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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... lead to surprisingly similar performance. 00:00 – OpenAI OSS Launch 01:00 – Comparing Open Source LLM Architectures 01:46 – GPT OSS Overview 02:37 – Under The Hood of GPT OSS 03:25 – Qwen-3 Architecture 04:17 – Qwen-3 Training 05:12 – Qwen-3 Post-Training 06:08 – Qwen-3 Reasoning & RL Innovations 06:52 – DeepSeek V3 Overview 07:40 – DeepSeek V3.1 Updates 08:39 – Attention Mechanism (MLA) 09:39 – Comparing Model Sizes 10:35 – Long Context Strategies 11:25 – Reflections on Methods 12:00 – Takeawaysshow more

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208,828 次观看 • 1 年前 •via X (Twitter)

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*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

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

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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

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

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168,007 次观看 • 9 个月前

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

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

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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

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

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101,314 次观看 • 3 个月前

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:

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 次观看 • 4 个月前

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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

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51,484 次观看 • 9 个月前

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

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1,154,836 次观看 • 1 年前

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?

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 次观看 • 9 个月前

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

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 次观看 • 4 个月前

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

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 次观看 • 9 个月前

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

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

45,470 次观看 • 9 个月前

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

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 次观看 • 1 年前

When should you start post-training your own models? Fireworks CEO Lin Qiao’s answer: after product-market fit. Not because it's hard... but because only after PMF is the data coming off your product surface worth training on. Lin joined us for our Sequoia Capital "Own Your Intelligence" event to host a workshop on all things post-training; what works, what breaks, and how not to let the model outsmart you. Must listen!! 00:00 Introduction 00:37 What Fireworks sees across thousands of AI applications 02:47 Off-the-shelf APIs and the problem of keeping your taste 03:58 What "owning your intelligence" actually means 05:43 The progression: prompting → RAG → SFT → preferences → RL 07:20 Why this mirrors how humans learn 09:03 Matching the technique to the problem you actually have 10:46 Where teams get stuck: data quality and vibe evals 12:28 Reward hacking: the model that wrote zero lines of code 13:59 Training-to-serving alignment (and why quality drops) 15:55 Post-training in healthcare and security 17:31 From coding to every co-work domain 19:35 Incumbents, cost burden, and not scaling into bankruptcy 21:26 How much control do you want? 23:24 Q&A: What makes a good reward signal 25:00 Q&A: When to start thinking about post-training

When should you start post-training your own models? Fireworks CEO Lin Qiao’s answer: after product-market fit. Not because it's hard... but because only after PMF is the data coming off your product surface worth training on. Lin joined us for our Sequoia Capital "Own Your Intelligence" event to host a workshop on all things post-training; what works, what breaks, and how not to let the model outsmart you. Must listen!! 00:00 Introduction 00:37 What Fireworks sees across thousands of AI applications 02:47 Off-the-shelf APIs and the problem of keeping your taste 03:58 What "owning your intelligence" actually means 05:43 The progression: prompting → RAG → SFT → preferences → RL 07:20 Why this mirrors how humans learn 09:03 Matching the technique to the problem you actually have 10:46 Where teams get stuck: data quality and vibe evals 12:28 Reward hacking: the model that wrote zero lines of code 13:59 Training-to-serving alignment (and why quality drops) 15:55 Post-training in healthcare and security 17:31 From coding to every co-work domain 19:35 Incumbents, cost burden, and not scaling into bankruptcy 21:26 How much control do you want? 23:24 Q&A: What makes a good reward signal 25:00 Q&A: When to start thinking about post-training

Sonya Huang 🐥

28,142 次观看 • 1 个月前

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

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

65,392 次观看 • 10 个月前

A model is only as good as its data, and we’ve long since exhausted the internet. From here on out, model progress is gated by data production. ’s Brendan (can/do) joined us at our Sovereign AI event to talk about how RL environments get built, and why your data might be your real moat: 00:00 Introduction 00:47 A short history of the data market: crowdsourcing to agentic data 02:29 What an RL environment is: worlds, apps, tasks 03:57 Why only humans can measure the frontier 05:35 Building verifiers is the hard part 06:44 Walkthrough: a real legal RL environment 08:18 Leaderboards — and what open weights change 09:45 Post-training results on Apex Agents 11:17 Three ways companies buy data 12:49 Q&A: How do you price data? 14:17 Q&A: What "data quality" actually means 16:42 Q&A: The misunderstanding about synthetic data 18:17 Q&A: Why RL environments now — and what comes after 21:20 Q&A: Can you scale rubric generation with models? 23:00 Q&A: RL environments for cyber defense 25:33 Q&A: Build data in-house or partner?

