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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...

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Dan Shipper 📧

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35,342 次观看 • 1 年前 •via X (Twitter)

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We're entering a new era of software where a single person, working with AI agents, can build products that previously required entire teams. In this episode of the Lightcone Podcast, they break down the rise of AI coding agents, "tokenmaxxing", and the emerging workflows behind tools like Claude Code and OpenClaw. They discuss why AI systems today feel less like productivity tools and more like collaborators, why the future of AI should be personal and user-controlled, and how founders are starting to build software in completely new ways. 00:00 — Will you control your AI? 00:47 — Coding again after 13 years 01:56 — Rebuilding a startup with Claude Code 05:50 — Software that thinks like a journalist 07:09 — The rise of “tokenmaxxing” 10:07 — The accidental creation of GStack 14:21 — The workflow behind 400x output 20:59 — Thin Harness, Fat Skills 24:35 — AI agents are like Ferraris 27:12 — The future of personal AI 38:37 — Buying back time with tokens
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We're entering a new era of software where a single person, working with AI agents, can build products that previously required entire teams. In this episode of the Lightcone Podcast, they break down the rise of AI coding agents, "tokenmaxxing", and the emerging workflows behind tools like Claude Code and OpenClaw. They discuss why AI systems today feel less like productivity tools and more like collaborators, why the future of AI should be personal and user-controlled, and how founders are starting to build software in completely new ways. 00:00 — Will you control your AI? 00:47 — Coding again after 13 years 01:56 — Rebuilding a startup with Claude Code 05:50 — Software that thinks like a journalist 07:09 — The rise of “tokenmaxxing” 10:07 — The accidental creation of GStack 14:21 — The workflow behind 400x output 20:59 — Thin Harness, Fat Skills 24:35 — AI agents are like Ferraris 27:12 — The future of personal AI 38:37 — Buying back time with tokens

Y Combinator

67,145 次观看 • 4 个月前

New episode with Scott Wu (Scott Wu), CEO and co-founder of @cognition, the company behind Devin Unlike other AI coding tools, Devin works like an autonomous engineer that you interact with through Slack, Linear, and GitHub, just like with a remote engineer. Each of Cognition's 15 engineers works with a team of Devins, and these Devins are writing about 25% of Cognition’s code today. By the end of the year, they expect over 50% of their code to be written by Devin. In our conversation, we discuss: 🔸 How Devin has evolved from a high school CS student to a strong junior engineer just in the past year 🔸 Why software engineering will shift from “bricklayers” to “architects” 🔸 Why AI tools will lead to *more* engineering jobs rather than fewer 🔸 The eight pivots Cognition went through before landing on their current approach 🔸 The cultural shifts required to successfully adopt AI engineers like Devin at your company 🔸 Much more Listen now 👇 • YouTube: • Spotify: • Apple: Thank you to our wonderful sponsors for supporting the podcast: 🏆 Enterpret — Transform customer feedback into product growth: 🏆 Paragon — Ship every SaaS integration your customers want: 🏆 Attio — The powerful, flexible CRM for fast-growing startups:
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New episode with Scott Wu (Scott Wu), CEO and co-founder of @cognition, the company behind Devin Unlike other AI coding tools, Devin works like an autonomous engineer that you interact with through Slack, Linear, and GitHub, just like with a remote engineer. Each of Cognition's 15 engineers works with a team of Devins, and these Devins are writing about 25% of Cognition’s code today. By the end of the year, they expect over 50% of their code to be written by Devin. In our conversation, we discuss: 🔸 How Devin has evolved from a high school CS student to a strong junior engineer just in the past year 🔸 Why software engineering will shift from “bricklayers” to “architects” 🔸 Why AI tools will lead to *more* engineering jobs rather than fewer 🔸 The eight pivots Cognition went through before landing on their current approach 🔸 The cultural shifts required to successfully adopt AI engineers like Devin at your company 🔸 Much more Listen now 👇 • YouTube: • Spotify: • Apple: Thank you to our wonderful sponsors for supporting the podcast: 🏆 Enterpret — Transform customer feedback into product growth: 🏆 Paragon — Ship every SaaS integration your customers want: 🏆 Attio — The powerful, flexible CRM for fast-growing startups:

