchmaking site, and the promise there was ‘Find a date.’ It wasn’t growing and we had to buy all this traffic from Google and elsewhere to keep the whole thing going. So what we did was we changed the language and the value proposition to say, ‘Help people find a date.’ When we did that, we registered 28 million people in about 9 months, virally with no cost to us. We changed the language, and then we changed the product subsequently.” Video source: Greylock Partners (2016)

Startup Archive

18,388 Aufrufe • vor 9 Monaten

I asked Dan Martell to walk me through every level of making money with AI. He gave me the most simple, practical advice I've ever heard on this subject. Level 1 - Making $0 - $100k Level 2 - Making $1m - $10m Level 3 - Building a $10m++ enterprise. 0:00 Only 5% of the World Has Ever Paid for AI 0:46 The Easiest Thing to Sell With AI Right Now 1:56 The Marcus and Sophie Framework 4:24 Theory of Constraints (Right Problem to Solve) 5:33 What Is the Number One Business Constraint 7:13 How to Leave Your Job and Go All In 8:27 Business Is Simple Find a Problem and Solve It 9:08 Stop Getting Ready to Get Ready 9:33 The Sarah Story One Text and $10K 9:53 Pull Up Your Phone and Message Your Contacts 11:05 Dan's Son Gets His First Client at $800/Month 12:41 Best Employee vs. Best Employer 13:59 What Other Services Can You Sell With AI 14:44 Sales Is Not Talking It's Asking 17:01 What to Do When You Hate Your Business 18:40 Pain and Pleasure Are the Only Two Motivators 19:13 They Haven't Made It a Must Yet 20:29 Make It a Must Not a Nice to Have 21:06 The Jen Story and the Gasping Moment 22:17 How to Find Your First 10 to 15 Clients 28:38 The Personal Brand Play 33:06 Vision Is What AI Cannot Do 34:55 Hard for Computers Easy for Humans 36:13 Level 2 Making Your First Million With AI 37:18 The Replacement Ladder Framework 37:39 Admin First Then Delivery Then Marketing 39:09 Why Marketing Is the Biggest AI Category 39:32 Why You Should Keep Sales for Yourself 40:00 Level 5 Leadership and AI Agents 41:41 What a Fully AI Systems Business Looks Like 43:13 The Gym Owner With Three Locations 46:16 Shutting Down the Company for Two Days 46:37 Teaching the Whole Team to Code in Claude 49:28 Wayne the 62 Year Old Who Made $12K a Month 52:38 I Only Share What Actually Works 53:21 Whisper Flow and Talking to Your AI 56:41 Claude Chat Claude Coworker and Claude Code 57:57 The Claude Browser Extension 58:49 Claude Code Is Not Just for Developers 1:00:06 How to Migrate Your AI Memory Across Tools 1:01:08 Level 3 $1M to $10M and the Brand Play 1:02:05 Nobody Buys AI They Buy Trust 1:03:25 Brand Is Association and Association Is Trust 1:05:12 A Million Followers Is $10M in Activated Revenue 1:07:03 How to Keep AI From Becoming Slop 1:07:42 Human in the Loop 1:08:16 The 10 80 10 Rule and Why AI Is Now the 80 1:10:01 The Team FIRED Themselves 1:11:45 Dan's Free AI Curriculum for Your Team
1:13:20

I asked Dan Martell to walk me through every level of making money with AI. He gave me the most simple, practical advice I've ever heard on this subject. Level 1 - Making $0 - $100k Level 2 - Making $1m - $10m Level 3 - Building a $10m++ enterprise. 0:00 Only 5% of the World Has Ever Paid for AI 0:46 The Easiest Thing to Sell With AI Right Now 1:56 The Marcus and Sophie Framework 4:24 Theory of Constraints (Right Problem to Solve) 5:33 What Is the Number One Business Constraint 7:13 How to Leave Your Job and Go All In 8:27 Business Is Simple Find a Problem and Solve It 9:08 Stop Getting Ready to Get Ready 9:33 The Sarah Story One Text and $10K 9:53 Pull Up Your Phone and Message Your Contacts 11:05 Dan's Son Gets His First Client at $800/Month 12:41 Best Employee vs. Best Employer 13:59 What Other Services Can You Sell With AI 14:44 Sales Is Not Talking It's Asking 17:01 What to Do When You Hate Your Business 18:40 Pain and Pleasure Are the Only Two Motivators 19:13 They Haven't Made It a Must Yet 20:29 Make It a Must Not a Nice to Have 21:06 The Jen Story and the Gasping Moment 22:17 How to Find Your First 10 to 15 Clients 28:38 The Personal Brand Play 33:06 Vision Is What AI Cannot Do 34:55 Hard for Computers Easy for Humans 36:13 Level 2 Making Your First Million With AI 37:18 The Replacement Ladder Framework 37:39 Admin First Then Delivery Then Marketing 39:09 Why Marketing Is the Biggest AI Category 39:32 Why You Should Keep Sales for Yourself 40:00 Level 5 Leadership and AI Agents 41:41 What a Fully AI Systems Business Looks Like 43:13 The Gym Owner With Three Locations 46:16 Shutting Down the Company for Two Days 46:37 Teaching the Whole Team to Code in Claude 49:28 Wayne the 62 Year Old Who Made $12K a Month 52:38 I Only Share What Actually Works 53:21 Whisper Flow and Talking to Your AI 56:41 Claude Chat Claude Coworker and Claude Code 57:57 The Claude Browser Extension 58:49 Claude Code Is Not Just for Developers 1:00:06 How to Migrate Your AI Memory Across Tools 1:01:08 Level 3 $1M to $10M and the Brand Play 1:02:05 Nobody Buys AI They Buy Trust 1:03:25 Brand Is Association and Association Is Trust 1:05:12 A Million Followers Is $10M in Activated Revenue 1:07:03 How to Keep AI From Becoming Slop 1:07:42 Human in the Loop 1:08:16 The 10 80 10 Rule and Why AI Is Now the 80 1:10:01 The Team FIRED Themselves 1:11:45 Dan's Free AI Curriculum for Your Team

Grant

167,714 Aufrufe • vor 2 Monaten

How is CI/CD changing because of AI, and what are sensible deployment practices more teams should be doing? Robert Erez is a CI/CD expert, and also my former teammate at Skype. Timestamps: 00:00 Intro 02:09 Canary deployments at Skype 05:01 Joining at Octopus Deploy 06:15 Continuous deployment 10:26 Why Kubernetes won 15:51 Kubernetes on-prem 18:50 How GitOps works 25:00 The uses and limitations of GitOps 31:04 The rise of platform teams 35:51 How AI is changing CI/CD 39:49 Progressive delivery explained 47:31 Rollbacks and roll-forwards 50:14 Feature flags 54:32 How development environments are evolving 57:40 Cloud development environments (CDEs) 1:03:45 Self-hosting CI/CD 1:09:25 Getting started with progressive delivery 1:11:15 Book recommendations 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. Three interesting thoughts from Rob: 1. Roll forward, never backwards. When a system has state – which typically means it uses databases – then doing a rollback can leave the code talking to a schema that’s no longer in sync. Rob’s advice is to not treat a failure in v2 as a trip back to v1, but rather as a push to v3 with the fix in it. 2. GitOps isn’t actually about Git. None of the four pillars of GitOps – 1) declarative, 2) versioned and immutable, 3) pulled, not pushed, 4) continuously reconciled – require Git, although Git can work under these constraints. Yet, the term ‘GitOps’ has made the industry dogmatic about cramming everything into a repo – even things like secrets that absolutely shouldn’t be there! 3. There’s a trend of ephemeral environments replacing test/staging environments across the industry. Companies used to have a few testers fighting over a handful of static test environments, but today, it’s trivial to spin up a full environment, per-feature branch, pre-merge. This is an “ephemeral” environment for evaluating that things work, which is then torn down once something is merged. It helps speed up the feedback process.
1:15:35