A model is only as good as its data, and we’ve long since exhausted the internet. From here on out, model progress is gated by data production. ’s Brendan (can/do) joined us at our Sovereign AI event to talk about how RL environments get built, and why your data might be your real moat: 00:00 Introduction 00:47 A short history of the data market: crowdsourcing to agentic data 02:29 What an RL environment is: worlds, apps, tasks 03:57 Why only humans can measure the frontier 05:35 Building verifiers is the hard part 06:44 Walkthrough: a real legal RL environment 08:18 Leaderboards — and what open weights change 09:45 Post-training results on Apex Agents 11:17 Three ways companies buy data 12:49 Q&A: How do you price data? 14:17 Q&A: What "data quality" actually means 16:42 Q&A: The misunderstanding about synthetic data 18:17 Q&A: Why RL environments now — and what comes after 21:20 Q&A: Can you scale rubric generation with models? 23:00 Q&A: RL environments for cyber defense 25:33 Q&A: Build data in-house or partner?

Pat Grady

66,577 次观看 • 1 个月前

GPT-6 Astra is by far the best inside Codex. & this is THE Complete Beginners Guide to Codex. The 28 things you need to know about Codex and GPT-6 to start using it for knowledge work and coding work. My goal with this video: give you one guide you can send to anyone on your team so they can get comfortable with Codex, understand what’s possible, and start finding ways to use AI in your business. 00:00 Intro 01:15 The Codex Platform 02:02 Downloading Codex (ChatGPT Desktop) 03:25 1. Codex Is the App, GPT-6 Is the Model 06:10 2. Build Any App 10:32 3. Host Sites with GPT Sites 18:12 4A. Building Desktop App 21:33 4B. Building an iOS app 24:09 5. Built-In Web Browser 32:27 6. Create Any Document 35:00 7. Annotation Mode 38:41 8. Production-Ready Apps 44:29 9. Chats & Projects 48:54 10. Multitasking 50:09 10B Side Chat 51:13 11. Sections 54:27 12. Connect Every App (Plugins) 01:03:17 13. Ask Codex What to Work On 01:05:39 14. Blender & 3D Assets 01:08:25 15. Video Games 01:09:24 16. Interactive Learning Websites 01:13:12 17. Scheduled Tasks 01:16:19 18. Email & iMessage 01:18:56 19. Skills 01:22:54 20. Record and Replay 01:26:45 21. External APIs in Skills 01:30:00 22. Video Understanding Skill 01:35:51 23. Inline Charts 01:38:03 24. Control Codex from Your Phone 01:42:49 25. Generate & Edit Images 01:45:17 26. Sub-Agents 01:48:09 27. Computer Use 01:49:35 28. Whatever You Want

GPT-6 Astra is by far the best inside Codex. & this is THE Complete Beginners Guide to Codex. The 28 things you need to know about Codex and GPT-6 to start using it for knowledge work and coding work. My goal with this video: give you one guide you can send to anyone on your team so they can get comfortable with Codex, understand what’s possible, and start finding ways to use AI in your business. 00:00 Intro 01:15 The Codex Platform 02:02 Downloading Codex (ChatGPT Desktop) 03:25 1. Codex Is the App, GPT-6 Is the Model 06:10 2. Build Any App 10:32 3. Host Sites with GPT Sites 18:12 4A. Building Desktop App 21:33 4B. Building an iOS app 24:09 5. Built-In Web Browser 32:27 6. Create Any Document 35:00 7. Annotation Mode 38:41 8. Production-Ready Apps 44:29 9. Chats & Projects 48:54 10. Multitasking 50:09 10B Side Chat 51:13 11. Sections 54:27 12. Connect Every App (Plugins) 01:03:17 13. Ask Codex What to Work On 01:05:39 14. Blender & 3D Assets 01:08:25 15. Video Games 01:09:24 16. Interactive Learning Websites 01:13:12 17. Scheduled Tasks 01:16:19 18. Email & iMessage 01:18:56 19. Skills 01:22:54 20. Record and Replay 01:26:45 21. External APIs in Skills 01:30:00 22. Video Understanding Skill 01:35:51 23. Inline Charts 01:38:03 24. Control Codex from Your Phone 01:42:49 25. Generate & Edit Images 01:45:17 26. Sub-Agents 01:48:09 27. Computer Use 01:49:35 28. Whatever You Want