Lenny Rachitsky

251,873 次观看 • 1 年前

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

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Guillermo Rauch (Guillermo Rauch) is one of the most prolific coders of this generation. But he doesn’t think of himself as a coder anymore. Coding, he says, is a specific skill that AI is becoming great at. Instead, he thinks the future of coding is more holistic, full-stack engineers who can ideate, design, and execute all together. Guillermo is the founder and CEO of Vercel (Vercel), the creator of NextJS, and SocketIO. We spent an hour talking about the future of software development in an AI world—and the meta-skills that are essential for the coders of today to master—in order to use tomorrow’s tools to their fullest extent. Here are a few takeaways: - One of the most important keys to his success is taste—and developing taste is all about paying better attention to everything you experience day to day. - He’s great at recognizing bleeding-edge technologies with extremely practical applications but that have bad user experiences. If you can learn to recognize those and build with them, you might build the next NextJs or SocketIO. - Why prototype cultures are becoming common in AI—and the benefits of written cultures like Amazon vs. prototype cultures like Apple for different kinds of companies. - For developers building frameworks, always put the product first; a framework in isolation without a “customer zero” is never going to be a good tool. - The theory of “recursive founder mode”—if you want to build a scalable business, you have to scale yourself by creating an atmosphere that nurtures talent and ambition. - AI tools are shifting software toward consumption-based billing models, making us capital allocators who decide how much compute the AI consumes. - The future of AI is agents with the taste, knowledge, and tools to perform specialized tasks. Watch below! Timestamps: Introduction: 00:01:33 How to spot trends early: 00:03:18 Why you should be your own customer: 00:07:34 How to create an ecosystem of talent and ambition: 00:14:55 Why Guillermo doesn't identify as a coder: 00:17:29 AI is gearing us toward an allocation economy: 00:20:50 How Vercel’s copilot compares with other coding agents: 00:28:34 Guillermo’s advice on having better taste: 00:40:35 The future of AI agents is specialized: 00:42:46 How AI startups can compete with big tech: 00:47:50

Dan Shipper

187,107 次观看 • 1 年前

SaaS isn’t dead, it just needs to become agent-native. Linear (Linear) is a great example of how: They pivoted the product to be used by both humans and agents, and that has made them one of the premier software tools in the agent-native era. I had Linear’s cofounder and CEO Karri Saarinen on Every 📧's AI & I to talk about how a product management tool for human software developers became an agent-native tool—and how Linear’s trajectory reveals a bright future for SaaS businesses: - Speed means decisions matter more, not less. AI makes it easy to have an idea and build it without considering whether its existence is justified. When ChatGPT was released, SaaS companies were launching their own chatbots left, right, and center. Instead of jumping on the bandwagon, Linear stopped to consider whether the application was useful. (It wasn’t.) - Just because the technology has changed doesn’t mean your mission should. Karri attributes Linear’s success to never losing sight of what matters: helping teams develop great software. Instead of chasing trends, Linear focused on understanding how AI was impacting its customers’ workflows—and updating its product accordingly. - Agents are now first-class users. Linear never tried to change what it was or did well; it just expanded the user base. Companies can now kick off agents inside Linear, manage them, and track what they're working on alongside the humans on the team, which explains why Codex, Coinbase, and Brex all run their agents on Linear. This is a must watch for anyone interested in how an agent-native SaaS company operates. Watch below! Timestamps: Introduction and how Every first discovered Linear: 00:00:39 Why Linear waited to ship AI features instead of rushing to chatbots: 00:02:00 Linear's agent platform and becoming the system that guides AI agents: 00:05:06 Why "SaaS is dead" is a simplistic narrative: 00:07:42 How Linear adopted AI coding tools internally: 00:12:18 AI's impact on product building workflows—speed versus thoughtfulness: 00:17:45 The value of conceptual work and thinking before shipping: 00:22:18 How AI is reshaping Linear's product strategy: 00:29:30 Demo: Linear's agent skills, shared context, and code review workflow: 00:37:18 The future of product development and the enduring role of human judgment: 00:47:48
52:48

SaaS isn’t dead, it just needs to become agent-native. Linear (Linear) is a great example of how: They pivoted the product to be used by both humans and agents, and that has made them one of the premier software tools in the agent-native era. I had Linear’s cofounder and CEO Karri Saarinen on Every 📧's AI & I to talk about how a product management tool for human software developers became an agent-native tool—and how Linear’s trajectory reveals a bright future for SaaS businesses: - Speed means decisions matter more, not less. AI makes it easy to have an idea and build it without considering whether its existence is justified. When ChatGPT was released, SaaS companies were launching their own chatbots left, right, and center. Instead of jumping on the bandwagon, Linear stopped to consider whether the application was useful. (It wasn’t.) - Just because the technology has changed doesn’t mean your mission should. Karri attributes Linear’s success to never losing sight of what matters: helping teams develop great software. Instead of chasing trends, Linear focused on understanding how AI was impacting its customers’ workflows—and updating its product accordingly. - Agents are now first-class users. Linear never tried to change what it was or did well; it just expanded the user base. Companies can now kick off agents inside Linear, manage them, and track what they're working on alongside the humans on the team, which explains why Codex, Coinbase, and Brex all run their agents on Linear. This is a must watch for anyone interested in how an agent-native SaaS company operates. Watch below! Timestamps: Introduction and how Every first discovered Linear: 00:00:39 Why Linear waited to ship AI features instead of rushing to chatbots: 00:02:00 Linear's agent platform and becoming the system that guides AI agents: 00:05:06 Why "SaaS is dead" is a simplistic narrative: 00:07:42 How Linear adopted AI coding tools internally: 00:12:18 AI's impact on product building workflows—speed versus thoughtfulness: 00:17:45 The value of conceptual work and thinking before shipping: 00:22:18 How AI is reshaping Linear's product strategy: 00:29:30 Demo: Linear's agent skills, shared context, and code review workflow: 00:37:18 The future of product development and the enduring role of human judgment: 00:47:48

