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Bootstrap, Twitter, MooTools, and building the right primitives for AI with Jacob Thornton. 00:00 Why Jacob is Jacob on every platform 04:30 Early years at Twitter, joining the JS illuminati 07:01 Creating Bootstrap with Mark Otto 13:31 Bootstrap and open source sustainability 23:16 Why Diffs is used by every...

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PlanetScale

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13,882 görüntüleme • 1 ay önce •via X (Twitter)

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EPISODE 154: Pax Silica vs G7; AI and the New Global Order Joe Lonsdale talks supply chains, the bull case for AI, and diplomacy reimagined with Under Secretary of State Jacob S. Helberg Jacob Helberg (00:00) Episode intro (02:30) Historic deal with the Philippines (05:25) Jacob's background (07:30) Why is Europe falling behind? (13:00) The asymmetric power of tech (15:35) Jacob responds to AI doomers & skeptics (19:15) China, AI, and Diverging Growth (21:45) What is Pax Silica? (23:45) AI and the new global order (29:30) Is China pushing back? (31:45) Optimism for the future
34:02

EPISODE 154: Pax Silica vs G7; AI and the New Global Order Joe Lonsdale talks supply chains, the bull case for AI, and diplomacy reimagined with Under Secretary of State Jacob S. Helberg Jacob Helberg (00:00) Episode intro (02:30) Historic deal with the Philippines (05:25) Jacob's background (07:30) Why is Europe falling behind? (13:00) The asymmetric power of tech (15:35) Jacob responds to AI doomers & skeptics (19:15) China, AI, and Diverging Growth (21:45) What is Pax Silica? (23:45) AI and the new global order (29:30) Is China pushing back? (31:45) Optimism for the future

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FULL INTERVIEW: Mach Industries founder Ethan Thornton joins TBPN to discuss the US defense industrial complex, modern warfare, US reliance on Taiwan, asymmetric warfare, and more. 00:00 - Why Ethan started Mach Industries 01:38 - Why the US cannot out-manufacture China 02:16 - Asymmetric warfare 04:41 - Drone warfare 06:24 - Hypersonic missiles 07:23 - The rise of decentralized warfare 08:31 - Taiwan, semiconductors, and why losing access would be existential 10:01 - Why future wars will be decided by logistics, sensors, and comms 16:23 - Why the defense industrial base is broken
24:59

FULL INTERVIEW: Mach Industries founder Ethan Thornton joins TBPN to discuss the US defense industrial complex, modern warfare, US reliance on Taiwan, asymmetric warfare, and more. 00:00 - Why Ethan started Mach Industries 01:38 - Why the US cannot out-manufacture China 02:16 - Asymmetric warfare 04:41 - Drone warfare 06:24 - Hypersonic missiles 07:23 - The rise of decentralized warfare 08:31 - Taiwan, semiconductors, and why losing access would be existential 10:01 - Why future wars will be decided by logistics, sensors, and comms 16:23 - Why the defense industrial base is broken

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36,664 görüntüleme • 5 ay önce

.Eric Jang is spearheading AI at 1X. Eric believes 1X is on the path to becoming an AGI company — not just a robotics company. I sat down with him at 1X’s new Palo Alto office to discuss the key AI building blocks needed to bring humanoid robots into homes at scale. 00:00 Eric Jang joins Humanoid Hub 00:27 Move to Palo Alto 01:02 Why 1X targets the home 02:01 The AGI company 02:49 Redwood AI overview 04:01 Unified control, modular stack 06:07 Data labeling at scale 07:10 RL mobility update for NEO 10:03 GPU and onboard compute 11:09 World model explained 13:34 Trusting world models for evaluation 15:05 Synthetic data generation 16:30 A world model Turing Test 17:41 RL in manipulation 20:08 Lessons from early NEO deployments 21:00 Full autonomy 22:18 1X’s unique edge in AI 24:02 Eric asks Devang a question
26:53

.Eric Jang is spearheading AI at 1X. Eric believes 1X is on the path to becoming an AGI company — not just a robotics company. I sat down with him at 1X’s new Palo Alto office to discuss the key AI building blocks needed to bring humanoid robots into homes at scale. 00:00 Eric Jang joins Humanoid Hub 00:27 Move to Palo Alto 01:02 Why 1X targets the home 02:01 The AGI company 02:49 Redwood AI overview 04:01 Unified control, modular stack 06:07 Data labeling at scale 07:10 RL mobility update for NEO 10:03 GPU and onboard compute 11:09 World model explained 13:34 Trusting world models for evaluation 15:05 Synthetic data generation 16:30 A world model Turing Test 17:41 RL in manipulation 20:08 Lessons from early NEO deployments 21:00 Full autonomy 22:18 1X’s unique edge in AI 24:02 Eric asks Devang a question

