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Forgot to post on twitter - my conversation with dax from OpenCode Talked a lot about how programming is changing & not changing. I really enjoyed a lot of the historical takes that Dax has. He's building one of these "agentic coding" tools, so he sees the value in...

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NeetCode

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41,213 次观看 • 5 个月前 •via X (Twitter)

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Get the inside story on the development of Gemini's coding capabilities. Listen as the product and research leads for Gemini share their philosophy on what makes a great coding model, the impact of "vibe coding," and the future of programming languages with Logan Kilpatrick, Connie Fan and Danny Tarlow. Timecodes: 0:00 Intro 1:10 Defining Early Coding Goals 6:23 Ingredients of a Great Coding Model 9:28 Adapting to Developer Workflows 11:40 The Rise of Vibe Coding 14:43 Code as a Reasoning Tool 17:20 Code as a Universal Solver 20:47 Evaluating Coding Models 24:30 Leveraging Internal Googler Feedback 26:52 Winning Over AI Skeptics 28:04 Performance Across Programming Languages 33:05 The Future of Programming Languages 36:16 Strategies for Large Codebases 41:06 Hill Climbing New Benchmarks 42:46 Short-Term Improvements 44:42 Model Style and Taste 47:43 2.5 Pro’s Breakthrough 51:06 Early AI Coding Experiences 56:19 Specialist vs. Generalist Models
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Get the inside story on the development of Gemini's coding capabilities. Listen as the product and research leads for Gemini share their philosophy on what makes a great coding model, the impact of "vibe coding," and the future of programming languages with Logan Kilpatrick, Connie Fan and Danny Tarlow. Timecodes: 0:00 Intro 1:10 Defining Early Coding Goals 6:23 Ingredients of a Great Coding Model 9:28 Adapting to Developer Workflows 11:40 The Rise of Vibe Coding 14:43 Code as a Reasoning Tool 17:20 Code as a Universal Solver 20:47 Evaluating Coding Models 24:30 Leveraging Internal Googler Feedback 26:52 Winning Over AI Skeptics 28:04 Performance Across Programming Languages 33:05 The Future of Programming Languages 36:16 Strategies for Large Codebases 41:06 Hill Climbing New Benchmarks 42:46 Short-Term Improvements 44:42 Model Style and Taste 47:43 2.5 Pro’s Breakthrough 51:06 Early AI Coding Experiences 56:19 Specialist vs. Generalist Models

Google AI Developers

65,474 次观看 • 1 年前

Here's my conversation with the founding team of Cursor, a popular code editor (based on VSCode) that specializes in AI-assisted programming. This is a super technical conversation that is bigger than just about one code editor. It's about the future of programming and, in general, the future of human-AI collaboration. It's here on X in full, and is up on everywhere else. Timestamps: 0:00 - Introduction 0:59 - Code editor basics 3:09 - GitHub Copilot 10:27 - Cursor 16:54 - Cursor Tab 23:08 - Code diff 31:20 - ML details 36:54 - GPT vs Claude 43:28 - Prompt engineering 50:54 - AI agents 1:04:51 - Running code in background 1:09:31 - Debugging 1:14:58 - Dangerous code 1:26:09 - Branching file systems 1:29:20 - Scaling challenges 1:43:32 - Context 1:48:39 - OpenAI o1 2:00:01 - Synthetic data 2:03:48 - RLHF vs RLAIF 2:05:34 - Fields Medal for AI 2:08:17 - Scaling laws 2:17:06 - The future of programming
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Here's my conversation with the founding team of Cursor, a popular code editor (based on VSCode) that specializes in AI-assisted programming. This is a super technical conversation that is bigger than just about one code editor. It's about the future of programming and, in general, the future of human-AI collaboration. It's here on X in full, and is up on everywhere else. Timestamps: 0:00 - Introduction 0:59 - Code editor basics 3:09 - GitHub Copilot 10:27 - Cursor 16:54 - Cursor Tab 23:08 - Code diff 31:20 - ML details 36:54 - GPT vs Claude 43:28 - Prompt engineering 50:54 - AI agents 1:04:51 - Running code in background 1:09:31 - Debugging 1:14:58 - Dangerous code 1:26:09 - Branching file systems 1:29:20 - Scaling challenges 1:43:32 - Context 1:48:39 - OpenAI o1 2:00:01 - Synthetic data 2:03:48 - RLHF vs RLAIF 2:05:34 - Fields Medal for AI 2:08:17 - Scaling laws 2:17:06 - The future of programming

Lex Fridman

1,261,340 次观看 • 1 年前

Claude Code is a major (and accidental!) hit for Anthropic that surprised even its creator, Boris Cherny. Claude Code, an Agentic AI coding product that lives in the terminal. Most of the new code at Anthropic is created through it today. And in the last 5 months since it was launched publicly, Claude Code went from $0 to $400M in revenue run rate (as per The Information). 00:00 – Intro 01:15 – Did You Expect Claude Code’s Success? 04:22 – How Claude Code Works and Origins 08:05 – Command Line vs IDE: Why Start Claude Code in the Terminal? 11:31 – The Evolution of Programming: From Punch Cards to Agents 13:20 – Product Follows Model: Simple Interfaces and Fast Evolution 15:17 – Who Is Claude Code For? (Engineers, Designers, PMs & More) 17:46 – What Can Claude Code Actually Do? (Actions & Capabilities) 21:14 – Agentic Actions, Subagents, and Workflows 25:30 – Claude Code’s Awareness, Memory, and Knowledge Sharing 33:28 – Model Context Protocol (MCP) and Customization 35:30 – Safety, Human Oversight, and Enterprise Considerations 38:10 – UX/UI: Making Claude Code Useful and Enjoyable 40:44 – Pricing for Power Users and Subscription Models 43:36 – Real-World Use Cases: Debugging, Testing, and More 46:44 – How Does Claude Code Transform Onboarding? 49:36 – The Future of Coding: Agents, Teams, and Collaboration 54:11 – The AI Coding Wars: Competition & Ecosystem 57:27 – The Future of Coding as a Profession 58:41 – What’s Next for Claude Code
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Claude Code is a major (and accidental!) hit for Anthropic that surprised even its creator, Boris Cherny. Claude Code, an Agentic AI coding product that lives in the terminal. Most of the new code at Anthropic is created through it today. And in the last 5 months since it was launched publicly, Claude Code went from $0 to $400M in revenue run rate (as per The Information). 00:00 – Intro 01:15 – Did You Expect Claude Code’s Success? 04:22 – How Claude Code Works and Origins 08:05 – Command Line vs IDE: Why Start Claude Code in the Terminal? 11:31 – The Evolution of Programming: From Punch Cards to Agents 13:20 – Product Follows Model: Simple Interfaces and Fast Evolution 15:17 – Who Is Claude Code For? (Engineers, Designers, PMs & More) 17:46 – What Can Claude Code Actually Do? (Actions & Capabilities) 21:14 – Agentic Actions, Subagents, and Workflows 25:30 – Claude Code’s Awareness, Memory, and Knowledge Sharing 33:28 – Model Context Protocol (MCP) and Customization 35:30 – Safety, Human Oversight, and Enterprise Considerations 38:10 – UX/UI: Making Claude Code Useful and Enjoyable 40:44 – Pricing for Power Users and Subscription Models 43:36 – Real-World Use Cases: Debugging, Testing, and More 46:44 – How Does Claude Code Transform Onboarding? 49:36 – The Future of Coding: Agents, Teams, and Collaboration 54:11 – The AI Coding Wars: Competition & Ecosystem 57:27 – The Future of Coding as a Profession 58:41 – What’s Next for Claude Code