How is CI/CD changing because of AI, and what are sensible deployment practices more teams should be doing? Robert Erez is a CI/CD expert, and also my former teammate at Skype. Timestamps: 00:00 Intro 02:09 Canary deployments at Skype 05:01 Joining at Octopus Deploy 06:15 Continuous deployment 10:26 Why Kubernetes won 15:51 Kubernetes on-prem 18:50 How GitOps works 25:00 The uses and limitations of GitOps 31:04 The rise of platform teams 35:51 How AI is changing CI/CD 39:49 Progressive delivery explained 47:31 Rollbacks and roll-forwards 50:14 Feature flags 54:32 How development environments are evolving 57:40 Cloud development environments (CDEs) 1:03:45 Self-hosting CI/CD 1:09:25 Getting started with progressive delivery 1:11:15 Book recommendations 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. Three interesting thoughts from Rob: 1. Roll forward, never backwards. When a system has state – which typically means it uses databases – then doing a rollback can leave the code talking to a schema that’s no longer in sync. Rob’s advice is to not treat a failure in v2 as a trip back to v1, but rather as a push to v3 with the fix in it. 2. GitOps isn’t actually about Git. None of the four pillars of GitOps – 1) declarative, 2) versioned and immutable, 3) pulled, not pushed, 4) continuously reconciled – require Git, although Git can work under these constraints. Yet, the term ‘GitOps’ has made the industry dogmatic about cramming everything into a repo – even things like secrets that absolutely shouldn’t be there! 3. There’s a trend of ephemeral environments replacing test/staging environments across the industry. Companies used to have a few testers fighting over a handful of static test environments, but today, it’s trivial to spin up a full environment, per-feature branch, pre-merge. This is an “ephemeral” environment for evaluating that things work, which is then torn down once something is merged. It helps speed up the feedback process.

Gergely Orosz

19,307 Aufrufe • vor 2 Monaten

Why is the creator of OpenCode pretty skeptical about AI productivity gains, and the hype around AI? A very conversation dax (and lots of truth bombs:) Timestamps: 00:00 Intro 07:03 Dax’s path into tech 09:04 Early startup experience 13:16 Getting involved with open source 16:13 OpenCode 23:17 Anthropic banning OpenCode 30:34 From terminal to GUI 32:34 OpenCode’s business model 36:33 Why inference is profitable 39:11 GPU bottlenecks 40:54 AI hype 45:50 AI spending 48:47 Dax’s memo 55:41 Dax’s skepticism of predictions 58:58 Engineering culture at OpenCode 1:02:38 How building works at OpenCode 1:05:36 Taste and quality 1:11:32 Dax’s work setup 1:12:35 The role of engineers and EMs 1:15:50 Advice for engineers 1:18:12 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 Three interesting thoughts from Dax: 1. No AI-native coding agent company is “winning” by being better with AI. Dax says that none of OpenCode’s competitors are crushing them, and that nobody is using AI so well that others cannot compete. 2. Most software engineers profit from AI as time gained, not increased output — unless you change incentives! Dax says the natural way for software engineers to “cash out” their AI tooling gains is with time savings, by doing the same work as before, but faster. Until compensation and motivation structures change, most teams should expect output to stay flat while engineers go home earlier. There’s nothing wrong with this, but AI vendors sell a different outcome to CFOs: increased output. 3. AI code generation mutes the “guilt” of doing the wrong thing, but this builds up tech debt. Pre-AI, writing a hack felt bad, the second time it felt really bad, and by the third time you’d often just refactor in order to fix up the code. Now, the agent hides the hack, which skews devs’ judgment and results in less tech debt being cleaned up.
1:21:01

Why is the creator of OpenCode pretty skeptical about AI productivity gains, and the hype around AI? A very conversation dax (and lots of truth bombs:) Timestamps: 00:00 Intro 07:03 Dax’s path into tech 09:04 Early startup experience 13:16 Getting involved with open source 16:13 OpenCode 23:17 Anthropic banning OpenCode 30:34 From terminal to GUI 32:34 OpenCode’s business model 36:33 Why inference is profitable 39:11 GPU bottlenecks 40:54 AI hype 45:50 AI spending 48:47 Dax’s memo 55:41 Dax’s skepticism of predictions 58:58 Engineering culture at OpenCode 1:02:38 How building works at OpenCode 1:05:36 Taste and quality 1:11:32 Dax’s work setup 1:12:35 The role of engineers and EMs 1:15:50 Advice for engineers 1:18:12 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 Three interesting thoughts from Dax: 1. No AI-native coding agent company is “winning” by being better with AI. Dax says that none of OpenCode’s competitors are crushing them, and that nobody is using AI so well that others cannot compete. 2. Most software engineers profit from AI as time gained, not increased output — unless you change incentives! Dax says the natural way for software engineers to “cash out” their AI tooling gains is with time savings, by doing the same work as before, but faster. Until compensation and motivation structures change, most teams should expect output to stay flat while engineers go home earlier. There’s nothing wrong with this, but AI vendors sell a different outcome to CFOs: increased output. 3. AI code generation mutes the “guilt” of doing the wrong thing, but this builds up tech debt. Pre-AI, writing a hack felt bad, the second time it felt really bad, and by the third time you’d often just refactor in order to fix up the code. Now, the agent hides the hack, which skews devs’ judgment and results in less tech debt being cleaned up.

Gergely Orosz

231,584 Aufrufe • vor 3 Monaten

Marc Andreessen on the 3 things he looks for when investing in a startup The first thing Marc Andreesen looks for is a big market: “Is there a big existing market that you think you can go after and displace incumbents? Or do you believe there will be a new market that will be big?” The second thing he looks for is a 10x better product: “Is there a fundamental technology or economic change that justifies a new company? And the way I always think about that is: Is there a 10x change happening in the technology landscape? Is something 10x faster, 10x cheaper, or 10x better? If it’s not 10x, we as both VCs and entrepreneurs have to ask ourselves if it’s really worth doing because it’s really hard to start new companies . . . Existing companies are usually pretty good at what they do. So for a new company to exist, it has to bring a product to market that’s so much better than what exists that it punches through the status quo.” The third is the team: “Is the team outstanding? . . . You want to have a founding team of complementary skillsets. You want to have at least one super strong technologist — quite possibly more than one. Some of the best startups are actually more than one founding technologist. And then it often helps to have someone who is a marketing or salesperson who has a really good understanding of business.” Marc believes that you need all three of these, but if you’re going to compromise on one of those as an investor, it should be the product: “A great market is a lot easier to make up for with iterative product execution. The problem with a poor or small market is that even if you do a good job on the product, there just aren’t that many customers so it’s hard to ever get big and people get demoralized . . . And then we evaluate the team of a startup by its ability to get into a big market with a good product.”
2:43

Marc Andreessen on the 3 things he looks for when investing in a startup The first thing Marc Andreesen looks for is a big market: “Is there a big existing market that you think you can go after and displace incumbents? Or do you believe there will be a new market that will be big?” The second thing he looks for is a 10x better product: “Is there a fundamental technology or economic change that justifies a new company? And the way I always think about that is: Is there a 10x change happening in the technology landscape? Is something 10x faster, 10x cheaper, or 10x better? If it’s not 10x, we as both VCs and entrepreneurs have to ask ourselves if it’s really worth doing because it’s really hard to start new companies . . . Existing companies are usually pretty good at what they do. So for a new company to exist, it has to bring a product to market that’s so much better than what exists that it punches through the status quo.” The third is the team: “Is the team outstanding? . . . You want to have a founding team of complementary skillsets. You want to have at least one super strong technologist — quite possibly more than one. Some of the best startups are actually more than one founding technologist. And then it often helps to have someone who is a marketing or salesperson who has a really good understanding of business.” Marc believes that you need all three of these, but if you’re going to compromise on one of those as an investor, it should be the product: “A great market is a lot easier to make up for with iterative product execution. The problem with a poor or small market is that even if you do a good job on the product, there just aren’t that many customers so it’s hard to ever get big and people get demoralized . . . And then we evaluate the team of a startup by its ability to get into a big market with a good product.”