The Humanoid Hub

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Tobi Lütke (tobi lutke) and Lulu Cheng Meservey (Lulu Cheng Meservey) on building Shopify in the AI era, why "we're cooked" gets the moment wrong, and the bell-curve case for aiming at best in the world. 0:00 Era of the Entrepreneur 3:18 Starting Shopify by Accident 7:30 Every Purchase Is a Vote 15:30 Magic Eye Dealer Era 22:00 Why 1-Person Companies Are BS 24:00 Island of Misfit Toys 27:00 Every App Is Now a Prompt 30:55 Don't Negotiate "Best" 34:00 Craftsmanship vs. Academia 44:00 "We're Cooked" Is Just Dumb
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Tobi Lütke (tobi lutke) and Lulu Cheng Meservey (Lulu Cheng Meservey) on building Shopify in the AI era, why "we're cooked" gets the moment wrong, and the bell-curve case for aiming at best in the world. 0:00 Era of the Entrepreneur 3:18 Starting Shopify by Accident 7:30 Every Purchase Is a Vote 15:30 Magic Eye Dealer Era 22:00 Why 1-Person Companies Are BS 24:00 Island of Misfit Toys 27:00 Every App Is Now a Prompt 30:55 Don't Negotiate "Best" 34:00 Craftsmanship vs. Academia 44:00 "We're Cooked" Is Just Dumb

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My Pet Hooligan: Creating the future of AI-powered IP 01:38 Creating an IP Factory 07:34 The "Napkin sketch" Netflix era 09:22 Working with Coldplay and BTS 16:32 Creating an Agentic Commerce Platform 23:03 MPH's AI production stack exposed 31:24 What is $HOOLI and why does it matter? 43:47 The 1 year vision

Nick O’Neill

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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 📧

66,339 görüntüleme • 3 ay önce

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1:04:35

"What's drawn people together for thousands of years is stories." My full conversation with Phil & Dom, co-founders of EasyA - the Duolingo for blockchain, with over a million users learning crypto. We covered what it's really like building a company with your brother, why they left promising careers in finance and law, their partnerships with Ripple, Solana and Sui, why crypto matters in a post-quantum AI world, and why every brand will need a face to survive. 00:00 Intro 01:03 How This Pod Happened 03:20 Printing the Bitcoin Whitepaper at 16 05:11 Mining Bitcoin From the Basement 06:12 Wharton, Blackstone and Sullivan & Cromwell 07:55 What It's Like Building With Your Brother 28:47 The UK vs US Talent Drain 29:04 Their Friend Who Ran AI Safety at Anthropic 36:29 EasyA Is the Duolingo for Blockchain 38:06 Why They Teach Stories, Not Code 46:34 The Business Model and EasyA Labs 50:31 Why Crypto Solves the AI Trust Problem 54:04 Why Every Brand Needs a Face

JackGK

26,719 görüntüleme • 1 ay önce

AI INTERVIEW: OPENAI'S SECRET WEAPON AI agents are no longer just hype—they're here to revolutionize automation, Web3, and beyond. SwarmNode.ai is building a serverless AI agent platform for scalability, efficiency, and real-world impact. In this exclusive interview, he reveals how AI swarms can outperform single models, why OpenAI’s Operator is just the beginning, and how crypto is fueling AI innovation. Plus, he breaks down DeepSeek’s game-changing AI breakthrough, the future of agent monetization, and why serverless AI could be the next frontier in automation. 01:37 – From Engineering to AI: The journey into artificial intelligence. 02:43 – The GPT-3 Moment: How OpenAI’s tech pulled him in. 04:10 – AI’s Biggest Challenge: Why real-world use cases lag behind. 05:05 – OpenAI’s Operator: Why it’s “rudimentary” (for now). 06:25 – Crypto & AI: How tokens help bootstrap AI startups. 08:15 – Can You Bootstrap a Startup with a Token? The trade-offs. 09:56 – 90% of AI Token Holders Don’t Use the Product—Does It Matter? 11:18 – What is SwarmNode?: AI agents, hosted serverlessly. 14:23 – AI Swarms: Why multiple agents outperform single models. 16:08 – What is a Swarm? A simple definition of collaborative AI. 17:32 – “How Can I Make Money with AI?”: Real-world use cases. 18:41 – AI Bounties: Hiring devs to build your custom agent. 20:50 – The Future of AI Marketplaces: Monetizing pre-built agents. 23:15 – DeepSeek’s Disruption: Why it’s good news for AI. 24:46 – Is SwarmNode Compatible with DeepSeek? How it integrates. 26:17 – SwarmNode vs. AI Launchpads: What makes it different? 27:42 – Why Serverless Matters: Cost savings & efficiency. 29:53 – AI Agents in the Real World: Booking flights, managing workflows, and more. 31:11 – Building SwarmNode for Developers: Why it started as a personal project. 32:27 – Explosive Growth: 200,000 AI agent executions in 5 weeks. 34:41 – Why SwarmNode Agents Aren’t Visible on 𝕏 Yet. 36:46 – Startup Hiring Lessons: Finding top AI talent. 39:15 – Why SwarmNode is Built in Python (and What’s Next). 40:32 – Scaling AI Workloads: Handling traffic surges. 41:42 – AWS & Cost Challenges: The biggest monetization hurdle. 42:58 – 2025: The Year of Mass AI Adoption. 45:22 – Should We Be Worried About AI’s Rapid Growth? 46:46 – The Most Underrated AI Tools Right Now. 47:34 – What’s Next for SwarmNode?: Making AI accessible to everyone.
49:12

