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New podcast, new format! Three frontier founders join us. Waste Tokens, Save Time 00:00 Three Frontier Founders 01:27 AI Software Factories 04:15 Waste Tokens, Save Time 05:47 Models Instructing Humans 09:30 Is Pure Software Dead? 12:04 You Don't Get Stuck Anymore With Guillermo Rauch, Max Hodak, and Blake Scholl...

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Naval Podcast

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Full podcast episode with Guillermo Rauch, Max Hodak, and Blake Scholl 🛫. 40 minutes of unreleased material. The AI Industrial Revolution Part 1: Waste Tokens, Save Time 0:00 Three Frontier Founders 1:27 AI Software Factories 4:15 Waste Tokens, Save Time 5:47 Models Instructing Humans 9:29 Is Pure Software Dead? 12:03 You Don't Get Stuck Anymore Part 2: Vibe Coding Hardware 14:39 Vibe Coding a Turbine Blade 18:07 Open Source Compounds China's Advantage 20:15 You Always Want the Smartest Model 22:44 Software Still Needs Hands 24:43 Humans Are Becoming Verifiers Part 3: The Regulatory Frontier 27:53 The Regulatory Red Queen Race 32:32 Why There's No Innovation in Healthcare 36:49 We Need a True 50-State Experiment 40:31 China's FDA Is Beating Ours 43:37 Healthcare Is a Communist Society Inside Capitalism 45:57 Sid's Story: N-of-1 Medicine Part 4: The Autonomous Company 47:49 Autonomous Infrastructure 51:25 Your Job Is to Train the Agent 54:54 The Next Lord of the Rings 59:08 What's Your Definition of Art? 1:05:00 Can AI Have New Ideas? 1:07:03 A Large Number of Small Teams
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Naval

292,986 Aufrufe • vor 3 Monaten

Part 2 of the Naval Podcast with Guillermo Rauch, Max Hodak, and Blake Scholl 🛫. Vibe Coding Hardware 00:35 Vibe Coding a Turbine Blade 04:04 Open Source AI Compounds China’s Advantage 06:12 You Always Want the Smartest Model 08:41 Software Still Needs Hands 10:40 Humans Are Becoming Verifiers
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Part 2 of the Naval Podcast with Guillermo Rauch, Max Hodak, and Blake Scholl 🛫. Vibe Coding Hardware 00:35 Vibe Coding a Turbine Blade 04:04 Open Source AI Compounds China’s Advantage 06:12 You Always Want the Smartest Model 08:41 Software Still Needs Hands 10:40 Humans Are Becoming Verifiers

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Part 3 of our conversation with three founders. The Regulatory Frontier Battling red tape at scale 00:00 Previously… 00:34 The Regulatory Red Queen Race 05:13 Why There’s No Innovation in Health Care 09:30 We Need a True 50-State Experiment 13:12 China’s FDA Is Beating Ours 16:18 Healthcare Is a Communist Society Inside Capitalism 18:38 Sid’s Story: N-of-1 Medicine (Sid Sijbrandij) With Guillermo Rauch, Max Hodak, and Blake Scholl 🛫. Fourth and final part coming tomorrow.
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Part 3 of our conversation with three founders. The Regulatory Frontier Battling red tape at scale 00:00 Previously… 00:34 The Regulatory Red Queen Race 05:13 Why There’s No Innovation in Health Care 09:30 We Need a True 50-State Experiment 13:12 China’s FDA Is Beating Ours 16:18 Healthcare Is a Communist Society Inside Capitalism 18:38 Sid’s Story: N-of-1 Medicine (Sid Sijbrandij) With Guillermo Rauch, Max Hodak, and Blake Scholl 🛫. Fourth and final part coming tomorrow.