Startup Archive

17,333 Aufrufe • vor 6 Monaten

Gmail creator Paul Buchheit on how to build something 100 people love In the clip below, Paul talks about how he built the first version of Gmail. The first version was built in one day, and from there, he iterated his way to 100 people who loved the product: “The whole thing was just iterating, step by step trying to build something that made people happy.” The team decided that they needed to have 100 happy users before launching gmail to the world. To achieve this, Paul embedded a quick questionnaire in the interface that asked users: “Are you happy? Yes or No” Paul would then seek out all of the “No” responders and ask them directly: “What will it take to make you a happy user?” He ignored feedback from the people who said things like “it basically needs to be a clone of Outlook” because it was unlikely he would be able to convert them. But other people just needed a minor feature or a bug fix. So Paul worked on those requests one-by-one until he hit 100 happy users. Email was 30 years old when Paul started building gmail, and it’s pretty much impossible to enter a space like that and build something that appeals to everyone. If you try, what you will end up building is a mediocre product that nobody really loves. What Paul recommends doing instead—and what he did with gmail—is: “Build a thing that has really deep appeal. Even if it’s to just a tiny fraction of people—if you can make that small fraction of people obsessively love what you’re building, it’s easier to just grow that group. There’s always people at the margin where if you make the thing slightly better, they’re going to join into that group. It’s easier to start with deep, narrow appeal and broaden it over time than it is to start with broad ‘meh’ and convert ‘meh’ to loving your thing en masse.”
4:51

Gmail creator Paul Buchheit on how to build something 100 people love In the clip below, Paul talks about how he built the first version of Gmail. The first version was built in one day, and from there, he iterated his way to 100 people who loved the product: “The whole thing was just iterating, step by step trying to build something that made people happy.” The team decided that they needed to have 100 happy users before launching gmail to the world. To achieve this, Paul embedded a quick questionnaire in the interface that asked users: “Are you happy? Yes or No” Paul would then seek out all of the “No” responders and ask them directly: “What will it take to make you a happy user?” He ignored feedback from the people who said things like “it basically needs to be a clone of Outlook” because it was unlikely he would be able to convert them. But other people just needed a minor feature or a bug fix. So Paul worked on those requests one-by-one until he hit 100 happy users. Email was 30 years old when Paul started building gmail, and it’s pretty much impossible to enter a space like that and build something that appeals to everyone. If you try, what you will end up building is a mediocre product that nobody really loves. What Paul recommends doing instead—and what he did with gmail—is: “Build a thing that has really deep appeal. Even if it’s to just a tiny fraction of people—if you can make that small fraction of people obsessively love what you’re building, it’s easier to just grow that group. There’s always people at the margin where if you make the thing slightly better, they’re going to join into that group. It’s easier to start with deep, narrow appeal and broaden it over time than it is to start with broad ‘meh’ and convert ‘meh’ to loving your thing en masse.”

Startup Archive

44,476 Aufrufe • vor 2 Jahren

THE MOST IMPORTANT Q&A OF MEDIA DAY. Mariana: You came from a very solid weekend on top of everything, but at the same time, it seems that you don't feel that the team is listening to you. Am I right? And how do you balance that? Lewis: I feel like we're going in the right direction. Rome wasn't built in one day, so it takes time to build. For me, coming into the team, I wanted to be respectful of the way they've done things in the past and just to really observe and see where our strengths and where our weaknesses are and to highlight where our weaknesses are and areas that we need to work on. But I do feel that they've been responding. I think you're starting to see, hopefully, some of the impact of the work that we're doing in the background and also into next year's car. This is a car that I've had nothing to do with in terms of developing this car over the years. Hopefully, from next year, my input goes into that car, and that will be a car that I've hopefully been a part of or will have been a part of developing. But I think we've got a really great rapport. I think we're really progressing, particularly since the summer break. I think things have started to get better, and it's all just about building trust and communication. Also, I'm coming into a team that English is not the first language, and I don't speak Italian, so it's finding a common ground. And the fact is we all want to win. We're all here to achieve the same thing, and we've got to just keep pushing. So that's why I'm trying to keep everyone motivated on difficult weekends, trying to keep everyone lifted up. But there have been many, many things we've changed this year that I suggested that they hadn't done in the past, and so they have been listening. It doesn't change straight away, just like that. It takes time to build. And as engineers, they really need proof. They need numbers. That's what they work on. So you have to sometimes push to get certain changes to be made, and then when you change it and then it works, you're like, okay. Mariana: That's what I was talking about.. Lewis: Yeah! - F1 2025 Mexico -
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THE MOST IMPORTANT Q&A OF MEDIA DAY. Mariana: You came from a very solid weekend on top of everything, but at the same time, it seems that you don't feel that the team is listening to you. Am I right? And how do you balance that? Lewis: I feel like we're going in the right direction. Rome wasn't built in one day, so it takes time to build. For me, coming into the team, I wanted to be respectful of the way they've done things in the past and just to really observe and see where our strengths and where our weaknesses are and to highlight where our weaknesses are and areas that we need to work on. But I do feel that they've been responding. I think you're starting to see, hopefully, some of the impact of the work that we're doing in the background and also into next year's car. This is a car that I've had nothing to do with in terms of developing this car over the years. Hopefully, from next year, my input goes into that car, and that will be a car that I've hopefully been a part of or will have been a part of developing. But I think we've got a really great rapport. I think we're really progressing, particularly since the summer break. I think things have started to get better, and it's all just about building trust and communication. Also, I'm coming into a team that English is not the first language, and I don't speak Italian, so it's finding a common ground. And the fact is we all want to win. We're all here to achieve the same thing, and we've got to just keep pushing. So that's why I'm trying to keep everyone motivated on difficult weekends, trying to keep everyone lifted up. But there have been many, many things we've changed this year that I suggested that they hadn't done in the past, and so they have been listening. It doesn't change straight away, just like that. It takes time to build. And as engineers, they really need proof. They need numbers. That's what they work on. So you have to sometimes push to get certain changes to be made, and then when you change it and then it works, you're like, okay. Mariana: That's what I was talking about.. Lewis: Yeah! - F1 2025 Mexico -

