Video wird geladen...

Video konnte nicht geladen werden

Zur Startseite

OpenAI recently released its first open-weights model since GPT-2, entering a field led by DeepSeek and Alibaba's Qwen. Ankit () breaks down these top OSS models, including what sets them apart under the hood: mixture-of-experts, long-context training, and post-training techniques that shape reasoning and alignment—and how different design choices...

ycombinator's profile picture

Y Combinator

1,614,198 subscribers

208,680 Aufrufe • vor 11 Monaten •via X (Twitter)

Wissenschaft & TechnologieNachrichten & PolitikBildung
LIVE

Anya Rossi• Live Now

Private livecam show

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

*Major* open source AI drop today. Can America win the Open AI race? My conversation with Nathan Lambert and Luca Soldaini 🎀 of Ai2 about the launch of Olmo 3 00:00 – Cold Open 00:39 – Welcome & today’s big announcement 01:18 – Introducing the Olmo 3 model family 02:07 – What “base models” really are (and why they matter) 05:51 – Dolma 3: the data behind Olmo 3 08:06 – Performance vs Qwen, Gemma, DeepSeek 10:28 – What true open source means (and why it’s rare) 12:51 – Intermediate checkpoints, transparency, and why AI2 publishes everything 16:37 – Why Qwen is everywhere (including U.S. startups) 18:31 – Why Chinese labs go open source (and why U.S. labs don’t) 20:28 – Inside ATOM: the U.S. response to China’s model surge 22:13 – The rise of “thinking models” and inference-time scaling 35:58 – The full Olmo pipeline, explained simply 46:52 – Pre-training: data, scale, and avoiding catastrophic spikes 50:27 – Mid-training (tail patching) and avoiding test leakage 52:06 – Why long-context training matters 55:28 – SFT: building the foundation for reasoning 1:04:53 – Preference tuning & why DPO still works 1:10:51 – The hard part: RLVR, long reasoning chains, and infrastructure pain 1:13:59 – Why RL is so technically brutal 1:18:17 – Complexity tax vs AGI hype 1:21:58 – How everyone can contribute to the future of AI 1:27:26 – Closing thoughts
1:28:10

*Major* open source AI drop today. Can America win the Open AI race? My conversation with Nathan Lambert and Luca Soldaini 🎀 of Ai2 about the launch of Olmo 3 00:00 – Cold Open 00:39 – Welcome & today’s big announcement 01:18 – Introducing the Olmo 3 model family 02:07 – What “base models” really are (and why they matter) 05:51 – Dolma 3: the data behind Olmo 3 08:06 – Performance vs Qwen, Gemma, DeepSeek 10:28 – What true open source means (and why it’s rare) 12:51 – Intermediate checkpoints, transparency, and why AI2 publishes everything 16:37 – Why Qwen is everywhere (including U.S. startups) 18:31 – Why Chinese labs go open source (and why U.S. labs don’t) 20:28 – Inside ATOM: the U.S. response to China’s model surge 22:13 – The rise of “thinking models” and inference-time scaling 35:58 – The full Olmo pipeline, explained simply 46:52 – Pre-training: data, scale, and avoiding catastrophic spikes 50:27 – Mid-training (tail patching) and avoiding test leakage 52:06 – Why long-context training matters 55:28 – SFT: building the foundation for reasoning 1:04:53 – Preference tuning & why DPO still works 1:10:51 – The hard part: RLVR, long reasoning chains, and infrastructure pain 1:13:59 – Why RL is so technically brutal 1:18:17 – Complexity tax vs AGI hype 1:21:58 – How everyone can contribute to the future of AI 1:27:26 – Closing thoughts

Matt Turck

37,531 Aufrufe • vor 8 Monaten

Thanksgiving-week treat: an epic conversation on Frontier AI with Lukasz Kaiser -co-author of “Attention Is All You Need” (Transformers) and leading research scientist at OpenAI working on GPT-5.1-era reasoning models. 00:00 – Cold open and intro 01:29 – “AI slowdown” vs a wild week of new frontier models 08:03 – Low-hanging fruit, infra, RL training and better data 11:39 – What is a reasoning model, in plain language 17:02 – Chain-of-thought and training the thinking process with RL 21:39 – Łukasz’s path: from logic and France to Google and Kurzweil 24:20 – Inside the Transformer story and what “attention” really means 28:42 – From Google Brain to OpenAI: culture, scale and GPUs 32:49 – What’s next for pre-training, GPUs and distillation 37:29 – Can we still understand these models? Circuits, sparsity and black boxes 39:42 – GPT-4 → GPT-5 → GPT-5.1: what actually changed 42:40 – Post-training, safety and teaching GPT-5.1 different tones 46:16 – How long should GPT-5.1 think? Reasoning tokens and jagged abilities 47:43 – The five-year-old’s dot puzzle that still breaks frontier models 52:22 – Generalization, child-like learning and whether reasoning is enough 53:48 – Beyond Transformers: ARC, LeCun’s ideas and multimodal bottlenecks 56:10 – GPT-5.1 Codex Max, long-running agents and compaction 1:00:06 – Will foundation models eat most apps? The translation analogy and trust 1:02:34 – What still needs to be solved, and where AI might go next
1:05:25

Thanksgiving-week treat: an epic conversation on Frontier AI with Lukasz Kaiser -co-author of “Attention Is All You Need” (Transformers) and leading research scientist at OpenAI working on GPT-5.1-era reasoning models. 00:00 – Cold open and intro 01:29 – “AI slowdown” vs a wild week of new frontier models 08:03 – Low-hanging fruit, infra, RL training and better data 11:39 – What is a reasoning model, in plain language 17:02 – Chain-of-thought and training the thinking process with RL 21:39 – Łukasz’s path: from logic and France to Google and Kurzweil 24:20 – Inside the Transformer story and what “attention” really means 28:42 – From Google Brain to OpenAI: culture, scale and GPUs 32:49 – What’s next for pre-training, GPUs and distillation 37:29 – Can we still understand these models? Circuits, sparsity and black boxes 39:42 – GPT-4 → GPT-5 → GPT-5.1: what actually changed 42:40 – Post-training, safety and teaching GPT-5.1 different tones 46:16 – How long should GPT-5.1 think? Reasoning tokens and jagged abilities 47:43 – The five-year-old’s dot puzzle that still breaks frontier models 52:22 – Generalization, child-like learning and whether reasoning is enough 53:48 – Beyond Transformers: ARC, LeCun’s ideas and multimodal bottlenecks 56:10 – GPT-5.1 Codex Max, long-running agents and compaction 1:00:06 – Will foundation models eat most apps? The translation analogy and trust 1:02:34 – What still needs to be solved, and where AI might go next

Matt Turck

168,007 Aufrufe • vor 8 Monaten

How GPT-5 thinks, with OpenAI VP of Research Jerry Tworek 00:00 - Intro 01:01 - What Reasoning Actually Means in AI 02:32 - Chain of Thought: Models Thinking in Words 05:25 - How Models Decide How Long to Think 07:24 - Evolution from o1 to o3 to GPT-5 11:00 - The Road to OpenAI: Growing up in Poland, Dropping out of School, Trading 20:32 - Working on Robotics and Rubik's Cube Solving 23:02 - A Day in the Life: Talking to Researchers 24:06 - How Research Priorities Are Determined 26:53 - OpenAI's Culture of Transparency 29:32 - Balancing Research with Shipping Fast 31:52 - Using OpenAI's Own Tools Daily 32:43 - Pre-Training Plus RL: The Modern AI Stack 35:10 - Reinforcement Learning 101: Training Dogs 40:17 - The Evolution of Deep Reinforcement Learning 42:09 - When GPT-4 Seemed Underwhelming at First 45:39 - How RLHF Made GPT-4 Actually Useful 48:02 - Unsupervised vs Supervised Learning 49:59 - GRPO and How DeepSeek Accelerated US Research 53:05 - What It Takes to Scale Reinforcement Learning 55:36 - Agentic AI and Long-Horizon Thinking 59:19 - Alignment as an RL Problem 1:01:11 - Winning ICPC World Finals Without Specific Training 1:05:53 - Applying RL Beyond Math and Coding 1:09:15 - The Path from Here to AGI 1:12:23 - Pure RL vs Language Models
1:16:04

How GPT-5 thinks, with OpenAI VP of Research Jerry Tworek 00:00 - Intro 01:01 - What Reasoning Actually Means in AI 02:32 - Chain of Thought: Models Thinking in Words 05:25 - How Models Decide How Long to Think 07:24 - Evolution from o1 to o3 to GPT-5 11:00 - The Road to OpenAI: Growing up in Poland, Dropping out of School, Trading 20:32 - Working on Robotics and Rubik's Cube Solving 23:02 - A Day in the Life: Talking to Researchers 24:06 - How Research Priorities Are Determined 26:53 - OpenAI's Culture of Transparency 29:32 - Balancing Research with Shipping Fast 31:52 - Using OpenAI's Own Tools Daily 32:43 - Pre-Training Plus RL: The Modern AI Stack 35:10 - Reinforcement Learning 101: Training Dogs 40:17 - The Evolution of Deep Reinforcement Learning 42:09 - When GPT-4 Seemed Underwhelming at First 45:39 - How RLHF Made GPT-4 Actually Useful 48:02 - Unsupervised vs Supervised Learning 49:59 - GRPO and How DeepSeek Accelerated US Research 53:05 - What It Takes to Scale Reinforcement Learning 55:36 - Agentic AI and Long-Horizon Thinking 59:19 - Alignment as an RL Problem 1:01:11 - Winning ICPC World Finals Without Specific Training 1:05:53 - Applying RL Beyond Math and Coding 1:09:15 - The Path from Here to AGI 1:12:23 - Pure RL vs Language Models