Matt Turck

82,161 次观看 • 1 年前

The end of coding? *Epic* conversation with Guillermo Rauch of Vercel on vibe coding, MCP, agents and building products in the AI era. (00:00) - Intro: Guillermo's Story (02:08) - What is v0 and Why Did It Take Off So Fast? (04:10) - Building a hit product with < people (07:51) - Cursor, Replit, etc: v0 vs Other AI Coding Tools (10:35) - Everyone can cook - What is Vibe Coding? (17:05) - Is v0 Just Frontend? Moving Toward Full Stack 19:40 - How to Become a Great Vibe Coder (23:35) - A GUI for AI: The Future of Interfaces (29:46) - Developer Love vs Agent Love (33:41) - The Importance of Taste in the AI Era (39:10) - MCP: The Protocol for AI-to-AI Communication (43:11) - Security and the Risks of Agentic Web (45:25) - Are Enterprises Ready for the Agentic Future? (49:42) - Building a Data Flywheel (56:06) - The Vercel AI Cloud: From Pixels to Tokens (1:10:14) - Adapting to a Non-Developer Persona (1:13:47) - Retention, Margins, and the Business of AI (1:16:51) - The Secret Behind Vercel Last Year Growth (1:24:15) - The importance of Online Presence (1:30:49) - Everything, Everywhere, All at Once: Keeping your Sanity as a CEO (1:34:59) - Guillermo's Advice to Younger Self
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Matt Turck

86,153 次观看 • 1 年前

Clawdbot creator Peter Steinberger offers a sobering reframe: Programming isn't dying, it's just becoming a hobby. He's being honest about where it's headed. He recently came across an article making the case that it's okay to mourn programming as a craft, and it struck a chord: "A part of me very strongly resonates with that because in my past, I spent a lot of time tinkering, just being really deep in the flow and just like cranking out code and like finding really beautiful solutions." But mourning, he says, is not the same as resisting. "It's okay to mourn it, but that's not something we can fight." His reasoning cuts to the core of why developer salaries ballooned in the first place: "The world for a long time had a lack of intelligence — if you see it like that — of people building things, and that's why salaries of software developers reached stupidly high amounts. And that will go away." The scarcity that made programming so financially rewarding was never really about the beauty of the craft. It was about supply and demand. AI is eroding that scarcity fast. And Peter Steinberger 🦞 isn't just theorising. He has shipped over 6,600 commits in a single month, much of it AI-assisted, sometimes without reading every line of code. That's what adaptation looks like in practice. The programmers who thrive won't be the ones who insist on writing every line themselves. They'll be the ones who understand systems deeply enough to direct, review, and build with AI as a collaborator. As Steinberger puts it: "The actual art of programming, it will stay there, but it's going to be like knitting — people do that because they like it, not because it makes any sense." The passion for code will always exist, but the era of it being a scarce and highly paid profession is coming to an end.
0:55

Clawdbot creator Peter Steinberger offers a sobering reframe: Programming isn't dying, it's just becoming a hobby. He's being honest about where it's headed. He recently came across an article making the case that it's okay to mourn programming as a craft, and it struck a chord: "A part of me very strongly resonates with that because in my past, I spent a lot of time tinkering, just being really deep in the flow and just like cranking out code and like finding really beautiful solutions." But mourning, he says, is not the same as resisting. "It's okay to mourn it, but that's not something we can fight." His reasoning cuts to the core of why developer salaries ballooned in the first place: "The world for a long time had a lack of intelligence — if you see it like that — of people building things, and that's why salaries of software developers reached stupidly high amounts. And that will go away." The scarcity that made programming so financially rewarding was never really about the beauty of the craft. It was about supply and demand. AI is eroding that scarcity fast. And Peter Steinberger 🦞 isn't just theorising. He has shipped over 6,600 commits in a single month, much of it AI-assisted, sometimes without reading every line of code. That's what adaptation looks like in practice. The programmers who thrive won't be the ones who insist on writing every line themselves. They'll be the ones who understand systems deeply enough to direct, review, and build with AI as a collaborator. As Steinberger puts it: "The actual art of programming, it will stay there, but it's going to be like knitting — people do that because they like it, not because it makes any sense." The passion for code will always exist, but the era of it being a scarce and highly paid profession is coming to an end.

Big Brain AI

15,023 次观看 • 4 个月前

AI has changed software engineering more in the last 3 years than it has changed in the previous 30. What’s needed is not a debate about whether it’s going away—instead it’s a serious discussion about its future: What are the new primitives, techniques, and best practices for software engineering in the age of AI. That’s why I brought Scott Wu (Scott Wu) on AI & I. He’s the founder of Cognition, the company behind the world’s first autonomous AI coding agent, Devin. Cognition got to $73M ARR in less than 2 years—and they just acquired Windsurf to accelerate their growth. I had Scott on the show to talk about where the programming goes from here. We get into: - What the new tools and workflows are for AI engineers. In the near term, Scott sees software engineering defined by a spectrum of tools. At one end are AI features that speed up coding, like tab complete; at the other are agentic systems, like Devin, that can take on tasks independently. Until engineers can operate entirely at the higher layer of abstraction, he argues, both are essential. - Why Scott thinks AGI is already here. By the benchmarks of a decade ago—passing the Turing test, solving hard math problems, and operating agentically—AGI is already here. The line keeps moving, he argues, because humans constantly redefine work around what machines can’t yet do. - Why developers will turn into product architects. Scott sees the long-term future of software engineering as a steady climb up the ladder of abstraction. Just as programming went from assembly to languages like Python and JavaScript, he thinks the future is humans focusing on the product, while AI agents execute. - How Devin stacks up against Anthropic’s Claude Code. Scott credits Claude Code’s success to great product design and the models becoming capable enough to support autonomous workflows. But according to him, the CLI itself isn’t the breakthrough, it’s how a tool fits into a developer’s workflow. Claude Code’s paradigm is that the AI is you, taking the wheel of your computer, he says, while Devin is like the engineer sitting beside you: it runs in its own cloud environment, manages the repo, and improves over time at testing and refining code. This episode of Every 📧’s AI & I is a must-watch for anyone interested in the brass tacks of how AI changes the future of programming. Watch below! Timestamps: Introduction: 00:02:02 Why Scott thinks AGI is here: 00:02:32 Scott’s personal journey as a founder: 00:09:27 Why the fundamentals of computer science still matter: 00:16:55 How the future of programming will evolve: 00:22:30 A new workflow for the AI-first software engineer: 00:26:50 How Devin stacks up against Claude Code: 00:29:33 Reinforcement learning to build better coding agents: 00:40:05 What excites Scott about AI beyond Cognition: 00:50:05
53:21