sim

170,303 Aufrufe • vor 10 Monaten

Just in $AMD Anush "Speed is the moat"|ROCm🎙️ In the race to define the future of AI, what's the one advantage that truly lasts? It's not proprietary tech, argues Anush Elangovan Elangovan, VP of AI Software at AMD , but the sustainable speed of innovation. He explains why AMD is rejecting the "walled garden" model for its open source ROCm stack, betting that an open community flywheel is the key to victory. Listen to understand how this open strategy is designed to out-innovate closed systems by empowering developers to solve everything from frontier-model challenges to the mundane, everyday problems that define the "last mile" of AI. AMD ROCm Software: Part 1 Transcript [00:00:00] Andrew Zigler: Joining me is Anush Elangovan, VP of AI software at AMD. And when people talk about AI compute, the conversation often stops at hardware specs, but it's more than just physical chips that win the game. It's also the software ecosystems supporting them. [00:00:18] Andrew Zigler: The prevailing strategy in the industry has been to build something like a walled garden. You know, something closed, proprietary locks, developers in. But AMD is betting on an entirely different play, open source acceleration, and with rock, their open source AI software stack. AMD is building not just hardware parity, but an innovation flywheel that's powered by the community with interoperability and the freedom to scale without all of that pesky lockin. [00:00:48] Andrew Zigler: And in this world, speed is your moat and how fast you can innovate while your platform remains open, flexible, and standardize across all of its applications. That's what we're gonna explore [00:01:00] today. So Anush, I'm really excited to have you here. Welcome to Dev Interrupted. [00:01:04] Anush Elangovan: Thanks for having me. Uh, super excited to chat about it. [00:01:07] Andrew Zigler: Amazing. Well, let's go ahead and dive right in with kind of what I laid it out with in the beginning, the idea of the moat and it being about speed. I wanna unpack that a bit because that came from you when you and I first spoke. And I, and I want to know, you know, how do you define speed inside of AMD beyond just things like hardware, benchmarks. [00:01:27] Anush Elangovan: Yeah, that's a very good question. So when we typically talk about speed, everyone's like, Hey, hardware benchmark specs, right? Like, uh, memory bandwidth or, or flops. And that is one important part of it, uh, AMD does very well. With that, we do have, a, a very good history of executing on that axis. [00:01:47] Anush Elangovan: But when I say speed is the moat, it is about, uh, how we prepare, how we build the muscle to run the race for a long time and run it fast. And it is [00:02:00] not about a single point in time that you've, you've beat some you know, benchmark and, and you declare victory. It's about building the ability to consistently develop and deliver. [00:02:13] Anush Elangovan: Both hardware and software innovation at scale and do it fast, right? Like, you know, we we're increasingly getting to a point where models come out and they're, uh, you know, a year or two ago it was like, Hey, they work on AMD on day zero, which is great, but now they are performing on AMD the day it releases, right? [00:02:32] Anush Elangovan: So, what does it take to Prefetch where the industry is going? Be prepared to intercept. At that point is what you know, I, I refer to as you know, the, the speed factor in, in creating this mode, right? And the mode is just shed all things that hold you back and run as fast as you can. [00:02:53] Anush Elangovan: Uh, because the pace of innovation that is, uh, being seen in, in AI [00:03:00] industries is just. Amazing. Right? And it's like, it's transformational at at how you generate electricity. It's transformational as at how you build data centers. It's transformational at how you deploy compute, networking. It's transformational at what kind of use cases you, you know, uh, use AI for. [00:03:17] Anush Elangovan: Uh, and for that, you need to be prepared to, see what comes tomorrow and be prepared to run the race tomorrow. [00:03:23] Andrew Zigler: Yeah, it's a really great perspective because it highlights that it's not just like a checkpoint that you run through. I like how you called out, like it's not just hitting that benchmark or being the best in class at that moment, in that snapshot, it's about having a. The throughput and about having that dedication to the idea and continuing to deliver on it. [00:03:43] Andrew Zigler: It's not just crossing the threshold, but it's also being the engine. And that's what, that's what protects a business. That is the moat, because the moat is that innovation layer, the faster and more, uh, future forward. That you can work and think, [00:04:00] you know, the better. Uh, we, we talk a lot about like future forward work styles. [00:04:04] Andrew Zigler: Like what are the things I could be doing right now today that are gonna be like, way more useful tomorrow? Let, let's abandon those, workflows that are older and that kind of like, that translates into. An advantage when you work that way. You know, what kind of things have you learned working with, uh, like across all spectrums of people who would use ROCm, right? [00:04:23] Andrew Zigler: You have like the developers, but then you also have the enterprises and you have this large span of adoptees, right? So what is the, what does that look like that you learn? [00:04:32] Anush Elangovan: Yeah, so, so the way I look at it is there are gonna be pockets of different, uh, you know, cadences, right? Like, so people who are deploying in enterprises, for example, right? The validation and how long it takes for them to deploy an LLM that's secure. It's, with guardrails, et cetera, maybe longer. [00:04:52] Anush Elangovan: but you still have to go through the process and you have to be prepared to like, walk that walk to deploy an enterprises. That doesn't mean it's [00:05:00] not fast, that's as fast as you can do for that industry, right? And if you are deploying AI in healthcare, right, it's, it's got its own, uh, cycle. [00:05:07] Anush Elangovan: but in each one of these, you want to see how, like, go down to the essence of what is it that you actually have to do. And, you know, I, I, I like how you framed it. It's like it's, you shed your prior assumptions of how things are done, right. And, and you kind of build up from a, uh, first principles, uh, approach to say, this is how I could use AI to unlock, whatever I'm doing. [00:05:33] Anush Elangovan: And, and, some of it, you know, it's good to really step back and look at. Just question every part of it, right? Like right now you're getting chat GPT and, Gemini competing for like, math, olympiads and, and, uh, college, uh, reasoning, uh, tests. Right? And, and those are like that, that is amazing and increasingly like complex tasks that they're trying to do. [00:05:58] Anush Elangovan: But there may also be like. [00:06:00] More mundane things that AI could, could get applied to. Right? And, and so when we think about shedding old ways, you wanna shed it not just in like the tip of the spear. It's like, you know, I'm gonna see what's the frontier model. It's also, it could be something as simple as. [00:06:18] Anush Elangovan: How do you choose a, a movie, uh, you know, like a recommendation system, right? Or, or, uh, an automated, uh, flight, uh, rebooking system. So the moment, you know, your flight is late, uh, right now it's a notification, right? It's like, oh, you got a text message saying your flight's late. And I got that like three times this week. [00:06:38] Anush Elangovan: But anyway, uh, and, and, and, and, I was just like, okay, so if I were to rethink this. All this MCPs that we have that should be hooked up into an MCP that says, your flight's delayed. Here are your options. If you want, you know, these are the paid options. Yeah. Here are the free options. This will get you back into your you know, Toronto airport [00:07:00] tonight. [00:07:00] Anush Elangovan: Or if you stay, here's a hotel plus this, plus this, plus. It's