Dan Shipper 📧

36,359 次观看 • 5 个月前

EPISODE 147: The New Era of Software Abundance Joe Lonsdale visits Scott Wu & Russell Kaplan at Cognition HQ 00:00 Episode intro 01:35 Why technical talent & execution matters in AI 06:10 Do young people have an edge in the AI era? 08:26 Cognition's rapid growth 11:55 The new era of software abundance 14:30 Cognition engineers don't type code anymore 19:20 "Never sleep while Devin is idling" 21:25 The case for AI disinflation 23:50 How Devin generates 12X productivity gains 28:25 Cognition for government / taking on complex, broken systems 36:40 The AI race / competition with Anthropic 39:00 Forward deployed engineers? 43:40 How fast are LLMs improving? 47:10 The AI-led small business explosion
53:19

EPISODE 147: The New Era of Software Abundance Joe Lonsdale visits Scott Wu & Russell Kaplan at Cognition HQ 00:00 Episode intro 01:35 Why technical talent & execution matters in AI 06:10 Do young people have an edge in the AI era? 08:26 Cognition's rapid growth 11:55 The new era of software abundance 14:30 Cognition engineers don't type code anymore 19:20 "Never sleep while Devin is idling" 21:25 The case for AI disinflation 23:50 How Devin generates 12X productivity gains 28:25 Cognition for government / taking on complex, broken systems 36:40 The AI race / competition with Anthropic 39:00 Forward deployed engineers? 43:40 How fast are LLMs improving? 47:10 The AI-led small business explosion

American Optimist

836,792 次观看 • 5 个月前

Scott Wu says Karpathy's idea of jagged intelligence explains why humans and AI work best together. AI is superhuman at some tasks, shockingly weak at others. “As long as that tail exists of what humans can do, that's what we'll own.” We still decide what to build. In the end, software engineering is just telling the computer what to do.
1:09

Scott Wu says Karpathy's idea of jagged intelligence explains why humans and AI work best together. AI is superhuman at some tasks, shockingly weak at others. “As long as that tail exists of what humans can do, that's what we'll own.” We still decide what to build. In the end, software engineering is just telling the computer what to do.

vitrupo

38,496 次观看 • 1 年前

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 • 10 个月前

Mustafa Suleyman on why the AGI race is FAKE, the coming $1 million agent revolution, and Microsoft's ultimate plan for self-sufficiency. - The new Turing Test: agents turning $100K into $1M in the real economy - Containment > alignment in the short term - Why AI personhood is an extinction-level mistake - Short-term social unrest is inevitable - How AGI is bigger than math, science, or any single discovery (00:00) - The Evolution of AI and Microsoft's Role (09:12) - Economic Benchmarks for AI Agents (18:37) - The Future of AI in Science and Engineering (27:54) - AI Alignment and Containment Strategies (36:42) - Anthropomorphism and AI Personhood (46:10) - The Role of AI in Government and Society Mustafa Suleyman is the CEO of Microsoft AI and a co-founder of DeepMind and Inflection AI. He joined me, @DavidBlundin, and Dr. Alex Wissner-Gross for a conversation on why the “AGI race” is a misleading frame, how AI agents will reshape the economy before they solve scientific problems, the potential for future social unrest, and why AI legal personhood is a red line for human survival.
1:24:58

Mustafa Suleyman on why the AGI race is FAKE, the coming $1 million agent revolution, and Microsoft's ultimate plan for self-sufficiency. - The new Turing Test: agents turning $100K into $1M in the real economy - Containment > alignment in the short term - Why AI personhood is an extinction-level mistake - Short-term social unrest is inevitable - How AGI is bigger than math, science, or any single discovery (00:00) - The Evolution of AI and Microsoft's Role (09:12) - Economic Benchmarks for AI Agents (18:37) - The Future of AI in Science and Engineering (27:54) - AI Alignment and Containment Strategies (36:42) - Anthropomorphism and AI Personhood (46:10) - The Role of AI in Government and Society Mustafa Suleyman is the CEO of Microsoft AI and a co-founder of DeepMind and Inflection AI. He joined me, @DavidBlundin, and Dr. Alex Wissner-Gross for a conversation on why the “AGI race” is a misleading frame, how AI agents will reshape the economy before they solve scientific problems, the potential for future social unrest, and why AI legal personhood is a red line for human survival.