AI has changed software engineering more in the last 3 years than it has changed in the previous 30. What’s needed is not a debate about whether it’s going away—instead it’s a serious discussion about its future: What are the new primitives, techniques, and best practices for software engineering in the age of AI. That’s why I brought Scott Wu (Scott Wu) on AI & I. He’s the founder of Cognition, the company behind the world’s first autonomous AI coding agent, Devin. Cognition got to $73M ARR in less than 2 years—and they just acquired Windsurf to accelerate their growth. I had Scott on the show to talk about where the programming goes from here. We get into: - What the new tools and workflows are for AI engineers. In the near term, Scott sees software engineering defined by a spectrum of tools. At one end are AI features that speed up coding, like tab complete; at the other are agentic systems, like Devin, that can take on tasks independently. Until engineers can operate entirely at the higher layer of abstraction, he argues, both are essential. - Why Scott thinks AGI is already here. By the benchmarks of a decade ago—passing the Turing test, solving hard math problems, and operating agentically—AGI is already here. The line keeps moving, he argues, because humans constantly redefine work around what machines can’t yet do. - Why developers will turn into product architects. Scott sees the long-term future of software engineering as a steady climb up the ladder of abstraction. Just as programming went from assembly to languages like Python and JavaScript, he thinks the future is humans focusing on the product, while AI agents execute. - How Devin stacks up against Anthropic’s Claude Code. Scott credits Claude Code’s success to great product design and the models becoming capable enough to support autonomous workflows. But according to him, the CLI itself isn’t the breakthrough, it’s how a tool fits into a developer’s workflow. Claude Code’s paradigm is that the AI is you, taking the wheel of your computer, he says, while Devin is like the engineer sitting beside you: it runs in its own cloud environment, manages the repo, and improves over time at testing and refining code. This episode of Every 📧’s AI & I is a must-watch for anyone interested in the brass tacks of how AI changes the future of programming. Watch below! Timestamps: Introduction: 00:02:02 Why Scott thinks AGI is here: 00:02:32 Scott’s personal journey as a founder: 00:09:27 Why the fundamentals of computer science still matter: 00:16:55 How the future of programming will evolve: 00:22:30 A new workflow for the AI-first software engineer: 00:26:50 How Devin stacks up against Claude Code: 00:29:33 Reinforcement learning to build better coding agents: 00:40:05 What excites Scott about AI beyond Cognition: 00:50:05

Dan Shipper 📧

35,342 次观看 • 10 个月前

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 次观看 • 2 个月前

Claude Code cracked something open for us Every 🧱. Now I ship to codebases I barely know, every feature we ship makes the next one easier, and non-technical members of the team use the terminal. I’m genuinely grateful. So I brought its creators, Cat Wu (cat) and Boris Cherny (Boris Cherny) from Anthropic, on AI & I to say thank you—and to talk about everything they’ve learned from building Claude Code. We get into: • The workflows Anthropic’s smartest engineers use to push Claude Code to its limits. Why they pit subagents against each other to get cleaner results, how they turn past code into leverage, and the slash commands and MCPs they rely on most. • The product lessons behind one of the most loved AI agents in the world. How the team balances simplicity and power—building a tool that anyone can use, but that experts can bend to their will—and their philosophy of “unshipping,” or cutting back whenever there’s a simpler, more intuitive path to user intent. • A peek into the future of coding with AI. The new form factors they’re experimenting with to make Claude Code more autonomous, more reliable, and more accessible to non-technical users This is a must-watch for anyone—both technical and non-technical—who wants to learn how to use Claude Code like the people who built it. Watch below! Timestamps: Introduction: 00:01:26 Claude Code’s origin story: 00:02:25 How Anthropic dogfoods Claude Code: 00:07:03 Boris and Cat’s favorite slash commands: 00:14:06 How Boris uses Claude Code to plan feature development: 00:15:49 Everything Anthropic has learned about using sub-agents well: 00:21:53 Use Claude Code to turn past code into leverage: 00:26:16 The product decisions for building an agent that’s simple and powerful: 00:33:14 Making Claude Code accessible to the non-technical user: 00:36:38 The next form factor for coding with AI: 00:45:12
1:10:11

Claude Code cracked something open for us Every 🧱. Now I ship to codebases I barely know, every feature we ship makes the next one easier, and non-technical members of the team use the terminal. I’m genuinely grateful. So I brought its creators, Cat Wu (cat) and Boris Cherny (Boris Cherny) from Anthropic, on AI & I to say thank you—and to talk about everything they’ve learned from building Claude Code. We get into: • The workflows Anthropic’s smartest engineers use to push Claude Code to its limits. Why they pit subagents against each other to get cleaner results, how they turn past code into leverage, and the slash commands and MCPs they rely on most. • The product lessons behind one of the most loved AI agents in the world. How the team balances simplicity and power—building a tool that anyone can use, but that experts can bend to their will—and their philosophy of “unshipping,” or cutting back whenever there’s a simpler, more intuitive path to user intent. • A peek into the future of coding with AI. The new form factors they’re experimenting with to make Claude Code more autonomous, more reliable, and more accessible to non-technical users This is a must-watch for anyone—both technical and non-technical—who wants to learn how to use Claude Code like the people who built it. Watch below! Timestamps: Introduction: 00:01:26 Claude Code’s origin story: 00:02:25 How Anthropic dogfoods Claude Code: 00:07:03 Boris and Cat’s favorite slash commands: 00:14:06 How Boris uses Claude Code to plan feature development: 00:15:49 Everything Anthropic has learned about using sub-agents well: 00:21:53 Use Claude Code to turn past code into leverage: 00:26:16 The product decisions for building an agent that’s simple and powerful: 00:33:14 Making Claude Code accessible to the non-technical user: 00:36:38 The next form factor for coding with AI: 00:45:12

Dan Shipper 📧

57,619 次观看 • 9 个月前

Nat Eliason’s (Nat Eliason) career arc is borderline absurd—but it works. He’ll spot a new tool or trend, master it, build a business around it, and move on. Nat’s pulled it off with the note-taking wave ($600k in sales from a Roam Research course), real estate (6x return flipping property in Austin), and crypto (published his insider story with Random House). Now it’s AI: he’s running a viral course on building apps with AI—$200k in pre-sales in just a week, 800 students and counting. I’ve known Nat for a long time and I think he has a great sense for where the puck is headed. He was one of the first guests I had on the podcast and I was delighted to have him on again. Here are a few takeaways from our conversation: - Coding with AI has become orders of magnitude easier for non-technical people over the last 2 years—Nat rarely has to help students fix bugs; they troubleshoot in Cursor on their own. - AI coding assistants are creating new behaviours in programming, like using a speech-to-text model to talk to an agent and having it write code for you. - The traditional learning curve of coding is flattening because AI tools let beginners build and iterate in faster feedback loops. - AI has given Nat leverage in spades—it increases his ability to be a creator while also building a robust business with as few people to manage as possible. He demos an AI book editor he coded for his sci-fi novel. - In the age of AI, software is becoming content and the barriers to create are lower than ever—but custom software for everything isn’t the answer. Nat’s model is that personalized tools make sense for that one thing you care the most about. - Nat believes that the future of writing with AI is a Cursor-style interface with a model that’s trained on your style and voice. This episode is a must-watch for writers, creators, and anyone interested in the future of product building. Watch below! Timestamps: Introduction: 00:01:45 The origins of Nat’s viral course on building apps with AI: 00:11:45 How coding with AI has evolved over the last two years: 00:18:46 Nat creates an app using Composer, Cursor’s AI assistant: 00:22:22 Tactical tips for coding with Cursor: 00:26:06 How coding with AI is creating new behaviours in programming: 00:29:06 What excites Nat the most about the future of AI: 00:32:41 A demo of Hubbard, the AI editor Nat built for his science fiction writing: 00:38:58 When does it makes sense to build custom software: 00:44:52 Nat’s take on the future of writing with AI: 00:49:18
1:00:52