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New episode of Uncapped with Max Junestrand from Legora and my partner Chetan Puttagunta. Every time I'm with Max I come away thinking "this is what it takes to build an AI native software company." He's one of my favorite founders, hope you enjoy. (0:00) Intro (0:31) Legora's origin story (9:05) Building an AI-native company (18:16) No sacred cows, the models will be amazing (27:36) Winning pilots and global expansion (36:43) Starting in Europe (47:15) Stockholm culture and "blodsmak"
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EPISODE 142: Understanding the AI Wave Joe Lonsdale sits down with 8VC Partners Alex Kolicich & Jack Moshkovich Why is AI fundamentally disruptive? What does it mean for large software incumbents? Will LLMs become commodities? And are we entering a bubble, or just getting started? (00:00) Episode intro (01:40) Smart enterprise thesis (04:00) Did enterprise software dramatically boost productivity? (08:30) Understanding the AI wave (11:45) Will LLMs become commodities? (17:10) OpenAI vs Anthropic vs Meta vs Grok (20:30) Frontier models vs enterprise incumbents (25:27) What did Palantir get right? (32:30) AI case studies/new possibilities (43:00) The most surprising aspect of the AI wave (46:12) Are we entering a bubble? Or just getting started? (52:40) Biggest concern with AI (58:00) Biggest reason for optimism
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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

39,041 Aufrufe • vor 8 Monaten

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26,037 Aufrufe • vor 1 Jahr

.signüll and i went through some of my favorite posts and accounts of all time and attempted to explain why they are good. thank you a16z for having us. thank you for the golf cart rides. thank you for the vodka sodas 00:00 - Why we started posting 05:26 - Defining “banger” and what goes viral now 06:59 - @basedsrini reaction 12:18 - bowser reaction 15:27 - hope hopes hoping reaction 17:03 - Gabriel reaction 25:47 - The Murray Hill Guy format (listing things) 28:04 - Max Mönch (rip) 34:08 - Landshark reaction 36:28 - Marc Andreessen 🇺🇸 and the thread format 37:35 - HowlingMutant reaction 38:57 - roon reaction 46:25 - “paula” reaction 49:19 - Getting good and finding your audience 53:04 - “Imagine the smell” gate 56:26 - How to get an account started 01:02:59 - Ragebait 01:04:01 - Finding your edge 01:07:27 - How the algorithm works 01:10:59 - Crossposting 01:14:15 - Monetizing
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.signüll and i went through some of my favorite posts and accounts of all time and attempted to explain why they are good. thank you a16z for having us. thank you for the golf cart rides. thank you for the vodka sodas 00:00 - Why we started posting 05:26 - Defining “banger” and what goes viral now 06:59 - @basedsrini reaction 12:18 - bowser reaction 15:27 - hope hopes hoping reaction 17:03 - Gabriel reaction 25:47 - The Murray Hill Guy format (listing things) 28:04 - Max Mönch (rip) 34:08 - Landshark reaction 36:28 - Marc Andreessen 🇺🇸 and the thread format 37:35 - HowlingMutant reaction 38:57 - roon reaction 46:25 - “paula” reaction 49:19 - Getting good and finding your audience 53:04 - “Imagine the smell” gate 56:26 - How to get an account started 01:02:59 - Ragebait 01:04:01 - Finding your edge 01:07:27 - How the algorithm works 01:10:59 - Crossposting 01:14:15 - Monetizing

