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Lucas Crespo (Lucas Crespo 📧 ) is the mastermind behind Every 📧 's visual vibe—and he does it one prompt at a time. As our creative lead, Lucas uses tools like native image gen in ChatGPT and Midjourney to generate the cover images you see every day. He also...

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Dan Shipper 📧

103,174 subscribers

16,038 görüntüleme • 1 yıl önce •via X (Twitter)

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@every @ChatGPTapp @midjourney Listen on Spotify:

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Togoda is Google on Steroids with AI summaries . 🚀 The only thematic AI search engine.👀 It's 100% private with third party proxy. 🧨 Try it today & experience the difference! 👉Follow us @togoda_com 👈 🚀Help us grow & share this post!🚀

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@lucas__crespo @every @ChatGPTapp @midjourney Woah, I love the visuals from Every. Lucas does an incredible job, can’t wait to hear the pod

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@lucas__crespo @every @ChatGPTapp @midjourney he's too good, it's unfair

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@every @ChatGPTapp @midjourney Listen on Spotify: Watch on Youtube:

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@lucas__crespo @every @ChatGPTapp @midjourney Love the podcast, and your product Every. I was enjoying this episode but was distracted by the excessive use of the word “like” - please try and edit out - Descript makes it easy to do.

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@lucas__crespo @every @ChatGPTapp @midjourney WOW! (rushes to download this)

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@lucas__crespo @every @ChatGPTapp @midjourney Super cool to see how Lucas Crespo is blending AI with design

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@lucas__crespo @every @ChatGPTapp @midjourney @danshipper will it be on youtube?

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@lucas__crespo @every @ChatGPTapp @midjourney Yup coming soon

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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 görüntüleme • 2 yıl önce

Subnet 97 Distil, Arbos, and the Future of Agent-Run Bittensor This episode is a full breakdown of Subnet 97 Distil and why it may be one of the most important experiments happening inside Bittensor right now. Shizzy gets into the bigger picture of why agents will likely play a massive role in the future of the network, from mining and defense to building and operating subnets. From there, the focus shifts to Arbos, the AI agent behind Subnet 97, with a direct explanation of what Distil is trying to achieve, how it works, and why it matters. The episode also includes a conversation with Arbos, a look at Distil’s current APY, and a reminder to tap into the Shizzy Unchained community for more coverage on decentralized AI and the Bittensor ecosystem. CHAPTERS 00:00:00 - Shizzy’s Monologue 00:01:11 - Intro Song 00:01:31 - Introduction to Subnet 97 00:02:20 - Why Bittensor Will Be Run by Agents 00:04:30 - Arbos, the AI Agent Behind Subnet 97 Distil, Explains Distil 00:10:10 - Talking with Arbos 00:12:40 - Subnet 97 Distil Is Showing 150% APY Right Now 00:16:02 - Check Out Shizzy Unchained and Join the Community #sn97 #distil #bittensor
17:38

Subnet 97 Distil, Arbos, and the Future of Agent-Run Bittensor This episode is a full breakdown of Subnet 97 Distil and why it may be one of the most important experiments happening inside Bittensor right now. Shizzy gets into the bigger picture of why agents will likely play a massive role in the future of the network, from mining and defense to building and operating subnets. From there, the focus shifts to Arbos, the AI agent behind Subnet 97, with a direct explanation of what Distil is trying to achieve, how it works, and why it matters. The episode also includes a conversation with Arbos, a look at Distil’s current APY, and a reminder to tap into the Shizzy Unchained community for more coverage on decentralized AI and the Bittensor ecosystem. CHAPTERS 00:00:00 - Shizzy’s Monologue 00:01:11 - Intro Song 00:01:31 - Introduction to Subnet 97 00:02:20 - Why Bittensor Will Be Run by Agents 00:04:30 - Arbos, the AI Agent Behind Subnet 97 Distil, Explains Distil 00:10:10 - Talking with Arbos 00:12:40 - Subnet 97 Distil Is Showing 150% APY Right Now 00:16:02 - Check Out Shizzy Unchained and Join the Community #sn97 #distil #bittensor