Nat Eliason’s (Nat Eliason) career arc is borderline absurd—but it works. He’ll spot a new tool or trend, master it, build a business around it, and move on. Nat’s pulled it off with the note-taking wave ($600k in sales from a Roam Research course), real estate (6x return flipping property in Austin), and crypto (published his insider story with Random House). Now it’s AI: he’s running a viral course on building apps with AI—$200k in pre-sales in just a week, 800 students and counting. I’ve known Nat for a long time and I think he has a great sense for where the puck is headed. He was one of the first guests I had on the podcast and I was delighted to have him on again. Here are a few takeaways from our conversation: - Coding with AI has become orders of magnitude easier for non-technical people over the last 2 years—Nat rarely has to help students fix bugs; they troubleshoot in Cursor on their own. - AI coding assistants are creating new behaviours in programming, like using a speech-to-text model to talk to an agent and having it write code for you. - The traditional learning curve of coding is flattening because AI tools let beginners build and iterate in faster feedback loops. - AI has given Nat leverage in spades—it increases his ability to be a creator while also building a robust business with as few people to manage as possible. He demos an AI book editor he coded for his sci-fi novel. - In the age of AI, software is becoming content and the barriers to create are lower than ever—but custom software for everything isn’t the answer. Nat’s model is that personalized tools make sense for that one thing you care the most about. - Nat believes that the future of writing with AI is a Cursor-style interface with a model that’s trained on your style and voice. This episode is a must-watch for writers, creators, and anyone interested in the future of product building. Watch below! Timestamps: Introduction: 00:01:45 The origins of Nat’s viral course on building apps with AI: 00:11:45 How coding with AI has evolved over the last two years: 00:18:46 Nat creates an app using Composer, Cursor’s AI assistant: 00:22:22 Tactical tips for coding with Cursor: 00:26:06 How coding with AI is creating new behaviours in programming: 00:29:06 What excites Nat the most about the future of AI: 00:32:41 A demo of Hubbard, the AI editor Nat built for his science fiction writing: 00:38:58 When does it makes sense to build custom software: 00:44:52 Nat’s take on the future of writing with AI: 00:49:18

Dan Shipper 📧

27,207 次观看 • 1 年前

WARP SPEED: EPISODE 4 - starring Daksh Gupta, CEO of Greptile Daksh Gupta The core difference between human productivity and every other species? Tools. On the margin, slightly better tools create long-term efficiencies that unlock time for more important things. Daksh, Soohoon Choi , and Vaishant Kameswaran built Greptile into one of the fastest-growing AI code review platforms in the world. $25M Series A led by Benchmark . YC-backed. 16 people. 500M+ lines of code reviewed this month alone for companies like Brex, Substack, and PostHog. The insight: AI coding tools are exploding. Cursor, Claude Code, Devin. Everyone's writing more code than ever. But the systems for validating that code before it ships? Breaking down. Greptile is the independent, centralized validation layer - AI that reviews pull requests, catches bugs, enforces standards. Daksh didn't plan to start a company. He was in senior year when he realized he'd lost his way. Remembered why he chose CS in the first place: to build things. He then convinced his college roommate - "the smartest person I know" - to move to San Francisco with barely enough angel money to survive. Now they're one of the most craft-obsessed teams in SF - using Linear, Claude Code, Cursor, and Warp to ship at warp speed. In this conversation: (0:00) - From losing his way in college to moving to San Francisco (1:24) - Applying to Y Combinator and building the foundations of Greptile (2:37) - Hiring more senior than typical: arbitraging ageism in Silicon Valley (3:40) - Building a company worthy of people who could go anywhere (4:14) - The new bottleneck: producing code is worthless without validating it (4:52) - The Greptile stack: Linear, Claude Code, Cursor, Warp (5:55) - "People talked about Warp with a passion you wouldn't expect for back-office software" (6:17) - The magic of heavy automation: "I'm never in it" (6:42) - Built for engineers, not HR people: "When you're used to confusing software, simple things are just delightful" (7:46) - Scaling from 15 to 50: "Whatever will go wrong, Warp will have functionality for it" (8:55) - Discovering Warp on Twitter: "Why are people so excited about payroll?" (9:35) - "It inspires you to build more intuitive things"
10:17

WARP SPEED: EPISODE 4 - starring Daksh Gupta, CEO of Greptile Daksh Gupta The core difference between human productivity and every other species? Tools. On the margin, slightly better tools create long-term efficiencies that unlock time for more important things. Daksh, Soohoon Choi , and Vaishant Kameswaran built Greptile into one of the fastest-growing AI code review platforms in the world. $25M Series A led by Benchmark . YC-backed. 16 people. 500M+ lines of code reviewed this month alone for companies like Brex, Substack, and PostHog. The insight: AI coding tools are exploding. Cursor, Claude Code, Devin. Everyone's writing more code than ever. But the systems for validating that code before it ships? Breaking down. Greptile is the independent, centralized validation layer - AI that reviews pull requests, catches bugs, enforces standards. Daksh didn't plan to start a company. He was in senior year when he realized he'd lost his way. Remembered why he chose CS in the first place: to build things. He then convinced his college roommate - "the smartest person I know" - to move to San Francisco with barely enough angel money to survive. Now they're one of the most craft-obsessed teams in SF - using Linear, Claude Code, Cursor, and Warp to ship at warp speed. In this conversation: (0:00) - From losing his way in college to moving to San Francisco (1:24) - Applying to Y Combinator and building the foundations of Greptile (2:37) - Hiring more senior than typical: arbitraging ageism in Silicon Valley (3:40) - Building a company worthy of people who could go anywhere (4:14) - The new bottleneck: producing code is worthless without validating it (4:52) - The Greptile stack: Linear, Claude Code, Cursor, Warp (5:55) - "People talked about Warp with a passion you wouldn't expect for back-office software" (6:17) - The magic of heavy automation: "I'm never in it" (6:42) - Built for engineers, not HR people: "When you're used to confusing software, simple things are just delightful" (7:46) - Scaling from 15 to 50: "Whatever will go wrong, Warp will have functionality for it" (8:55) - Discovering Warp on Twitter: "Why are people so excited about payroll?" (9:35) - "It inspires you to build more intuitive things"

Ayush S

27,984 次观看 • 8 个月前

Old software engineering patterns are coming back because of coding agents. Dax Raad(dax), co-founder of AI coding agent OpenCode, on why DDD is becoming more relevant and less painful: “I think it's the same problem as always, which is how do you make code bases that are easy to work in, scalable, that can flex to new requirements. A lot of the old patterns for me are coming back. We've always been a big domain-driven design company. We did it in a very light way. We're now doing it in a much heavier way because we find that these, kind of boring, enterprisey patterns end up being pretty useful because you have a bunch of idiots on your team now. Coding agents are a bunch of idiots. They are going to work 24/7 and they're going to ship a lot of stuff so you need way more guardrails than you used to. What's nice is some of these old patterns we hated because they were very verbose, they produced reliable code that was modular and safe but they were very verbose and annoying to type out… you're not typing it out anymore. Now you can get the benefits of these patterns without the downsides."
1:03