atlas

78,979 Aufrufe • vor 3 Monaten

Dust ( helps companies build AI teammates that work across their organization. Founded by former OpenAI researcher Stanislas Polu (Stanislas Polu) and his team, the company has spent the last three years building AI for work while the capabilities of frontier models have advanced at breakneck speed. At Startup School Paris, YC's Mathilde Collin sat down with Stanislas to talk about what it takes to build alongside frontier AI labs, why staying model agnostic is a competitive advantage, how AI is changing the way we work, and why the best startups are built around a problem founders can't stop thinking about. 01:09 — From Stripe to OpenAI to Founder 01:40 — Why Stan Left OpenAI 02:49 — What Dust Actually Does 03:46 — What Work Looks Like in 3 Years 04:53 — Where the Thesis Was Right (and Wrong) 07:05 — Building Alongside Frontier Labs 09:15 — Model Agnostic as a Moat 10:49 — How Frontier Labs Changed Fundraising 12:19 — Raising Small on Purpose 14:39 — What Mistral Got Right 15:11 — Building in France 16:37 — What Happens When Frontier Labs Enter Your Market? 18:27 — The Margin Problem
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Dust ( helps companies build AI teammates that work across their organization. Founded by former OpenAI researcher Stanislas Polu (Stanislas Polu) and his team, the company has spent the last three years building AI for work while the capabilities of frontier models have advanced at breakneck speed. At Startup School Paris, YC's Mathilde Collin sat down with Stanislas to talk about what it takes to build alongside frontier AI labs, why staying model agnostic is a competitive advantage, how AI is changing the way we work, and why the best startups are built around a problem founders can't stop thinking about. 01:09 — From Stripe to OpenAI to Founder 01:40 — Why Stan Left OpenAI 02:49 — What Dust Actually Does 03:46 — What Work Looks Like in 3 Years 04:53 — Where the Thesis Was Right (and Wrong) 07:05 — Building Alongside Frontier Labs 09:15 — Model Agnostic as a Moat 10:49 — How Frontier Labs Changed Fundraising 12:19 — Raising Small on Purpose 14:39 — What Mistral Got Right 15:11 — Building in France 16:37 — What Happens When Frontier Labs Enter Your Market? 18:27 — The Margin Problem

Y Combinator

54,342 Aufrufe • vor 1 Monat

State of AI compute 2026: my conversation with stephen balaban of Lambda on the neocloud boom, data centers, GPUs and what's ahead 00:00 — Cold open 01:21 — Why GPU compute was never a commodity 02:45 — The H100 price index and what it gets wrong 04:02 — The real moat: technology or financing? 05:57 — Winner-take-all, or room for many neoclouds 06:48 — Are we overbuilding or underbuilding AI compute? 09:26 — What if AI gets 10x more compute-efficient? 10:44 — The real bottleneck: land, power, and shell 11:38 — The backlash against data centers — and the misinformation 15:00 — Opening the hood: from photons to tokens 17:11 — Extracting more value from the same chip 19:26 — Frontier inference and distributed training, explained 23:26 — What actually drives compute cost 25:21 — Lambda's chip stack and the NVIDIA relationship 26:17 — A multi-silicon world? CUDA, CUDNN, and NVIDIA's real moat 28:59 — Networking, storage, and the one-click cluster 34:46 — Renting vs. owning, and full vertical integration 36:24 — How global is Lambda? Does location still matter? 38:44 — The financing stack: off-take agreements, SPVs, and credit 41:16 — Why a 2023 GPU leases for more today 42:36 — A futures market for compute? 43:54 — Origin story: facial recognition, Perceptio, and Apple 47:03 — The Lambda hat and Dream Scope 48:59 — The $60K bet that became a cloud business 52:00 — Holding the team together through the hard times 54:30 — Bringing on a new CEO; Stephen as CTO 57:33 — Matching xAI on high-velocity deployment 59:29 — "AI won't write software — it will become the software" 01:01:30 — Neural software vs. vibe coding 01:04:25 — Do agents change the compute layer 01:06:14 — Self-assembling software inside Lambda 01:08:18 — Gigawatt-scale AI factories 01:08:57 — One person, one GPU 01:12:04 — Hot takes: overrated and underrated in AI
1:14:27