just like, go ahead is all I should say. Versus now I'm like, okay, can someone, you know, can I call a travel agent? Can I do this? Can I go online and log into And you know, so we gotta fundamentally rethink even those like small, nuances of, things that we do that can be automated out and AI is really, really good at doing something like this, right? Maybe I just explained an AI startup idea right now. Somebody should just start that. [00:07:29] Andrew Zigler: I think you did. Yeah, you definitely did. Someone, one of our listeners is definitely going to lift that off of you. I, I, I, you know, I hate being on the receiving end of those. You feel a little helpless and then you have to like, follow the whole flow. So I know what you mean. Like I, I like how you called out that the build and this like. [00:07:45] Andrew Zigler: Where speed is your moat and the innovation layer is protecting you, is what makes you better than your competitors. How you scale that and you bring that to market. So by understanding the problems that you're solving, uh, throwing away those older assumptions, but also [00:08:00] recognizing that like. We're building every single day, new things and new ways of using stuff that we're still figuring out the implications of. [00:08:08] Andrew Zigler: And so when you have a lot of velocity and you're introducing a lot of new ideas, and maybe you have that workflow now that automatically rebook your flight off of your late flight text message, and uh, I know I would certainly use it, but you know, what kind of philosophies guide the way that y'all think about building this ecosystem to manage that stability while letting folks. [00:08:29] Andrew Zigler: Play with the speed and the assumptions and the airplane re bookings. [00:08:34] Anush Elangovan: so, so I think, you know, we need to peel one layer down, right? and the philosophy is, Hey, we, we just discovered electricity, right? And you know what we're gonna do? We are gonna make motors, uh, or dynamos, right? Like engines. Uh, sure. We don't know if it's gonna be a Ferrari that you're gonna make, or it's a a a a dump truck. [00:08:57] Anush Elangovan: That's good for doing this. But let's [00:09:00] let, which is also required, right? You need a dump truck. You need a garbage truck. And, [00:09:04] Andrew Zigler: Yeah. You need the [00:09:04] Anush Elangovan: course you need, uh, a Ferrari for a midlife crisis, right? So, [00:09:09] Andrew Zigler: precisely. [00:09:10] Anush Elangovan: But, but my, uh, point is what do we build next? And, uh, and this is what I meant by like, okay, let's, let's take those baby steps to build the. [00:09:20] Anush Elangovan: Infrastructure that's required that we know we'll have to use, right? So, so if I just discovered electricity, okay, great. Now one, how do I save this electricity and how do I use it? So there's battery technology, so you need to do something like that, right? Like so. But then you also want to make it into an actionable thing. [00:09:37] Anush Elangovan: You want to make it for like automobiles, or you wanna use it for, you know, powering, uh, entire cities. So it is that transformational. So, uh, AI is that transformational. So, if you distill down, it'll, it'll come down to how do we think about, what we can do with this this fundamental technology that, We may not be aware of what it [00:10:00] is gonna unlock next, but at least you know the next step is clear, right? It's like a dense fog, you know, it's gonna be like, it, it's the right path. You see the light, but it's kind of like out there and, and the steps you're taking are concrete and you're like, okay, this is good. [00:10:16] Anush Elangovan: I, this is better than where I was or where we were. So we are moving forward. So you can build with the. Intuition from what you see in the short term and a tactical view, but towards what you think the future is gonna be. [00:10:28] Andrew Zigler: Right. You almost like we're all in this like fog of war, right? And like you said, you're reaching out and you're trying to step through it. You could think of it too, as like you're in the dark and your hands are up in front of you and you know that. You're, you're not gonna run your face into a wall because your hands are out in front of you, but you're not gonna maybe do much better than that. [00:10:45] Andrew Zigler: So that's kind of like, I think the eco, the, the industry, the world that we find ourselves in, uh, and we all have to, then this becomes the power of an ecosystem, of a group of people working together to create that layer of, [00:11:00] uh, of establishing the [00:11:01] Anush Elangovan: exactly. And I, I, I just, instead of, you know, saying fog of war I describe it as like, you're in this. Beautiful valley with like a morning, uh, fog that's in. You can smell the flowers. You, you hear the birds. You are like, okay, it's, we are in like, uh, utopian paradise and yes, I just need to like, continue the walk, right? [00:11:24] Anush Elangovan: and then move forward with that, conviction that you're in the right spot. [00:11:27] Andrew Zigler: Yeah. So let's talk about that ecosystem world. This nice, I love how you describe it, this grassy side of a hill in the morning that's covered in some mist and maybe we can't see 30 feet in one direction, but it sure is a beautiful hill and it smells nice. And so we're all here. And why is, in that world, why is. [00:11:44] Andrew Zigler: You know, open source, their strategic advantage that y'all are going for in the AI hardware market. And, and then how does like ROCm turn that into wins for people within that ecosystem? [00:11:56] Anush Elangovan: you know, the, the way we look at it is this, is kind of like how I view [00:12:00] AI and the ecosystem, right? But, but it is for everyone to enjoy. Uh, and so we do want to make sure that. You know, it is, uh, beneficial for everyone. [00:12:09] Anush Elangovan: The ecosystem can come in and, and innovate. It's an open innovation engine. and uh, it is very different from, you know, having a walled garden with, Hey, only I know how to do this and I'm gonna do it and throw it over the fence and you can use it or keep walking, right? So we'd like to be good citizens that way, but also. [00:12:30] Anush Elangovan: Uh, it is self-fulfilling in a way, right? Like it, the, the pace at which we innovate with open source is unmatched. Like, you know, our serving engines are like VLLM and, and sg l. Those things, uh, those frameworks are like super, super aggressive in terms of how fast they come out with features and how fast they can you know, get performant models out. [00:12:52] Anush Elangovan: And that compared with what, uh, you'd get from, you know, the likes of like T-R-T-L-L-M or something is always lagging, right? Because you [00:13:00] just can't keep up with you know, 200 commits a week just on one particular model to get that model really performant [00:13:06] Andrew Zigler: And, and, and in that world where, you know, everyone can enjoy the winds of this, what kind of customer stories or innovation stories have really stood out to you and excite you about building and creating this place for developers? [00:13:19] Anush Elangovan: Yeah. So I think the parts that are super exciting for me are when when we get to see a customer that is first skeptical. Then they start a little like, okay, fine, we'll give you a chance. Uh, we do a simple, uh, POC and then they're like, huh, this seems to work. Yeah, we told you it works. [00:13:42] Anush Elangovan: You don't have to change one line of code. Really? Yes, no need to change one line of code. Okay, let's try a production workload. So then they try it. Oh, you're more performant than the competition. Yes. We're more performant than, than the competition. So how much does it cost? And we're like, oh, it's your TCO is better with, uh, [00:14:00] AMD. [00:14:00] Anush Elangovan: So again, they're like, wow, okay, good. So now how do we deploy at scale? And then we go deploy it at scale. And when they give a thumbs up on that and they say, this is good, right? That's when you know, you, you see it go full circle from like, oh, we, we've