Peter H. Diamandis, MD

214,454 次观看 • 9 个月前

Michael Truell (Michael Truell), co-founder and CEO of Anysphere, the company behind Cursor, joined Garry Tan to talk about building one of the fastest-growing startups of all time—and why he's betting everything on a world beyond code. He walks through the early insights that led him and his co-founders to leave a promising AI-powered CAD project and instead chase a bigger dream: reinventing how software is written. From the hard years of false starts and rewrites to Cursor's breakthrough moment, Michael explains what it really takes to build a tool that could eventually replace programming as we know it. He also reflects on their first 10 hires, why taste still matters, and how the decade ahead will unlock a new kind of creativity for builders everywhere. 01:00 - A new way to build software 04:50 - Two ways to view LLMs 05:50 - Bottlenecks to superhuman agents 08:30 - New approaches to coding UI 09:40 - Why taste still matters 12:15 - Niche software opportunities 13:30 - Cursor origin story 16:00 - The first problem they tried solving 17:20 - Why they abandoned the CAD idea 21:00 - Pivoting to Cursor 23:00 - Following the scaling laws 27:00 - Getting to PMF 30:00 - “Dogfooding” 31:00 - First 10 hires 32:50 - How to evaluate great engineers 33:45 - Maintaining the hacker mindset 35:00 - What are the moats for AI coding tools? 37:00 - Looking ahead
37:28

Michael Truell (Michael Truell), co-founder and CEO of Anysphere, the company behind Cursor, joined Garry Tan to talk about building one of the fastest-growing startups of all time—and why he's betting everything on a world beyond code. He walks through the early insights that led him and his co-founders to leave a promising AI-powered CAD project and instead chase a bigger dream: reinventing how software is written. From the hard years of false starts and rewrites to Cursor's breakthrough moment, Michael explains what it really takes to build a tool that could eventually replace programming as we know it. He also reflects on their first 10 hires, why taste still matters, and how the decade ahead will unlock a new kind of creativity for builders everywhere. 01:00 - A new way to build software 04:50 - Two ways to view LLMs 05:50 - Bottlenecks to superhuman agents 08:30 - New approaches to coding UI 09:40 - Why taste still matters 12:15 - Niche software opportunities 13:30 - Cursor origin story 16:00 - The first problem they tried solving 17:20 - Why they abandoned the CAD idea 21:00 - Pivoting to Cursor 23:00 - Following the scaling laws 27:00 - Getting to PMF 30:00 - “Dogfooding” 31:00 - First 10 hires 32:50 - How to evaluate great engineers 33:45 - Maintaining the hacker mindset 35:00 - What are the moats for AI coding tools? 37:00 - Looking ahead

Y Combinator

337,015 次观看 • 1 年前

In the future, you’ll be able to accomplish a goal by just giving Claude an outcome and a budget. That’s the direction Anthropic is building in with its new Managed Agents features, announced at this week’s Code with Claude developer event. The basic idea: Claude, wrapped in a computer in the cloud, that you can spin up, scale, and manage as needed. Anthropic is taking on the infrastructure that kills most agent products, and making sure that it scales to meet the needs of agents running 24/7. On this week’s AI & I from Every 📧, I talk with Angela Jiang (Angela Jiang), head of product for the Claude platform, and Katelyn Lesse (Katelyn Lesse), head of engineering for the Claude platform, about what Anthropic is building and what it takes to make agents reliable in production. We get into: - Why the "build a generic harness, hot-swap any model behind it" playbook is already outdated. Angela points to eval data on Memory where the same task across different harnesses performed drastically differently. - The infrastructure wall every team hits in production—and why Katelyn thinks “my sandbox died and took the agent with it” is the real reason internal agents don't ship. - Why Anthropic is so bullish on using file systems and skills within Claude, including Angela's argument that those early design choices can compound for years. This is a must-watch for anyone trying to take an agent past the demo and into production. Watch below! Timestamps: How the Claude platform evolved from API to agents: 00:01:48 The primitives that make up Claude Managed Agents: 00:04:09 Why the harness and the model are becoming a single unit: 00:10:37 The infrastructure wall that kills most agent projects in production: 00:18:49 Why team agents need a different shape than individual productivity tools: 00:24:49 How Anthropic's legal team uses an agent to review marketing copy: 00:26:36 Using multi-agent orchestration for advisor strategies, adversarial pairs, and swarms: 00:34:24 How to measure agent success with outcome and budget as the end state: 00:35:50 What the platform looks like a year from now, when Claude writes its own harness: 00:39:11
43:20