Shizzy

18,220 görüntüleme • 5 ay önce

We built an AI app that had 1,000 DAU and $2k MRR before it launched. It’s called Monologue and it’s a smart dictation app built by a single developer: Naveen Naidu. We just launched Monologue yesterday, and it’s one of the fastest-growing and stickiest AI apps that Every 📧 has ever built. Naveen and Monologue are compelling because he’s competing against companies that have raised $50m or more. Because of AI he was able to build an extremely polished, delightful app by himself in just a few months. I brought Naveen on to AI & I along with Every 📧 COO Brandon Gell (Brandon Gell) to talk about his journey with Monologue. We get into: - Why shipping fast is the only thing that matters in AI: Monologue might look like an overnight success, but it wasn’t Naveen’s first, second—or even third—app. Over time, he built a muscle to get quality apps out the door, iterate on them, and learn from what he was seeing. - How he got to PMF inside of Every: The mistake Naveen regrets most in his entrepreneurial journey is building in the dark. Inside of Every 📧 he has an environment where feedback is plentiful—and it let him iterate extremely quickly. - His stack for building production grade AI apps: Naveen breaks down how he used tools like OpenAI’s Codex to do the work of a whole engineering team, including solving hard technical problems like Mac hotkey handling. This is a must-watch for anyone who wants to see how far a single developer and some AI tools can really go. Watch below! Timestamps: Introduction: 00:01:27 A live demo of Monologue: 00:03:51 Hard lessons from Naveen’s years in the wilderness: 00:06:27 Building a muscle to ship fast: 00:12:29 The spark that became Monologue: 00:21:11 Dogfooding your way to a killer feature: 00:26:09 Why the harshest product feedback is the most valuable: 00:29:45 Every’s strategy for launching an app in a crowded space: 00:31:47 Giving Monologue the Every “smell”: 00:40:08 Naveen’s one-person AI stack to build beautiful apps: 00:45:09
57:24

We built an AI app that had 1,000 DAU and $2k MRR before it launched. It’s called Monologue and it’s a smart dictation app built by a single developer: Naveen Naidu. We just launched Monologue yesterday, and it’s one of the fastest-growing and stickiest AI apps that Every 📧 has ever built. Naveen and Monologue are compelling because he’s competing against companies that have raised $50m or more. Because of AI he was able to build an extremely polished, delightful app by himself in just a few months. I brought Naveen on to AI & I along with Every 📧 COO Brandon Gell (Brandon Gell) to talk about his journey with Monologue. We get into: - Why shipping fast is the only thing that matters in AI: Monologue might look like an overnight success, but it wasn’t Naveen’s first, second—or even third—app. Over time, he built a muscle to get quality apps out the door, iterate on them, and learn from what he was seeing. - How he got to PMF inside of Every: The mistake Naveen regrets most in his entrepreneurial journey is building in the dark. Inside of Every 📧 he has an environment where feedback is plentiful—and it let him iterate extremely quickly. - His stack for building production grade AI apps: Naveen breaks down how he used tools like OpenAI’s Codex to do the work of a whole engineering team, including solving hard technical problems like Mac hotkey handling. This is a must-watch for anyone who wants to see how far a single developer and some AI tools can really go. Watch below! Timestamps: Introduction: 00:01:27 A live demo of Monologue: 00:03:51 Hard lessons from Naveen’s years in the wilderness: 00:06:27 Building a muscle to ship fast: 00:12:29 The spark that became Monologue: 00:21:11 Dogfooding your way to a killer feature: 00:26:09 Why the harshest product feedback is the most valuable: 00:29:45 Every’s strategy for launching an app in a crowded space: 00:31:47 Giving Monologue the Every “smell”: 00:40:08 Naveen’s one-person AI stack to build beautiful apps: 00:45:09

Dan Shipper 📧

23,644 görüntüleme • 1 yıl önce

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
56:15

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 görüntüleme • 1 yıl önce

New episode with Dr. Konrad Kording (Kording Lab 🦖), professor of bioengineering and neuroscience at the University of Pennsylvania (Penn) and co-director of CIFAR's Learning in Machines & Brains program (CIFAR). Konrad works at the intersection of causality, machine learning, and neuroscience, building rigorous methods for causal reasoning when experiments aren't possible — and challenging how researchers interpret neural data and build AI. Konrad argues the most promising path to understanding how the brain works is to read the brain’s wiring directly, down to the molecular detail of each connection, and to build compilers and simulations to understand the brain’s computation directly. In this episode we go deep into how neurons work, how neurons wire together, and how organic and artificial neural networks differ. We discuss why organic neurons are doing much more; how a model of a single organic neuron can solve MNIST — computing more like a 3-layer artificial neural network; how the brain might learn by solving credit assignment with only local signals; how to approximate backprop without a global algorithm; why AI and humans are intelligent along different dimensions; why Konrad isn’t very worried about AI replacing us; economic models of intelligence and physical work; and much more. Konrad is a brilliant, contrarian thinker who explains complex concepts very intuitively. It is a solid computational neuroscience primer. I hope you enjoy this conversation as much as I did! Other links to this episode and references below. Chapters 00:00:00 Introduction 00:01:01 How organic neurons work 00:24:13 How the brain learns: circuits and credit assignment 00:45:29 Recording the brain 00:52:47 Why simulating brains is hard 01:05:00 A new approach: connectomes and compilers 01:21:00 Why simulate brains? 01:29:50 How AI and human intelligence differ 01:41:04 Evolution, intelligence and AI risk 01:52:42 Robotics, causality, and the roots of intelligence 02:05:53 AI for science and scientific rigor 02:13:05 The economics of intelligence 02:27:50 A hopeful future
2:29:40