never heard about AMD to like actually deploy to tens of thousands of GPUs In the order of a few months, right? It, it, it really is fascinating to see and very exciting and invigorating to [00:14:28] Andrew Zigler: Yeah. At like a great exposure to a lot of interesting problems. And, and then people using the infrastructure, the, the technology available to solve those problems. Really specific problems by the way, that's often why they're bringing their data and AI to it, uh, is because it is really specific and important for them. [00:14:45] Andrew Zigler: And there's a, a lot I think that other engineering orgs can learn and even emulate from AMD's success and, and having this open source ecosystem and it causing this acceleration within. You [00:15:00] know, uh, customers and enterprises that use and adopt the tools and, and, and that creates an advantage. And that goes back to why we're talking and like the real thesis of our conversation today. [00:15:10] Andrew Zigler: So how do you think engineering leaders that are listening to this and obviously tapping into this great success AMD has from an open source flywheel, how do you think other, other folks building in the same space can foster that open, first, that open source oriented culture in order to, you know, accelerate their innovation goals? [00:15:29] Anush Elangovan: Yeah, that's a very good question. So the startup that um, was acquired by AMD we, we built, I mean, we started off doing iot stuff and you know, smart ring and all that, right? But in the, the end of like, uh, and not the end, the last six years of the company was building ML compilers. [00:15:47] Anush Elangovan: And ml, ML compilers are like super, uh, complicated, sophisticated, advanced algorithms, dah, dah, dah. but it was all open source, right? So our VCs were like, wait, what do you mean your core [00:16:00] IP is open source? And um, the speed is the moat applied even then, right? It was just like, yes, if you have an idea that. [00:16:08] Anush Elangovan: Because someone saw this idea that you are, they're gonna be able to catch up, then you probably have the wrong idea anyway. But if they are, you know, you execute and they're gonna catch up, that you should assume they're gonna catch up. Right? So you gotta move forward. So keeping it open source is super important. [00:16:25] Anush Elangovan: But also to your question on like, you know, the learnings from an AMD standpoint, right? If there are, hard problems, I'd say dig in and work through it, right? Like there's no way but through it, right? That should be the simple mentality. And more, uh, frequently than not. you'll see that you'll just make it through in a, in, in good form. [00:16:52] Anush Elangovan: But if you doubt it and you're like, oh, I don't know if I should commit, if I'm, I, you know, what should just commit to do the right thing [00:17:00] every step, right? Every step, and just keep taking one step in front of the other. And in no time you'll see that you'll be running. Right. And, and yes, the first few steps will be like, yeah, everyone's complaining about your software quality. [00:17:15] Anush Elangovan: Everyone's complaining about this and that, and it doesn't work. And, and a few steps in, you know, you get, you get the hang of all the complaints that are coming in. You get the feedback loop. You're like, okay, what, what are you prioritizing again? One step in front of the other, right? You just keep knocking that out and then you get to a point where you're, it just becomes second nature, right? To do the, to do the right thing. And, and then yes, if someone gives you two options, you'll be like, fine. This is, uh, you know, there's always the resource trade off. There's always a human capital trade off, but what's the right thing to do? of course, I, I'm pragmatic about what we choose, but, but if the right thing for your long-term success is dig in, go first, principles, make it [00:18:00] happen. [00:18:00] Anush Elangovan: Well. Then just go for that. There's, there is no shortcut to [00:18:04] Andrew Zigler: acknowledging, you know, how it aligns with your mission, your core company goals, and what you're looking to achieve. And, and I, I love how you rightfully called out that in the open source world and you know, you have your technology that you've built, what you think is your moat upon, right? [00:18:22] Andrew Zigler: It's your code and, and to open source that, or to just make it where anyone could peer in is, you know. Scary in one regard, but two, it just kind of feels like you're handing away your throne room in some kind of sense, a very direct feeling sense. But the ultimately, you were really right to call out, and this is something I think about all the time, that the real power there is still the speed This the speed. [00:18:42] Andrew Zigler: That was the moat at the beginning of our conversation. It's the speed in combination with your. Very specific domain understanding of what you're building and what you're creating, and your new role as the steward of that world and how people plug into it, which [00:19:00] has frankly, a lot more influence and power than lording over a closed. [00:19:04] Andrew Zigler: You know, repository or an ecosystem, and like you said, like throwing things over the wall. Sure. There, there might be people always on the other side of that wall, but you're not gonna have a great connection with them. You're not gonna be able to really clearly understand them. I, I like your metaphor of the side of the field of the mountain a lot more. [00:19:23] Andrew Zigler: But, but in the, in this world, you know, where. That speed is, is the power and, and open source is just one way that you can harness that speed to get really far ahead and to innovate. , There's other parts of this equation that you can be experimenting with too, and I'd love to pick your brain about them as a software leader and, and, and one of them is about looking forward and kind of understanding that future that we're all building towards and beyond today's models and hardware. [00:19:48] Andrew Zigler: You know, what do you see as the next major bottleneck or opportunity in the AI compute space? As, as you know, enterprises and folks start to get a little more mature about what's available to [00:20:00] them. [00:20:00] Anush Elangovan: Yeah, I think, the bottleneck and opportunity is, uh, what I'd call, call walking the last mile of ai. Right. Uh, and like I I, I gave you an example, uh, previously, but, but it's similar to that. It's like there are cases where Humans have so many, uh, things to do in your day. You know, like the, if we sit down and actually had a customer focus like, okay, these customers lives, I'm gonna save four hours of this customer's life. And if you actually sit down and look at all of that, it'll be. Easily automatable, easily you know, uh, applicable, uh, for ai, right? [00:20:39] Anush Elangovan: Like, but then making it happen is gonna take a little bit, right? It's like maybe it's, uh, paying your utility bill, right? Or something like that, right? Or, or, your healthcare explanation of benefits. Uh, like, I'm sure you get an explanation of benefits, and I'm like, I, I don't even know what that thing is. [00:20:55] Anush Elangovan: It's just like EOB and like. [00:20:57] Andrew Zigler: it's a big, a big old PDF. Yeah, [00:21:00] exactly. [00:21:01] Anush Elangovan: Like, like, I'm like great straight to the, uh, shredder, right? And but that could be, you know, automated with the ai, right? It, it, it'd be like, Hey, the summary of this thing is you went and visited this day. Everything is okay. Everything is paid for, so don't worry, it's not a bill. [00:21:17] Anush Elangovan: That again, the same, uh, thing, but the sense of what that information overload is could be. Digested by ai, uh, accumulated over time and retrieved when you need it. Like, I don't, I actually don't even need to know this EOB right now, unless of course, whenever I need to know it, that maybe, you know, like for some benefits I need to figure out what do, what did I do over the past year and how do I apply it? Source:
31:35