In the future, you’ll be able to accomplish a goal by just giving Claude an outcome and a budget. That’s the direction Anthropic is building in with its new Managed Agents features, announced at this week’s Code with Claude developer event. The basic idea: Claude, wrapped in a computer in the cloud, that you can spin up, scale, and manage as needed. Anthropic is taking on the infrastructure that kills most agent products, and making sure that it scales to meet the needs of agents running 24/7. On this week’s AI & I from Every 📧, I talk with Angela Jiang (Angela Jiang), head of product for the Claude platform, and Katelyn Lesse (Katelyn Lesse), head of engineering for the Claude platform, about what Anthropic is building and what it takes to make agents reliable in production. We get into: - Why the "build a generic harness, hot-swap any model behind it" playbook is already outdated. Angela points to eval data on Memory where the same task across different harnesses performed drastically differently. - The infrastructure wall every team hits in production—and why Katelyn thinks “my sandbox died and took the agent with it” is the real reason internal agents don't ship. - Why Anthropic is so bullish on using file systems and skills within Claude, including Angela's argument that those early design choices can compound for years. This is a must-watch for anyone trying to take an agent past the demo and into production. Watch below! Timestamps: How the Claude platform evolved from API to agents: 00:01:48 The primitives that make up Claude Managed Agents: 00:04:09 Why the harness and the model are becoming a single unit: 00:10:37 The infrastructure wall that kills most agent projects in production: 00:18:49 Why team agents need a different shape than individual productivity tools: 00:24:49 How Anthropic's legal team uses an agent to review marketing copy: 00:26:36 Using multi-agent orchestration for advisor strategies, adversarial pairs, and swarms: 00:34:24 How to measure agent success with outcome and budget as the end state: 00:35:50 What the platform looks like a year from now, when Claude writes its own harness: 00:39:11

Dan Shipper

66,862 次观看 • 4 个月前

One line from Prasanna Sankar has stayed with me: "You're not helping AI anymore. You're getting in the way by being in the way." That's exactly why he's building Vorflux Not another AI coding assistant. His vision is an autonomous AI software engineer that can build software with minimal human involvement. Coming from someone who was India's #1 competitive programmer, helped build Rippling into a $15B company, and once turned down Facebook's $100M acquisition offer, it's a perspective worth paying attention to. In this conversation, we go deep on: • Why AI has already surpassed humans at software engineering. • Why tools like Cursor and Claude Code are only the beginning—and how Vorflux is thinking differently. • Why the engineer of the future becomes a "builder." • The story behind building Rippling and starting over from scratch. • Why he turned down Facebook's $100M acquisition offer. • Why he believes we're moving toward zero-employee companies. • What humans will be uniquely valuable for in an AI-first world. One of the most thought-provoking conversations I've had on AI and the future of software. Full conversation is now live on the 1947 Rise Podcast. Link in comments Prasanna S Vorflux
1:21

One line from Prasanna Sankar has stayed with me: "You're not helping AI anymore. You're getting in the way by being in the way." That's exactly why he's building Vorflux Not another AI coding assistant. His vision is an autonomous AI software engineer that can build software with minimal human involvement. Coming from someone who was India's #1 competitive programmer, helped build Rippling into a $15B company, and once turned down Facebook's $100M acquisition offer, it's a perspective worth paying attention to. In this conversation, we go deep on: • Why AI has already surpassed humans at software engineering. • Why tools like Cursor and Claude Code are only the beginning—and how Vorflux is thinking differently. • Why the engineer of the future becomes a "builder." • The story behind building Rippling and starting over from scratch. • Why he turned down Facebook's $100M acquisition offer. • Why he believes we're moving toward zero-employee companies. • What humans will be uniquely valuable for in an AI-first world. One of the most thought-provoking conversations I've had on AI and the future of software. Full conversation is now live on the 1947 Rise Podcast. Link in comments Prasanna S Vorflux

Shiva Singh Sangwan

72,199 次观看 • 1 个月前

Maths savant turned Cognition founder Scott Wu joins me to discuss their AI software engineer, acquiring Windsurf over a weekend, the Moneyball-ification of everything, math contests with Alexandr Wang in 6th grade, the future of independent coding tools, and why he thinks we already have AGI. Subscribe on Spotify, Apple Podcasts, and YouTube for future Cheeky Pint episodes. Timestamps: 00:00 Intro 01:13 Early life and maths competitions 03:47 Addepar job as a high schooler 05:43 Where are all the young founders? 08:45 Moneyball-ification of everything 11:42 Cognition’s AI software engineer, Devin 15:46 Essential and accidental complexity 17:59 How Devin works with enterprises 19:48 IDE productivity 21:56 Nihilist computer use argument 25:55 Benchmarking Devin 27:15 Market structure 30:32 Agent economy 37:21 Cognition’s team of founders 39:31 Jevons paradox and software 42:00 When will we see AI UIs? 45:52 “I think we have AGI” 47:03 Windsurf deal 52:37 M&A in AI 54:21 Cognition’s culture 55:48 Learning as a CEO 57:12 Scott’s information diet
59:36

