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WHY THE BEST DESIGNERS MAKE THE WORST EMPLOYEES Simon Corry (Simon Corry), design leader now at Basis, joins Khushi (khushi) and me to talk about how he's built his career on his own terms, why he hires spiky builders, and what agents taught him building a game solo. 00:53... From call center manager to designer 03:39 Why he's the worst employee 10:21 How he hires for spiky, interesting people 13:51 How his agents decided to build a game engine 20:06 AI brain rot & untested agent code in prod 23:52 Design, eng, and product collapsing into one 26:29 Ramp Labs as a blueprint for experiments 31:08 Building out a neolab Basis 32:19 New media and testing the bleeding edge Dry Runshow more

Eshita

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Scott Jennings has built a reputation for speaking truth to power on CNN, but what stood out to me in this conversation was his honesty, his perspective, and his heart. We filmed this episode over a month ago in Washington DC, and it was the perfect backdrop for a raw and real discussion about politics, faith, and family. We talked about what it takes to sit in the lion’s den night after night, debating politics in front of a national audience, and why authenticity matters more now than ever. Scott opened up about his career path from Kentucky to the Bush White House, to campaign war rooms, and now to CNN, where his voice has become one of the most recognizable conservative perspectives in America. He shared stories about Donald Trump, Mitch McConnell, and the lessons he has learned from decades inside politics and media. We also talked about faith, family, and why staying grounded matters when the world is coming at you full speed. Scott Jennings is sharp, candid, and passionate about America’s future, but what I walked away with was a reminder of his integrity and his courage to stand firm in the face of constant pressure. Chapters: 00:03:08:12 – Thoughts on Donald Trump's presidency so far 00:06:32:00 – How has President Trump improved since 2016 00:08:35:01 – How the incident in Butler PA changed everything 00:13:11:23 – Scott Jennings on CNN 00:18:10:19 – How the debates work on CNN 00:20:52:15 – How does Scott Jennings keep his cool while debating 00:24:25:11 – Any strained relationships on set 00:27:18:25 – Scott Jennings' best and worst moments on air 00:31:06:08 – How does Scott Jennings deal with being outnumbered 00:41:12:23 – Scott's career in politics 00:44:39:16 – Mitch McConnell 00:52:11:11 – Political run in Kentucky for Scott 00:54:37:24 – Family 01:00:11:02 – Closing thoughts 01:03:29:11 – The Yellow Ribbon

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He built 3000+ AI agents at Google Brain - in 60 minutes at Stanford he revealed how he built each one from scratch 00:00 - solo engineer saves $20K/month with AI 08:34 - one agent replaces the research department 19:47 - one prompt builds a Graph from scratch after watching I understood why Andrew Ng built 3000 agents - and started building my first one that same day save this & read the article below to replace your team with one agent

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Bankless

23,815 просмотров • 5 месяцев назад

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Y Combinator

151,649 просмотров • 5 месяцев назад

The rules of professional product development are being rewritten in real time. - PMs and designers can ship software as easily as engineers. - Software is no longer just built for humans—it’s also built for agents as first-class citizens. To better understand how we build products in this world, I invited Mike Krieger (Mike Krieger) on Every 📧’s AI & I podcast. Mike cofounded Instagram and is now a member of the technical staff at Anthropic, co-leading Anthropic Labs, their internal incubator for experimental products. He's been at the frontier of two transformative technology waves: mobile/social and now agent-native software. We discussed: - How to build a truly agent-native product. The best products today, like Claude Code, allow users to do things that their creators never intended. But that requires hard trade-offs between freedom and safety/reliability for frontier products, an issue that Mike's team is learning how to solve. - What's different about building now versus building Instagram. At Instagram, it took months to hit dead ends and learn what to cut. Now, that cycle runs in hours. - The trap of building too much, too fast with agents. You can go from idea to a nearly-shipped product in a day, but that process doesn’t give you the incremental feedback that used to tell you what not to build. The models are great at adding features, but can create a product that lacks coherence. - How Anthropic Labs structures product teams. New product experiments are led by only two people, usually a product manager or designer paired with an engineer. Mike says bigger teams tend to be too slow because of coordination costs. - Why you need to throw out your product and start over every three to six months. AI progress means most of your harness will be outdated quickly—the best teams build this into their product strategy. And much more! You should watch this one. Timestamps Introduction: What's gotten easier—and what hasn't—about building products in the age of AI: Why vibe coding creates "indoor trees": How rewrites have become a normal part of the development process: What "agent native" product design means: How Mike's labs team is structured and the cofounder model: The best signal for a product bet is someone with "break through walls" conviction: Navigating enterprise customers while keeping pace with rapid AI change: OpenClaw, personal agents, and the product question defining 2026:

The rules of professional product development are being rewritten in real time. - PMs and designers can ship software as easily as engineers. - Software is no longer just built for humans—it’s also built for agents as first-class citizens. To better understand how we build products in this world, I invited Mike Krieger (Mike Krieger) on Every 📧’s AI & I podcast. Mike cofounded Instagram and is now a member of the technical staff at Anthropic, co-leading Anthropic Labs, their internal incubator for experimental products. He's been at the frontier of two transformative technology waves: mobile/social and now agent-native software. We discussed: - How to build a truly agent-native product. The best products today, like Claude Code, allow users to do things that their creators never intended. But that requires hard trade-offs between freedom and safety/reliability for frontier products, an issue that Mike's team is learning how to solve. - What's different about building now versus building Instagram. At Instagram, it took months to hit dead ends and learn what to cut. Now, that cycle runs in hours. - The trap of building too much, too fast with agents. You can go from idea to a nearly-shipped product in a day, but that process doesn’t give you the incremental feedback that used to tell you what not to build. The models are great at adding features, but can create a product that lacks coherence. - How Anthropic Labs structures product teams. New product experiments are led by only two people, usually a product manager or designer paired with an engineer. Mike says bigger teams tend to be too slow because of coordination costs. - Why you need to throw out your product and start over every three to six months. AI progress means most of your harness will be outdated quickly—the best teams build this into their product strategy. And much more! You should watch this one. Timestamps Introduction: What's gotten easier—and what hasn't—about building products in the age of AI: Why vibe coding creates "indoor trees": How rewrites have become a normal part of the development process: What "agent native" product design means: How Mike's labs team is structured and the cofounder model: The best signal for a product bet is someone with "break through walls" conviction: Navigating enterprise customers while keeping pace with rapid AI change: OpenClaw, personal agents, and the product question defining 2026:

Dan Shipper 📧

58,849 просмотров • 5 месяцев назад

DROPS E27: Ben from Talus Labs: Building the Future of Decentralized AI Agents In this episode Ben breaks down why AI isn’t a bubble, how Talus is redefining on-chain agents, and why optimism is something you earn and not something you just feel. We talk about: - Why AI is not a bubble and how mega-cap tech is funding the next AI wave through massive CapEx - How Talus enables decentralized AI agents that can act and transact trustlessly - The three big crypto × AI intersections: compute, data ownership, and intelligent on-chain workflows - Why AI needs verifiable, censorship-resistant execution layers and why blockchains provide that - Nuclear energy as a prerequisite for AGI-scale compute demand - Why SWE’s object model + parallelization is perfect for autonomous agent payments and priority fees And much more! Timestamps: 0:00 Introduction 1:31 Welcome To Drops 2:10 Getting Backers Like Polychain 3:16 Building While In College 7:51 VC’s vs Founders Differences Explained 10:01 Is There An AI Bubble 11:53 How Do We Quantify The AI Opportunity In Crypto 12:33 AI Is Difficult To Invest In, Why? 13:35 Risks To Look Out For To Ensure AI Investments Deliver 16:47 Energy Being A Big Risk For AI 20:50 How You Became Fascinated By Crypto & Blockchain 22:00 Why Does AI & Blockchain Intersection Make Sense Compute Side 24:39 Why Does AI & Blockchain Intersection Make Sense for Data Ownership 26:30 Complex Workflows With Intelligent Execution Meaning 28:19 Why Do We Need This On Blockchain 30:26 How Adding This Decentralized Layer Helps With The Black Box 32:01 Explain Talus To Your Mom 33:39 Talus Solved Blockchain Determinism Limitation Explained 35:43 Two Examples That Can Be Built On The Zapier Of Web 3 39:32 Why Get Investment From SUI 42:56 What’s Needed To Get To The Next Level 43:35 One Takeaway From Conversation 43:51 One Thing Pessimistic People Are Missing