State of AI compute 2026: my conversation with stephen balaban of Lambda on the neocloud boom, data centers, GPUs and what's ahead 00:00 — Cold open 01:21 — Why GPU compute was never a commodity 02:45 — The H100 price index and what it gets wrong 04:02 — The real moat: technology or financing? 05:57 — Winner-take-all, or room for many neoclouds 06:48 — Are we overbuilding or underbuilding AI compute? 09:26 — What if AI gets 10x more compute-efficient? 10:44 — The real bottleneck: land, power, and shell 11:38 — The backlash against data centers — and the misinformation 15:00 — Opening the hood: from photons to tokens 17:11 — Extracting more value from the same chip 19:26 — Frontier inference and distributed training, explained 23:26 — What actually drives compute cost 25:21 — Lambda's chip stack and the NVIDIA relationship 26:17 — A multi-silicon world? CUDA, CUDNN, and NVIDIA's real moat 28:59 — Networking, storage, and the one-click cluster 34:46 — Renting vs. owning, and full vertical integration 36:24 — How global is Lambda? Does location still matter? 38:44 — The financing stack: off-take agreements, SPVs, and credit 41:16 — Why a 2023 GPU leases for more today 42:36 — A futures market for compute? 43:54 — Origin story: facial recognition, Perceptio, and Apple 47:03 — The Lambda hat and Dream Scope 48:59 — The $60K bet that became a cloud business 52:00 — Holding the team together through the hard times 54:30 — Bringing on a new CEO; Stephen as CTO 57:33 — Matching xAI on high-velocity deployment 59:29 — "AI won't write software — it will become the software" 01:01:30 — Neural software vs. vibe coding 01:04:25 — Do agents change the compute layer 01:06:14 — Self-assembling software inside Lambda 01:08:18 — Gigawatt-scale AI factories 01:08:57 — One person, one GPU 01:12:04 — Hot takes: overrated and underrated in AI

Matt Turck

70,916 Aufrufe • vor 2 Monaten

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

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

Y Combinator

337,015 Aufrufe • vor 1 Jahr

Why AI Progress Suddenly Feels Real - my conversation with Yann Dubois, who co-leads the Post-Training Frontiers team at OpenAI 00:00 - Intro 01:30 - Why recent AI progress feels like a step function 04:13 - Model reliability & the emotional rollercoaster of shipping GPT-5.5 07:33 - How OpenAI structures vertical and horizontal teams 09:49 - Improving model efficiency and test-time compute 12:32 - Yann's journey from Switzerland to OpenAI 15:37 - Reasoning in 2026: Real-world utility vs verifiable rewards 18:34 - GPT-5.5 Thinking vs Pro: Scaling test-time compute 20:09 - How reasoning models become more efficient 23:23 - Pre-training scaling and overcoming the data wall 27:03 - Multimodal data, synthetic data, and embodied AI 31:05 - Demystifying mid-training and post-training 37:21 - Does RL create new capabilities in AI? 38:53 - The challenges and frontier of scaling RL 43:09 - Is building AI models a craft or a strict science 48:21 - How AI models generalize across different domains 54:18 - How reinforcement learning cures AI hallucinations 56:04 - Negative generalization and conflicting instructions 58:05 - Can RL scale to law, medicine, and the broader economy? 1:00:19 - The evaluation bottleneck and Model as a Judge 1:04:21 - Continuous AI progress & continual learning 1:08:49 - Will foundation models eat the agent harness 1:11:23 - Why startups should focus on the last mile of AI
1:13:56

Why AI Progress Suddenly Feels Real - my conversation with Yann Dubois, who co-leads the Post-Training Frontiers team at OpenAI 00:00 - Intro 01:30 - Why recent AI progress feels like a step function 04:13 - Model reliability & the emotional rollercoaster of shipping GPT-5.5 07:33 - How OpenAI structures vertical and horizontal teams 09:49 - Improving model efficiency and test-time compute 12:32 - Yann's journey from Switzerland to OpenAI 15:37 - Reasoning in 2026: Real-world utility vs verifiable rewards 18:34 - GPT-5.5 Thinking vs Pro: Scaling test-time compute 20:09 - How reasoning models become more efficient 23:23 - Pre-training scaling and overcoming the data wall 27:03 - Multimodal data, synthetic data, and embodied AI 31:05 - Demystifying mid-training and post-training 37:21 - Does RL create new capabilities in AI? 38:53 - The challenges and frontier of scaling RL 43:09 - Is building AI models a craft or a strict science 48:21 - How AI models generalize across different domains 54:18 - How reinforcement learning cures AI hallucinations 56:04 - Negative generalization and conflicting instructions 58:05 - Can RL scale to law, medicine, and the broader economy? 1:00:19 - The evaluation bottleneck and Model as a Judge 1:04:21 - Continuous AI progress & continual learning 1:08:49 - Will foundation models eat the agent harness 1:11:23 - Why startups should focus on the last mile of AI