Maths savant turned Cognition founder Scott Wu joins me to discuss their AI software engineer, acquiring Windsurf over a weekend, the Moneyball-ification of everything, math contests with Alexandr Wang in 6th grade, the future of independent coding tools, and why he thinks we already have AGI. Subscribe on Spotify, Apple Podcasts, and YouTube for future Cheeky Pint episodes. Timestamps: 00:00 Intro 01:13 Early life and maths competitions 03:47 Addepar job as a high schooler 05:43 Where are all the young founders? 08:45 Moneyball-ification of everything 11:42 Cognition’s AI software engineer, Devin 15:46 Essential and accidental complexity 17:59 How Devin works with enterprises 19:48 IDE productivity 21:56 Nihilist computer use argument 25:55 Benchmarking Devin 27:15 Market structure 30:32 Agent economy 37:21 Cognition’s team of founders 39:31 Jevons paradox and software 42:00 When will we see AI UIs? 45:52 “I think we have AGI” 47:03 Windsurf deal 52:37 M&A in AI 54:21 Cognition’s culture 55:48 Learning as a CEO 57:12 Scott’s information diet

John Collison

346,157 次观看 • 1 年前

In the first episode of our new series Full Stack, Conductor CEO and co-founder Charlie Holtz takes us into the details of how he sets up his workflow for coding and managing AI agents. 00:00 – Building Conductor With Conductor 01:05 – Managing a Team of Coding Agents 02:39 – Do You Still Write Code? 04:17 – Charlie's AI Stack and Setup 05:48 – “Slop-free” Zones 07:25 – Don’t Let the AI Be Your Architect 09:15 – The Future of Cloud Workspaces 10:40 – Claude vs Codex 12:01 – Tokenmaxxing 14:17 – The Future of Human–AI Collaboration 15:07 – "Code Is Becoming Sawdust"
16:34

In the first episode of our new series Full Stack, Conductor CEO and co-founder Charlie Holtz takes us into the details of how he sets up his workflow for coding and managing AI agents. 00:00 – Building Conductor With Conductor 01:05 – Managing a Team of Coding Agents 02:39 – Do You Still Write Code? 04:17 – Charlie's AI Stack and Setup 05:48 – “Slop-free” Zones 07:25 – Don’t Let the AI Be Your Architect 09:15 – The Future of Cloud Workspaces 10:40 – Claude vs Codex 12:01 – Tokenmaxxing 14:17 – The Future of Human–AI Collaboration 15:07 – "Code Is Becoming Sawdust"

Y Combinator

156,937 次观看 • 3 个月前

BREAKING: Devin now writes ~95% of Devin's code Scott Wu (Scott Wu), CEO of Cognition says the abundance era of AI is real & coming soon.. FULL INTERVIEW Cog Stats: › $2.5B raised, $26B valuation › $500M+ run rate, up from $37M LY › Customer usage is up 11x-12x in 6 months › Total code shipped inside Cognition has grown 7x › Devin writes ~95% of Cog's code, up from 89% at May Series D › The entire Windsurf acquisition was negotiated over 1 weekend, ~40 people buying a 200 person company with $82M of ARR › Goldman Sachs, Mercedes-Benz, Citi, Dell, Santander, NASA, the US Navy & the US Army all run Devin R I P : "The tokenmaxx era lasted from January to May of 2026, roughly." "I'm excited to have AI that allows me to spend all of my time & focus on the things that I care about & take care of the rest. I think the abundance era is real, & I think we'll be there pretty soon." Recorded 7 July 2026. On 23 July 2026 Cognition acquired Poke maker The Interaction Company 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Scott Wu, Co-Founder & CEO at Cognition (01:00) The Identity crisis of building Devin (01:42) How Cognition 7X-ed its output in 6 months (02:55) The Metric every AI company gets wrong (04:18) Cognition's 12x growth curve (07:46) The Windsurf acquisition (13:26) Scaling overnight: Cognition's hardest week (15:11) The strategy behind the integration of Cognition products (17:23) Cognition's M&A playbook (20:01) Why the DMV is Cognition's next big target (22:59) Why Cognition bet against AI centralization (25:09) Why enterprises refuse to bet on a single AI lab (28:32) Scott's honest take on Superintelligence (32:36) Is Silicon Valley losing its mind over the AI gold rush? (33:26) Devin's origin story (37:17) The question Scott thinks nobody's asking yet (39:59) Poker with Peter Thiel & Napoleon ? (41:24) Scott's vision for the next year
43:24