AI INTERVIEW: OPENAI'S SECRET WEAPON AI agents are no longer just hype—they're here to revolutionize automation, Web3, and beyond. SwarmNode.ai is building a serverless AI agent platform for scalability, efficiency, and real-world impact. In this exclusive interview, he reveals how AI swarms can outperform single models, why OpenAI’s Operator is just the beginning, and how crypto is fueling AI innovation. Plus, he breaks down DeepSeek’s game-changing AI breakthrough, the future of agent monetization, and why serverless AI could be the next frontier in automation. 01:37 – From Engineering to AI: The journey into artificial intelligence. 02:43 – The GPT-3 Moment: How OpenAI’s tech pulled him in. 04:10 – AI’s Biggest Challenge: Why real-world use cases lag behind. 05:05 – OpenAI’s Operator: Why it’s “rudimentary” (for now). 06:25 – Crypto & AI: How tokens help bootstrap AI startups. 08:15 – Can You Bootstrap a Startup with a Token? The trade-offs. 09:56 – 90% of AI Token Holders Don’t Use the Product—Does It Matter? 11:18 – What is SwarmNode?: AI agents, hosted serverlessly. 14:23 – AI Swarms: Why multiple agents outperform single models. 16:08 – What is a Swarm? A simple definition of collaborative AI. 17:32 – “How Can I Make Money with AI?”: Real-world use cases. 18:41 – AI Bounties: Hiring devs to build your custom agent. 20:50 – The Future of AI Marketplaces: Monetizing pre-built agents. 23:15 – DeepSeek’s Disruption: Why it’s good news for AI. 24:46 – Is SwarmNode Compatible with DeepSeek? How it integrates. 26:17 – SwarmNode vs. AI Launchpads: What makes it different? 27:42 – Why Serverless Matters: Cost savings & efficiency. 29:53 – AI Agents in the Real World: Booking flights, managing workflows, and more. 31:11 – Building SwarmNode for Developers: Why it started as a personal project. 32:27 – Explosive Growth: 200,000 AI agent executions in 5 weeks. 34:41 – Why SwarmNode Agents Aren’t Visible on 𝕏 Yet. 36:46 – Startup Hiring Lessons: Finding top AI talent. 39:15 – Why SwarmNode is Built in Python (and What’s Next). 40:32 – Scaling AI Workloads: Handling traffic surges. 41:42 – AWS & Cost Challenges: The biggest monetization hurdle. 42:58 – 2025: The Year of Mass AI Adoption. 45:22 – Should We Be Worried About AI’s Rapid Growth? 46:46 – The Most Underrated AI Tools Right Now. 47:34 – What’s Next for SwarmNode?: Making AI accessible to everyone.

Mario Nawfal

338,265 görüntüleme • 1 yıl önce

Ben Horowitz and Alex Rampell on AI Infrastructure, Crypto, and the Future of Venture a16z's Ben Horowitz and Alex Rampell sit down to discuss why the fundamental rules of building software companies have changed, how AI is creating massive demand for crypto infrastructure, why the next 15 years will make everyone's lives dramatically better, and more. 00:00 Intro 01:39 The new laws of physics for tech companies 06:37 Why not every legacy SaaS company is dead 10:30 America's AI infrastructure bottleneck 14:27 AI + crypto: Why they're more connected than you think 19:54 The future of VC 24:06 AI and the history of technological change benahorowitz.eth Alex Rampell
28:49

Ben Horowitz and Alex Rampell on AI Infrastructure, Crypto, and the Future of Venture a16z's Ben Horowitz and Alex Rampell sit down to discuss why the fundamental rules of building software companies have changed, how AI is creating massive demand for crypto infrastructure, why the next 15 years will make everyone's lives dramatically better, and more. 00:00 Intro 01:39 The new laws of physics for tech companies 06:37 Why not every legacy SaaS company is dead 10:30 America's AI infrastructure bottleneck 14:27 AI + crypto: Why they're more connected than you think 19:54 The future of VC 24:06 AI and the history of technological change benahorowitz.eth Alex Rampell

a16z

24,904 görüntüleme • 3 ay 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 • 3 ay önce