Matt Turck

101,314 Aufrufe • vor 3 Monaten

The Arcee AI Podcast is here! In this episode, Lucas Atkins and stochasm join us to discuss the story of Trinity models and everything frontier. I can say, this talk has been one of the most amazing and technical conversations we've had on Ground Zero. 0:00:00 - Intro 0:00:59 - Varun's transition from SWE to Pre-Training Lead 0:04:20 - Trinity Manifesto, Openclaw Ecosystem 0:12:15 - Arcee's Post-Training to Pre-Training Pivot 0:23:45 - Varun's first Pre-Training run (you can just do things!) 0:27:33 - Saturation in Pre-Training?, Mid-Training 0:37:00 - Tweaking the Training Architecture, Adam vs Muon, Evals 01:09:07 - Inference Engineering, Quick Fire, Post-Training Recipe 01:18:02 - Alpha in RL Envs, Harness Design 01:23:00 - American Open Source is trailing Chinese Competitors, Trinity Adoption 01:29:25 - Hiring at Arcee, Advice to 20yo
1:38:31

The Arcee AI Podcast is here! In this episode, Lucas Atkins and stochasm join us to discuss the story of Trinity models and everything frontier. I can say, this talk has been one of the most amazing and technical conversations we've had on Ground Zero. 0:00:00 - Intro 0:00:59 - Varun's transition from SWE to Pre-Training Lead 0:04:20 - Trinity Manifesto, Openclaw Ecosystem 0:12:15 - Arcee's Post-Training to Pre-Training Pivot 0:23:45 - Varun's first Pre-Training run (you can just do things!) 0:27:33 - Saturation in Pre-Training?, Mid-Training 0:37:00 - Tweaking the Training Architecture, Adam vs Muon, Evals 01:09:07 - Inference Engineering, Quick Fire, Post-Training Recipe 01:18:02 - Alpha in RL Envs, Harness Design 01:23:00 - American Open Source is trailing Chinese Competitors, Trinity Adoption 01:29:25 - Hiring at Arcee, Advice to 20yo