BREAKING: Devin now writes ~95% of Devin's code Scott Wu (Scott Wu), CEO of Cognition says the abundance era of AI is real & coming soon.. FULL INTERVIEW Cog Stats: › $2.5B raised, $26B valuation › $500M+ run rate, up from $37M LY › Customer usage is up 11x-12x in 6 months › Total code shipped inside Cognition has grown 7x › Devin writes ~95% of Cog's code, up from 89% at May Series D › The entire Windsurf acquisition was negotiated over 1 weekend, ~40 people buying a 200 person company with $82M of ARR › Goldman Sachs, Mercedes-Benz, Citi, Dell, Santander, NASA, the US Navy & the US Army all run Devin R I P : "The tokenmaxx era lasted from January to May of 2026, roughly." "I'm excited to have AI that allows me to spend all of my time & focus on the things that I care about & take care of the rest. I think the abundance era is real, & I think we'll be there pretty soon." Recorded 7 July 2026. On 23 July 2026 Cognition acquired Poke maker The Interaction Company 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Scott Wu, Co-Founder & CEO at Cognition (01:00) The Identity crisis of building Devin (01:42) How Cognition 7X-ed its output in 6 months (02:55) The Metric every AI company gets wrong (04:18) Cognition's 12x growth curve (07:46) The Windsurf acquisition (13:26) Scaling overnight: Cognition's hardest week (15:11) The strategy behind the integration of Cognition products (17:23) Cognition's M&A playbook (20:01) Why the DMV is Cognition's next big target (22:59) Why Cognition bet against AI centralization (25:09) Why enterprises refuse to bet on a single AI lab (28:32) Scott's honest take on Superintelligence (32:36) Is Silicon Valley losing its mind over the AI gold rush? (33:26) Devin's origin story (37:17) The question Scott thinks nobody's asking yet (39:59) Poker with Peter Thiel & Napoleon ? (41:24) Scott's vision for the next year

Molly O’Shea

677,522 次观看 • 1 个月前

This AI can read emotions better than you can. It was created by Hume (Hume AI) an AI research lab developing models that can read your face and your voice with uncanny accuracy. Their hope is that models that can read your emotions will help create AI that optimizes for human well-being. I sat down with Alan Cowen (Alan Cowen), the co-founder and CEO of Hume to talk about how all of this works: the science of emotion, AI that optimizes for human well-being, and more. Before starting Hume, Alan helped set up Google’s research into affective computing and got a Ph.D. in computational psychology from Berkeley. He's one of the bright lights in AI, and this was an incredible conversation. We get into: - What an emotion actually is - Why traditional psychological theories of emotion are inadequate - How Hume is able to model human emotions - How Hume's API enables developers to build empathetic voice interfaces - Applications of the model in customer service, gaming, and therapy - Why Hume is designed to optimize for human well-being instead of engagement - The ethical concerns around creating an AI that can interpret human emotions - The future of psychology as a science This is a must-watch for anyone interested in the science of emotion and the future of human-AI interactions. Watch! --- Timestamps: I tell Hume’s empathetic AI model a secret: 00:00:00 Introduction: 00:01:13 What traditional psychology tells us about emotions: 00:10:17 Alan’s radical approach to studying human emotion: 00:13:46 Methods that Hume’s AI model uses to understand emotion: 00:16:46 How the model accounts for individual differences: 00:21:08 My pet theory on why it’s been hard to make progress in psychology: 00:27:19 The ways in which Alan thinks Hume can be used: 00:38:12 How Alan is thinking about the API v. consumer product question: 00:41:22
56:12

This AI can read emotions better than you can. It was created by Hume (Hume AI) an AI research lab developing models that can read your face and your voice with uncanny accuracy. Their hope is that models that can read your emotions will help create AI that optimizes for human well-being. I sat down with Alan Cowen (Alan Cowen), the co-founder and CEO of Hume to talk about how all of this works: the science of emotion, AI that optimizes for human well-being, and more. Before starting Hume, Alan helped set up Google’s research into affective computing and got a Ph.D. in computational psychology from Berkeley. He's one of the bright lights in AI, and this was an incredible conversation. We get into: - What an emotion actually is - Why traditional psychological theories of emotion are inadequate - How Hume is able to model human emotions - How Hume's API enables developers to build empathetic voice interfaces - Applications of the model in customer service, gaming, and therapy - Why Hume is designed to optimize for human well-being instead of engagement - The ethical concerns around creating an AI that can interpret human emotions - The future of psychology as a science This is a must-watch for anyone interested in the science of emotion and the future of human-AI interactions. Watch! --- Timestamps: I tell Hume’s empathetic AI model a secret: 00:00:00 Introduction: 00:01:13 What traditional psychology tells us about emotions: 00:10:17 Alan’s radical approach to studying human emotion: 00:13:46 Methods that Hume’s AI model uses to understand emotion: 00:16:46 How the model accounts for individual differences: 00:21:08 My pet theory on why it’s been hard to make progress in psychology: 00:27:19 The ways in which Alan thinks Hume can be used: 00:38:12 How Alan is thinking about the API v. consumer product question: 00:41:22