Matt Turck

451,229 Aufrufe • vor 9 Monaten

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

100,966 Aufrufe • vor 2 Monaten

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 Aufrufe • vor 3 Monaten

Here's my 5-hour conversation with Dylan Patel and Nathan Lambert on DeepSeek, China, OpenAI, NVIDIA, xAI, Google, Anthropic, Meta, Microsoft, TSMC, Stargate, megacluster buildouts, RL, reasoning, and a lot of other topics at the cutting edge of AI. This is was a mind-blowing, super-technical, and fun conversation. Yes, we discuss r1 and o3-mini, but more importantly we look into the future of technology, geopolitics, and humanity in a world that stands on the precipice of a global AI revolution. The first 4 hours are here on X (4 hours is current limit), and the full 5 hours are up everywhere else. Links in comment. Timestamps: 0:00 - Introduction 3:33 - DeepSeek-R1 and DeepSeek-V3 25:07 - Low cost of training 51:25 - DeepSeek compute cluster 58:57 - Export controls on GPUs to China 1:09:16 - AGI timeline 1:18:41 - China's manufacturing capacity 1:26:36 - Cold war with China 1:31:05 - TSMC and Taiwan 1:54:44 - Best GPUs for AI 2:09:36 - Why DeepSeek is so cheap 2:22:55 - Espionage 2:31:57 - Censorship 2:44:52 - Andrej Karpathy and magic of RL 2:55:23 - OpenAI o3-mini vs DeepSeek r1 3:14:31 - NVIDIA 3:18:58 - GPU smuggling 3:25:36 - DeepSeek training on OpenAI data 3:36:04 - AI megaclusters 4:11:26 - Who wins the race to AGI? 4:21:39 - AI agents 4:30:21 - Programming and AI 4:37:49 - Open source 4:47:01 - Stargate 4:54:30 - Future of AI
3:59:56

Here's my 5-hour conversation with Dylan Patel and Nathan Lambert on DeepSeek, China, OpenAI, NVIDIA, xAI, Google, Anthropic, Meta, Microsoft, TSMC, Stargate, megacluster buildouts, RL, reasoning, and a lot of other topics at the cutting edge of AI. This is was a mind-blowing, super-technical, and fun conversation. Yes, we discuss r1 and o3-mini, but more importantly we look into the future of technology, geopolitics, and humanity in a world that stands on the precipice of a global AI revolution. The first 4 hours are here on X (4 hours is current limit), and the full 5 hours are up everywhere else. Links in comment. Timestamps: 0:00 - Introduction 3:33 - DeepSeek-R1 and DeepSeek-V3 25:07 - Low cost of training 51:25 - DeepSeek compute cluster 58:57 - Export controls on GPUs to China 1:09:16 - AGI timeline 1:18:41 - China's manufacturing capacity 1:26:36 - Cold war with China 1:31:05 - TSMC and Taiwan 1:54:44 - Best GPUs for AI 2:09:36 - Why DeepSeek is so cheap 2:22:55 - Espionage 2:31:57 - Censorship 2:44:52 - Andrej Karpathy and magic of RL 2:55:23 - OpenAI o3-mini vs DeepSeek r1 3:14:31 - NVIDIA 3:18:58 - GPU smuggling 3:25:36 - DeepSeek training on OpenAI data 3:36:04 - AI megaclusters 4:11:26 - Who wins the race to AGI? 4:21:39 - AI agents 4:30:21 - Programming and AI 4:37:49 - Open source 4:47:01 - Stargate 4:54:30 - Future of AI

Lex Fridman

1,153,999 Aufrufe • vor 1 Jahr

Gemini 3, scaling laws and the 'finite data' era: my conversation with Sebastian Borgeaud, research engineer at Google DeepMind and a pre-training lead for Gemini 3 00:00 – Cold intro: “We’re ahead of schedule” + AI is now a system 00:58 – Oriol Vinyals's “secret recipe”: better pre- + post-training 02:09 – Why AI progress still isn’t slowing down 03:04 – Are models actually getting smarter? 04:36 – Two–three years out: what changes first? 06:34 – AI doing AI research: faster, not automated 07:45 – Frontier labs: same playbook or different bets? 10:19 – Post-transformers: will a disruption happen? 10:51 – DeepMind’s advantage: research × engineering × infra 12:26 – What a Gemini 3 pre-training lead actually does 13:59 – From Europe to Cambridge to DeepMind 18:06 – Why he left RL for real-world data 20:05 – From Gopher to Chinchilla to RETRO (and why it matters) 20:28 – “Research taste”: integrate or slow everyone down 23:00 – Fixes vs moonshots: how they balance the pipeline 24:37 – Research vs product pressure (and org structure) 26:24 – Gemini 3 under the hood: MoE in plain English 28:30 – Native multimodality: the hidden costs 30:03 – Scaling laws aren’t dead (but scale isn’t everything) 33:07 – Synthetic data: powerful, dangerous? 35:00 – Reasoning traces: what he can’t say (and why) 37:18 – Long context + attention: what’s next 38:40 – Retrieval vs RAG vs long context 41:49 – The real boss fight: evals (and contamination) 42:28 – Alignment: pre-training vs post-training 43:32 – Deep Think + agents + “vibe coding” 46:34 – Continual learning: updating models over time 49:35 – Advice for researchers + founders 53:35 – “No end in sight” for progress + closing
54:56

Gemini 3, scaling laws and the 'finite data' era: my conversation with Sebastian Borgeaud, research engineer at Google DeepMind and a pre-training lead for Gemini 3 00:00 – Cold intro: “We’re ahead of schedule” + AI is now a system 00:58 – Oriol Vinyals's “secret recipe”: better pre- + post-training 02:09 – Why AI progress still isn’t slowing down 03:04 – Are models actually getting smarter? 04:36 – Two–three years out: what changes first? 06:34 – AI doing AI research: faster, not automated 07:45 – Frontier labs: same playbook or different bets? 10:19 – Post-transformers: will a disruption happen? 10:51 – DeepMind’s advantage: research × engineering × infra 12:26 – What a Gemini 3 pre-training lead actually does 13:59 – From Europe to Cambridge to DeepMind 18:06 – Why he left RL for real-world data 20:05 – From Gopher to Chinchilla to RETRO (and why it matters) 20:28 – “Research taste”: integrate or slow everyone down 23:00 – Fixes vs moonshots: how they balance the pipeline 24:37 – Research vs product pressure (and org structure) 26:24 – Gemini 3 under the hood: MoE in plain English 28:30 – Native multimodality: the hidden costs 30:03 – Scaling laws aren’t dead (but scale isn’t everything) 33:07 – Synthetic data: powerful, dangerous? 35:00 – Reasoning traces: what he can’t say (and why) 37:18 – Long context + attention: what’s next 38:40 – Retrieval vs RAG vs long context 41:49 – The real boss fight: evals (and contamination) 42:28 – Alignment: pre-training vs post-training 43:32 – Deep Think + agents + “vibe coding” 46:34 – Continual learning: updating models over time 49:35 – Advice for researchers + founders 53:35 – “No end in sight” for progress + closing

Matt Turck

51,317 Aufrufe • vor 7 Monaten

We don't know what most microbial genes do. Can genomic language models help? there's only one way to find out! this is a 1 hour and 42 minute interview with an MIT professor (the famous Yunha Hwang) chatting about these questions, her work in solving them at Tatta Bio, and more. zoomer captions are back too Links in reply! Timestamps: 00:00:00 - Clips + sponsor roll from the wonderful LatchBio 00:02:07 – Introduction 00:02:23 – Why do microbial genomes matter 00:04:07 – Deep learning acceptance in metagenomics 00:05:25 – The case for genomic “context” over sequence matching 00:06:43 – OMG: the only ML-ready metagenomic dataset 00:09:27 – gLM2: A multimodal genomic language model 00:11:06 – What do you do with the output of genomic language models? 00:17:41 – How will OMG evolve? 00:20:26 – Why train on only microbial genomes, as opposed to all genomes? 00:22:58 – Do we need more sequences or more annotations? 00:23:54 – Is there a conserved microbial genome ‘language’? 00:28:11 – What non-obvious things can this genomic language model tell you? 00:33:08 – Semantic deduplication and evaluation 00:37:33 – How does benchmarking work for these types of models? 00:41:31 – Gaia: A genomic search engine 00:44:18 – Even ‘well-studied’ genomes are mostly unannotated 00:50:51 – Using agents on Gaia 00:54:53 – Will genomic language models reshape the tree of life? 00:59:18 – Current limitations of genomic language models 01:08:54 – Directed evolution as training data 01:12:35 – What is Tatta Bio? 01:19:02 – Building Google for genomic sequences (SeqHub) 01:25:46 – How to create communities around scientific OSS 01:29:06 – What’s the purpose in the centralization of the software? 01:35:37 – How will the way science is done change in 10 years?
1:42:41

We don't know what most microbial genes do. Can genomic language models help? there's only one way to find out! this is a 1 hour and 42 minute interview with an MIT professor (the famous Yunha Hwang) chatting about these questions, her work in solving them at Tatta Bio, and more. zoomer captions are back too Links in reply! Timestamps: 00:00:00 - Clips + sponsor roll from the wonderful LatchBio 00:02:07 – Introduction 00:02:23 – Why do microbial genomes matter 00:04:07 – Deep learning acceptance in metagenomics 00:05:25 – The case for genomic “context” over sequence matching 00:06:43 – OMG: the only ML-ready metagenomic dataset 00:09:27 – gLM2: A multimodal genomic language model 00:11:06 – What do you do with the output of genomic language models? 00:17:41 – How will OMG evolve? 00:20:26 – Why train on only microbial genomes, as opposed to all genomes? 00:22:58 – Do we need more sequences or more annotations? 00:23:54 – Is there a conserved microbial genome ‘language’? 00:28:11 – What non-obvious things can this genomic language model tell you? 00:33:08 – Semantic deduplication and evaluation 00:37:33 – How does benchmarking work for these types of models? 00:41:31 – Gaia: A genomic search engine 00:44:18 – Even ‘well-studied’ genomes are mostly unannotated 00:50:51 – Using agents on Gaia 00:54:53 – Will genomic language models reshape the tree of life? 00:59:18 – Current limitations of genomic language models 01:08:54 – Directed evolution as training data 01:12:35 – What is Tatta Bio? 01:19:02 – Building Google for genomic sequences (SeqHub) 01:25:46 – How to create communities around scientific OSS 01:29:06 – What’s the purpose in the centralization of the software? 01:35:37 – How will the way science is done change in 10 years?