himanshu

32,305 Aufrufe • vor 4 Monaten

Updated: Vocals & Rap - #성화 #Seonghwa 00:00 Still Here 00:15 Wonderland 00:36 Halazia 00:47 Answer 01:21 Cyberpunk 01:27 Wave 01:37 Utopia 01:50 My Way 02:00 With U 02:16 Be With You 02:54 Star 1117 03:21 Turbulence (야간비행) 03:39 Days 04:26 Promise 04:38 Fever 04:54 Sunrise 05:19 Mist 05:43 Take Me Home 06:11 Better 06:34 All About You 06:43 Aurora 06:55 Blue Summer 07:23 Diamond 07:46 Time Of Love 07:58 Stay 08:07 Desire 08:21 WDIG 08:28 New World 08:35 Dreamers 08:53 Not Too Late 09:03 The Letter 09:19 One Day At A Time 09:28 Dazzling Light 09:40 Light 09:50 The Ring 10:04 Paradigm 10:18 This World 10:38 Bouncy 10:51 Not Okay 11:15 The King 11:23 Inception 11:52 Fireworks 12:03 Limitless 12:19 Precious 12:28 Rocky 12:41 Let’s Get Together 13:07 White Love 13:17 The Leaders 13:27 Crazy Form 13:43 Arriba 13:57 Silver Light 14:06 Dreamy Day 14:26 Matz 15:15 Outlaw 15:23 Twilight 15:31 Dune 15:38 Hala Hala 15:43 Deja Vu 15:50 Horizon 15:54 Say My Name 15:57 Sector 1 16:02 Good Lil Boy 16:09 Don’t Stop 16:12 Still Here (Acoustic Ver) 16:19 Siren 16:31 Guerrilla 16:45 We Know 17:00 Empty Box 17:28 Wake Up 17:40 Dancing Like Butterfly Wings 17:49 Work 18:09 Thanxx 18:26 If Without U 18:45 Shaboom 19:01 Outlaw 19:18 Treasure 19:33 Eternal Sunshine 20:03 Django 20:31 Turbulence Japanese Ver. 20:55 Blind 21:16 Pirate King 21:24 Forevermore 22:00 Birthday 22:11 Deep Dive 22:26 Ice On My Teeth 22:50 Man On Fire 23:10 Selfish Waltz 23:25 Enough
23:39

Updated: Vocals & Rap - #성화 #Seonghwa 00:00 Still Here 00:15 Wonderland 00:36 Halazia 00:47 Answer 01:21 Cyberpunk 01:27 Wave 01:37 Utopia 01:50 My Way 02:00 With U 02:16 Be With You 02:54 Star 1117 03:21 Turbulence (야간비행) 03:39 Days 04:26 Promise 04:38 Fever 04:54 Sunrise 05:19 Mist 05:43 Take Me Home 06:11 Better 06:34 All About You 06:43 Aurora 06:55 Blue Summer 07:23 Diamond 07:46 Time Of Love 07:58 Stay 08:07 Desire 08:21 WDIG 08:28 New World 08:35 Dreamers 08:53 Not Too Late 09:03 The Letter 09:19 One Day At A Time 09:28 Dazzling Light 09:40 Light 09:50 The Ring 10:04 Paradigm 10:18 This World 10:38 Bouncy 10:51 Not Okay 11:15 The King 11:23 Inception 11:52 Fireworks 12:03 Limitless 12:19 Precious 12:28 Rocky 12:41 Let’s Get Together 13:07 White Love 13:17 The Leaders 13:27 Crazy Form 13:43 Arriba 13:57 Silver Light 14:06 Dreamy Day 14:26 Matz 15:15 Outlaw 15:23 Twilight 15:31 Dune 15:38 Hala Hala 15:43 Deja Vu 15:50 Horizon 15:54 Say My Name 15:57 Sector 1 16:02 Good Lil Boy 16:09 Don’t Stop 16:12 Still Here (Acoustic Ver) 16:19 Siren 16:31 Guerrilla 16:45 We Know 17:00 Empty Box 17:28 Wake Up 17:40 Dancing Like Butterfly Wings 17:49 Work 18:09 Thanxx 18:26 If Without U 18:45 Shaboom 19:01 Outlaw 19:18 Treasure 19:33 Eternal Sunshine 20:03 Django 20:31 Turbulence Japanese Ver. 20:55 Blind 21:16 Pirate King 21:24 Forevermore 22:00 Birthday 22:11 Deep Dive 22:26 Ice On My Teeth 22:50 Man On Fire 23:10 Selfish Waltz 23:25 Enough