Dan Shipper 📧

45,941 次观看 • 2 年前

In this episode of the Lightcone Podcast, we sat down with Mukund Jha and Madhav Jha, the founders of Emergent - an AI platform that lets anyone build and ship production-ready software. In just eight months, users have created more than 7 million apps on Emergent, with the number doubling in just the last 45 days. We discuss how they built one of the most powerful AI coding agents, why they focused on non-technical users and what it's like building for a global audience from India. 00:00 - Intro 01:06 - What Is Emergent? 01:18 - Founder Backstory 02:09 - From AI Testing to General Coding Agents 02:52 - Getting Ahead of the Market 04:18 - The Pivot to Non-Technical Users 05:22 - Why Second Movers Can Win in AI 09:04 - Building for Production, Not Just Prototypes 18:21 - Live Demo: Building Apps with Emergent 24:40 - How Emergent Hires and Runs a Lean Team 29:04 - Is SaaS Dead? The Rise of Personalized Software 34:04 - The Future: Niche Apps, Solo Builders and AI Agency
39:32

In this episode of the Lightcone Podcast, we sat down with Mukund Jha and Madhav Jha, the founders of Emergent - an AI platform that lets anyone build and ship production-ready software. In just eight months, users have created more than 7 million apps on Emergent, with the number doubling in just the last 45 days. We discuss how they built one of the most powerful AI coding agents, why they focused on non-technical users and what it's like building for a global audience from India. 00:00 - Intro 01:06 - What Is Emergent? 01:18 - Founder Backstory 02:09 - From AI Testing to General Coding Agents 02:52 - Getting Ahead of the Market 04:18 - The Pivot to Non-Technical Users 05:22 - Why Second Movers Can Win in AI 09:04 - Building for Production, Not Just Prototypes 18:21 - Live Demo: Building Apps with Emergent 24:40 - How Emergent Hires and Runs a Lean Team 29:04 - Is SaaS Dead? The Rise of Personalized Software 34:04 - The Future: Niche Apps, Solo Builders and AI Agency

Y Combinator

107,806 次观看 • 6 个月前

Jensen Huang just said this is the greatest era in history to build software. AI agents will not kill software. They will do the exact opposite: create a massive new wave of software demand. at NVIDIA GTC Taipei 2026 "Click and type. We now replace that with explaining to the AI what we want, our intent, and the AI generates the code or uses tools to produce the necessary output. This is how computers are going to work in the future. This is Agentic AI. For two years, we've been building toward this, and now it has arrived. One of the big breakthroughs, of course, is tool use. A lot of people have said, “Jensen, AI is coming. Agentic AI is coming. Therefore, all the software companies are going to go out of business.” This is exactly the opposite. Because there are going to be so many agents, the world is no longer limited by the number of people. Therefore, those agents are going to use more tools than ever. This is actually an incredible time to be a software company. But the software has to be presented to the agent in a way that the agent can use it. This is a big breakthrough. And in fact, what we have done, as you know, what Nvidia’s treasure..." ---- From 'NVIDIA' YT channel (link in comment)
0:59

Jensen Huang just said this is the greatest era in history to build software. AI agents will not kill software. They will do the exact opposite: create a massive new wave of software demand. at NVIDIA GTC Taipei 2026 "Click and type. We now replace that with explaining to the AI what we want, our intent, and the AI generates the code or uses tools to produce the necessary output. This is how computers are going to work in the future. This is Agentic AI. For two years, we've been building toward this, and now it has arrived. One of the big breakthroughs, of course, is tool use. A lot of people have said, “Jensen, AI is coming. Agentic AI is coming. Therefore, all the software companies are going to go out of business.” This is exactly the opposite. Because there are going to be so many agents, the world is no longer limited by the number of people. Therefore, those agents are going to use more tools than ever. This is actually an incredible time to be a software company. But the software has to be presented to the agent in a way that the agent can use it. This is a big breakthrough. And in fact, what we have done, as you know, what Nvidia’s treasure..." ---- From 'NVIDIA' YT channel (link in comment)

Rohan Paul

16,584 次观看 • 3 个月前