owl

44,279 Aufrufe • vor 7 Monaten

A 7-million parameter model outperforming models a thousand times its size on tasks like ARC Prize. That's what recursive reasoning unlocks. In this episode of Decoded, YC's Ankit Gupta and Francois Chaubard break down two recent papers on recursive AI models, HRMs and TRMs, that are achieving state-of-the-art results with a fraction of the parameters of today's largest models. They explain why standard LLMs hit a fundamental ceiling on certain reasoning tasks, how recursion at inference time gives small models the compute depth to break through it, and what happens when you combine these ideas with the power of large-scale foundation models. 00:35 - Model Foundations 01:15 - RNN Limits and LLM Contrast 02:36 - Reasoning Limits and Sorting Analogy 04:22 - HRM Paper Introduction 05:25 - HRM Architecture and Intuition 07:36 - HRM Results and Outer Loop 09:46 - TRM Paper Overview 11:20 - TRM Training and Fixed Point 13:30 - Detailed HRM Summary 20:46 - Comparing HRM and TRM 34:45 - Future Outlook
37:52

A 7-million parameter model outperforming models a thousand times its size on tasks like ARC Prize. That's what recursive reasoning unlocks. In this episode of Decoded, YC's Ankit Gupta and Francois Chaubard break down two recent papers on recursive AI models, HRMs and TRMs, that are achieving state-of-the-art results with a fraction of the parameters of today's largest models. They explain why standard LLMs hit a fundamental ceiling on certain reasoning tasks, how recursion at inference time gives small models the compute depth to break through it, and what happens when you combine these ideas with the power of large-scale foundation models. 00:35 - Model Foundations 01:15 - RNN Limits and LLM Contrast 02:36 - Reasoning Limits and Sorting Analogy 04:22 - HRM Paper Introduction 05:25 - HRM Architecture and Intuition 07:36 - HRM Results and Outer Loop 09:46 - TRM Paper Overview 11:20 - TRM Training and Fixed Point 13:30 - Detailed HRM Summary 20:46 - Comparing HRM and TRM 34:45 - Future Outlook

Y Combinator

127,645 Aufrufe • vor 3 Monaten

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 Aufrufe • vor 8 Monaten

"Projects like the New Deal, the Apollo program pale in comparison to what we're doing right now." 🆕 Greg Brockman (Greg Brockman) joins us to talk GPT-5, GPT-OSS, and what's next on OpenAI's road to crystallizing all of human intelligence! “Energy turns into compute, turns into intelligence… crystallizing compute into potential energy you can release again and again.” 0:00:04 - Introductions 0:01:04 - The Evolution of Reasoning at OpenAI 0:04:01 - Online vs Offline Learning in Language Models 0:06:44 - Sample Efficiency and Human Curation in Reinforcement Learning 0:08:16 - Scaling Compute and Supercritical Learning 0:13:21 - Wall clock time limitations in RL and real-world interactions 0:16:34 - Experience with ARC Institute and DNA neural networks 0:19:33 - Defining the GPT-5 Era 0:22:46 - Evaluating Model Intelligence and Task Difficulty 0:25:06 - Practical Advice for Developers Using GPT-5 0:31:48 - Model Specs 0:37:21 - Challenges in RL Preferences (e.g., try/catch) 0:39:13 - Model Routing and Hybrid Architectures in GPT-5 0:43:58 - GPT-5 pricing and compute efficiency improvements 0:46:04 - Self-Improving Coding Agents and Tool Usage 0:49:11 - On-Device Models and Local vs Remote Agent Systems 0:51:34 - Engineering at OpenAI and Leveraging LLMs 0:54:16 - Structuring Codebases and Teams for AI Optimization 0:55:27 - The Value of Engineers in the Age of AGI 0:58:42 - Current state of AI research and lab diversity 1:01:11 - OpenAI’s Prioritization and Focus Areas 1:03:05 - Advice for Founders - It's Not Too Late 1:04:20 - Future outlook and closing thoughts 1:04:33 - Time Capsule to 2045 - Future of Compute and Abundance 1:07:07 - Time Capsule to 2005 - More Problems Will Emerge
1:08:36

"Projects like the New Deal, the Apollo program pale in comparison to what we're doing right now." 🆕 Greg Brockman (Greg Brockman) joins us to talk GPT-5, GPT-OSS, and what's next on OpenAI's road to crystallizing all of human intelligence! “Energy turns into compute, turns into intelligence… crystallizing compute into potential energy you can release again and again.” 0:00:04 - Introductions 0:01:04 - The Evolution of Reasoning at OpenAI 0:04:01 - Online vs Offline Learning in Language Models 0:06:44 - Sample Efficiency and Human Curation in Reinforcement Learning 0:08:16 - Scaling Compute and Supercritical Learning 0:13:21 - Wall clock time limitations in RL and real-world interactions 0:16:34 - Experience with ARC Institute and DNA neural networks 0:19:33 - Defining the GPT-5 Era 0:22:46 - Evaluating Model Intelligence and Task Difficulty 0:25:06 - Practical Advice for Developers Using GPT-5 0:31:48 - Model Specs 0:37:21 - Challenges in RL Preferences (e.g., try/catch) 0:39:13 - Model Routing and Hybrid Architectures in GPT-5 0:43:58 - GPT-5 pricing and compute efficiency improvements 0:46:04 - Self-Improving Coding Agents and Tool Usage 0:49:11 - On-Device Models and Local vs Remote Agent Systems 0:51:34 - Engineering at OpenAI and Leveraging LLMs 0:54:16 - Structuring Codebases and Teams for AI Optimization 0:55:27 - The Value of Engineers in the Age of AGI 0:58:42 - Current state of AI research and lab diversity 1:01:11 - OpenAI’s Prioritization and Focus Areas 1:03:05 - Advice for Founders - It's Not Too Late 1:04:20 - Future outlook and closing thoughts 1:04:33 - Time Capsule to 2045 - Future of Compute and Abundance 1:07:07 - Time Capsule to 2005 - More Problems Will Emerge

Latent.Space

305,090 Aufrufe • vor 11 Monaten

Today, we're joined by Aakanksha Chowdhery, member of technical staff at Reflection, to explore the fundamental shifts required to build true agentic AI. While the industry has largely focused on post-training techniques to improve reasoning, Aakanksha draws on her experience leading pre-training efforts for Google’s PaLM and early Gemini models to argue that pre-training itself must be rethought to move beyond static benchmarks. We explore the limitations of next-token prediction for multi-step workflows and examine how attention mechanisms, loss objectives, and training data must evolve to support long-form reasoning and planning. Aakanksha shares insights on the difference between context retrieval and actual reasoning, the importance of "trajectory" training data, and why scaling remains essential for discovering emergent agentic capabilities like error recovery and dynamic tool learning. 🗒️ For the full list of resources for this episode, visit the show notes page: 📖 CHAPTERS =============================== 00:00 - Introduction 02:26 - Reflection 04:54 - Limitations of post-training for building agents 07:31 - Rethinking pre-training in agents 10:51 - Scaling 11:27 - Evolving attention mechanisms for agentic capabilities 12:39 - Memory as a tool 14:13 - Loss objectives and training data 15:50 - Fine-tuning loss in agent performance 19:37 - Training data 21:29 - Augmenting dominant training data source 24:11 - Overcoming challenges in training on synthetic data 25:47 - Benchmarks 30:44 - Scaling laws in large models versus small models 33:20 - Long-form versus short-form reasoning 37:57 - Agent’s ability to recover from failure 40:15 - Hallucinations and failure recovery 43:53 - Tool use in agents 46:38 - Coding agents 48:37 - How researchers can contribute to agentic AI
51:54

Today, we're joined by Aakanksha Chowdhery, member of technical staff at Reflection, to explore the fundamental shifts required to build true agentic AI. While the industry has largely focused on post-training techniques to improve reasoning, Aakanksha draws on her experience leading pre-training efforts for Google’s PaLM and early Gemini models to argue that pre-training itself must be rethought to move beyond static benchmarks. We explore the limitations of next-token prediction for multi-step workflows and examine how attention mechanisms, loss objectives, and training data must evolve to support long-form reasoning and planning. Aakanksha shares insights on the difference between context retrieval and actual reasoning, the importance of "trajectory" training data, and why scaling remains essential for discovering emergent agentic capabilities like error recovery and dynamic tool learning. 🗒️ For the full list of resources for this episode, visit the show notes page: 📖 CHAPTERS =============================== 00:00 - Introduction 02:26 - Reflection 04:54 - Limitations of post-training for building agents 07:31 - Rethinking pre-training in agents 10:51 - Scaling 11:27 - Evolving attention mechanisms for agentic capabilities 12:39 - Memory as a tool 14:13 - Loss objectives and training data 15:50 - Fine-tuning loss in agent performance 19:37 - Training data 21:29 - Augmenting dominant training data source 24:11 - Overcoming challenges in training on synthetic data 25:47 - Benchmarks 30:44 - Scaling laws in large models versus small models 33:20 - Long-form versus short-form reasoning 37:57 - Agent’s ability to recover from failure 40:15 - Hallucinations and failure recovery 43:53 - Tool use in agents 46:38 - Coding agents 48:37 - How researchers can contribute to agentic AI