Old software engineering patterns are coming back because of coding agents. Dax Raad(dax), co-founder of AI coding agent OpenCode, on why DDD is becoming more relevant and less painful: “I think it's the same problem as always, which is how do you make code bases that are easy to work in, scalable, that can flex to new requirements. A lot of the old patterns for me are coming back. We've always been a big domain-driven design company. We did it in a very light way. We're now doing it in a much heavier way because we find that these, kind of boring, enterprisey patterns end up being pretty useful because you have a bunch of idiots on your team now. Coding agents are a bunch of idiots. They are going to work 24/7 and they're going to ship a lot of stuff so you need way more guardrails than you used to. What's nice is some of these old patterns we hated because they were very verbose, they produced reliable code that was modular and safe but they were very verbose and annoying to type out… you're not typing it out anymore. Now you can get the benefits of these patterns without the downsides."

The Pragmatic Engineer

175,050 次观看 • 2 个月前

In 2025, it was rational to be skeptical about whether AI would change the future of software development. In 2026, it's not, anymore. With Charity Majors: Timestamps: 00:00 Intro 02:56 How Parse led to Honeycomb 06:00 The limits of individual productivity metrics 09:08 How Charity’s perspective on AI has evolved 13:50 Rewriting code vs. editing code 19:20 Production as a stage of development 22:14 Code reviews 26:56 Non-deterministic systems 31:11 Sensible uses of AI 37:41 The two AI camps 44:40 Why AI works so well for building software 49:42 DevOps 55:13 Modern observability 1:00:40 Handling context overload 1:01:56 What’s new in Observability Engineering’s 2nd edition 1:07:45 What effective leadership looks like 1:10:25 Engineering management: what is changing? 1:16:31 Junior engineers 1:18:01 AI fatigue 1:21:39 Book recommendations Brought to you by: • Antithesis — turbocharge testing of your systems by running your whole system under aggressive fault injection. Teams like Jane Street, and the etcd community rely on Antithesis. • Buildkite — the CI platform trusted by OpenAI, Anthropic, Cursor, Meta, Uber, NVIDIA, Airbnb and many more. Engineered to absorb whatever your coding agents throw at the build queue. • WorkOS — make your app and agents Enterprise Ready, with SSO, SCIM, RBAC, and more. 1. The question engineers need to answer: what would it take for you to be fully comfortable shipping code you have not read? Charity believes it is a “when” and not an “if” that professional software engineers will ship code they never looked at – and thus do not understand – to production. Engineering is building the systems that validate this code, and allow shipping with full confidence. 2. AI could have the software industry go through the “pets” to “cattle” change that compute infra went through in the 2010s. Up to now, writing software from scratch was far more expensive than editing existing software. But now, generating hundreds of variants of a function can be done faster than how long it would take you to hand-write it once. Charity believes that we might be at the beginning of the transition from “pets” to “cattle” that happened at the hardware infrastructure layer. Before the 2010s, configuring and repairing individual servers was commonly done. But with tools like Terraform and Kubernetes, individual servers having issues are no longer fixed up: they are re-created instead. Charity thinks the same might happen with code, sooner rather than later. When there’s an issue with the code, generate new code that solves it, and is verifyably correct.​ 3. Non-deterministic systems require more engineering discipline versus before. With code written by AI, we’re reducing the trust in the code (because we no longer wrote it), so we need to increase trust at the other part of the development process. Specifically, at validation: with things like tests, evals, and conformance testing.
1:26:22

In 2025, it was rational to be skeptical about whether AI would change the future of software development. In 2026, it's not, anymore. With Charity Majors: Timestamps: 00:00 Intro 02:56 How Parse led to Honeycomb 06:00 The limits of individual productivity metrics 09:08 How Charity’s perspective on AI has evolved 13:50 Rewriting code vs. editing code 19:20 Production as a stage of development 22:14 Code reviews 26:56 Non-deterministic systems 31:11 Sensible uses of AI 37:41 The two AI camps 44:40 Why AI works so well for building software 49:42 DevOps 55:13 Modern observability 1:00:40 Handling context overload 1:01:56 What’s new in Observability Engineering’s 2nd edition 1:07:45 What effective leadership looks like 1:10:25 Engineering management: what is changing? 1:16:31 Junior engineers 1:18:01 AI fatigue 1:21:39 Book recommendations Brought to you by: • Antithesis — turbocharge testing of your systems by running your whole system under aggressive fault injection. Teams like Jane Street, and the etcd community rely on Antithesis. • Buildkite — the CI platform trusted by OpenAI, Anthropic, Cursor, Meta, Uber, NVIDIA, Airbnb and many more. Engineered to absorb whatever your coding agents throw at the build queue. • WorkOS — make your app and agents Enterprise Ready, with SSO, SCIM, RBAC, and more. 1. The question engineers need to answer: what would it take for you to be fully comfortable shipping code you have not read? Charity believes it is a “when” and not an “if” that professional software engineers will ship code they never looked at – and thus do not understand – to production. Engineering is building the systems that validate this code, and allow shipping with full confidence. 2. AI could have the software industry go through the “pets” to “cattle” change that compute infra went through in the 2010s. Up to now, writing software from scratch was far more expensive than editing existing software. But now, generating hundreds of variants of a function can be done faster than how long it would take you to hand-write it once. Charity believes that we might be at the beginning of the transition from “pets” to “cattle” that happened at the hardware infrastructure layer. Before the 2010s, configuring and repairing individual servers was commonly done. But with tools like Terraform and Kubernetes, individual servers having issues are no longer fixed up: they are re-created instead. Charity thinks the same might happen with code, sooner rather than later. When there’s an issue with the code, generate new code that solves it, and is verifyably correct.​ 3. Non-deterministic systems require more engineering discipline versus before. With code written by AI, we’re reducing the trust in the code (because we no longer wrote it), so we need to increase trust at the other part of the development process. Specifically, at validation: with things like tests, evals, and conformance testing.