DROPS E27: Ben from Talus Labs: Building the Future of Decentralized AI Agents In this episode Ben breaks down why AI isn’t a bubble, how Talus is redefining on-chain agents, and why optimism is something you earn and not something you just feel. We talk about: - Why AI is not a bubble and how mega-cap tech is funding the next AI wave through massive CapEx - How Talus enables decentralized AI agents that can act and transact trustlessly - The three big crypto × AI intersections: compute, data ownership, and intelligent on-chain workflows - Why AI needs verifiable, censorship-resistant execution layers and why blockchains provide that - Nuclear energy as a prerequisite for AGI-scale compute demand - Why SWE’s object model + parallelization is perfect for autonomous agent payments and priority fees And much more! Timestamps: 0:00 Introduction 1:31 Welcome To Drops 2:10 Getting Backers Like Polychain 3:16 Building While In College 7:51 VC’s vs Founders Differences Explained 10:01 Is There An AI Bubble 11:53 How Do We Quantify The AI Opportunity In Crypto 12:33 AI Is Difficult To Invest In, Why? 13:35 Risks To Look Out For To Ensure AI Investments Deliver 16:47 Energy Being A Big Risk For AI 20:50 How You Became Fascinated By Crypto & Blockchain 22:00 Why Does AI & Blockchain Intersection Make Sense Compute Side 24:39 Why Does AI & Blockchain Intersection Make Sense for Data Ownership 26:30 Complex Workflows With Intelligent Execution Meaning 28:19 Why Do We Need This On Blockchain 30:26 How Adding This Decentralized Layer Helps With The Black Box 32:01 Explain Talus To Your Mom 33:39 Talus Solved Blockchain Determinism Limitation Explained 35:43 Two Examples That Can Be Built On The Zapier Of Web 3 39:32 Why Get Investment From SUI 42:56 What’s Needed To Get To The Next Level 43:35 One Takeaway From Conversation 43:51 One Thing Pessimistic People Are Missing

MR SHIFT 🦁

41,057 просмотров • 8 месяцев назад

In this episode of Design Review, Aaron Epstein sits down with Jorn van Dijk, the CEO and Co-founder of Framer. Together they unpack the brutal “gray zone” between early revenue and real product-market fit, the pivot that reignited their growth, and how to adapt as AI reshapes how we build and design software. 00:00 - Intro to Jorn van Dijk, the CEO and co-founder of Framer 00:34 - Framer’s Scale: $2B Valuation, 120 People around the globe 01:06 - Jorn’s Design Roots 01:49 - Why Dutch Design Culture Is So Strong 03:43 - Sofa: The Studio That Shaped Jorn (and His Co-Founder) 04:39 - Building Mac Products: Checkout, Versions, Kaleidoscope 06:13 - Apple Design Awards And A Game Changing Email 07:24 - Facebook Acquisition: Why They Wanted Designers 09:27 - Culture Shock: From 15-Person Studio to Facebook Scale 10:27 - Leaving Facebook: Intentionally Learning to Build a Big Company 13:12 - Early Experiments: Bitcoin App, Fashion App, Framer Prototype Tool 18:37 - The Plateau: Why Framer Studio Hit a Ceiling Around $4–5M ARR 23:52 - The Decision: Sell, Quit, or Pivot 25:30 - The Big Insight: People Hate Rebuilding 34:55 - AI + Design: What’s Changing & What Isn’t

In this episode of Design Review, Aaron Epstein sits down with Jorn van Dijk, the CEO and Co-founder of Framer. Together they unpack the brutal “gray zone” between early revenue and real product-market fit, the pivot that reignited their growth, and how to adapt as AI reshapes how we build and design software. 00:00 - Intro to Jorn van Dijk, the CEO and co-founder of Framer 00:34 - Framer’s Scale: $2B Valuation, 120 People around the globe 01:06 - Jorn’s Design Roots 01:49 - Why Dutch Design Culture Is So Strong 03:43 - Sofa: The Studio That Shaped Jorn (and His Co-Founder) 04:39 - Building Mac Products: Checkout, Versions, Kaleidoscope 06:13 - Apple Design Awards And A Game Changing Email 07:24 - Facebook Acquisition: Why They Wanted Designers 09:27 - Culture Shock: From 15-Person Studio to Facebook Scale 10:27 - Leaving Facebook: Intentionally Learning to Build a Big Company 13:12 - Early Experiments: Bitcoin App, Fashion App, Framer Prototype Tool 18:37 - The Plateau: Why Framer Studio Hit a Ceiling Around $4–5M ARR 23:52 - The Decision: Sell, Quit, or Pivot 25:30 - The Big Insight: People Hate Rebuilding 34:55 - AI + Design: What’s Changing & What Isn’t