The TWIML AI Podcast

44,888 Aufrufe • vor 7 Monaten

No one: *insert lack of people here*: Me: Here's over four hours of CART 1996-02 onboard footage with timestamps if you want to find a driver that you like #INDYCAR Timestamps (Driver, Year, Track) 0:00 JPM '99 Long Beach 1:00 Andretti '97 Portland 1:43 Takagi '02 Motegi 2:08 JPM '00 Denver 2:57 Fernandez '99 Fontana 3:57 Gordon '96 Toronto 4:53 Andretti '98 Rio de Janeiro 6:41 Brack '02 Toronto 7:37 de Ferran '97 Fontana 8:59 da Matta '02 Monterrey 12:13 Gugelmin '01 Texas 12:40 Papis '00 Toronto 13:40 Bruno '01 Nazareth 14:10 de Ferran '96 Cleveland 15:06 Vasser '00 Michigan 15:37 Fernandez '98 Toronto 16:47 Dixon '01 Rockingham 17:29 Johansson '96 Vancouver 18:05 Tracy '02 Chicago 19:11 Rahal '96 Long Beach 19:59 Servia '02 Rockingham 20:43 Vasser '96 Toronto 22:16 Andretti '97 Fontana 23:08 Bruno '02 Denver 25:43 Pruett '98 Michigan 26:16 JPM '00 Road America 26:54 de Ferran '97 Gateway 27:26 Brack '02 Laguna Seca 29:33 Andretti '96 Milwaukee 31:33 Zanardi '98 Toronto 33:01 Papis '02 Fontana 33:53 Bruno '01 Toronto 35:15 Fittipaldi '96 Portland 36:12 Servia '02 Rockingham 36:40 Zanardi '98 Toronto 37:49 Andretti '96 Rio de Janeiro 38:34 Tracy '02 Vancouver 40:32 JPM '99 Homestead 41:11 Papis '01 Long Beach 41:39 Franchitti '02 Rockingham 42:11 Zanardi '98 Toronto 44:30 Gugelmin '01 Monterrey 45:10 Hearn '99 Milwaukee 46:15 Andretti '02 Monterrey 48:56 Brack '01 Chicago 49:26 da Matta '02 Road America 51:21 Pruett '98 Gateway 51:53 Tags '02 Toronto 52:34 Vasser '98 Motegi 53:06 Papis '02 Mid-Ohio 53:41 Hearn '98 Nazareth 54:18 da Matta '02 Monterrey 55:24 Vasser '00 Nazareth 56:25 Franchitti '02 Mexico City 57:40 Zanardi '98 Michigan 58:06 Franchitti '02 Portland 1:00:46 Vasser '00 Rio de Janeiro 1:01:34 Fittipaldi '98 Vancouver 1:02:42 Vasser '99 Nazareth 1:03:42 de Ferran '98 Denver 1:04:36 Fittipaldi '96 Mid-Ohio 1:05:29 Dixon '01 Vancouver 1:06:26 Rahal '98 Laguna Seca 1:08:10 Brack '02 Vancouver 1:09:16 Pruett '97 Rio de Janeiro 1:09:55 Rahal '98 Surfers Paradise 1:11:13 Minassian '01 Texas 1:12:48 Franchitti '02 Monterrey 1:14:27 Kanaan '99 Surfers Paradise 1:15:09 Vasser '96 Homestead 1:16:01 Franchitti '02 Mid-Ohio 1:17:16 Papis '00 Cleveland 1:18:18 Bruno '02 Road America 1:20:32 Andretti '99 Homestead 1:20:54 Bruno '01 Vancouver 1:22:07 Andretti '99 Motegi 1:23:11 JPM '00 Vancouver 1:23:41 Tracy '02 Laguna Seca 1:25:22 Minassian '01 Denver 1:26:23 Andretti '99 Gateway 1:26:49 Bruno '02 Denver 1:28:46 Andretti '99 Road America 1:30:04 Dixon '01 Surfers Paradise 1:30:37 Franchitti '02 Portland 1:32:48 Vasser '96 Vancouver 1:33:24 Andretti '98 Nazareth 1:34:00 Tracy '02 Toronto 1:34:56 JPM '99 Portland 1:36:45 Rahal '97 Homestead 1:37:24 Carpentier '02 Road America 1:39:02 da Matta '00 Nazareth 1:40:14 Zanardi '98 Toronto 1:40:59 Johansson '96 Portland 1:41:54 Brack '02 Long Beach 1:42:33 Boesel '97 Laguna Seca 1:43:28 Andretti '99 Homestead 1:44:26 Rahal '98 Denver 1:45:57 Johnstone '96 Portland 1:46:55 JPM '00 Chicago 1:47:35 da Matta '02 Laguna Seca 1:49:36 Brack '01 Chicago 1:50:44 Carpentier '02 Montreal 1:52:25 Gidley '01 Rockingham 1:53:01 Vasser '02 Mid-Ohio 1:54:23 Dixon '01 Houston 1:55:12 JPM '99 Milwaukee 1:56:15 Gugelmin '01 Mid-Ohio 1:57:13 Tracy '02 Miami 1:59:02 Pruett '98 Fontana 1:59:37 Andretti '99 Portland 2:00:32 Papis '00 Michigan 2:01:23 Andretti '96 Laguna Seca 2:03:21 Fernandez '99 Surfers Paradise 2:04:23 Brack '02 Fontana 2:05:24 Papis '00 Cleveland 2:06:08 da Matta '02 Laguna Seca 2:10:12 Ribiero '97 Michigan 2:10:59 Brack '02 Vancouver 2:12:26 Fernandez '99 Laguna Seca 2:13:49 Rahal '97 Fontana 2:14:27 Gidley '01 Road America 2:15:14 Fernandez '02 Milwaukee 2:16:26 Zanardi '98 Vancouver 2:17:54 Fittipaldi '97 Laguna Seca 2:18:58 Bell '02 Motegi 2:19:39 JPM '00 Laguna Seca 2:20:32 Pruett '98 Gateway 2:21:07 Franchitti '02 Toronto 2:23:08 JPM '99 Portland 2:24:08 Dixon '01 Lausitz 2:24:44 Vasser '02 Denver 2:25:55 Fernandez '01 Motegi 2:27:19 Tracy '02 Mexico City 2:28:20 Fittipaldi '99 Gateway 2:29:38 Carpentier '02 Mid-Ohio 2:30:13 Andretti '97 Surfers Paradise 2:31:40 Vasser '96 Nazareth 2:32:05 de Ferran '97 Long Beach 2:33:13 Vasser '99 Homestead 2:33:39 Kanaan '02 Cleveland 2:34:30 Fernandez '01 Chicago 2:35:58 Brack '02 Laguna Seca 2:37:21 Fernandez '01 Texas 2:37:49 JPM '99 Denver 2:38:44 de Ferran '97 Rio de Janeiro 2:39:28 Hearn '98 Road America 2:44:50 Brack '02 Vancouver 2:44:02 Andretti '98 Homestead 2:45:00 Tracy '02 Laguna Seca 2:46:16 Gidley '01 Chicago 2:47:12 Fittipaldi '96 Cleveland 2:48:18 JPM '00 Portland 2:49:57 Rahal '98 Motegi 2:50:28 Blundell '00 Long Beach 2:51:39 Franchitti '02 Rockingham 2:52:03 Andretti '99 Mid-Ohio 2:53:25 Tracy '02 Vancouver 2:54:33 Gugelmin '01 Portland 2:55:41 JPM '00 Denver 2:56:44 Franchitti '02 Motegi 2:57:25 Zanardi '98 Toronto 2:59:41 Franchitti '02 Rockingham 3:00:17 JPM '00 Laguna Seca 3:01:35 Tracy '02 Toronto 3:04:18 Brack '01 Michigan 3:04:51 da Matta '02 Monterrey 3:06:04 Jones '99 Rio de Janeiro 3:06:57 JPM '00 Houston 3:07:33 Fernandez '02 Monterrey 3:09:02 Bruno '01 Nazareth 3:10:20 Rahal '96 Toronto 3:11:23 da Matta '02 Road America 3:13:42 Pruett '96 Nazareth 3:14:40 JPM '00 Road America 3:15:23 Fittipaldi '99 Cleveland 3:16:31 Hearn '98 Rio de Janeiro 3:17:58 Andretti '02 Road America 3:18:54 Vasser '96 Milwaukee 3:19:37 Franchitti '02 Portland 3:20:34 Andretti '97 Toronto 3:21:26 Vasser '99 Milwaukee 3:22:06 Papis '00 Vancouver 3:22:48 Bruno '01 Laguna Seca 3:24:10 Rahal '97 Rio de Janeiro 3:25:22 Brack '02 Denver 3:26:15 Gordon '99 Chicago 3:27:03 Fittipaldi '96 Portland 3:27:41 Nakano '01 Motegi 3:28:34 Vasser '02 Denver 3:29:46 Andretti '99 Milwaukee 3:30:27 Pruett '98 Mid-Ohio 3:31:23 Papis '00 Milwaukee 3:31:55 Andretti '99 Denver 3:33:01 Bruno '01 Nazareth 3:33:34 Carpentier '02 Montreal 3:35:11 Andretti '99 Nazareth 3:36:01 JPM '00 Mid-Ohio 3:37:05 Rahal '98 Rio de Janeiro 3:37:48 Kanaan '02 Cleveland 3:39:01 Vasser '00 Motegi 3:40:10 Fittipaldi '02 Montreal 3:41:20 da Matta '00 Michigan 3:41:55 Zanardi '98 Toronto 3:42:57 Papis '00 Milwaukee 3:43:53 Bruno '02 Portland 3:44:48 da Matta '01 Fontana 3:45:42 Zanardi '98 Portland 3:46:42 Andretti '99 Nazareth 3:47:44 Carpentier '02 Mid-Ohio 3:48:55 Andretti '99 Fontana 3:49:42 Vasser '01 Houston 3:50:34 Rahal '98 Fontana 3:51:57 JPM '00 Portland 3:53:15 Andretti '99 Cleveland 3:54:30 Vasser '97 Homestead 3:55:30 Andretti '99 Cleveland 3:56:32 Servia '02 Rockingham 3:56:59 Fernandez '01 Surfers Paradise 3:58:27 Tracy '02 Fontana 3:59:08 JPM '99 Denver 4:00:16 Papis '00 Homestead 4:00:44 Franchitti '02 Laguna Seca 4:02:39 Bruno '01 Milwaukee 4:03:25 Carpentier '02 Vancouver 4:04:34 JPM '99 Milwaukee 4:05:04 Kanaan '02 Cleveland 4:05:55 Jones '99 Gateway 4:06:30 da Matta '02 Laguna Seca 4:07:38 Vasser '99 Motegi 4:08:13 Fontana '00 Long Beach 4:09:49 Bruno '01 Milwaukee 4:10:54 Carpentier '02 Cleveland 4:11:59 Andretti '99 Gateway 4:12:51 Franchitti '02 Road America 4:14:29 Zanardi '98 Toronto 4:15:51 da Matta '02 Fontana 4:16:51 Ribiero '97 Toronto 4:19:16 Nakano '00 Laguna Seca 4:20:12 Gidley '01 Fontana 4:20:42 Kanaan '99 Mid-Ohio 4:21:48 da Matta '01 Fontana 4:22:33 Andretti '02 Monterrey 4:24:22 Pruett '96 Toronto 4:26:40 Vasser '99 Michigan
4:27:33