Gergely Orosz

26,623 次观看 • 2 天前

David Malan (David J. Malan) is a professor at Harvard famous for transforming CS50 into a freely-available, world-class online course. I interviewed him about: • How he went from student -> teacher of CS50 • The knowledge that matters most in the future (not just programming) • How to be engaging in long-form lectures • How AI has impacted CS education including cheating / academic dishonesty • Advice for his younger self 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 1:09 - Getting into computer science 3:27 - Becoming the professor of CS50 11:19 - How to lecture well 14:25 - Depth vs engagement in education 18:11 - Why don't we consolidate educational resources 23:20 - Why start with C 31:51 - The ideal use of AI in education 34:54 - Cheating and AI 38:21 - Should we really learn CS still? 45:24 - College vs online education 47:06 - The most difficult concept to learn 51:00 - Growth vs fixed mindset 52:35 - The future of CS50 55:56 - Biggest career regret 1:00:29 - Top book recommendations 1:02:36 - Advice for his younger self 1:03:35 - Outro
1:04:30

David Malan (David J. Malan) is a professor at Harvard famous for transforming CS50 into a freely-available, world-class online course. I interviewed him about: • How he went from student -> teacher of CS50 • The knowledge that matters most in the future (not just programming) • How to be engaging in long-form lectures • How AI has impacted CS education including cheating / academic dishonesty • Advice for his younger self 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 1:09 - Getting into computer science 3:27 - Becoming the professor of CS50 11:19 - How to lecture well 14:25 - Depth vs engagement in education 18:11 - Why don't we consolidate educational resources 23:20 - Why start with C 31:51 - The ideal use of AI in education 34:54 - Cheating and AI 38:21 - Should we really learn CS still? 45:24 - College vs online education 47:06 - The most difficult concept to learn 51:00 - Growth vs fixed mindset 52:35 - The future of CS50 55:56 - Biggest career regret 1:00:29 - Top book recommendations 1:02:36 - Advice for his younger self 1:03:35 - Outro

Ryan Peterman

23,116 次观看 • 3 个月前

Here's my conversation with Dario Amodei, CEO of Anthropic, the company that created Claude, one of the best AI systems in the world. We talk about scaling, AI safety, regulation, and a lot of super technical details about the present and future of AI and humanity. It's a 5+ hour conversation in total. Amanda Askell and Chris Olah (Chris Olah) join us for an hour each to talk about Claude's character and mechanistic interpretability, respectively. This was a fascinating, wide-ranging, super-technical, and fun conversation! First 4 hours are here on X (4 hours is current limit), and is up on everywhere else in full. Links in comment. Timestamps: 0:00 - Introduction 3:14 - Scaling laws 12:20 - Limits of LLM scaling 20:45 - Competition with OpenAI, Google, xAI, Meta 26:08 - Claude 29:44 - Opus 3.5 34:30 - Sonnet 3.5 37:50 - Claude 4.0 42:02 - Criticism of Claude 54:49 - AI Safety Levels 1:05:37 - ASL-3 and ASL-4 1:09:40 - Computer use 1:19:35 - Government regulation of AI 1:38:24 - Hiring a great team 1:47:14 - Post-training 1:52:39 - Constitutional AI 1:58:05 - Machines of Loving Grace 2:17:11 - AGI timeline 2:29:46 - Programming 2:36:46 - Meaning of life 2:42:53 - Amanda Askell - Philosophy 2:45:21 - Programming advice for non-technical people 2:49:09 - Talking to Claude 3:05:41 - Prompt engineering 3:14:15 - Post-training 3:18:54 - Constitutional AI 3:23:48 - System prompts 3:29:54 - Is Claude getting dumber? 3:41:56 - Character training 3:42:56 - Nature of truth 3:47:32 - Optimal rate of failure 3:54:43 - AI consciousness 4:09:14 - AGI 4:17:52 - Chris Olah - Mechanistic Interpretability 4:22:44 - Features, Circuits, Universality 4:40:17 - Superposition 4:51:16 - Monosemanticity 4:58:08 - Scaling Monosemanticity 5:06:56 - Macroscopic behavior of neural networks 5:11:50 - Beauty of neural networks
3:58:08

Here's my conversation with Dario Amodei, CEO of Anthropic, the company that created Claude, one of the best AI systems in the world. We talk about scaling, AI safety, regulation, and a lot of super technical details about the present and future of AI and humanity. It's a 5+ hour conversation in total. Amanda Askell and Chris Olah (Chris Olah) join us for an hour each to talk about Claude's character and mechanistic interpretability, respectively. This was a fascinating, wide-ranging, super-technical, and fun conversation! First 4 hours are here on X (4 hours is current limit), and is up on everywhere else in full. Links in comment. Timestamps: 0:00 - Introduction 3:14 - Scaling laws 12:20 - Limits of LLM scaling 20:45 - Competition with OpenAI, Google, xAI, Meta 26:08 - Claude 29:44 - Opus 3.5 34:30 - Sonnet 3.5 37:50 - Claude 4.0 42:02 - Criticism of Claude 54:49 - AI Safety Levels 1:05:37 - ASL-3 and ASL-4 1:09:40 - Computer use 1:19:35 - Government regulation of AI 1:38:24 - Hiring a great team 1:47:14 - Post-training 1:52:39 - Constitutional AI 1:58:05 - Machines of Loving Grace 2:17:11 - AGI timeline 2:29:46 - Programming 2:36:46 - Meaning of life 2:42:53 - Amanda Askell - Philosophy 2:45:21 - Programming advice for non-technical people 2:49:09 - Talking to Claude 3:05:41 - Prompt engineering 3:14:15 - Post-training 3:18:54 - Constitutional AI 3:23:48 - System prompts 3:29:54 - Is Claude getting dumber? 3:41:56 - Character training 3:42:56 - Nature of truth 3:47:32 - Optimal rate of failure 3:54:43 - AI consciousness 4:09:14 - AGI 4:17:52 - Chris Olah - Mechanistic Interpretability 4:22:44 - Features, Circuits, Universality 4:40:17 - Superposition 4:51:16 - Monosemanticity 4:58:08 - Scaling Monosemanticity 5:06:56 - Macroscopic behavior of neural networks 5:11:50 - Beauty of neural networks

Lex Fridman

1,375,630 次观看 • 1 年前

Why did spec-driven development never take off - the workflow tools like Amazon’s Kiro or GitHub Workspaces encouraged? dex, founder of HumanLayer: “These projects have a really interesting idea: you maintain a set of specifications for your software, and then you maintain the code itself, and the dream is the coding part is just compiling specs into code. But that part never really materialized. I’m on a GitHub issue in spec kit that’s been open for a year, and every couple of weeks I get a new email on the thread of people complaining about the same problem: I edit my specs and then I edit the code, and the code drifts from the specs. How do I keep the specs up to date as the code is changing? You now have two sources of truth, and it stops being useful. That’s why with RPI (Research, Plan, Implement), the docs are tactical execution docs - I do the research, the plan, the implementation, and I throw the docs out. The next time I need research I just do it from scratch, because tokens are cheap and my time is expensive.”
1:36

Why did spec-driven development never take off - the workflow tools like Amazon’s Kiro or GitHub Workspaces encouraged? dex, founder of HumanLayer: “These projects have a really interesting idea: you maintain a set of specifications for your software, and then you maintain the code itself, and the dream is the coding part is just compiling specs into code. But that part never really materialized. I’m on a GitHub issue in spec kit that’s been open for a year, and every couple of weeks I get a new email on the thread of people complaining about the same problem: I edit my specs and then I edit the code, and the code drifts from the specs. How do I keep the specs up to date as the code is changing? You now have two sources of truth, and it stops being useful. That’s why with RPI (Research, Plan, Implement), the docs are tactical execution docs - I do the research, the plan, the implementation, and I throw the docs out. The next time I need research I just do it from scratch, because tokens are cheap and my time is expensive.”

