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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

43,309 subscribers

14,084 просмотров • 3 месяцев назад •via X (Twitter)

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Комментарии: 4

Фото профиля Jonathan Grahl
Jonathan Grahl3 месяцев назад

@fat @mdo Holy shit what a combo

Фото профиля Alexander Vivas Jernström
Alexander Vivas Jernström3 месяцев назад

@fat @mdo The goat

Фото профиля Dan DiGangi
Dan DiGangi3 месяцев назад

@fat @mdo @hollylawly how do you put up with these hoodlums

Фото профиля Dave Eckel
Dave Eckel3 месяцев назад

@fat @mdo Jacob is a legend 🐐

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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

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1:04:35

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Shane Parrish

450,952 просмотров • 5 месяцев назад

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

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23,264 просмотров • 5 месяцев назад

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 просмотров • 7 месяцев назад

🚨 Special Friday episode - this one couldn't wait. OpenAI's model hacked Hugging Face. As a side quest. Co-founder and CSO Thomas Wolf takes us inside the first autonomous AI attack, why GLM 5.2, rather than Claude, had to stop it, and what it all means for the future of open source. 00:00 An AI Agent Hacked Hugging Face 00:30 Introduction 01:00 17,000 Attacker Events, and a Strange Target 04:28 The Attack Was a “Side Quest” 06:13 AI Training Runs Left Notes for Each Other 07:09 Closed AI Refused to Help 09:47 Fighting Back With an Open-Source Model 13:15 Open vs. Closed Is the Wrong Safety Debate 15:46 AI Agents Start Social-Engineering Humans 22:24 The Three Walls: Sandboxes, Guardrails, Alignment 24:34 “Neuralese”: Can Humans Still Read AI Reasoning? 25:28 Why Monitoring AI Agents Gets So Hard 28:10 Reward Hacking and the “Paperclip Problem” 32:02 The State of Open-Source AI in 2026 33:47 Router Models and the Enterprise Shift to Open 37:01 The Real Economics of Open Models 39:41 Can Chinese AI Models Be Trusted? 41:37 AI Sovereignty: Who Controls the Switch? 43:16 Why Western Open-Source AI Matters 48:16 Is AI Heading Toward an Oligopoly? 49:41 The Race Toward Recursive Self-Improvement 51:54 Why Thomas Signed the AI Slowdown Letter 55:14 AI Slowdown - or Regulatory Capture?
57:41

🚨 Special Friday episode - this one couldn't wait. OpenAI's model hacked Hugging Face. As a side quest. Co-founder and CSO Thomas Wolf takes us inside the first autonomous AI attack, why GLM 5.2, rather than Claude, had to stop it, and what it all means for the future of open source. 00:00 An AI Agent Hacked Hugging Face 00:30 Introduction 01:00 17,000 Attacker Events, and a Strange Target 04:28 The Attack Was a “Side Quest” 06:13 AI Training Runs Left Notes for Each Other 07:09 Closed AI Refused to Help 09:47 Fighting Back With an Open-Source Model 13:15 Open vs. Closed Is the Wrong Safety Debate 15:46 AI Agents Start Social-Engineering Humans 22:24 The Three Walls: Sandboxes, Guardrails, Alignment 24:34 “Neuralese”: Can Humans Still Read AI Reasoning? 25:28 Why Monitoring AI Agents Gets So Hard 28:10 Reward Hacking and the “Paperclip Problem” 32:02 The State of Open-Source AI in 2026 33:47 Router Models and the Enterprise Shift to Open 37:01 The Real Economics of Open Models 39:41 Can Chinese AI Models Be Trusted? 41:37 AI Sovereignty: Who Controls the Switch? 43:16 Why Western Open-Source AI Matters 48:16 Is AI Heading Toward an Oligopoly? 49:41 The Race Toward Recursive Self-Improvement 51:54 Why Thomas Signed the AI Slowdown Letter 55:14 AI Slowdown - or Regulatory Capture?

Matt Turck

95,724 просмотров • 2 месяцев назад

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

188,549 просмотров • 2 месяцев назад

"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

22,036 просмотров • 3 месяцев назад

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 просмотров • 5 месяцев назад

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

151,929 просмотров • 6 месяцев назад

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 просмотров • 10 месяцев назад