No one: *insert lack of people here*: Me: Here's over four hours of CART 1996-02 onboard footage with timestamps if you want to find a driver that you like #INDYCAR Timestamps (Driver, Year, Track) 0:00 JPM '99 Long Beach 1:00 Andretti '97 Portland 1:43 Takagi '02 Motegi 2:08 JPM '00 Denver 2:57 Fernandez '99 Fontana 3:57 Gordon '96 Toronto 4:53 Andretti '98 Rio de Janeiro 6:41 Brack '02 Toronto 7:37 de Ferran '97 Fontana 8:59 da Matta '02 Monterrey 12:13 Gugelmin '01 Texas 12:40 Papis '00 Toronto 13:40 Bruno '01 Nazareth 14:10 de Ferran '96 Cleveland 15:06 Vasser '00 Michigan 15:37 Fernandez '98 Toronto 16:47 Dixon '01 Rockingham 17:29 Johansson '96 Vancouver 18:05 Tracy '02 Chicago 19:11 Rahal '96 Long Beach 19:59 Servia '02 Rockingham 20:43 Vasser '96 Toronto 22:16 Andretti '97 Fontana 23:08 Bruno '02 Denver 25:43 Pruett '98 Michigan 26:16 JPM '00 Road America 26:54 de Ferran '97 Gateway 27:26 Brack '02 Laguna Seca 29:33 Andretti '96 Milwaukee 31:33 Zanardi '98 Toronto 33:01 Papis '02 Fontana 33:53 Bruno '01 Toronto 35:15 Fittipaldi '96 Portland 36:12 Servia '02 Rockingham 36:40 Zanardi '98 Toronto 37:49 Andretti '96 Rio de Janeiro 38:34 Tracy '02 Vancouver 40:32 JPM '99 Homestead 41:11 Papis '01 Long Beach 41:39 Franchitti '02 Rockingham 42:11 Zanardi '98 Toronto 44:30 Gugelmin '01 Monterrey 45:10 Hearn '99 Milwaukee 46:15 Andretti '02 Monterrey 48:56 Brack '01 Chicago 49:26 da Matta '02 Road America 51:21 Pruett '98 Gateway 51:53 Tags '02 Toronto 52:34 Vasser '98 Motegi 53:06 Papis '02 Mid-Ohio 53:41 Hearn '98 Nazareth 54:18 da Matta '02 Monterrey 55:24 Vasser '00 Nazareth 56:25 Franchitti '02 Mexico City 57:40 Zanardi '98 Michigan 58:06 Franchitti '02 Portland 1:00:46 Vasser '00 Rio de Janeiro 1:01:34 Fittipaldi '98 Vancouver 1:02:42 Vasser '99 Nazareth 1:03:42 de Ferran '98 Denver 1:04:36 Fittipaldi '96 Mid-Ohio 1:05:29 Dixon '01 Vancouver 1:06:26 Rahal '98 Laguna Seca 1:08:10 Brack '02 Vancouver 1:09:16 Pruett '97 Rio de Janeiro 1:09:55 Rahal '98 Surfers Paradise 1:11:13 Minassian '01 Texas 1:12:48 Franchitti '02 Monterrey 1:14:27 Kanaan '99 Surfers Paradise 1:15:09 Vasser '96 Homestead 1:16:01 Franchitti '02 Mid-Ohio 1:17:16 Papis '00 Cleveland 1:18:18 Bruno '02 Road America 1:20:32 Andretti '99 Homestead 1:20:54 Bruno '01 Vancouver 1:22:07 Andretti '99 Motegi 1:23:11 JPM '00 Vancouver 1:23:41 Tracy '02 Laguna Seca 1:25:22 Minassian '01 Denver 1:26:23 Andretti '99 Gateway 1:26:49 Bruno '02 Denver 1:28:46 Andretti '99 Road America 1:30:04 Dixon '01 Surfers Paradise 1:30:37 Franchitti '02 Portland 1:32:48 Vasser '96 Vancouver 1:33:24 Andretti '98 Nazareth 1:34:00 Tracy '02 Toronto 1:34:56 JPM '99 Portland 1:36:45 Rahal '97 Homestead 1:37:24 Carpentier '02 Road America 1:39:02 da Matta '00 Nazareth 1:40:14 Zanardi '98 Toronto 1:40:59 Johansson '96 Portland 1:41:54 Brack '02 Long Beach 1:42:33 Boesel '97 Laguna Seca 1:43:28 Andretti '99 Homestead 1:44:26 Rahal '98 Denver 1:45:57 Johnstone '96 Portland 1:46:55 JPM '00 Chicago 1:47:35 da Matta '02 Laguna Seca 1:49:36 Brack '01 Chicago 1:50:44 Carpentier '02 Montreal 1:52:25 Gidley '01 Rockingham 1:53:01 Vasser '02 Mid-Ohio 1:54:23 Dixon '01 Houston 1:55:12 JPM '99 Milwaukee 1:56:15 Gugelmin '01 Mid-Ohio 1:57:13 Tracy '02 Miami 1:59:02 Pruett '98 Fontana 1:59:37 Andretti '99 Portland 2:00:32 Papis '00 Michigan 2:01:23 Andretti '96 Laguna Seca 2:03:21 Fernandez '99 Surfers Paradise 2:04:23 Brack '02 Fontana 2:05:24 Papis '00 Cleveland 2:06:08 da Matta '02 Laguna Seca 2:10:12 Ribiero '97 Michigan 2:10:59 Brack '02 Vancouver 2:12:26 Fernandez '99 Laguna Seca 2:13:49 Rahal '97 Fontana 2:14:27 Gidley '01 Road America 2:15:14 Fernandez '02 Milwaukee 2:16:26 Zanardi '98 Vancouver 2:17:54 Fittipaldi '97 Laguna Seca 2:18:58 Bell '02 Motegi 2:19:39 JPM '00 Laguna Seca 2:20:32 Pruett '98 Gateway 2:21:07 Franchitti '02 Toronto 2:23:08 JPM '99 Portland 2:24:08 Dixon '01 Lausitz 2:24:44 Vasser '02 Denver 2:25:55 Fernandez '01 Motegi 2:27:19 Tracy '02 Mexico City 2:28:20 Fittipaldi '99 Gateway 2:29:38 Carpentier '02 Mid-Ohio 2:30:13 Andretti '97 Surfers Paradise 2:31:40 Vasser '96 Nazareth 2:32:05 de Ferran '97 Long Beach 2:33:13 Vasser '99 Homestead 2:33:39 Kanaan '02 Cleveland 2:34:30 Fernandez '01 Chicago 2:35:58 Brack '02 Laguna Seca 2:37:21 Fernandez '01 Texas 2:37:49 JPM '99 Denver 2:38:44 de Ferran '97 Rio de Janeiro 2:39:28 Hearn '98 Road America 2:44:50 Brack '02 Vancouver 2:44:02 Andretti '98 Homestead 2:45:00 Tracy '02 Laguna Seca 2:46:16 Gidley '01 Chicago 2:47:12 Fittipaldi '96 Cleveland 2:48:18 JPM '00 Portland 2:49:57 Rahal '98 Motegi 2:50:28 Blundell '00 Long Beach 2:51:39 Franchitti '02 Rockingham 2:52:03 Andretti '99 Mid-Ohio 2:53:25 Tracy '02 Vancouver 2:54:33 Gugelmin '01 Portland 2:55:41 JPM '00 Denver 2:56:44 Franchitti '02 Motegi 2:57:25 Zanardi '98 Toronto 2:59:41 Franchitti '02 Rockingham 3:00:17 JPM '00 Laguna Seca 3:01:35 Tracy '02 Toronto 3:04:18 Brack '01 Michigan 3:04:51 da Matta '02 Monterrey 3:06:04 Jones '99 Rio de Janeiro 3:06:57 JPM '00 Houston 3:07:33 Fernandez '02 Monterrey 3:09:02 Bruno '01 Nazareth 3:10:20 Rahal '96 Toronto 3:11:23 da Matta '02 Road America 3:13:42 Pruett '96 Nazareth 3:14:40 JPM '00 Road America 3:15:23 Fittipaldi '99 Cleveland 3:16:31 Hearn '98 Rio de Janeiro 3:17:58 Andretti '02 Road America 3:18:54 Vasser '96 Milwaukee 3:19:37 Franchitti '02 Portland 3:20:34 Andretti '97 Toronto 3:21:26 Vasser '99 Milwaukee 3:22:06 Papis '00 Vancouver 3:22:48 Bruno '01 Laguna Seca 3:24:10 Rahal '97 Rio de Janeiro 3:25:22 Brack '02 Denver 3:26:15 Gordon '99 Chicago 3:27:03 Fittipaldi '96 Portland 3:27:41 Nakano '01 Motegi 3:28:34 Vasser '02 Denver 3:29:46 Andretti '99 Milwaukee 3:30:27 Pruett '98 Mid-Ohio 3:31:23 Papis '00 Milwaukee 3:31:55 Andretti '99 Denver 3:33:01 Bruno '01 Nazareth 3:33:34 Carpentier '02 Montreal 3:35:11 Andretti '99 Nazareth 3:36:01 JPM '00 Mid-Ohio 3:37:05 Rahal '98 Rio de Janeiro 3:37:48 Kanaan '02 Cleveland 3:39:01 Vasser '00 Motegi 3:40:10 Fittipaldi '02 Montreal 3:41:20 da Matta '00 Michigan 3:41:55 Zanardi '98 Toronto 3:42:57 Papis '00 Milwaukee 3:43:53 Bruno '02 Portland 3:44:48 da Matta '01 Fontana 3:45:42 Zanardi '98 Portland 3:46:42 Andretti '99 Nazareth 3:47:44 Carpentier '02 Mid-Ohio 3:48:55 Andretti '99 Fontana 3:49:42 Vasser '01 Houston 3:50:34 Rahal '98 Fontana 3:51:57 JPM '00 Portland 3:53:15 Andretti '99 Cleveland 3:54:30 Vasser '97 Homestead 3:55:30 Andretti '99 Cleveland 3:56:32 Servia '02 Rockingham 3:56:59 Fernandez '01 Surfers Paradise 3:58:27 Tracy '02 Fontana 3:59:08 JPM '99 Denver 4:00:16 Papis '00 Homestead 4:00:44 Franchitti '02 Laguna Seca 4:02:39 Bruno '01 Milwaukee 4:03:25 Carpentier '02 Vancouver 4:04:34 JPM '99 Milwaukee 4:05:04 Kanaan '02 Cleveland 4:05:55 Jones '99 Gateway 4:06:30 da Matta '02 Laguna Seca 4:07:38 Vasser '99 Motegi 4:08:13 Fontana '00 Long Beach 4:09:49 Bruno '01 Milwaukee 4:10:54 Carpentier '02 Cleveland 4:11:59 Andretti '99 Gateway 4:12:51 Franchitti '02 Road America 4:14:29 Zanardi '98 Toronto 4:15:51 da Matta '02 Fontana 4:16:51 Ribiero '97 Toronto 4:19:16 Nakano '00 Laguna Seca 4:20:12 Gidley '01 Fontana 4:20:42 Kanaan '99 Mid-Ohio 4:21:48 da Matta '01 Fontana 4:22:33 Andretti '02 Monterrey 4:24:22 Pruett '96 Toronto 4:26:40 Vasser '99 Michigan