Michael Bolin (Michael Bolin) is the tech lead of the Codex open source repository at OpenAI and formerly a distinguished eng (E9) at Meta. I asked him for all the details on his career story and how he uses Codex for max benefit. Timestamps: 00:00:00 - Intro 00:00:56 - Chickenfoot 00:02:45 - Working at Google 00:06:34 - Overhauling Facebook's build system 00:16:36 - Rewriting Facebook's IDE 00:26:01 - Struggles after Principal Eng (E8) promo 00:28:39 - Building a virtual filesystem for Facebook 00:35:47 - Delayed Distinguished promo (E9) and learnings 00:39:56 - Joining OpenAI 00:43:05 - Research-led vs engineering-led cultures 00:44:53 - The story behind Codex 00:51:00 - How he uses Codex 00:57:00 - Why Codex's harness is open source 00:59:50 - Top technical book recommendations 01:05:02 - Why deep technical skills are still valuable (for now) 01:11:07 - How to start projects well 01:14:27 - Advice on writing better and career planning 01:17:06 - Advice for his younger self 01:19:10 - Outro He was a dream guest of mine and I'm excited to share his story with you all! Other places to watch: • YouTube: • Spotify: • Apple Podcasts: • Transcript:
1:20:03

Michael Bolin (Michael Bolin) is the tech lead of the Codex open source repository at OpenAI and formerly a distinguished eng (E9) at Meta. I asked him for all the details on his career story and how he uses Codex for max benefit. Timestamps: 00:00:00 - Intro 00:00:56 - Chickenfoot 00:02:45 - Working at Google 00:06:34 - Overhauling Facebook's build system 00:16:36 - Rewriting Facebook's IDE 00:26:01 - Struggles after Principal Eng (E8) promo 00:28:39 - Building a virtual filesystem for Facebook 00:35:47 - Delayed Distinguished promo (E9) and learnings 00:39:56 - Joining OpenAI 00:43:05 - Research-led vs engineering-led cultures 00:44:53 - The story behind Codex 00:51:00 - How he uses Codex 00:57:00 - Why Codex's harness is open source 00:59:50 - Top technical book recommendations 01:05:02 - Why deep technical skills are still valuable (for now) 01:11:07 - How to start projects well 01:14:27 - Advice on writing better and career planning 01:17:06 - Advice for his younger self 01:19:10 - Outro He was a dream guest of mine and I'm excited to share his story with you all! Other places to watch: • YouTube: • Spotify: • Apple Podcasts: • Transcript:

Ryan Peterman

119,601 görüntüleme • 5 ay önce

The State of Digital Asset Investing in 2026 (What’s Changed) with Mike Dudas of 6MV. Timestamps: 00:00 Intro 01:06 Why Now For VC 03:00 Founders Lack Confidence 05:30 Where Flows Are Going 07:22 Alien Ideas Still Win 09:03 RWA Investing Paralysis 10:23 Pumpfun & Hyperliquid 13:05 Fundamentals Not Rewarded 15:28 Consumer Onchain Thesis 16:35 MegaETH & Base 19:10 Plasma, Arc, Tempo Thesis 21:18 Agent Payments Changing 23:00 General Purpose Chain Risk 24:45 Circle vs. Tether 29:04 New Stablecoins 31:20 Stablecoin Issuers: M0 & Bridge 34:16 Kraken & Paxos IPO 36:08 AI IPOs As A Signal 38:57 Why Prices Hold 41:30 Vaults & Risk Management 44:00 What Comes Next
45:40

The State of Digital Asset Investing in 2026 (What’s Changed) with Mike Dudas of 6MV. Timestamps: 00:00 Intro 01:06 Why Now For VC 03:00 Founders Lack Confidence 05:30 Where Flows Are Going 07:22 Alien Ideas Still Win 09:03 RWA Investing Paralysis 10:23 Pumpfun & Hyperliquid 13:05 Fundamentals Not Rewarded 15:28 Consumer Onchain Thesis 16:35 MegaETH & Base 19:10 Plasma, Arc, Tempo Thesis 21:18 Agent Payments Changing 23:00 General Purpose Chain Risk 24:45 Circle vs. Tether 29:04 New Stablecoins 31:20 Stablecoin Issuers: M0 & Bridge 34:16 Kraken & Paxos IPO 36:08 AI IPOs As A Signal 38:57 Why Prices Hold 41:30 Vaults & Risk Management 44:00 What Comes Next