Hwanakin

62,764 Aufrufe • vor 1 Jahr

new greenpill.network pod out with Shaw (spirit/acc) founder of elizaOS ! we talk about agent-native games, open source culture, abundance networks, and why software is getting cheaper while agency, ownership, and coordination matter more than ever. love these talks on the frontier... 00:00 – Intro & why Shaw’s work matters 01:10 – Catching up after the bear market 03:00 – How Shaw’s perspective on AI has changed 05:10 – From Eliza v1 to ElizaOS 07:20 – Shipping fast & throwing code away 09:30 – AI agents as autonomous actors 12:00 – Agent-native games as experimentation spaces 15:10 – Open source, forks & losing mindshare 18:00 – Hype cycles, narratives & incentives 21:00 – Why “code is getting cheap” 24:00 – Rewriting systems & technical tradeoffs 27:00 – What AI agents still can’t do 30:00 – Builder culture vs speculation culture 33:00 – Centralized platforms & getting banned 36:00 – Decentralized social: what’s missing 39:00 – Are AI agents taking jobs? 41:30 – Meaning, agency & human creativity 44:00 – Ownership, UBI & coordination problems 46:30 – Abundance networks & positive-sum systems 49:00 – What Shaw wants to build next 51:00 – Closing thoughts
51:59

new greenpill.network pod out with Shaw (spirit/acc) founder of elizaOS ! we talk about agent-native games, open source culture, abundance networks, and why software is getting cheaper while agency, ownership, and coordination matter more than ever. love these talks on the frontier... 00:00 – Intro & why Shaw’s work matters 01:10 – Catching up after the bear market 03:00 – How Shaw’s perspective on AI has changed 05:10 – From Eliza v1 to ElizaOS 07:20 – Shipping fast & throwing code away 09:30 – AI agents as autonomous actors 12:00 – Agent-native games as experimentation spaces 15:10 – Open source, forks & losing mindshare 18:00 – Hype cycles, narratives & incentives 21:00 – Why “code is getting cheap” 24:00 – Rewriting systems & technical tradeoffs 27:00 – What AI agents still can’t do 30:00 – Builder culture vs speculation culture 33:00 – Centralized platforms & getting banned 36:00 – Decentralized social: what’s missing 39:00 – Are AI agents taking jobs? 41:30 – Meaning, agency & human creativity 44:00 – Ownership, UBI & coordination problems 46:30 – Abundance networks & positive-sum systems 49:00 – What Shaw wants to build next 51:00 – Closing thoughts

owockai

12,941 Aufrufe • vor 6 Monaten

NEW - Part 2 of my vitalik.eth interview is now live. Joined by guest cohosts sejalrekhan.eth and , we chat with Vitalik about deep funding via dependency graphs🌟 From measuring a project's value to humanity 👉 to assessing how much credit dependency A deserves for B's success... Deep Funding is a new primitive for valuing/funding dependancies! Deep Funding is a new frontier in onchain capital allocation! We go deep on deep funding with Vitalik in the Part 2 of the podcast. Don’t miss it! 👀🚀 00:00 Intro 01:06 Podcast Overview 02:06 Guest Introduction 03:24 Deep Funding Mechanism 08:00 Unintended consequences of Deep Funding 11:28 Alternatives in Market 12:13 Government AI initiatives filtering and eligibility 15:53 Protocol Guild 16:34 Deep funding cases other than software and GitHub 19:47 How can you contribute to the pilot's success? 23:21 Favorite underdog project for deep funding 25:11 Suggestion for other mechanism 26:54 Vote of Thanks 27:10 After Podcast Discussion 36:53 Outro
37:06

NEW - Part 2 of my vitalik.eth interview is now live. Joined by guest cohosts sejalrekhan.eth and , we chat with Vitalik about deep funding via dependency graphs🌟 From measuring a project's value to humanity 👉 to assessing how much credit dependency A deserves for B's success... Deep Funding is a new primitive for valuing/funding dependancies! Deep Funding is a new frontier in onchain capital allocation! We go deep on deep funding with Vitalik in the Part 2 of the podcast. Don’t miss it! 👀🚀 00:00 Intro 01:06 Podcast Overview 02:06 Guest Introduction 03:24 Deep Funding Mechanism 08:00 Unintended consequences of Deep Funding 11:28 Alternatives in Market 12:13 Government AI initiatives filtering and eligibility 15:53 Protocol Guild 16:34 Deep funding cases other than software and GitHub 19:47 How can you contribute to the pilot's success? 23:21 Favorite underdog project for deep funding 25:11 Suggestion for other mechanism 26:54 Vote of Thanks 27:10 After Podcast Discussion 36:53 Outro