Hickey

13,306 Aufrufe • vor 11 Monaten

Cursor Complete Guide for AI Coding... 1. The Basics, Composer, Cursor 2.0, Why use Cursor? 2. Multiple Agent Testing, Adding Database, Deploying to Vercel 3. Comparing the big 4: v0, Replit, Lovable, Cursor And more... with Senior Software Engineer Kehan Zhang TIME STAMPS --------------- 1. BASICS: 00:00 Introduction 01:01 Overview of Cursor and Its Features 01:47 Getting Started with Cursor 02:39 Understanding IDE and Vibe Coding 06:00 Cursor For Mobile Apps 10:26 Downloading and Installing Cursor 11:17 Creating and Managing Projects in Cursor 15:14 Building a Simple Game with Cursor 19:10 Advanced Features and Customization 40:28 Fixing Styling Rules 40:53 Redesigning the App 42:17 Exploring Cursor 2.0 Features 43:22 Setting Up the Project Structure 44:17 Adding and Testing Meme Templates 46:08 Debugging Text Issues 2. ADVANCED 49:46 Using Multiple Agents 01:10:40 Creating Custom Commands 01:14:15 Creating Commands in Settings Tab 01:15:11 Introduction to Instant DB 01:16:04 Setting Up Instant DB in Your Project 01:18:24 Building a Full Stack Application 01:19:04 Using the Agent to Plan and Build 01:26:06 Testing and Debugging the Application 01:53:02 Deploying the Application with Vercel 01:55:35 Setting Up the CLI 01:56:15 Understanding Command Line Interfaces (CLI) 01:57:32 Deploying Code to Vercel 01:58:07 Handling Environment Variables 01:58:44 Interacting with the Vercel Deployment 02:00:34 Exploring Cursor's Capabilities 3. COMPARING VIBE CODING TOOLS 02:09:48 Comparing Vibe Coding Tools 02:31:04 Final Thoughts and Recommendations
2:34:13

Cursor Complete Guide for AI Coding... 1. The Basics, Composer, Cursor 2.0, Why use Cursor? 2. Multiple Agent Testing, Adding Database, Deploying to Vercel 3. Comparing the big 4: v0, Replit, Lovable, Cursor And more... with Senior Software Engineer Kehan Zhang TIME STAMPS --------------- 1. BASICS: 00:00 Introduction 01:01 Overview of Cursor and Its Features 01:47 Getting Started with Cursor 02:39 Understanding IDE and Vibe Coding 06:00 Cursor For Mobile Apps 10:26 Downloading and Installing Cursor 11:17 Creating and Managing Projects in Cursor 15:14 Building a Simple Game with Cursor 19:10 Advanced Features and Customization 40:28 Fixing Styling Rules 40:53 Redesigning the App 42:17 Exploring Cursor 2.0 Features 43:22 Setting Up the Project Structure 44:17 Adding and Testing Meme Templates 46:08 Debugging Text Issues 2. ADVANCED 49:46 Using Multiple Agents 01:10:40 Creating Custom Commands 01:14:15 Creating Commands in Settings Tab 01:15:11 Introduction to Instant DB 01:16:04 Setting Up Instant DB in Your Project 01:18:24 Building a Full Stack Application 01:19:04 Using the Agent to Plan and Build 01:26:06 Testing and Debugging the Application 01:53:02 Deploying the Application with Vercel 01:55:35 Setting Up the CLI 01:56:15 Understanding Command Line Interfaces (CLI) 01:57:32 Deploying Code to Vercel 01:58:07 Handling Environment Variables 01:58:44 Interacting with the Vercel Deployment 02:00:34 Exploring Cursor's Capabilities 3. COMPARING VIBE CODING TOOLS 02:09:48 Comparing Vibe Coding Tools 02:31:04 Final Thoughts and Recommendations

Riley Brown

65,375 Aufrufe • vor 8 Monaten

Who touches every token that flows through Paul Jankura? It’s not any one model, but it’s Katelyn Lesse, Angela Jiang and the platform team. A year ago, it was just a messages API. Today, their platform sits under every API call, every workflow, and every agent built on Claude. Sonya Huang 🐥 and I explore why they make everything they can externally facing for customers, how they pick markets to target, what it takes to build the best agents, and more: 00:00 Introduction 01:49 Two North Stars 02:27 External Builders And Primitives 03:54 What To Externalize 06:00 From Messages To Agents 08:19 Managed Agents Adoption 09:07 Three Layer Cake 10:22 Execution Harnesses Explained 11:09 Coordination Strategies Roadmap 12:13 Ecosystem Standards And Safety 15:39 Open Ecosystem Not Walled 17:12 Vertical Products And Form Factors 22:26 Claude Tag Under The Hood 26:04 Harness Best Practices 38:13 Token Costs And Whats Next
48:54

Who touches every token that flows through Paul Jankura? It’s not any one model, but it’s Katelyn Lesse, Angela Jiang and the platform team. A year ago, it was just a messages API. Today, their platform sits under every API call, every workflow, and every agent built on Claude. Sonya Huang 🐥 and I explore why they make everything they can externally facing for customers, how they pick markets to target, what it takes to build the best agents, and more: 00:00 Introduction 01:49 Two North Stars 02:27 External Builders And Primitives 03:54 What To Externalize 06:00 From Messages To Agents 08:19 Managed Agents Adoption 09:07 Three Layer Cake 10:22 Execution Harnesses Explained 11:09 Coordination Strategies Roadmap 12:13 Ecosystem Standards And Safety 15:39 Open Ecosystem Not Walled 17:12 Vertical Products And Form Factors 22:26 Claude Tag Under The Hood 26:04 Harness Best Practices 38:13 Token Costs And Whats Next

Lauren Reeder

54,868 Aufrufe • vor 17 Tagen

AI has a trust problem. Verifiability is the solution. Our GM of AI Nima Vaziri sat down with a16z’s Ali Yahya and Dan Boneh of Stanford University to map the deepest fault lines in AI today. ☁️ Models we can’t trust ☁️ Current providers can censor, shut down, or shift rules overnight. Outsourced training hides backdoors. Even “open” weights don’t prove what’s actually running. Trust. Backdoors. Black boxes. The path forward is clear: 🔥 Verifiable evals 🔥 Verifiable inference 🔥 TEEs for hardware-backed integrity 🔥 Infra beyond single points of control 🔥 Blockchains as coordination layers for AI From “trust us” to “verify yourself.” That’s the shift. That’s the unlock. The frontier is here. The builders decide what comes next. Create and use AI that’s incentive aligned with you. Timestamps: 00:00:00 Introduction: AI & Crypto Intersection Overview 00:01:58 Four Major AI-Crypto Trends 00:02:44 AI Agents Need Financial Infrastructure 00:04:03 Proof of Humanity: Fighting AI-Generated Content 00:04:17 Decentralizing AI Infrastructure Networks 00:04:44 Synthetic Life: Autonomous AI Agents 00:06:20 Verifiable AI 00:10:16 Current Performance Numbers for AI Proofs 00:13:18 The Era of Experience in AI Learning 00:14:56 AI Agents Having Life of its Own 00:18:21 Algorithmic Fairness & Verifiable Models 00:23:18 Privacy in AI: Trusted Execution Environments 00:25:47 Economic Incentive for Open Weight Models 00:31:39 Attribution Problem: Who Gets Paid for AI Training? 00:35:52 Content Provenance & Authentication (C2PA) 00:48:03 AI Security: Finding Exploits & Vulnerabilities 00:54:53 Educational Applications: LLMs as Learning Partner 00:58:29 Reliance on LLMs and Cognitive Abilities 01:03:57 Content Providers’ Fear of LLM Training
1:07:13