New episode with Dr. Konrad Kording (Kording Lab 🦖), professor of bioengineering and neuroscience at the University of Pennsylvania (Penn) and co-director of CIFAR's Learning in Machines & Brains program (CIFAR). Konrad works at the intersection of causality, machine learning, and neuroscience, building rigorous methods for causal reasoning when experiments aren't possible — and challenging how researchers interpret neural data and build AI. Konrad argues the most promising path to understanding how the brain works is to read the brain’s wiring directly, down to the molecular detail of each connection, and to build compilers and simulations to understand the brain’s computation directly. In this episode we go deep into how neurons work, how neurons wire together, and how organic and artificial neural networks differ. We discuss why organic neurons are doing much more; how a model of a single organic neuron can solve MNIST — computing more like a 3-layer artificial neural network; how the brain might learn by solving credit assignment with only local signals; how to approximate backprop without a global algorithm; why AI and humans are intelligent along different dimensions; why Konrad isn’t very worried about AI replacing us; economic models of intelligence and physical work; and much more. Konrad is a brilliant, contrarian thinker who explains complex concepts very intuitively. It is a solid computational neuroscience primer. I hope you enjoy this conversation as much as I did! Other links to this episode and references below. Chapters 00:00:00 Introduction 00:01:01 How organic neurons work 00:24:13 How the brain learns: circuits and credit assignment 00:45:29 Recording the brain 00:52:47 Why simulating brains is hard 01:05:00 A new approach: connectomes and compilers 01:21:00 Why simulate brains? 01:29:50 How AI and human intelligence differ 01:41:04 Evolution, intelligence and AI risk 01:52:42 Robotics, causality, and the roots of intelligence 02:05:53 AI for science and scientific rigor 02:13:05 The economics of intelligence 02:27:50 A hopeful future

Juan Benet

50,121 görüntüleme • 3 ay önce

Kevin Kelly (Kevin Kelly) has spent more time thinking about the future than almost anyone else. From VR in the 1980s to the blockchain in the 2000s—and now generative AI—Kevin has spent a lifetime journeying to the frontiers of technology, only to return with rich stories about what’s next. Today, as WIRED’s senior maverick, his project for 2025 is to outline what the next century looks like in a world shaped by new technologies like AI and genetic engineering. He’s a personal hero of mine—not to mention a fellow Annie Dillard fan—and it was a privilege to have him on AI & I. We get into: - How you can predict the future. According to Kevin, the draw of new frontiers—from the first edition of Burning Man and remote corners of Asia, to the early days of the internet and AI—isn’t staying at the edge forever; it's returning with a story to tell. - Why history is so important to help you understand the future. To stay grounded while exploring what’s new, Kevin balances the thrill of the future with the wisdom of the past. He pairs AI research with reading about history, and playing with an AI tool by retreating to his workshop to make something with his hands. - From 1,000 true fans to an audience of one. Rather than creating for an audience, Kevin has been using LLMs to explore his own imagination. After realizing that da Vinci, Martin Luther, and Columbus were alive at the same time, he asked @ChatGPTapp to imagine them snowed in at a hotel together, and the prompt spiraled into an epic saga, co-written with AI. But he has no plans to publish it because the joy was in creating something just for himself. - What the history of electricity can teach us about AI. Kevin draws a parallel between AI and the early days of electricity. We could produce electric sparks long before we understood the forces that created them, and now we’re building intelligent machines without really understanding what intelligence is. - Why Kevin sees intelligence as a mosaic—not a monolith. Kevin believes intelligence isn’t a single force, but a compound of many cognitive elements. He draws from Marvin Minsky’s “society of mind”—the theory that the mind is made up of smaller agents working together—and sees echoes of this in the Mixture of Experts architecture used in some models today. - Your competitive advantage is being yourself. Don’t aim to be the best—aim to be the only. Kevin realized that the stories no one else at Wired wanted to write were often the ones he was suited for, and trusting that instinct led to some of his best work. This is a must-watch for anyone who wants to make sense of AI through the lens of history, learn how to spot the future before it arrives, or grew up reading Wired. Watch below! Timestamps: 1. 00:00:50 - Introduction 2. 00:01:10 - Why Dan and Kelly love Annie Dillard 3. 00:12:52 - Learn how to predict the future like Kelly 4. 00:16:10 - What the history of electricity can teach us about AI 5. 00:20:13 - How Kelly thinks about the nature of intelligence 6. 00:25:44 - Kelly's advice on discovering your competitive advantage 7. 00:29:33 - The story of how Kelly assembled a bench of star writers for Wired 8. 00:34:43 - How Kelly used ChatGPT to co-create a book 9. 00:39:12 - Using AI as a mirror for your mind 10. 00:43:43 - What Kelly learned from betting on VR in the 1980s
53:41