owockai

302,852 Aufrufe • vor 1 Jahr

Inside Nemotron and NVIDIA's AI lab: my conversation with Bryan Catanzaro (Bryan Catanzaro). NVIDIA is a chip company. So why does it put hundreds of researchers on building AI models - and then give them away for free? We go deep into the Nemotron models, what it takes to build a top AI lab, and the future of frontier AI. 01:33 - Is open source AI catching the frontier? 05:29 - Do closed labs blocking distillation slow open source down? 07:42 - Is the US falling behind China? 10:30 - Why companies actually choose open models 12:39 - A "crazy" 2008 bet: machine learning on GPUs 15:33 - Working with Andrew Ng and Dario Amodei at Baidu 17:41 - Coming back to NVIDIA: DLSS and the birth of Megatron 21:55 - The real reason NVIDIA builds its own models 24:28 - Is Moore's Law really dead? 33:37 - The Nemotron family: Nano, Super, Ultra 35:09 - Built for agents: why NVIDIA bets on speed 36:02 - How you train a 550B model in 4 bits 39:25 - Hybrid Mamba-Transformer, explained simply 42:31 - Mixture of experts, and why NVIDIA built NVL72 around it 47:26 - Why a 1-million-token context window matters 49:26 - Multi-token prediction: how the model predicts 5 tokens at once 52:47 - Multi-teacher distillation: teaching one model from many 58:01 - Where reinforcement learning goes next 01:00:16 - Inside NVIDIA's research org: "the mission is the boss" 01:04:03 - How NVIDIA decides who gets the GPUs 01:10:53 - Why NVIDIA still feels entrepreneurial after 33 years 01:12:58 - Why Bryan doesn't believe in the singularity 01:17:50 - The AI backlash 01:19:18 - The controversial case: open AI is safer than closed
1:22:57

Inside Nemotron and NVIDIA's AI lab: my conversation with Bryan Catanzaro (Bryan Catanzaro). NVIDIA is a chip company. So why does it put hundreds of researchers on building AI models - and then give them away for free? We go deep into the Nemotron models, what it takes to build a top AI lab, and the future of frontier AI. 01:33 - Is open source AI catching the frontier? 05:29 - Do closed labs blocking distillation slow open source down? 07:42 - Is the US falling behind China? 10:30 - Why companies actually choose open models 12:39 - A "crazy" 2008 bet: machine learning on GPUs 15:33 - Working with Andrew Ng and Dario Amodei at Baidu 17:41 - Coming back to NVIDIA: DLSS and the birth of Megatron 21:55 - The real reason NVIDIA builds its own models 24:28 - Is Moore's Law really dead? 33:37 - The Nemotron family: Nano, Super, Ultra 35:09 - Built for agents: why NVIDIA bets on speed 36:02 - How you train a 550B model in 4 bits 39:25 - Hybrid Mamba-Transformer, explained simply 42:31 - Mixture of experts, and why NVIDIA built NVL72 around it 47:26 - Why a 1-million-token context window matters 49:26 - Multi-token prediction: how the model predicts 5 tokens at once 52:47 - Multi-teacher distillation: teaching one model from many 58:01 - Where reinforcement learning goes next 01:00:16 - Inside NVIDIA's research org: "the mission is the boss" 01:04:03 - How NVIDIA decides who gets the GPUs 01:10:53 - Why NVIDIA still feels entrepreneurial after 33 years 01:12:58 - Why Bryan doesn't believe in the singularity 01:17:50 - The AI backlash 01:19:18 - The controversial case: open AI is safer than closed

Matt Turck

56,954 Aufrufe • vor 2 Monaten