AI has a trust problem. Verifiability is the solution. Our GM of AI Nima Vaziri sat down with a16z’s Ali Yahya and Dan Boneh of Stanford University to map the deepest fault lines in AI today. ☁️ Models we can’t trust ☁️ Current providers can censor, shut down, or shift rules overnight. Outsourced training hides backdoors. Even “open” weights don’t prove what’s actually running. Trust. Backdoors. Black boxes. The path forward is clear: 🔥 Verifiable evals 🔥 Verifiable inference 🔥 TEEs for hardware-backed integrity 🔥 Infra beyond single points of control 🔥 Blockchains as coordination layers for AI From “trust us” to “verify yourself.” That’s the shift. That’s the unlock. The frontier is here. The builders decide what comes next. Create and use AI that’s incentive aligned with you. Timestamps: 00:00:00 Introduction: AI & Crypto Intersection Overview 00:01:58 Four Major AI-Crypto Trends 00:02:44 AI Agents Need Financial Infrastructure 00:04:03 Proof of Humanity: Fighting AI-Generated Content 00:04:17 Decentralizing AI Infrastructure Networks 00:04:44 Synthetic Life: Autonomous AI Agents 00:06:20 Verifiable AI 00:10:16 Current Performance Numbers for AI Proofs 00:13:18 The Era of Experience in AI Learning 00:14:56 AI Agents Having Life of its Own 00:18:21 Algorithmic Fairness & Verifiable Models 00:23:18 Privacy in AI: Trusted Execution Environments 00:25:47 Economic Incentive for Open Weight Models 00:31:39 Attribution Problem: Who Gets Paid for AI Training? 00:35:52 Content Provenance & Authentication (C2PA) 00:48:03 AI Security: Finding Exploits & Vulnerabilities 00:54:53 Educational Applications: LLMs as Learning Partner 00:58:29 Reliance on LLMs and Cognitive Abilities 01:03:57 Content Providers’ Fear of LLM Training

EigenCloud

62,099 Aufrufe • vor 10 Monaten

New episode goes deep into the science of peak athletic performance for the pros and recreational athletes with Andy Galpin, PhD. Galpin’s dual expertise as a research scientist investigating nutrition, supplementation, training, and recovery, and as a performance coach working directly with professional athletes and Olympians, informs an integrated understanding of what truly works in practice. In our conversation, we explore how athletes and fitness enthusiasts can effectively balance immediate athletic goals with long-term health, tackling practical questions such as: • Micronutrient optimization (including magnesium, omega-3, and iron) • Fueling strategies around fasted vs. fed training • What truly matters when fueling for performance • Electrolyte management and hydration timing • Recovery techniques addressing inflammation, soreness, and overtraining • Supplement myths and evidence-based recommendations Links to YouTube, Apple Podcasts, and Spotify in the comments. Timestamps: 0:00 - Introduction 2:07 - Eating to perform vs. eating to live longer 5:27 - Training fasted 12:00 - What to eat before morning strength training 13:59 - Nutrient timing 15:50 - Is intermittent fasting killing your gains? 26:31 - Carbs before resistance training 28:14 - Endurance fueling strategies 33:09 - Post-exercise carb intake 34:42 - Game day fueling 37:32 - Carb supplements vs. whole foods 40:25 - Rethinking fat intake 43:21 - Metabolic flexibility 47:46 - The real test of metabolic health 49:02 - Anaerobic vs. aerobic systems 53:03 - Protein timing 55:33 - Whole foods vs. protein powders 1:00:27 - Fat timing 1:01:54 - Seed oils and saturated fat 1:06:49 - Magnesium 1:08:49 - The problem with magnesium blood tests 1:10:07 - Why the magnesium RDA might not be enough 1:11:00 - Magnesium citrate, glycinate, or threonate? 1:13:01 - Magnesium dosing 1:15:40 - Omega-3 supplementation 1:19:16 - Can omega-3s prevent muscle loss? 1:23:20 - "Performance anchors" vs. supplements 1:27:53 - Iron deficiency 1:30:48 - Caffeine before workouts 1:32:35 - Caffeine cycling 1:35:51 - Can music enhance performance? 1:37:03 - Rhodiola rosea 1:41:44 - Nitric oxide boosters (beetroot, citrulline, arginine) 1:52:13 - Beta-alanine 1:58:11 - Creatine 1:59:24 - Sodium bicarbonate 2:01:42 - Pre-workouts 2:04:00 - Does excess caffeine impair performance? 2:04:46 - Antioxidants & high-dose vitamin C 2:12:18 - Anti-inflammatories 2:14:43 - Tart cherry juice 2:18:10 - Glutamine 2:26:17 - Collagen 2:30:32 - Glucosamine chondroitin 2:31:33 - Recovery—signaling vs. inflammation 2:34:31 - The most important recovery metric 2:36:11 - Blood flow for recovery 2:41:01 - Persistent soreness 2:44:08 - Compression boots 2:45:02 - Water immersion 2:48:19 - Sauna vs. extra miles 2:50:18 - Can localized heat preserve muscle? 2:51:37 - Cold water immersion 2:58:22 - Pre-bed cold exposure for sleep 3:01:37 - HRV vs. resting heart rate 3:09:50 - Respiratory rate 3:14:03 - Are you overtrained—or just overreached? 3:18:47 - Hormones and overtraining 3:22:54 - Does training hard increase your sleep need? 3:25:00 - How to know if you're getting enough sleep 3:28:22 - Sleep trackers 3:30:16 - Hydration timing 3:32:00 - Wind-down index 3:33:08 - Bedroom CO₂ buildup 3:36:46 - Nasal allergies 3:38:29 - Sleep hacks
3:48:34

New episode goes deep into the science of peak athletic performance for the pros and recreational athletes with Andy Galpin, PhD. Galpin’s dual expertise as a research scientist investigating nutrition, supplementation, training, and recovery, and as a performance coach working directly with professional athletes and Olympians, informs an integrated understanding of what truly works in practice. In our conversation, we explore how athletes and fitness enthusiasts can effectively balance immediate athletic goals with long-term health, tackling practical questions such as: • Micronutrient optimization (including magnesium, omega-3, and iron) • Fueling strategies around fasted vs. fed training • What truly matters when fueling for performance • Electrolyte management and hydration timing • Recovery techniques addressing inflammation, soreness, and overtraining • Supplement myths and evidence-based recommendations Links to YouTube, Apple Podcasts, and Spotify in the comments. Timestamps: 0:00 - Introduction 2:07 - Eating to perform vs. eating to live longer 5:27 - Training fasted 12:00 - What to eat before morning strength training 13:59 - Nutrient timing 15:50 - Is intermittent fasting killing your gains? 26:31 - Carbs before resistance training 28:14 - Endurance fueling strategies 33:09 - Post-exercise carb intake 34:42 - Game day fueling 37:32 - Carb supplements vs. whole foods 40:25 - Rethinking fat intake 43:21 - Metabolic flexibility 47:46 - The real test of metabolic health 49:02 - Anaerobic vs. aerobic systems 53:03 - Protein timing 55:33 - Whole foods vs. protein powders 1:00:27 - Fat timing 1:01:54 - Seed oils and saturated fat 1:06:49 - Magnesium 1:08:49 - The problem with magnesium blood tests 1:10:07 - Why the magnesium RDA might not be enough 1:11:00 - Magnesium citrate, glycinate, or threonate? 1:13:01 - Magnesium dosing 1:15:40 - Omega-3 supplementation 1:19:16 - Can omega-3s prevent muscle loss? 1:23:20 - "Performance anchors" vs. supplements 1:27:53 - Iron deficiency 1:30:48 - Caffeine before workouts 1:32:35 - Caffeine cycling 1:35:51 - Can music enhance performance? 1:37:03 - Rhodiola rosea 1:41:44 - Nitric oxide boosters (beetroot, citrulline, arginine) 1:52:13 - Beta-alanine 1:58:11 - Creatine 1:59:24 - Sodium bicarbonate 2:01:42 - Pre-workouts 2:04:00 - Does excess caffeine impair performance? 2:04:46 - Antioxidants & high-dose vitamin C 2:12:18 - Anti-inflammatories 2:14:43 - Tart cherry juice 2:18:10 - Glutamine 2:26:17 - Collagen 2:30:32 - Glucosamine chondroitin 2:31:33 - Recovery—signaling vs. inflammation 2:34:31 - The most important recovery metric 2:36:11 - Blood flow for recovery 2:41:01 - Persistent soreness 2:44:08 - Compression boots 2:45:02 - Water immersion 2:48:19 - Sauna vs. extra miles 2:50:18 - Can localized heat preserve muscle? 2:51:37 - Cold water immersion 2:58:22 - Pre-bed cold exposure for sleep 3:01:37 - HRV vs. resting heart rate 3:09:50 - Respiratory rate 3:14:03 - Are you overtrained—or just overreached? 3:18:47 - Hormones and overtraining 3:22:54 - Does training hard increase your sleep need? 3:25:00 - How to know if you're getting enough sleep 3:28:22 - Sleep trackers 3:30:16 - Hydration timing 3:32:00 - Wind-down index 3:33:08 - Bedroom CO₂ buildup 3:36:46 - Nasal allergies 3:38:29 - Sleep hacks