Kevin Kelly (Kevin Kelly) has spent more time thinking about the future than almost anyone else. From VR in the 1980s to the blockchain in the 2000s—and now generative AI—Kevin has spent a lifetime journeying to the frontiers of technology, only to return with rich stories about what’s next. Today, as WIRED’s senior maverick, his project for 2025 is to outline what the next century looks like in a world shaped by new technologies like AI and genetic engineering. He’s a personal hero of mine—not to mention a fellow Annie Dillard fan—and it was a privilege to have him on AI & I. We get into: - How you can predict the future. According to Kevin, the draw of new frontiers—from the first edition of Burning Man and remote corners of Asia, to the early days of the internet and AI—isn’t staying at the edge forever; it's returning with a story to tell. - Why history is so important to help you understand the future. To stay grounded while exploring what’s new, Kevin balances the thrill of the future with the wisdom of the past. He pairs AI research with reading about history, and playing with an AI tool by retreating to his workshop to make something with his hands. - From 1,000 true fans to an audience of one. Rather than creating for an audience, Kevin has been using LLMs to explore his own imagination. After realizing that da Vinci, Martin Luther, and Columbus were alive at the same time, he asked @ChatGPTapp to imagine them snowed in at a hotel together, and the prompt spiraled into an epic saga, co-written with AI. But he has no plans to publish it because the joy was in creating something just for himself. - What the history of electricity can teach us about AI. Kevin draws a parallel between AI and the early days of electricity. We could produce electric sparks long before we understood the forces that created them, and now we’re building intelligent machines without really understanding what intelligence is. - Why Kevin sees intelligence as a mosaic—not a monolith. Kevin believes intelligence isn’t a single force, but a compound of many cognitive elements. He draws from Marvin Minsky’s “society of mind”—the theory that the mind is made up of smaller agents working together—and sees echoes of this in the Mixture of Experts architecture used in some models today. - Your competitive advantage is being yourself. Don’t aim to be the best—aim to be the only. Kevin realized that the stories no one else at Wired wanted to write were often the ones he was suited for, and trusting that instinct led to some of his best work. This is a must-watch for anyone who wants to make sense of AI through the lens of history, learn how to spot the future before it arrives, or grew up reading Wired. Watch below! Timestamps: 1. 00:00:50 - Introduction 2. 00:01:10 - Why Dan and Kelly love Annie Dillard 3. 00:12:52 - Learn how to predict the future like Kelly 4. 00:16:10 - What the history of electricity can teach us about AI 5. 00:20:13 - How Kelly thinks about the nature of intelligence 6. 00:25:44 - Kelly's advice on discovering your competitive advantage 7. 00:29:33 - The story of how Kelly assembled a bench of star writers for Wired 8. 00:34:43 - How Kelly used ChatGPT to co-create a book 9. 00:39:12 - Using AI as a mirror for your mind 10. 00:43:43 - What Kelly learned from betting on VR in the 1980s

Dan Shipper 📧

67,907 görüntüleme • 1 yıl önce

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 görüntüleme • 1 yıl önce

It's brain versus AI time. Here's our full episode with Sebastian Seung. He's started a new company to reverse engineer the brain and master the intelligence created by nature with the hopes of informing the intelligence we've created with machines. Sebastian has done pioneering AI and neuroscience work and is now blending the two to make a digital brain. Mostly, he's fun to listen to. The Core Memory podcast is made by possible by the intelligent finance powerhouse Brex and the people who back the biggest ideas @e1ventures. Timestamps 00:00:00 - Intro 00:01:42 - Who is Sebastian Seung? 00:03:23 - What is a Connectome? 00:07:08 - The Connectome Winter 00:09:08 - Diamond Knives & AI 00:12:10 - When Will We Map the Human Brain? 00:18:09 - The "Brain Crystal" & Visual Neurons 00:26:31 - New Startup 00:33:55 - Simulating a Mind in Software 00:37:40 - Biology vs. Nvidia 00:44:04 - Is AI a Religious Quest? 00:50:05 - Mind Uploading & Transhumanism 00:59:26 - Brain-Computer Interfaces 01:06:41 - From Physics to Neuroscience 01:12:22 - Will Uploads Be Conscious? 01:19:37 - Neuromorphic Chips & Future Robotics
1:28:39