Just in $AMD Anush "Speed is the moat"|ROCm🎙️ In the race to define the future of AI, what's the one advantage that truly lasts? It's not proprietary tech, argues Anush Elangovan Elangovan, VP of AI Software at AMD , but the sustainable speed of innovation. He explains why AMD is rejecting the "walled garden" model for its open source ROCm stack, betting that an open community flywheel is the key to victory. Listen to understand how this open strategy is designed to out-innovate closed systems by empowering developers to solve everything from frontier-model challenges to the mundane, everyday problems that define the "last mile" of AI. AMD ROCm Software: Part 1 Transcript [00:00:00] Andrew Zigler: Joining me is Anush Elangovan, VP of AI software at AMD. And when people talk about AI compute, the conversation often stops at hardware specs, but it's more than just physical chips that win the game. It's also the software ecosystems supporting them. [00:00:18] Andrew Zigler: The prevailing strategy in the industry has been to build something like a walled garden. You know, something closed, proprietary locks, developers in. But AMD is betting on an entirely different play, open source acceleration, and with rock, their open source AI software stack. AMD is building not just hardware parity, but an innovation flywheel that's powered by the community with interoperability and the freedom to scale without all of that pesky lockin. [00:00:48] Andrew Zigler: And in this world, speed is your moat and how fast you can innovate while your platform remains open, flexible, and standardize across all of its applications. That's what we're gonna explore [00:01:00] today. So Anush, I'm really excited to have you here. Welcome to Dev Interrupted. [00:01:04] Anush Elangovan: Thanks for having me. Uh, super excited to chat about it. [00:01:07] Andrew Zigler: Amazing. Well, let's go ahead and dive right in with kind of what I laid it out with in the beginning, the idea of the moat and it being about speed. I wanna unpack that a bit because that came from you when you and I first spoke. And I, and I want to know, you know, how do you define speed inside of AMD beyond just things like hardware, benchmarks. [00:01:27] Anush Elangovan: Yeah, that's a very good question. So when we typically talk about speed, everyone's like, Hey, hardware benchmark specs, right? Like, uh, memory bandwidth or, or flops. And that is one important part of it, uh, AMD does very well. With that, we do have, a, a very good history of executing on that axis. [00:01:47] Anush Elangovan: But when I say speed is the moat, it is about, uh, how we prepare, how we build the muscle to run the race for a long time and run it fast. And it is [00:02:00] not about a single point in time that you've, you've beat some you know, benchmark and, and you declare victory. It's about building the ability to consistently develop and deliver. [00:02:13] Anush Elangovan: Both hardware and software innovation at scale and do it fast, right? Like, you know, we we're increasingly getting to a point where models come out and they're, uh, you know, a year or two ago it was like, Hey, they work on AMD on day zero, which is great, but now they are performing on AMD the day it releases, right? [00:02:32] Anush Elangovan: So, what does it take to Prefetch where the industry is going? Be prepared to intercept. At that point is what you know, I, I refer to as you know, the, the speed factor in, in creating this mode, right? And the mode is just shed all things that hold you back and run as fast as you can. [00:02:53] Anush Elangovan: Uh, because the pace of innovation that is, uh, being seen in, in AI [00:03:00] industries is just. Amazing. Right? And it's like, it's transformational at at how you generate electricity. It's transformational as at how you build data centers. It's transformational at how you deploy compute, networking. It's transformational at what kind of use cases you, you know, uh, use AI for. [00:03:17] Anush Elangovan: Uh, and for that, you need to be prepared to, see what comes tomorrow and be prepared to run the race tomorrow. [00:03:23] Andrew Zigler: Yeah, it's a really great perspective because it highlights that it's not just like a checkpoint that you run through. I like how you called out, like it's not just hitting that benchmark or being the best in class at that moment, in that snapshot, it's about having a. The throughput and about having that dedication to the idea and continuing to deliver on it. [00:03:43] Andrew Zigler: It's not just crossing the threshold, but it's also being the engine. And that's what, that's what protects a business. That is the moat, because the moat is that innovation layer, the faster and more, uh, future forward. That you can work and think, [00:04:00] you know, the better. Uh, we, we talk a lot about like future forward work styles. [00:04:04] Andrew Zigler: Like what are the things I could be doing right now today that are gonna be like, way more useful tomorrow? Let, let's abandon those, workflows that are older and that kind of like, that translates into. An advantage when you work that way. You know, what kind of things have you learned working with, uh, like across all spectrums of people who would use ROCm, right? [00:04:23] Andrew Zigler: You have like the developers, but then you also have the enterprises and you have this large span of adoptees, right? So what is the, what does that look like that you learn? [00:04:32] Anush Elangovan: Yeah, so, so the way I look at it is there are gonna be pockets of different, uh, you know, cadences, right? Like, so people who are deploying in enterprises, for example, right? The validation and how long it takes for them to deploy an LLM that's secure. It's, with guardrails, et cetera, maybe longer. [00:04:52] Anush Elangovan: but you still have to go through the process and you have to be prepared to like, walk that walk to deploy an enterprises. That doesn't mean it's [00:05:00] not fast, that's as fast as you can do for that industry, right? And if you are deploying AI in healthcare, right, it's, it's got its own, uh, cycle. [00:05:07] Anush Elangovan: but in each one of these, you want to see how, like, go down to the essence of what is it that you actually have to do. And, you know, I, I, I like how you framed it. It's like it's, you shed your prior assumptions of how things are done, right. And, and you kind of build up from a, uh, first principles, uh, approach to say, this is how I could use AI to unlock, whatever I'm doing. [00:05:33] Anush Elangovan: And, and, some of it, you know, it's good to really step back and look at. Just question every part of it, right? Like right now you're getting chat GPT and, Gemini competing for like, math, olympiads and, and, uh, college, uh, reasoning, uh, tests. Right? And, and those are like that, that is amazing and increasingly like complex tasks that they're trying to do. [00:05:58] Anush Elangovan: But there may also be like. [00:06:00] More mundane things that AI could, could get applied to. Right? And, and so when we think about shedding old ways, you wanna shed it not just in like the tip of the spear. It's like, you know, I'm gonna see what's the frontier model. It's also, it could be something as simple as. [00:06:18] Anush Elangovan: How do you choose a, a movie, uh, you know, like a recommendation system, right? Or, or, uh, an automated, uh, flight, uh, rebooking system. So the moment, you know, your flight is late, uh, right now it's a notification, right? It's like, oh, you got a text message saying your flight's late. And I got that like three times this week. [00:06:38] Anush Elangovan: But anyway, uh, and, and, and, and, I was just like, okay, so if I were to rethink this. All this MCPs that we have that should be hooked up into an MCP that says, your flight's delayed. Here are your options. If you want, you know, these are the paid options. Yeah. Here are the free options. This will get you back into your you know, Toronto airport [00:07:00] tonight. [00:07:00] Anush Elangovan: Or if you stay, here's a hotel plus this, plus this, plus. It's just like, go ahead is all I should say. Versus now I'm like, okay, can someone, you know, can I call a travel agent? Can I do this? Can I go online and log into And you know, so we gotta fundamentally rethink even those like small, nuances of, things that we do that can be automated out and AI is really, really good at doing something like this, right? Maybe I just explained an AI startup idea right now. Somebody should just start that. [00:07:29] Andrew Zigler: I think you did. Yeah, you definitely did. Someone, one of our listeners is definitely going to lift that off of you. I, I, I, you know, I hate being on the receiving end of those. You feel a little helpless and then you have to like, follow the whole flow. So I know what you mean. Like I, I like how you called out that the build and this like. [00:07:45] Andrew Zigler: Where speed is your moat and the innovation layer is protecting you, is what makes you better than your competitors. How you scale that and you bring that to market. So by understanding the problems that you're solving, uh, throwing away those older assumptions, but also [00:08:00] recognizing that like. We're building every single day, new things and new ways of using stuff that we're still figuring out the implications of. [00:08:08] Andrew Zigler: And so when you have a lot of velocity and you're introducing a lot of new ideas, and maybe you have that workflow now that automatically rebook your flight off of your late flight text message, and uh, I know I would certainly use it, but you know, what kind of philosophies guide the way that y'all think about building this ecosystem to manage that stability while letting folks. [00:08:29] Andrew Zigler: Play with the speed and the assumptions and the airplane re bookings. [00:08:34] Anush Elangovan: so, so I think, you know, we need to peel one layer down, right? and the philosophy is, Hey, we, we just discovered electricity, right? And you know what we're gonna do? We are gonna make motors, uh, or dynamos, right? Like engines. Uh, sure. We don't know if it's gonna be a Ferrari that you're gonna make, or it's a a a a dump truck. [00:08:57] Anush Elangovan: That's good for doing this. But let's [00:09:00] let, which is also required, right? You need a dump truck. You need a garbage truck. And, [00:09:04] Andrew Zigler: Yeah. You need the [00:09:04] Anush Elangovan: course you need, uh, a Ferrari for a midlife crisis, right? So, [00:09:09] Andrew Zigler: precisely. [00:09:10] Anush Elangovan: But, but my, uh, point is what do we build next? And, uh, and this is what I meant by like, okay, let's, let's take those baby steps to build the. [00:09:20] Anush Elangovan: Infrastructure that's required that we know we'll have to use, right? So, so if I just discovered electricity, okay, great. Now one, how do I save this electricity and how do I use it? So there's battery technology, so you need to do something like that, right? Like so. But then you also want to make it into an actionable thing. [00:09:37] Anush Elangovan: You want to make it for like automobiles, or you wanna use it for, you know, powering, uh, entire cities. So it is that transformational. So, uh, AI is that transformational. So, if you distill down, it'll, it'll come down to how do we think about, what we can do with this this fundamental technology that, We may not be aware of what it [00:10:00] is gonna unlock next, but at least you know the next step is clear, right? It's like a dense fog, you know, it's gonna be like, it, it's the right path. You see the light, but it's kind of like out there and, and the steps you're taking are concrete and you're like, okay, this is good. [00:10:16] Anush Elangovan: I, this is better than where I was or where we were. So we are moving forward. So you can build with the. Intuition from what you see in the short term and a tactical view, but towards what you think the future is gonna be. [00:10:28] Andrew Zigler: Right. You almost like we're all in this like fog of war, right? And like you said, you're reaching out and you're trying to step through it. You could think of it too, as like you're in the dark and