Dr. Rhonda Patrick

216,737 Aufrufe • vor 1 Jahr

My dear friend, Vlad Tenev, changed the landscape of investing forever! The rise of the retail investor is largely due to Robinhood's success... and in this new Journey Man, we discuss it all... Enjoy! 00:00 - Intro 00:53 - Introducing Vlad Tenev of Robinhood 01:27 - Why Take on Wall Street? 01:54 - Robinhood’s Zero-Fee Origin Story 02:53 - Inspiration from Instagram and Uber 04:24 - Reimagining Trading for Mobile 05:05 - The Challenge of Disrupting Finance 05:42 - Why Everything Is Hard 06:34 - Early Wrong Assumptions 07:42 - Raising Capital with a Small Vision 08:48 - Funding Robinhood on AngelList 09:50 - Early Investors Changed Their Lives 10:38 - The Crypto Explosion Begins 11:07 - Considering a Bitcoin Exchange First 12:17 - Bitcoin’s Early Skepticism and Growth 13:08 - Robinhood Launches Crypto in 2018 14:03 - 2020: Crypto Revenue Surges Overnight 15:04 - The Challenge of Crypto Cyclicality 16:11 - Staffing a Volatile Business 17:10 - Building Robinhood’s Lean Crypto Team 18:46 - Robinhood’s First Crypto Event Coming 19:38 - Where TradFi Meets DeFi 20:34 - Tokenizing Everything 21:09 - Robinhood’s Vision for Crypto + Finance 21:47 - Thoughts on Crypto Options Demand 23:04 - Why Crypto Options Haven’t Taken Off 24:09 - Millennials and the Speculative Economy 25:22 - Democratizing Trading for Everyone 26:08 - Why Buy-and-Hold Doesn’t Work for All 27:15 - Trading vs Investing: A Matter of Wealth 28:01 - Trading as a Skill Anyone Can Build 29:13 - Robinhood’s Role in Onboarding Millions 30:06 - The Fed's Role and Retail Insight 31:03 - The Rise of the Retail Macro Trader 32:17 - Helping Users Succeed with Robinhood Strategies 33:35 - Power of Community and the Hive Mind 34:55 - Will AI Disrupt Community Too? 36:14 - Technological Waves and Investor Opportunity 37:10 - Human Purpose in an AI World 37:52 - Tokenizing Human Connection 38:28 - Creators, Platforms, and Future-Proofing 39:26 - Vlad’s Long-Term View of the Future 40:05 - Financial Services at the Heart of Disruption 41:14 - If AI Replaces Jobs, What Happens to Investing? 42:25 - Entering the Economic Singularity 43:31 - What Happens When AIs Win the Markets? 44:16 - AI's Role in Capital and Markets 45:07 - Will AI Eliminate Human Emotion from Markets? 46:06 - HFT: The Original AI Traders 47:20 - AI and Long-Term Probabilistic Forecasting 48:48 - GPUs, Gaming, and the Origins of AI 50:01 - Nvidia, CUDA, and Wall Street Arms Races 51:04 - Flash Boys and Microwave Trading 51:54 - Will AI Costs Go to Zero? 52:52 - Lower Cost, Higher Usage 53:41 - Robinhood’s UX Won’t Be Just a Chatbox 55:16 - Cortex: AI-Powered Features at Robinhood 56:54 - Tokenization and the Future of Asset Management 57:44 - Crowdsourced, Tokenized Hedge Funds 58:48 - Portability of Tokenized Assets 59:39 - Blockchain as the New Rails of Finance 01:00:09 - The Trump Token and Capital Formation 01:01:00 - Capital Access Unlocks Innovation 01:01:49 - Why Crypto Needs Regulatory Clarity 01:03:17 - From Meme Coins to Real Assets 01:04:17 - Crypto's Path to $100 Trillion? 01:05:15 - The Financial System Will Run on Blockchains 01:06:00 - Platform Layer vs Application Layer Wealth 01:06:29 - AI Raises Money and Launches Tokens 01:07:39 - AIs Creating Software and Capital Formation 01:08:00 - Final Thoughts: A Wild Future Ahead 01:08:20 - When Will Vlad Buy a CryptoPunk? 01:08:51 - Wrapping Up: AI, Crypto, and the Road Ahead
1:09:53

My dear friend, Vlad Tenev, changed the landscape of investing forever! The rise of the retail investor is largely due to Robinhood's success... and in this new Journey Man, we discuss it all... Enjoy! 00:00 - Intro 00:53 - Introducing Vlad Tenev of Robinhood 01:27 - Why Take on Wall Street? 01:54 - Robinhood’s Zero-Fee Origin Story 02:53 - Inspiration from Instagram and Uber 04:24 - Reimagining Trading for Mobile 05:05 - The Challenge of Disrupting Finance 05:42 - Why Everything Is Hard 06:34 - Early Wrong Assumptions 07:42 - Raising Capital with a Small Vision 08:48 - Funding Robinhood on AngelList 09:50 - Early Investors Changed Their Lives 10:38 - The Crypto Explosion Begins 11:07 - Considering a Bitcoin Exchange First 12:17 - Bitcoin’s Early Skepticism and Growth 13:08 - Robinhood Launches Crypto in 2018 14:03 - 2020: Crypto Revenue Surges Overnight 15:04 - The Challenge of Crypto Cyclicality 16:11 - Staffing a Volatile Business 17:10 - Building Robinhood’s Lean Crypto Team 18:46 - Robinhood’s First Crypto Event Coming 19:38 - Where TradFi Meets DeFi 20:34 - Tokenizing Everything 21:09 - Robinhood’s Vision for Crypto + Finance 21:47 - Thoughts on Crypto Options Demand 23:04 - Why Crypto Options Haven’t Taken Off 24:09 - Millennials and the Speculative Economy 25:22 - Democratizing Trading for Everyone 26:08 - Why Buy-and-Hold Doesn’t Work for All 27:15 - Trading vs Investing: A Matter of Wealth 28:01 - Trading as a Skill Anyone Can Build 29:13 - Robinhood’s Role in Onboarding Millions 30:06 - The Fed's Role and Retail Insight 31:03 - The Rise of the Retail Macro Trader 32:17 - Helping Users Succeed with Robinhood Strategies 33:35 - Power of Community and the Hive Mind 34:55 - Will AI Disrupt Community Too? 36:14 - Technological Waves and Investor Opportunity 37:10 - Human Purpose in an AI World 37:52 - Tokenizing Human Connection 38:28 - Creators, Platforms, and Future-Proofing 39:26 - Vlad’s Long-Term View of the Future 40:05 - Financial Services at the Heart of Disruption 41:14 - If AI Replaces Jobs, What Happens to Investing? 42:25 - Entering the Economic Singularity 43:31 - What Happens When AIs Win the Markets? 44:16 - AI's Role in Capital and Markets 45:07 - Will AI Eliminate Human Emotion from Markets? 46:06 - HFT: The Original AI Traders 47:20 - AI and Long-Term Probabilistic Forecasting 48:48 - GPUs, Gaming, and the Origins of AI 50:01 - Nvidia, CUDA, and Wall Street Arms Races 51:04 - Flash Boys and Microwave Trading 51:54 - Will AI Costs Go to Zero? 52:52 - Lower Cost, Higher Usage 53:41 - Robinhood’s UX Won’t Be Just a Chatbox 55:16 - Cortex: AI-Powered Features at Robinhood 56:54 - Tokenization and the Future of Asset Management 57:44 - Crowdsourced, Tokenized Hedge Funds 58:48 - Portability of Tokenized Assets 59:39 - Blockchain as the New Rails of Finance 01:00:09 - The Trump Token and Capital Formation 01:01:00 - Capital Access Unlocks Innovation 01:01:49 - Why Crypto Needs Regulatory Clarity 01:03:17 - From Meme Coins to Real Assets 01:04:17 - Crypto's Path to $100 Trillion? 01:05:15 - The Financial System Will Run on Blockchains 01:06:00 - Platform Layer vs Application Layer Wealth 01:06:29 - AI Raises Money and Launches Tokens 01:07:39 - AIs Creating Software and Capital Formation 01:08:00 - Final Thoughts: A Wild Future Ahead 01:08:20 - When Will Vlad Buy a CryptoPunk? 01:08:51 - Wrapping Up: AI, Crypto, and the Road Ahead

Raoul Pal

172,640 Aufrufe • vor 1 Jahr

Episode w Dylan Patel and Asianometry out! A bonanza on how the semiconductor industry actually works. & what Xi could do if he becomes scaling pilled, how we can train models with 10,000x GPT-4's compute by end of decade, & much more. This one is very fun. Enjoy! Links below. 0:00:00 – Xi’s path to AGI 0:05:05 – Liang Mong Song 0:09:10 – How semiconductors get better 0:12:01 – China can centralize compute 0:19:35 – Export controls & sanctions 0:33:45 – Huawei’s intense culture 0:39:45 – Why the semiconductor industry is so stratified 0:41:50 – N2 should not exist 0:46:47 – Taiwan invasion hypothetical 0:50:15 – Mind-boggling complexity of semiconductors 1:00:06 – Chip architecture design 1:05:29 – Architectures lead to different AI models? China vs. US 1:11:06 – Being head of compute at an AI lab 1:17:17 – Scaling costs and power demand 1:37:59 – Are we financing an AI bubble?
2:11:02

Episode w Dylan Patel and Asianometry out! A bonanza on how the semiconductor industry actually works. & what Xi could do if he becomes scaling pilled, how we can train models with 10,000x GPT-4's compute by end of decade, & much more. This one is very fun. Enjoy! Links below. 0:00:00 – Xi’s path to AGI 0:05:05 – Liang Mong Song 0:09:10 – How semiconductors get better 0:12:01 – China can centralize compute 0:19:35 – Export controls & sanctions 0:33:45 – Huawei’s intense culture 0:39:45 – Why the semiconductor industry is so stratified 0:41:50 – N2 should not exist 0:46:47 – Taiwan invasion hypothetical 0:50:15 – Mind-boggling complexity of semiconductors 1:00:06 – Chip architecture design 1:05:29 – Architectures lead to different AI models? China vs. US 1:11:06 – Being head of compute at an AI lab 1:17:17 – Scaling costs and power demand 1:37:59 – Are we financing an AI bubble?

Dwarkesh Patel

261,054 Aufrufe • vor 1 Jahr