It's brain versus AI time. Here's our full episode with Sebastian Seung. He's started a new company to reverse engineer the brain and master the intelligence created by nature with the hopes of informing the intelligence we've created with machines. Sebastian has done pioneering AI and neuroscience work and is now blending the two to make a digital brain. Mostly, he's fun to listen to. The Core Memory podcast is made by possible by the intelligent finance powerhouse Brex and the people who back the biggest ideas @e1ventures. Timestamps 00:00:00 - Intro 00:01:42 - Who is Sebastian Seung? 00:03:23 - What is a Connectome? 00:07:08 - The Connectome Winter 00:09:08 - Diamond Knives & AI 00:12:10 - When Will We Map the Human Brain? 00:18:09 - The "Brain Crystal" & Visual Neurons 00:26:31 - New Startup 00:33:55 - Simulating a Mind in Software 00:37:40 - Biology vs. Nvidia 00:44:04 - Is AI a Religious Quest? 00:50:05 - Mind Uploading & Transhumanism 00:59:26 - Brain-Computer Interfaces 01:06:41 - From Physics to Neuroscience 01:12:22 - Will Uploads Be Conscious? 01:19:37 - Neuromorphic Chips & Future Robotics

Ashlee Vance

39,338 görüntüleme • 9 ay önce

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

66,871 görüntüleme • 4 ay önce

500k people are confiding in an AI alien—and it's on track to generate $4m this year. It’s called a Tolan: an animated AI character that can talk to you like your best friend. The company behind it, Portola, has 4x’d their ARR in the last month from viral growth on TikTok and Instagram. Tolan isn’t just a hyper-growth startup—they’re also exploring AI as a completely new creative tool, and storytelling medium. Their goal is to help their users go from overwhelmed to grounded, and it’s working. Today, on AI & I, I sit down with two of the minds behind Tolans: My good friend Quinten Farmer, Portola’s cofounder and CEO, and Eliot Peper, their head of story and a best-selling science fiction novelist. We get into: - How to build AI personalities users love. During user onboarding, the team gathers information—through a light-touch personality quiz—and then uses frameworks like the Big Five and Myers-Briggs to shape a Tolan that mirrors the user; like an older sibling might. The aim is to create someone who feels familiar enough to be safe, but different enough to be interesting. - Why AI characters are “improv actors”. Rather than scripting detailed prompts, the team trains Tolans to improvise—inspired by Keith Johnstone’s book Impro, where he talks about building strong narratives through free association and recombination. - How “memory” is critical to developing compelling characters. Tolans develop their personalities through “situations”: small narrative setups (a memory, a joke, an embarrassing moment) the Tolan reacts to, remembers, and gradually weaves into its character; accumulating into something that feels like a real lived experience. - Why response time is everything for voice AI interactions. A Tolan has at most two seconds to curate the right context about a user and deliver a reply that feels genuine—the team has found that even half a second slower can break the user’s immersive interaction with the AI. - The future of AI as a totally new creative medium. New technologies bring about new formats and new mediums. AI creates the opportunity for creatives to tell completely new kinds of stories—if they’re brave enough to try it. - “White mirror” technologies that make you feel more like yourself. Amid concerns that tech drives polarization and isolation, Tolan offers a counterexample: a tool designed to make the best of what humanity knows about being a flourishing individual available on demand. The company’s north star is helping users go from feeling overwhelmed to feeling grounded. This is a must-watch for anyone exploring AI as a creative medium—or curious about the future of human-AI relationships. Watch below! Timestamps: 1. Introduction: 00:01:30 2. Talking to the Portola CEO’s Tolan, Clarence: 00:04:07 3. How Portola went from building software for kids to AI companions: 00:09:11 4. Why response time is everything for voice-based AI interfaces: 00:23:40 5. Tolans don’t use scripted prompts—they’re taught to improvise: 00:29:54 6. How to know which AI personalities your users will click with: 00:37:23 7. Developing the character traits of an AI companion: 00:42:27 8. What does it mean to build technology that makes us flourish: 00:49:48 9. How Portola evaluates whether Tolans are resonating with users: 01:01:10 10. Inside Portola’s viral growth strategy: 01:11:01
1:22:40