The Rollup

23,264 görüntüleme • 3 ay önce

There’s NO Chance Trillions Move Onchain Until We Solve This | Rand from Zama 00:00 Why Crypto Needs Privacy 01:24 The Biggest Problem with Public Blockchains 02:34 HTTPS for Crypto 05:38 How Zama Brings Privacy to Ethereum & Solana 06:34 Why Privacy Is Making a Comeback 08:18 Building One Privacy Layer for Every Blockchain 10:11 How Confidential Transactions Work for Users 12:17 Will Privacy Become the Default? 13:00 Confidential USDC and Private DeFi Yields 14:07 Why Institutions Can't Adopt Blockchains Yet 16:11 Zama's Story: From AI Privacy to Blockchain 19:22 The Apex/T-Rex Partnership 21:13 How Shielded Assets Actually Work 23:07 Compliance, KYC, and Preventing Illicit Activity 26:53 Zama's Goal: Encrypt 95% of On-Chain Finance 28:20 Why This Is a Winner-Takes-All Market 29:00 Zama vs. Canton Explained 30:06 Final Thoughts
30:49

There’s NO Chance Trillions Move Onchain Until We Solve This | Rand from Zama 00:00 Why Crypto Needs Privacy 01:24 The Biggest Problem with Public Blockchains 02:34 HTTPS for Crypto 05:38 How Zama Brings Privacy to Ethereum & Solana 06:34 Why Privacy Is Making a Comeback 08:18 Building One Privacy Layer for Every Blockchain 10:11 How Confidential Transactions Work for Users 12:17 Will Privacy Become the Default? 13:00 Confidential USDC and Private DeFi Yields 14:07 Why Institutions Can't Adopt Blockchains Yet 16:11 Zama's Story: From AI Privacy to Blockchain 19:22 The Apex/T-Rex Partnership 21:13 How Shielded Assets Actually Work 23:07 Compliance, KYC, and Preventing Illicit Activity 26:53 Zama's Goal: Encrypt 95% of On-Chain Finance 28:20 Why This Is a Winner-Takes-All Market 29:00 Zama vs. Canton Explained 30:06 Final Thoughts

The Wolf Of All Streets

186,891 görüntüleme • 22 gün önce

The attackers have AI. So do the defenders. Cybersecurity has a new shape: AI is both the threat and the only viable solution. Nikesh Arora, CEO of Palo Alto Networks, joined me at South Park Commons to break down what’s next. (00:30) Big problems vs. fast wins (04:00) Joining Google (07:00) Larry Page's product obsession (10:00) He read every hiring packet himself (13:00) What Silicon Valley gets wrong about Masa Son (16:00) Successful founders never wish they took less risk (17:00) Joining Palo Alto knowing nothing about cybersecurity (20:00) What incumbents got wrong when ChatGPT launched (23:00) Security was never built into AI (28:00) No enterprise knows what's running inside its stack (31:00) AI finds bad code faster than humans ever could (35:00) The only way to fix the chaos is more AI (38:00) AI won't just automate work—it raises the floor (41:00) Foundation models vs. specialized stacks (45:00) Why communication is 30% of the job at scale
49:27

The attackers have AI. So do the defenders. Cybersecurity has a new shape: AI is both the threat and the only viable solution. Nikesh Arora, CEO of Palo Alto Networks, joined me at South Park Commons to break down what’s next. (00:30) Big problems vs. fast wins (04:00) Joining Google (07:00) Larry Page's product obsession (10:00) He read every hiring packet himself (13:00) What Silicon Valley gets wrong about Masa Son (16:00) Successful founders never wish they took less risk (17:00) Joining Palo Alto knowing nothing about cybersecurity (20:00) What incumbents got wrong when ChatGPT launched (23:00) Security was never built into AI (28:00) No enterprise knows what's running inside its stack (31:00) AI finds bad code faster than humans ever could (35:00) The only way to fix the chaos is more AI (38:00) AI won't just automate work—it raises the floor (41:00) Foundation models vs. specialized stacks (45:00) Why communication is 30% of the job at scale

Aditya Agarwal

47,436 görüntüleme • 3 ay önce

"Aging is the process of a complex system slowly falling into its chaotic disequilibrium. And it's not like evolution necessarily even set a lifespan for us. It is merely the result of, once the system has been established, how long its intrinsic property stayed ordered before it became disordered." I’m excited to share my conversation with Jacob Kimmel of NewLimit. Every species who has ever lived has one shared certainty: we’ll age, decay, and die. Jacob and his team are among the small group of people on earth with the expertise and funding to work on this problem. We talked about why we age, whether cellular aging can actually be reversed, how evolution shaped our lifespan and our peaks, and how AI is fundamentally changing the next era of science. Recorded at New Limit’s HQ in South San Francisco. Timestamps: 00:00 Intro 01:25 Why Evolution Limits Lifespan 03:13 Evolution Tradeoffs Explained 07:44 Why Women Live Longer 10:15 Lessons from Long Lived Species 16:20 Peak Intelligence 23:26 What is Aging? 27:59 Yamanaka Factors 32:18 New Limit 33:56 AI's Role in Progress 37:28 Organ by Organ Longevity 40:28 Is Immortality Possible? 42:04 Lifespan Progress 45:44 How to Test Longevity Drugs 49:30 Placebo Mind Body Link 53:56 What longevity hacks actually work? 57:25 Peptides And Stem Cells 01:00:34 Gene Editing 01:05:10 Personalized Medicine 01:08:48 Philosophical Dilemmas of Longer Lifespans 01:14:57 Living Off Earth 01:16:55 AI And Bio Risks
1:23:37