The Pragmatic Engineer

168,579 次观看 • 22 天前

8 rules to improve your AI coding agent. All of these rules work with Claude Code, Cursor, VS Code, and with most programming languages. Automating these rules will 10x the code quality and security produced by your AI coding agents. 1. Dependency checks - Prevent your agent from suggesting insecure libraries based on outdated training data. 2. Secret exposure - Auto-fix the use of hardcoded credentials introduced by your coding agent. 3. File and function size - Automatically refactor any files or functions that exceed a reasonable length. 4. Complexity and parameter limits - Simplify overly complex code written by the agent. 5. SQL Injection - Auto-fix all database interactions with unsanitized user input. 6. Unused variables and imports - Detect and remove dead code. 7. Detect invisible unicode characters in AI rules files - Remove zero-width spaces, direction overrides, and other invisible characters that can hide malicious behavior. 8. Insecure OpenAI API usage - Enforce use of secure OpenAI endpoints, proper authentication, and context isolation Here is how you can automate this: Install the Codacy extension. This will give you access to a CLI for local scanning and an MCP server for agent communication. From here on out, every time you need to generate some code: 1. Your agent will write the code 2. It will then call Codacy's CLI to check it 3. It will find any issues in real time 4. Your coding agent will fix the issues 5. When the code passes all checks, you are done Level of effort on your side: literally zero! Code quality and security because of this: 100x better! Here is the link to download the extension for your IDE: Thanks to the Codacy team for collaborating with me on this post.
3:50

8 rules to improve your AI coding agent. All of these rules work with Claude Code, Cursor, VS Code, and with most programming languages. Automating these rules will 10x the code quality and security produced by your AI coding agents. 1. Dependency checks - Prevent your agent from suggesting insecure libraries based on outdated training data. 2. Secret exposure - Auto-fix the use of hardcoded credentials introduced by your coding agent. 3. File and function size - Automatically refactor any files or functions that exceed a reasonable length. 4. Complexity and parameter limits - Simplify overly complex code written by the agent. 5. SQL Injection - Auto-fix all database interactions with unsanitized user input. 6. Unused variables and imports - Detect and remove dead code. 7. Detect invisible unicode characters in AI rules files - Remove zero-width spaces, direction overrides, and other invisible characters that can hide malicious behavior. 8. Insecure OpenAI API usage - Enforce use of secure OpenAI endpoints, proper authentication, and context isolation Here is how you can automate this: Install the Codacy extension. This will give you access to a CLI for local scanning and an MCP server for agent communication. From here on out, every time you need to generate some code: 1. Your agent will write the code 2. It will then call Codacy's CLI to check it 3. It will find any issues in real time 4. Your coding agent will fix the issues 5. When the code passes all checks, you are done Level of effort on your side: literally zero! Code quality and security because of this: 100x better! Here is the link to download the extension for your IDE: Thanks to the Codacy team for collaborating with me on this post.

Santiago

49,331 次观看 • 9 个月前

Excited to share that I’ve joined Anomaly as founding designer. I’m thrilled to work with Jay dax Frank Wang and Adam — a team stacked with talent! I’ve always been fascinated by challenges that influence the way people work. Today, AI is reshaping software development at a pace we’ve never seen. From solo developers to global enterprises, the way we write code has shifted dramatically in the past year, and will continue to change in the years ahead. This is an opportunity for me to help shape these experiences for everyone, regardless of frameworks, IDEs, or tools. I want to explore how they’re adopted and how they can have the greatest impact. The team is hard at work on OpenCode — the open source AI coding agent for the terminal. With over 23K stars on GitHub, it’s already changing how thousands of developers write code every day. Follow OpenCode as we go beyond the terminal and design and build world-class AI developer experiences ✌️
0:25

Excited to share that I’ve joined Anomaly as founding designer. I’m thrilled to work with Jay dax Frank Wang and Adam — a team stacked with talent! I’ve always been fascinated by challenges that influence the way people work. Today, AI is reshaping software development at a pace we’ve never seen. From solo developers to global enterprises, the way we write code has shifted dramatically in the past year, and will continue to change in the years ahead. This is an opportunity for me to help shape these experiences for everyone, regardless of frameworks, IDEs, or tools. I want to explore how they’re adopted and how they can have the greatest impact. The team is hard at work on OpenCode — the open source AI coding agent for the terminal. With over 23K stars on GitHub, it’s already changing how thousands of developers write code every day. Follow OpenCode as we go beyond the terminal and design and build world-class AI developer experiences ✌️

David Hill

172,131 次观看 • 11 个月前

Here's my 6 hour conversation with DHH, a legendary programmer, creator of Ruby on Rails, author, and race car driver. This was a fun and inspiring conversation on everything from the future of programming & AI to the nature of happiness & productivity to the value of family, getting married and having kids. X limits video length to 6 hours. So this full convo doesn't fit (by a few minutes). So, the first 6 hours are here on X. The full version is up everywhere else (see comment). Timestamps: 0:00 - Episode highlight 1:21 - Introduction 2:32 - Programming - early days 19:57 - JavaScript 30:16 - Google Chrome and DOJ 38:03 - Ruby programming language 45:14 - Beautiful code 1:03:15 - Metaprogramming 1:06:36 - Dynamic typing 1:13:55 - Scaling 1:26:47 - Future of programming 1:44:18 - Future of AI 1:50:13 - Vibe coding 1:58:45 - Rails manifesto: Principles of a great programming language 2:23:11 - Why managers are useless 2:32:32 - Small teams 2:38:39 - Jeff Bezos 2:53:57 - Why meetings are toxic 3:01:43 - Case against retirement 3:09:00 - Hard work 3:14:38 - Why we left the cloud 3:17:48 - AWS 3:27:07 - Owning your own servers 3:33:19 - Elon Musk 3:43:01 - Apple 3:54:48 - Tim Sweeney 4:06:22 - Fatherhood 4:32:04 - Racing 4:59:08 - Cars 5:04:26 - Programming setup 5:19:35 - Programming language for beginners 5:32:53 - Open source 5:41:46 - WordPress drama 5:53:03 - Money and happiness 6:01:56 - Hope
5:59:58

Here's my 6 hour conversation with DHH, a legendary programmer, creator of Ruby on Rails, author, and race car driver. This was a fun and inspiring conversation on everything from the future of programming & AI to the nature of happiness & productivity to the value of family, getting married and having kids. X limits video length to 6 hours. So this full convo doesn't fit (by a few minutes). So, the first 6 hours are here on X. The full version is up everywhere else (see comment). Timestamps: 0:00 - Episode highlight 1:21 - Introduction 2:32 - Programming - early days 19:57 - JavaScript 30:16 - Google Chrome and DOJ 38:03 - Ruby programming language 45:14 - Beautiful code 1:03:15 - Metaprogramming 1:06:36 - Dynamic typing 1:13:55 - Scaling 1:26:47 - Future of programming 1:44:18 - Future of AI 1:50:13 - Vibe coding 1:58:45 - Rails manifesto: Principles of a great programming language 2:23:11 - Why managers are useless 2:32:32 - Small teams 2:38:39 - Jeff Bezos 2:53:57 - Why meetings are toxic 3:01:43 - Case against retirement 3:09:00 - Hard work 3:14:38 - Why we left the cloud 3:17:48 - AWS 3:27:07 - Owning your own servers 3:33:19 - Elon Musk 3:43:01 - Apple 3:54:48 - Tim Sweeney 4:06:22 - Fatherhood 4:32:04 - Racing 4:59:08 - Cars 5:04:26 - Programming setup 5:19:35 - Programming language for beginners 5:32:53 - Open source 5:41:46 - WordPress drama 5:53:03 - Money and happiness 6:01:56 - Hope