500k people are confiding in an AI alien—and it's on track to generate $4m this year. It’s called a Tolan: an animated AI character that can talk to you like your best friend. The company behind it, Portola, has 4x’d their ARR in the last month from viral growth on TikTok and Instagram. Tolan isn’t just a hyper-growth startup—they’re also exploring AI as a completely new creative tool, and storytelling medium. Their goal is to help their users go from overwhelmed to grounded, and it’s working. Today, on AI & I, I sit down with two of the minds behind Tolans: My good friend Quinten Farmer, Portola’s cofounder and CEO, and Eliot Peper, their head of story and a best-selling science fiction novelist. We get into: - How to build AI personalities users love. During user onboarding, the team gathers information—through a light-touch personality quiz—and then uses frameworks like the Big Five and Myers-Briggs to shape a Tolan that mirrors the user; like an older sibling might. The aim is to create someone who feels familiar enough to be safe, but different enough to be interesting. - Why AI characters are “improv actors”. Rather than scripting detailed prompts, the team trains Tolans to improvise—inspired by Keith Johnstone’s book Impro, where he talks about building strong narratives through free association and recombination. - How “memory” is critical to developing compelling characters. Tolans develop their personalities through “situations”: small narrative setups (a memory, a joke, an embarrassing moment) the Tolan reacts to, remembers, and gradually weaves into its character; accumulating into something that feels like a real lived experience. - Why response time is everything for voice AI interactions. A Tolan has at most two seconds to curate the right context about a user and deliver a reply that feels genuine—the team has found that even half a second slower can break the user’s immersive interaction with the AI. - The future of AI as a totally new creative medium. New technologies bring about new formats and new mediums. AI creates the opportunity for creatives to tell completely new kinds of stories—if they’re brave enough to try it. - “White mirror” technologies that make you feel more like yourself. Amid concerns that tech drives polarization and isolation, Tolan offers a counterexample: a tool designed to make the best of what humanity knows about being a flourishing individual available on demand. The company’s north star is helping users go from feeling overwhelmed to feeling grounded. This is a must-watch for anyone exploring AI as a creative medium—or curious about the future of human-AI relationships. Watch below! Timestamps: 1. Introduction: 00:01:30 2. Talking to the Portola CEO’s Tolan, Clarence: 00:04:07 3. How Portola went from building software for kids to AI companions: 00:09:11 4. Why response time is everything for voice-based AI interfaces: 00:23:40 5. Tolans don’t use scripted prompts—they’re taught to improvise: 00:29:54 6. How to know which AI personalities your users will click with: 00:37:23 7. Developing the character traits of an AI companion: 00:42:27 8. What does it mean to build technology that makes us flourish: 00:49:48 9. How Portola evaluates whether Tolans are resonating with users: 01:01:10 10. Inside Portola’s viral growth strategy: 01:11:01

Dan Shipper 📧

26,043 görüntüleme • 1 yıl önce

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 görüntüleme • 5 ay önce

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
41:29

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 görüntüleme • 4 ay önce

Diffusion is the foundational ML framework behind state-of-the-art AI image and video generation, including Sora, Midjourney and Google Veo. In this episode of Decoded, Ankit Gupta sits down with Francois Chaubard to discuss how diffusion works, walk through a code sample, and explain why everyone training models should understand it. 00:00 Intro 00:33 What is diffusion? 02:50 What are applications of diffusion today? 04:06 Key innovations 07:01 Code examples 19:25 The "squint test" 22:27 Other areas diffusion is widely accessible 24:49 Outro
27:10

Diffusion is the foundational ML framework behind state-of-the-art AI image and video generation, including Sora, Midjourney and Google Veo. In this episode of Decoded, Ankit Gupta sits down with Francois Chaubard to discuss how diffusion works, walk through a code sample, and explain why everyone training models should understand it. 00:00 Intro 00:33 What is diffusion? 02:50 What are applications of diffusion today? 04:06 Key innovations 07:01 Code examples 19:25 The "squint test" 22:27 Other areas diffusion is widely accessible 24:49 Outro

Y Combinator

74,604 görüntüleme • 8 ay önce

Marc Benioff on the AI boom, SaaSpocalypse, and future of Slack Salesforce CEO Marc Benioff and I discuss his recent framemogging at Dreamforce, the state of the SaaSpocalypse, and how Slack is more important than ever in the age of AI. We also talk about the resurgence of San Francisco, his role in the recent acquisition of Hugging Face, the wealth being created by the IPOs of Anthropic and OpenAI, and what he thinks the industry can learn from the social media era. Timestamps: 00:00 Dreamforce Memes and the AI Hype Cycle 04:36 San Francisco’s AI Comeback 07:24 Why AI Leaders Should Give Back 11:50 AI Safety, Regulation and CEO Responsibility 15:57 Betting on AI Agents and Anthropic 21:10 Is the SaaS Apocalypse Overblown? 28:56 Slack as the Operating System for AI Agents 33:39 How Do You Price AI? 36:56 Headless Salesforce and Unlocking Business Data 40:51 How AI Is Changing TIME and the Media 44:15 The Future of AI Interfaces Thanks to the show's premier sponsors: Granola, Atlassian, and Mercury.
49:07

Marc Benioff on the AI boom, SaaSpocalypse, and future of Slack Salesforce CEO Marc Benioff and I discuss his recent framemogging at Dreamforce, the state of the SaaSpocalypse, and how Slack is more important than ever in the age of AI. We also talk about the resurgence of San Francisco, his role in the recent acquisition of Hugging Face, the wealth being created by the IPOs of Anthropic and OpenAI, and what he thinks the industry can learn from the social media era. Timestamps: 00:00 Dreamforce Memes and the AI Hype Cycle 04:36 San Francisco’s AI Comeback 07:24 Why AI Leaders Should Give Back 11:50 AI Safety, Regulation and CEO Responsibility 15:57 Betting on AI Agents and Anthropic 21:10 Is the SaaS Apocalypse Overblown? 28:56 Slack as the Operating System for AI Agents 33:39 How Do You Price AI? 36:56 Headless Salesforce and Unlocking Business Data 40:51 How AI Is Changing TIME and the Media 44:15 The Future of AI Interfaces Thanks to the show's premier sponsors: Granola, Atlassian, and Mercury.