Y Combinator

19,897 просмотров • 6 месяцев назад

Figma co-founder and CEO Dylan Field (Dylan Field) on how AI will transform design and why craft and attention to detail will be the ultimate differentiators. At AI Startup School in San Francisco. 01:38 – How Dylan and Evan Started Figma 02:58 – WebGL: From Games to Tools 04:10 – Buying Time to Build 05:48 – Staying Motivated Through Early Exploration 06:10 – Cold Emails, Early Feedback & First Users 08:24 – Launch Timing and Lessons on Shipping Early 10:08 – Culture of Constraints & Creative Problem-Solving 10:50 – Recognizing Product-Market Pull 13:32 – Why Design is the Differentiator in the AI Era 16:34 – Figma’s AI Product Launches & Expanding Beyond Design 18:56 – The Blurring of Design, Development, and Product 21:00 – The “MS-DOS Era” of AI Interfaces & Future Surfaces 23:26 – The Role of Design in AI Research & the Future of Designers 27:36 – Audience Q&A: AI Tools, Open Source, Principles, and Advice

Figma co-founder and CEO Dylan Field (Dylan Field) on how AI will transform design and why craft and attention to detail will be the ultimate differentiators. At AI Startup School in San Francisco. 01:38 – How Dylan and Evan Started Figma 02:58 – WebGL: From Games to Tools 04:10 – Buying Time to Build 05:48 – Staying Motivated Through Early Exploration 06:10 – Cold Emails, Early Feedback & First Users 08:24 – Launch Timing and Lessons on Shipping Early 10:08 – Culture of Constraints & Creative Problem-Solving 10:50 – Recognizing Product-Market Pull 13:32 – Why Design is the Differentiator in the AI Era 16:34 – Figma’s AI Product Launches & Expanding Beyond Design 18:56 – The Blurring of Design, Development, and Product 21:00 – The “MS-DOS Era” of AI Interfaces & Future Surfaces 23:26 – The Role of Design in AI Research & the Future of Designers 27:36 – Audience Q&A: AI Tools, Open Source, Principles, and Advice

Y Combinator

178,646 просмотров • 1 год назад

Over the past year, we've been building our own internal agent infrastructure at YC: over 350 tools, self-improving skill loops, and a shared organizational brain that gets smarter overnight. In this episode of the Lightcone Podcast, we sat down with YC General Partner Pete Pete Koomen to talk about how he led the effort from the ground up. We cover how giving agents unrestricted access to one database was the key unlock, the self-improving skill loops that get smarter overnight, and why he thinks we've arrived at the personal computer moment for AI. 00:39 — YC's AI Stack 02:15 — The Finance Team Problem That Started It All 05:07 — SQL Access Changes Everything 07:20 — One Database to Rule Them All 09:14 — Jevons Paradox 10:07 — Denormalizing for Agents 12:15 — The Single-Player Era of Agents 14:16 — 350 Tools and a Shared Registry 16:24 — Skillify, DRY, and MECE Resolvers 18:23 — The Self-Improving Dream Cycle 20:26 — The Two-Sentence Pitch Skill 23:06 — How Super Intelligence Compounds 25:10 — Recording Everything as a Building Layer 27:10 — The Shared Organizational Brain 29:18 — Trust-Default Culture as a Requirement 30:44 — Raising the Floor for New Employees 32:35 — Horseless Carriages 34:24 — Why Chat Is the Best Interface for Agents 38:50 — Just-in-Time Software 40:49 — Centralizing vs. Decentralizing AI 43:32 — The Personal AI Revolution

Over the past year, we've been building our own internal agent infrastructure at YC: over 350 tools, self-improving skill loops, and a shared organizational brain that gets smarter overnight. In this episode of the Lightcone Podcast, we sat down with YC General Partner Pete Pete Koomen to talk about how he led the effort from the ground up. We cover how giving agents unrestricted access to one database was the key unlock, the self-improving skill loops that get smarter overnight, and why he thinks we've arrived at the personal computer moment for AI. 00:39 — YC's AI Stack 02:15 — The Finance Team Problem That Started It All 05:07 — SQL Access Changes Everything 07:20 — One Database to Rule Them All 09:14 — Jevons Paradox 10:07 — Denormalizing for Agents 12:15 — The Single-Player Era of Agents 14:16 — 350 Tools and a Shared Registry 16:24 — Skillify, DRY, and MECE Resolvers 18:23 — The Self-Improving Dream Cycle 20:26 — The Two-Sentence Pitch Skill 23:06 — How Super Intelligence Compounds 25:10 — Recording Everything as a Building Layer 27:10 — The Shared Organizational Brain 29:18 — Trust-Default Culture as a Requirement 30:44 — Raising the Floor for New Employees 32:35 — Horseless Carriages 34:24 — Why Chat Is the Best Interface for Agents 38:50 — Just-in-Time Software 40:49 — Centralizing vs. Decentralizing AI 43:32 — The Personal AI Revolution

Y Combinator

754,783 просмотров • 3 месяцев назад