your hands are up in front of you and you know that. You're, you're not gonna run your face into a wall because your hands are out in front of you, but you're not gonna maybe do much better than that. [00:10:45] Andrew Zigler: So that's kind of like, I think the eco, the, the industry, the world that we find ourselves in, uh, and we all have to, then this becomes the power of an ecosystem, of a group of people working together to create that layer of, [00:11:00] uh, of establishing the [00:11:01] Anush Elangovan: exactly. And I, I, I just, instead of, you know, saying fog of war I describe it as like, you're in this. Beautiful valley with like a morning, uh, fog that's in. You can smell the flowers. You, you hear the birds. You are like, okay, it's, we are in like, uh, utopian paradise and yes, I just need to like, continue the walk, right? [00:11:24] Anush Elangovan: and then move forward with that, conviction that you're in the right spot. [00:11:27] Andrew Zigler: Yeah. So let's talk about that ecosystem world. This nice, I love how you describe it, this grassy side of a hill in the morning that's covered in some mist and maybe we can't see 30 feet in one direction, but it sure is a beautiful hill and it smells nice. And so we're all here. And why is, in that world, why is. [00:11:44] Andrew Zigler: You know, open source, their strategic advantage that y'all are going for in the AI hardware market. And, and then how does like ROCm turn that into wins for people within that ecosystem? [00:11:56] Anush Elangovan: you know, the, the way we look at it is this, is kind of like how I view [00:12:00] AI and the ecosystem, right? But, but it is for everyone to enjoy. Uh, and so we do want to make sure that. You know, it is, uh, beneficial for everyone. [00:12:09] Anush Elangovan: The ecosystem can come in and, and innovate. It's an open innovation engine. and uh, it is very different from, you know, having a walled garden with, Hey, only I know how to do this and I'm gonna do it and throw it over the fence and you can use it or keep walking, right? So we'd like to be good citizens that way, but also. [00:12:30] Anush Elangovan: Uh, it is self-fulfilling in a way, right? Like it, the, the pace at which we innovate with open source is unmatched. Like, you know, our serving engines are like VLLM and, and sg l. Those things, uh, those frameworks are like super, super aggressive in terms of how fast they come out with features and how fast they can you know, get performant models out. [00:12:52] Anush Elangovan: And that compared with what, uh, you'd get from, you know, the likes of like T-R-T-L-L-M or something is always lagging, right? Because you [00:13:00] just can't keep up with you know, 200 commits a week just on one particular model to get that model really performant [00:13:06] Andrew Zigler: And, and, and in that world where, you know, everyone can enjoy the winds of this, what kind of customer stories or innovation stories have really stood out to you and excite you about building and creating this place for developers? [00:13:19] Anush Elangovan: Yeah. So I think the parts that are super exciting for me are when when we get to see a customer that is first skeptical. Then they start a little like, okay, fine, we'll give you a chance. Uh, we do a simple, uh, POC and then they're like, huh, this seems to work. Yeah, we told you it works. [00:13:42] Anush Elangovan: You don't have to change one line of code. Really? Yes, no need to change one line of code. Okay, let's try a production workload. So then they try it. Oh, you're more performant than the competition. Yes. We're more performant than, than the competition. So how much does it cost? And we're like, oh, it's your TCO is better with, uh, [00:14:00] AMD. [00:14:00] Anush Elangovan: So again, they're like, wow, okay, good. So now how do we deploy at scale? And then we go deploy it at scale. And when they give a thumbs up on that and they say, this is good, right? That's when you know, you, you see it go full circle from like, oh, we, we've never heard about AMD to like actually deploy to tens of thousands of GPUs In the order of a few months, right? It, it, it really is fascinating to see and very exciting and invigorating to [00:14:28] Andrew Zigler: Yeah. At like a great exposure to a lot of interesting problems. And, and then people using the infrastructure, the, the technology available to solve those problems. Really specific problems by the way, that's often why they're bringing their data and AI to it, uh, is because it is really specific and important for them. [00:14:45] Andrew Zigler: And there's a, a lot I think that other engineering orgs can learn and even emulate from AMD's success and, and having this open source ecosystem and it causing this acceleration within. You [00:15:00] know, uh, customers and enterprises that use and adopt the tools and, and, and that creates an advantage. And that goes back to why we're talking and like the real thesis of our conversation today. [00:15:10] Andrew Zigler: So how do you think engineering leaders that are listening to this and obviously tapping into this great success AMD has from an open source flywheel, how do you think other, other folks building in the same space can foster that open, first, that open source oriented culture in order to, you know, accelerate their innovation goals? [00:15:29] Anush Elangovan: Yeah, that's a very good question. So the startup that um, was acquired by AMD we, we built, I mean, we started off doing iot stuff and you know, smart ring and all that, right? But in the, the end of like, uh, and not the end, the last six years of the company was building ML compilers. [00:15:47] Anush Elangovan: And ml, ML compilers are like super, uh, complicated, sophisticated, advanced algorithms, dah, dah, dah. but it was all open source, right? So our VCs were like, wait, what do you mean your core [00:16:00] IP is open source? And um, the speed is the moat applied even then, right? It was just like, yes, if you have an idea that. [00:16:08] Anush Elangovan: Because someone saw this idea that you are, they're gonna be able to catch up, then you probably have the wrong idea anyway. But if they are, you know, you execute and they're gonna catch up, that you should assume they're gonna catch up. Right? So you gotta move forward. So keeping it open source is super important. [00:16:25] Anush Elangovan: But also to your question on like, you know, the learnings from an AMD standpoint, right? If there are, hard problems, I'd say dig in and work through it, right? Like there's no way but through it, right? That should be the simple mentality. And more, uh, frequently than not. you'll see that you'll just make it through in a, in, in good form. [00:16:52] Anush Elangovan: But if you doubt it and you're like, oh, I don't know if I should commit, if I'm, I, you know, what should just commit to do the right thing [00:17:00] every step, right? Every step, and just keep taking one step in front of the other. And in no time you'll see that you'll be running. Right. And, and yes, the first few steps will be like, yeah, everyone's complaining about your software quality. [00:17:15] Anush Elangovan: Everyone's complaining about this and that, and it doesn't work. And, and a few steps in, you know, you get, you get the hang of all the complaints that are coming in. You get the feedback loop. You're like, okay, what, what are you prioritizing again? One step in front of the other, right? You just keep knocking that out and then you get to a point where you're, it just becomes second nature, right? To do the, to do the right thing. And, and then yes, if someone gives you two options, you'll be like, fine. This is, uh, you know, there's always the resource trade off. There's always a human capital trade off, but what's the right thing to do? of course, I, I'm pragmatic about what we choose, but, but if the right thing for your long-term success is dig in, go first, principles, make it [00:18:00] happen. [00:18:00] Anush Elangovan: Well. Then just go for that. There's, there is no shortcut to [00:18:04] Andrew Zigler: acknowledging, you know, how it aligns with your mission, your core company goals, and what you're looking to achieve. And, and I, I love how you rightfully called out that in the open source world and you know, you have your technology that you've built, what you think is your moat upon, right? [00:18:22] Andrew Zigler: It's your code and, and to open source that, or to just make it where anyone could peer in is, you know. Scary in one regard, but two, it just kind of feels like you're handing away your throne room in some kind of sense, a very direct feeling sense. But the ultimately, you were really right to call out, and this is something I think about all the time, that the real power there is still the speed This the speed. [00:18:42] Andrew Zigler: That was the moat at the beginning of our conversation. It's the speed in combination with your. Very specific domain understanding of what you're building and what you're creating, and your new role as the steward of that world and how people plug into it, which [00:19:00] has frankly, a lot more influence and power than lording over a closed. [00:19:04] Andrew Zigler: You know, repository or an ecosystem, and like you said, like throwing things over the wall. Sure. There, there might be people always on the other side of that wall, but you're not gonna have a great connection with them. You're not gonna be able to really clearly understand them. I, I like your metaphor of the side of the field of the mountain a lot more. [00:19:23] Andrew Zigler: But, but in the, in this world, you know, where. That speed is, is the power and, and open source is just one way that you can harness that speed to get really far ahead and to innovate. , There's other parts of this equation that you can be experimenting with too, and I'd love to pick your brain about them as a software leader and, and, and one of them is about looking forward and kind of understanding that future that we're all building towards and beyond today's models and hardware. [00:19:48] Andrew Zigler: You know, what do you see as the next major bottleneck or opportunity in the AI compute space? As, as you know, enterprises and folks start to get a little more mature about what's available to [00:20:00] them. [00:20:00] Anush Elangovan: Yeah, I think, the bottleneck and opportunity is, uh, what I'd call, call walking the last mile of ai. Right. Uh, and like I I, I gave you an example, uh, previously, but, but it's similar to that. It's like there are cases where Humans have so many, uh, things to do in your day. You know, like the, if we sit down and actually had a customer focus like, okay, these customers lives, I'm gonna save four hours of this customer's life. And if you actually sit down and look at all of that, it'll be. Easily automatable, easily you know, uh, applicable, uh, for ai, right? [00:20:39] Anush Elangovan: Like, but then making it happen is gonna take a little bit, right? It's like maybe it's, uh, paying your utility bill, right? Or something like that, right? Or, or, your healthcare explanation of benefits. Uh, like, I'm sure you get an explanation of benefits, and I'm like, I, I don't even know what that thing is. [00:20:55] Anush Elangovan: It's just like EOB and like. [00:20:57] Andrew Zigler: it's a big, a big old PDF. Yeah, [00:21:00] exactly. [00:21:01] Anush Elangovan: Like, like, I'm like great straight to the, uh, shredder, right? And but that could be, you know, automated with the ai, right? It, it, it'd be like, Hey, the summary of this thing is you went and visited this day. Everything is okay. Everything is paid for, so don't worry, it's not a bill. [00:21:17] Anush Elangovan: That again, the same, uh, thing, but the sense of what that information overload is could be. Digested by ai, uh, accumulated over time and retrieved when you need it. Like, I don't, I actually don't even need to know this EOB right now, unless of course, whenever I need to know it, that maybe, you know, like for some benefits I need to figure out what do, what did I do over the past year and how do I apply it? Source:

Mike

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