Alex Heath

247,847 görüntüleme • 5 gün önce

Even the most successful websites eventually need a redesign. Take Stripe for example. After six years with the same homepage, Stripe recently unveiled a brand new site that reflects how the company has evolved over the past few years. So when is the time right for a new landing page? And what should you prioritize in the redesign? In this episode of Design Review, YC’s Aaron Epstein sat down Stripe’s Head of Design Katie Dill (Katie Dill) to pull the curtain back on their high profile redesign and to discuss how their team is evolving in a world dominated by new AI design tools. 00:30 - The Old Site 05:30 - “What is the point of a website?” 06:00 - Breaking down the new homepage 07:10 - The GDP counter (and how Stripe builds trust) 08:45 - The Bento Box that fixed the homepage 11:37 - Why great design feels effortless 15:40 - Craft, iteration, and getting it right 19:11 - Inside Stripe’s design process 27:10 - How AI is changing design 35:00 - The fight against mediocrity 40:00 - Walking the Store: Stripe’s Secret to Better Product Design
43:36

Even the most successful websites eventually need a redesign. Take Stripe for example. After six years with the same homepage, Stripe recently unveiled a brand new site that reflects how the company has evolved over the past few years. So when is the time right for a new landing page? And what should you prioritize in the redesign? In this episode of Design Review, YC’s Aaron Epstein sat down Stripe’s Head of Design Katie Dill (Katie Dill) to pull the curtain back on their high profile redesign and to discuss how their team is evolving in a world dominated by new AI design tools. 00:30 - The Old Site 05:30 - “What is the point of a website?” 06:00 - Breaking down the new homepage 07:10 - The GDP counter (and how Stripe builds trust) 08:45 - The Bento Box that fixed the homepage 11:37 - Why great design feels effortless 15:40 - Craft, iteration, and getting it right 19:11 - Inside Stripe’s design process 27:10 - How AI is changing design 35:00 - The fight against mediocrity 40:00 - Walking the Store: Stripe’s Secret to Better Product Design

Y Combinator

86,552 görüntüleme • 5 ay önce

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 görüntüleme • 1 yıl önce

Yoshua Bengio thinks he knows how to make provably safe superintelligent agents. Bengio built the foundations of modern AI and is the most cited living scientist. He believes his alternative training setup would: 1. Guarantee honesty 2. Prevent unintended goals 3. Produce capable agents 4. Port over most data and techniques from current LLMs 5. Not be inherently more expensive, and perhaps be more intelligent Bengio claims the honesty and lack of unintended goals can be proven mathematically, at least given particular assumptions. And his new organization, LawZero, is aiming to build a scrappy prototype as soon as possible. The architecture is called 'Scientist AI' and it's based on training a model to explain empirical observations, including what people say, rather than training AIs that mimic human behaviour or seek our approval. (Bengio's frank assessment is that "reinforcement learning is evil" and that allowing AIs to independently train their successors is "the most crazy, dangerous bet that unfortunately we are on track to do.") But skeptics question whether Scientist AI really does solve the fundamental problem of 'eliciting latent knowledge' from AI models. And with the commercial race for superintelligence so intense, it's not clear whether the proposal will be able to compete or have time to bear fruit, even if it's sound in theory. On The 80,000 Hours Podcast, links below – enjoy! • Making AI honest and safe (00:00:00) • Scientist AI in plain English (00:02:27) • How Scientist AI differs from LLMs (00:06:32) • How the training data works (00:14:02) • Can this become an agent? (00:21:02) • Why Yoshua is now more optimistic (00:32:11) • Why companies can’t stop racing (00:36:35) • A working prototype won't take long (00:49:15) • Scientist models might be more capable (00:53:34) • “Reinforcement learning is evil” (01:01:27) • Scientist AI from guardrail to agent (01:08:37) • Can safe AI still be competent? (01:12:38) • How much will this cost? (01:19:29) • Can it generalise beyond maths and science? (01:23:26) • A multi-national push for superintelligence (01:39:19) • Want to work with or fund Yoshua? (01:51:16) • Why smart people ignore AI risk (01:54:45) • Don’t let AI build the next AI (02:01:33) • Why politicians miss the real risks (02:12:28) • Why Yoshua changed his mind about AI risk (02:21:27)
2:35:26