Lex Fridman

4,520,818 次观看 • 1 年前

Everyone's talking, reading, or writing about how much is changing in how we build products in the future, but very few people have actually experienced what the most cutting edge tools are capable of. In a new series on my podcast, I'm going to sit down with the founders of the most advanced product development tools to live demo what these tools can do, and then talk about the implications of this on product people's careers and lives. To kick things off, I sat down with Amjad Masad (Amjad Masad), co-founder and CEO of Replit ⠕, a browser-based coding environment that allows anyone to write and deploy code. Replit has 34 million users globally and is one of the fastest-growing developer communities in the world. In our conversation, Amjad shares: 🔸 A live demo of Replit building a full-stack web app from a text prompt 🔸 The implications of AI-powered development for product managers, designers, and engineers 🔸 What skills will matter more, and less, in the future 🔸 How this might reshape companies and careers 🔸 Why being “generative” will become an increasingly valuable skill 🔸 “Amjad’s law” and how learning to debug AI-generated code is becoming ever more valuable 🔸 Much more Listen now 👇 - YouTube: - Spotify: - Apple: Thank you to our wonderful sponsors for supporting the podcast: 🏆 WorkOS — Modern identity platform for B2B SaaS, free up to 1 million MAUs: 🏆 — A global leader in digital identity verification: 🏆 @LinkedInMktg — Reach professionals and drive results for your business:
1:06

Everyone's talking, reading, or writing about how much is changing in how we build products in the future, but very few people have actually experienced what the most cutting edge tools are capable of. In a new series on my podcast, I'm going to sit down with the founders of the most advanced product development tools to live demo what these tools can do, and then talk about the implications of this on product people's careers and lives. To kick things off, I sat down with Amjad Masad (Amjad Masad), co-founder and CEO of Replit ⠕, a browser-based coding environment that allows anyone to write and deploy code. Replit has 34 million users globally and is one of the fastest-growing developer communities in the world. In our conversation, Amjad shares: 🔸 A live demo of Replit building a full-stack web app from a text prompt 🔸 The implications of AI-powered development for product managers, designers, and engineers 🔸 What skills will matter more, and less, in the future 🔸 How this might reshape companies and careers 🔸 Why being “generative” will become an increasingly valuable skill 🔸 “Amjad’s law” and how learning to debug AI-generated code is becoming ever more valuable 🔸 Much more Listen now 👇 - YouTube: - Spotify: - Apple: Thank you to our wonderful sponsors for supporting the podcast: 🏆 WorkOS — Modern identity platform for B2B SaaS, free up to 1 million MAUs: 🏆 — A global leader in digital identity verification: 🏆 @LinkedInMktg — Reach professionals and drive results for your business:

Lenny Rachitsky

81,955 次观看 • 1 年前

My conversation with OpenAI co-founder Greg Brockman This is the most detailed first-person account of the 72 hours after Sam Altman was fired. We also go deep on what comes next: the global race to AGI, why ChatGPT stopped showing reasoning, how much of OpenAI's own code is now written by AI ("it's hard to know what percent is not"), and the untold story of how OpenAI actually started in 2015. 00:00:00 Introduction 00:00:49 Meeting Sam Altman and Starting OpenAI 00:02:40 Building the Founding Team 00:04:25 DeepMind's Lead Over OpenAI 00:04:54 Changing OpenAI to a For-Profit Model 00:06:05 Breakthrough Moments at OpenAI 00:08:22 What Dota 2 Meant for OpenAI 00:10:04 Reasoning Versus Prediction 00:11:59 Tensions Grow at OpenAI 00:15:44 Sam Altman's Firing 00:17:49 Greg Quits OpenAI 00:19:56 Sam Explores Deal with Microsoft's Satya 00:20:28 Petition for Altman's Return 00:23:43 Ilya Sutskever Leaves OpenAI 00:24:59 Lessons Learned after Sam Ousting 00:28:22 The Thing Ilya Said that Greg Can't Forget 00:32:22 Is AI Going Parabolic? 00:33:24 How Much of OpenAI's Code is Written by AI? 00:36:21 Do AI Chatbots Tell Us What We Want to Hear? 00:38:06 The Global AI Race to Reach AGI 00:38:40 What Happens if US Doesn't Reach AGI First? 00:39:49 Are Countries Stealing AI Advancements? 00:40:38 Why ChatGPT No Longer Shows Reasoning 00:41:47 The Finite Constraints of Compute 00:43:38 On Investing Early in Data Centers 00:46:31 The Future of Data Center Specialization 00:47:52 How to Decide Whose Queries to Serve 00:49:08 OpenAI on Consumer vs Enterprise Models 00:53:05 Data Centers in Space? 01:00:56 What Should AI Regulation Look Like? 01:04:33 The Future of AI-Powered Entrepreneurship 01:04:44 AI and Job Loss 01:07:15 The Skills Young People Should Invest In 01:11:30 What Does Success Look Like For You? Full episode on X below. Also find it on: • YouTube: • Spotify: • Apple:
1:11:41

My conversation with OpenAI co-founder Greg Brockman This is the most detailed first-person account of the 72 hours after Sam Altman was fired. We also go deep on what comes next: the global race to AGI, why ChatGPT stopped showing reasoning, how much of OpenAI's own code is now written by AI ("it's hard to know what percent is not"), and the untold story of how OpenAI actually started in 2015. 00:00:00 Introduction 00:00:49 Meeting Sam Altman and Starting OpenAI 00:02:40 Building the Founding Team 00:04:25 DeepMind's Lead Over OpenAI 00:04:54 Changing OpenAI to a For-Profit Model 00:06:05 Breakthrough Moments at OpenAI 00:08:22 What Dota 2 Meant for OpenAI 00:10:04 Reasoning Versus Prediction 00:11:59 Tensions Grow at OpenAI 00:15:44 Sam Altman's Firing 00:17:49 Greg Quits OpenAI 00:19:56 Sam Explores Deal with Microsoft's Satya 00:20:28 Petition for Altman's Return 00:23:43 Ilya Sutskever Leaves OpenAI 00:24:59 Lessons Learned after Sam Ousting 00:28:22 The Thing Ilya Said that Greg Can't Forget 00:32:22 Is AI Going Parabolic? 00:33:24 How Much of OpenAI's Code is Written by AI? 00:36:21 Do AI Chatbots Tell Us What We Want to Hear? 00:38:06 The Global AI Race to Reach AGI 00:38:40 What Happens if US Doesn't Reach AGI First? 00:39:49 Are Countries Stealing AI Advancements? 00:40:38 Why ChatGPT No Longer Shows Reasoning 00:41:47 The Finite Constraints of Compute 00:43:38 On Investing Early in Data Centers 00:46:31 The Future of Data Center Specialization 00:47:52 How to Decide Whose Queries to Serve 00:49:08 OpenAI on Consumer vs Enterprise Models 00:53:05 Data Centers in Space? 01:00:56 What Should AI Regulation Look Like? 01:04:33 The Future of AI-Powered Entrepreneurship 01:04:44 AI and Job Loss 01:07:15 The Skills Young People Should Invest In 01:11:30 What Does Success Look Like For You? Full episode on X below. Also find it on: • YouTube: • Spotify: • Apple:

Shane Parrish

450,952 次观看 • 3 个月前