"Aging is the process of a complex system slowly falling into its chaotic disequilibrium. And it's not like evolution necessarily even set a lifespan for us. It is merely the result of, once the system has been established, how long its intrinsic property stayed ordered before it became disordered." I’m excited to share my conversation with Jacob Kimmel of NewLimit. Every species who has ever lived has one shared certainty: we’ll age, decay, and die. Jacob and his team are among the small group of people on earth with the expertise and funding to work on this problem. We talked about why we age, whether cellular aging can actually be reversed, how evolution shaped our lifespan and our peaks, and how AI is fundamentally changing the next era of science. Recorded at New Limit’s HQ in South San Francisco. Timestamps: 00:00 Intro 01:25 Why Evolution Limits Lifespan 03:13 Evolution Tradeoffs Explained 07:44 Why Women Live Longer 10:15 Lessons from Long Lived Species 16:20 Peak Intelligence 23:26 What is Aging? 27:59 Yamanaka Factors 32:18 New Limit 33:56 AI's Role in Progress 37:28 Organ by Organ Longevity 40:28 Is Immortality Possible? 42:04 Lifespan Progress 45:44 How to Test Longevity Drugs 49:30 Placebo Mind Body Link 53:56 What longevity hacks actually work? 57:25 Peptides And Stem Cells 01:00:34 Gene Editing 01:05:10 Personalized Medicine 01:08:48 Philosophical Dilemmas of Longer Lifespans 01:14:57 Living Off Earth 01:16:55 AI And Bio Risks

Sabrina Halper

20,154 görüntüleme • 26 gün önce

Just after Gemini 3 was released, OpenAI's research chief Mark Chen stopped by to chat. Here's our full Core Memory pod with Mark. We start with the Great Recruitment Wars and then get into Mark's career and how OpenAI's research arm works and the tricks the company thinks it has up its sleeve. Think you'll learn more about Mark's personality than ever before. This pod is brought to you by Brex. We run our company on Brex, and you should too. It's also made possible by the investing gurus E1 Ventures 🇺🇸. Timestamps 00:00:00 Intro 00:05:02 Allocating Compute and Priorities 00:08:36 Finding the Next AI Paradigm 00:14:14 Research vs. Engineering Dichotomy 00:16:25 Gemini 3 and Math Puzzles 00:20:07 AI’s "Move 37" Moment 00:28:42 ChatGPT Replacing Technical Interviews 00:35:58 From Wall Street to AI 00:44:26 Joining Early OpenAI in 2018 00:48:08 Inside the OpenAI Coup 00:53:46 Impact of Losing Top Researchers 01:00:10 Is AI Scaling Dead? 01:09:29 Automating Nobel Prize Discoveries 01:13:43 Automating Research 01:15:35 The Insatiable Demand for Compute 01:18:23 Jony Ive and AI Hardware 01:27:50 Reaction to China’s DeepSeek 01:34:03 Preventing Scheming AI Models
1:38:17

Just after Gemini 3 was released, OpenAI's research chief Mark Chen stopped by to chat. Here's our full Core Memory pod with Mark. We start with the Great Recruitment Wars and then get into Mark's career and how OpenAI's research arm works and the tricks the company thinks it has up its sleeve. Think you'll learn more about Mark's personality than ever before. This pod is brought to you by Brex. We run our company on Brex, and you should too. It's also made possible by the investing gurus E1 Ventures 🇺🇸. Timestamps 00:00:00 Intro 00:05:02 Allocating Compute and Priorities 00:08:36 Finding the Next AI Paradigm 00:14:14 Research vs. Engineering Dichotomy 00:16:25 Gemini 3 and Math Puzzles 00:20:07 AI’s "Move 37" Moment 00:28:42 ChatGPT Replacing Technical Interviews 00:35:58 From Wall Street to AI 00:44:26 Joining Early OpenAI in 2018 00:48:08 Inside the OpenAI Coup 00:53:46 Impact of Losing Top Researchers 01:00:10 Is AI Scaling Dead? 01:09:29 Automating Nobel Prize Discoveries 01:13:43 Automating Research 01:15:35 The Insatiable Demand for Compute 01:18:23 Jony Ive and AI Hardware 01:27:50 Reaction to China’s DeepSeek 01:34:03 Preventing Scheming AI Models