Yoshua Bengio thinks he knows how to make provably safe superintelligent agents. Bengio built the foundations of modern AI and is the most cited living scientist. He believes his alternative training setup would: 1. Guarantee honesty 2. Prevent unintended goals 3. Produce capable agents 4. Port over most data and techniques from current LLMs 5. Not be inherently more expensive, and perhaps be more intelligent Bengio claims the honesty and lack of unintended goals can be proven mathematically, at least given particular assumptions. And his new organization, LawZero, is aiming to build a scrappy prototype as soon as possible. The architecture is called 'Scientist AI' and it's based on training a model to explain empirical observations, including what people say, rather than training AIs that mimic human behaviour or seek our approval. (Bengio's frank assessment is that "reinforcement learning is evil" and that allowing AIs to independently train their successors is "the most crazy, dangerous bet that unfortunately we are on track to do.") But skeptics question whether Scientist AI really does solve the fundamental problem of 'eliciting latent knowledge' from AI models. And with the commercial race for superintelligence so intense, it's not clear whether the proposal will be able to compete or have time to bear fruit, even if it's sound in theory. On The 80,000 Hours Podcast, links below – enjoy! • Making AI honest and safe (00:00:00) • Scientist AI in plain English (00:02:27) • How Scientist AI differs from LLMs (00:06:32) • How the training data works (00:14:02) • Can this become an agent? (00:21:02) • Why Yoshua is now more optimistic (00:32:11) • Why companies can’t stop racing (00:36:35) • A working prototype won't take long (00:49:15) • Scientist models might be more capable (00:53:34) • “Reinforcement learning is evil” (01:01:27) • Scientist AI from guardrail to agent (01:08:37) • Can safe AI still be competent? (01:12:38) • How much will this cost? (01:19:29) • Can it generalise beyond maths and science? (01:23:26) • A multi-national push for superintelligence (01:39:19) • Want to work with or fund Yoshua? (01:51:16) • Why smart people ignore AI risk (01:54:45) • Don’t let AI build the next AI (02:01:33) • Why politicians miss the real risks (02:12:28) • Why Yoshua changed his mind about AI risk (02:21:27)

Rob Wiblin

65,088 görüntüleme • 4 ay önce

Packy McCormick’s (Packy McCormick) uses AI to find, articulate, and invest behind the next big idea. He writes Not Boring, a newsletter that analyzes technology and startups for 200,000 subscribers every week. He also invests in early stage companies through his fund Not Boring Capital and is an advisor at a16z crypto. I spent an hour with him to understand how he’s baked AI tools into the way he thinks, writes, and invests. We get into: - How he uses AI to understand dense concepts and refine his arguments - His thesis around vertically integrated businesses being the future of tech - How Packy uses Anthropic’s Claude Projects to edit his newsletter - How he makes interactive graphics that represent concepts from his essays - The tools Packy uses to research, write, and edit Not Boring - When he thinks the next crypto bull run will take place We also use Projects to build an AI tool that grades Packy’s essays live on the show This is a must-watch for writers, investors, and anyone trying to understand the cutting edge of technology. Watch below! -- Timestamps: Introduction: 00:01:24 Packy’s thesis about the future of technology: 00:02:40 What Packy quick takes on your crypto portfolio: 00:07:42 Use LLMs to validate your understanding of complex concepts: 00:14:31 How Packy used Claude Projects to write an essay he published recently: 00:18:26 Packy’s process to make interactive visual graphics for his essays: 00:24:00 How to use AI to be thorough in your research: 00:31:10 How Packy uses Claude to edit his writing: 00:35:04 The tools Packy uses to create his newsletter: 00:36:44 Using Claude Projects to make a tool that grades Packy’s essays: 00:44:12
1:13:29

Packy McCormick’s (Packy McCormick) uses AI to find, articulate, and invest behind the next big idea. He writes Not Boring, a newsletter that analyzes technology and startups for 200,000 subscribers every week. He also invests in early stage companies through his fund Not Boring Capital and is an advisor at a16z crypto. I spent an hour with him to understand how he’s baked AI tools into the way he thinks, writes, and invests. We get into: - How he uses AI to understand dense concepts and refine his arguments - His thesis around vertically integrated businesses being the future of tech - How Packy uses Anthropic’s Claude Projects to edit his newsletter - How he makes interactive graphics that represent concepts from his essays - The tools Packy uses to research, write, and edit Not Boring - When he thinks the next crypto bull run will take place We also use Projects to build an AI tool that grades Packy’s essays live on the show This is a must-watch for writers, investors, and anyone trying to understand the cutting edge of technology. Watch below! -- Timestamps: Introduction: 00:01:24 Packy’s thesis about the future of technology: 00:02:40 What Packy quick takes on your crypto portfolio: 00:07:42 Use LLMs to validate your understanding of complex concepts: 00:14:31 How Packy used Claude Projects to write an essay he published recently: 00:18:26 Packy’s process to make interactive visual graphics for his essays: 00:24:00 How to use AI to be thorough in your research: 00:31:10 How Packy uses Claude to edit his writing: 00:35:04 The tools Packy uses to create his newsletter: 00:36:44 Using Claude Projects to make a tool that grades Packy’s essays: 00:44:12

Dan Shipper 📧

52,701 görüntüleme • 2 yıl önce