Ashlee Vance

80,718 görüntüleme • 8 ay önce

François Chollet (François Chollet) has spent years asking a different question than most of the AI world. Instead of scaling what already works, he’s trying to understand what intelligence actually is and how to build it from first principles. In this episode of the Lightcone Podcast, he traces that path from his early work on deep learning to the creation of the ARC Prize, and the launch of ARC V3, a new benchmark designed to measure something deeper than performance: the ability to learn, adapt, and reason efficiently in entirely new environments. He explains why today’s systems may be hitting limits, what recent breakthroughs really mean, and why reaching true general intelligence may require a fundamentally different approach. 00:00 - AGI by 2030? 00:31 - Introducing Ndea: A New Path Beyond Deep Learning 01:08 - A New ML Paradigm 01:30 - Replacing neural nets with compact symbolic programs 03:04 - Why Ndea Isn’t Competing With Coding Agents 05:20 - Why Everyone Might Be Wrong About Scaling LLMs 07:22 - Why Coding Agents Suddenly Work So Well 08:50 - The Limits of LLMs in Non-Verifiable Domains 10:48 - What AGI Actually Means (And Why Most Definitions Are Wrong) 13:30 - Why Deep Learning Hits a Wall 14:00 - ARC’s Origin Story 18:20 - ARC Benchmarks Explained: From V1 to V3 22:49 - The RL Loop Powering Coding Agents Today 27:03 - ARC-AGI V3: Measuring “Agentic Intelligence” 31:14 - Inside the ARC Game Studio 35:31 - Could AGI Fit in 10,000 Lines of Code? 44:01 - Building Ndea: From Idea to Compounding Research Stack 46:46 - The Future of ARC: Benchmarks That Evolve With AI 47:21 - Why There’s Still Huge Opportunity for New AI Paradigms 53:37 - How to Build a Breakout Open Source Project - Lessons From Keras 56:39 - Advice For How To Think About AI
57:23

François Chollet (François Chollet) has spent years asking a different question than most of the AI world. Instead of scaling what already works, he’s trying to understand what intelligence actually is and how to build it from first principles. In this episode of the Lightcone Podcast, he traces that path from his early work on deep learning to the creation of the ARC Prize, and the launch of ARC V3, a new benchmark designed to measure something deeper than performance: the ability to learn, adapt, and reason efficiently in entirely new environments. He explains why today’s systems may be hitting limits, what recent breakthroughs really mean, and why reaching true general intelligence may require a fundamentally different approach. 00:00 - AGI by 2030? 00:31 - Introducing Ndea: A New Path Beyond Deep Learning 01:08 - A New ML Paradigm 01:30 - Replacing neural nets with compact symbolic programs 03:04 - Why Ndea Isn’t Competing With Coding Agents 05:20 - Why Everyone Might Be Wrong About Scaling LLMs 07:22 - Why Coding Agents Suddenly Work So Well 08:50 - The Limits of LLMs in Non-Verifiable Domains 10:48 - What AGI Actually Means (And Why Most Definitions Are Wrong) 13:30 - Why Deep Learning Hits a Wall 14:00 - ARC’s Origin Story 18:20 - ARC Benchmarks Explained: From V1 to V3 22:49 - The RL Loop Powering Coding Agents Today 27:03 - ARC-AGI V3: Measuring “Agentic Intelligence” 31:14 - Inside the ARC Game Studio 35:31 - Could AGI Fit in 10,000 Lines of Code? 44:01 - Building Ndea: From Idea to Compounding Research Stack 46:46 - The Future of ARC: Benchmarks That Evolve With AI 47:21 - Why There’s Still Huge Opportunity for New AI Paradigms 53:37 - How to Build a Breakout Open Source Project - Lessons From Keras 56:39 - Advice For How To Think About AI

Y Combinator

151,559 görüntüleme • 4 ay 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

18,173 görüntüleme • 4 ay önce

.Jerry Tworek is an AI legend. He came to OpenAI from the world of finance and spent years contributing to some of the company's most important projects. He played a major role in ushering in the era of reasoning and, well, he just left OpenAI. In this Core Memory pod, Kylie Robison and I bring you the exclusive not-so-formal exit interview with Jerry. He explains his dissatisfaction with the company's current research direction and his fears for the field overall. He goes into spots where OpenAI lost its way and Google and Anthropic thrived. And he talks about what he's chasing next. It's a banger. The Core Memory podcast is on all major platforms and on our YouTube channel. This podcast is sponsored by Brex , the intelligent finance platform built to help companies spend smarter and move faster. And it's brought to you by the VC gurus E1 Ventures 🇺🇸 Timestamps 00:00:00 Intro 00:03:37 Jerry Tworek Joins the Pod 00:05:34 Reflecting on Seven Years at OpenAI 00:09:08 Why Jerry Left: 00:12:28 Beyond Pre-Training 00:16:19 The "Sad" Homogeneity of Current AI Labs 00:23:35 The Mavericks: Carmack, Ilya, and LeCun 00:26:32 Training AI on Video Games 00:32:14 Two Big Bets 00:34:32 Updating AGI Timelines 00:37:16 Q-Star 00:41:10 The Coup 00:43:29 Is the Hype Justified? 00:49:23 The Polish Mafia 00:53:15 Google's Comeback and OpenAI's Fumble 00:59:27 Why Anthropic is Impressive 01:03:01 Jerry's Next Chapter 01:11:56 Is AI Research Star-Driven? 01:14:32 Meditation & Conclusion
1:16:29

.Jerry Tworek is an AI legend. He came to OpenAI from the world of finance and spent years contributing to some of the company's most important projects. He played a major role in ushering in the era of reasoning and, well, he just left OpenAI. In this Core Memory pod, Kylie Robison and I bring you the exclusive not-so-formal exit interview with Jerry. He explains his dissatisfaction with the company's current research direction and his fears for the field overall. He goes into spots where OpenAI lost its way and Google and Anthropic thrived. And he talks about what he's chasing next. It's a banger. The Core Memory podcast is on all major platforms and on our YouTube channel. This podcast is sponsored by Brex , the intelligent finance platform built to help companies spend smarter and move faster. And it's brought to you by the VC gurus E1 Ventures 🇺🇸 Timestamps 00:00:00 Intro 00:03:37 Jerry Tworek Joins the Pod 00:05:34 Reflecting on Seven Years at OpenAI 00:09:08 Why Jerry Left: 00:12:28 Beyond Pre-Training 00:16:19 The "Sad" Homogeneity of Current AI Labs 00:23:35 The Mavericks: Carmack, Ilya, and LeCun 00:26:32 Training AI on Video Games 00:32:14 Two Big Bets 00:34:32 Updating AGI Timelines 00:37:16 Q-Star 00:41:10 The Coup 00:43:29 Is the Hype Justified? 00:49:23 The Polish Mafia 00:53:15 Google's Comeback and OpenAI's Fumble 00:59:27 Why Anthropic is Impressive 01:03:01 Jerry's Next Chapter 01:11:56 Is AI Research Star-Driven? 01:14:32 Meditation & Conclusion

Ashlee Vance

11,701 görüntüleme • 6 ay önce

Agents Already Spent $76 Million: Circle's AI Agent stack, x402, stablecoins and the Agentic Economy Shoal Signal with Corey Cooper, Senior Manager, DevRel at Circle, hosted by Zaddycoin We cover how Circle is building the agent money stack with $USDC, Arc, x402, why EIP-3009 makes payments gasless, who is actually transacting today, whether SaaS is dead, and why agents buying their own services points to a trillion dollar TAM. 0:00 What Circle is building for agents 3:01 Why x402 matters for builders 6:54 USDC, gasless transfers, and facilitators 10:57 The Circle agent stack and nano payments 14:17 Who is using x402 and for what 16:46 Pay-per-use flattens the internet 19:50 A new persona, and $76 million transacted 23:03 What is missing for agent to agent 28:05 Every app has APIs, the TAM is trillions 29:26 Is SaaS dead 31:57 Discovery as the real value layer 36:28 How Circle curated its marketplace 41:23 Why it has to run on stablecoins 46:00 Cash App to agent in one second 49:38 Where the alpha is, trading on Robinhood 52:08 Try it yourself
54:16

Agents Already Spent $76 Million: Circle's AI Agent stack, x402, stablecoins and the Agentic Economy Shoal Signal with Corey Cooper, Senior Manager, DevRel at Circle, hosted by Zaddycoin We cover how Circle is building the agent money stack with $USDC, Arc, x402, why EIP-3009 makes payments gasless, who is actually transacting today, whether SaaS is dead, and why agents buying their own services points to a trillion dollar TAM. 0:00 What Circle is building for agents 3:01 Why x402 matters for builders 6:54 USDC, gasless transfers, and facilitators 10:57 The Circle agent stack and nano payments 14:17 Who is using x402 and for what 16:46 Pay-per-use flattens the internet 19:50 A new persona, and $76 million transacted 23:03 What is missing for agent to agent 28:05 Every app has APIs, the TAM is trillions 29:26 Is SaaS dead 31:57 Discovery as the real value layer 36:28 How Circle curated its marketplace 41:23 Why it has to run on stablecoins 46:00 Cash App to agent in one second 49:38 Where the alpha is, trading on Robinhood 52:08 Try it yourself

Shoal Research

20,578 görüntüleme • 1 ay önce