Riley Brown

104,491 次观看 • 2 天前

qwen 3.8 max vs deepseek v4 flash 0731 vs kimi k3 vs gpt 5.6 sol – on rubik's cube and chess four frontier models built a rubik's cube stand and solved it, then built a chess board and played claude opus 5 on it the setup: Nous Research's hermes agent cli on OpenRouter tasks: 1. cube – build a 3d rubik's cube with a cli and a Three.js viewer, then solve an identical scrambled position on your own stand 2. chess – build a 3d chess stand, then play white against claude opus 5 as black, live, one move at a time. no engine, no solver, no opening book on either side. stockfish depth 14 grades every chess ply afterwards; neither player sees the score models: DeepSeek v4 flash 0731, OpenAI gpt-5.6 sol, Kimi.ai kimi k3, Qwen qwen 3.8 max gpt-5.6 sol and deepseek v4 flash solved their cubes – sol in 24 moves and seventeen seconds, deepseek in 32. qwen and kimi never got there, giving up at 96 and 207 moves then all four built chess stands and played white against claude opus 5 on them, and all four resigned: deepseek on move 13, sol on 19, kimi on 21, qwen holding out longest at 29 - build time, both stands #1 gpt-5.6 sol – 16m 43s #2 deepseek v4 flash – 97m 39s #3 kimi k3 – 166m 09s #4 qwen 3.8 max – 215m 08s - build attempts before a working stand #1 gpt-5.6 sol – 3 #2 qwen 3.8 max – 4 #3 kimi k3 – 4 #4 deepseek v4 flash – 5 - total tokens #1 gpt-5.6 sol – 6,713,754 #2 qwen 3.8 max – 17,272,507 #3 kimi k3 – 22,427,504 #4 deepseek v4 flash – 27,417,442 - total price #1 deepseek v4 flash – $0.557 #2 gpt-5.6 sol – $6.319 #3 qwen 3.8 max – $10.270 #4 kimi k3 – $16.667 observations: • deepseek v4 flash is the cheapest model here by a margin nobody else is near, and it got there while being the least efficient of the four. it burned 27.4m tokens – more than anyone, 5m more than kimi – and still finished both benchmarks for $0.557. that is $0.02 per million tokens against kimi's $0.74. it also needed the most passes to produce working stands, five, and that did not matter: all five deepseek passes together cost a thirtieth of kimi's two • so what deepseek cannot do is get it right the first time. what it can do is get it right the fifth time, for half a dollar. that is a different thing to be buying – not a good first draft, but the option to keep asking • gpt-5.6 sol is the opposite profile and the strongest of the four on pure efficiency. 16m 43s to build both stands, 6.7m tokens, three passes – under 40% of the next lowest token count and a quarter of deepseek's, on an eighth of qwen's clock. it also solved the cube fastest of anyone, 24 moves in seventeen seconds. sol is what you reach for when you want the answer now and can absorb $0.94 per million • sol's weakness is in what it does not check. its chess viewer deleted the capturing piece instead of the captured one, so pieces disappeared off the board mid-game – a defect the fifty-cent deepseek stand did not have. fast and terse turns out to be the same dial as fast and unverified • qwen 3.8 max is not the cheap open-weights option it gets treated as. $10.270 across the two benchmarks, second most expensive of the four, 18x deepseek, and by a distance the slowest – 215 minutes of build time, nearly thirteen times sol's. what the money buys is judgment: it played eighteen moves without a single error worth a hundredth of a pawn, then made exactly one bad move in the whole game, and averaged 44.6 centipawns lost across the longest game any of the four managed. it also could not solve a rubik's cube in 96 tries • kimi k3 is the one line with no reading that flatters it. most expensive at $16.667, last on the cube at 207 moves, last at chess at 478 centipawns lost per move. it is also the model that verified hardest – on the cube it wrote its own integrity check instead of trusting its output. that makes the result worse rather than better: the checking was real, and the reasoning underneath it still was not follow thehype. for 24/7 ai news, analysis and breakdowns

thehype.

84,777 次观看 • 1 个月前

Want world class research capabilities, but don’t have the resources of a big lab? At our recent Sovereign AI event, Gabe Pereyra shared Harvey ’s “moneyball” approach. Here’s the playbook: 00:00 Introduction 00:37 Building a research lab on a budget 02:28 Legal Agent Bench, contracting, and the diligence dataset 03:57 Domain experts guiding synthetic data generation 05:23 Why Harvey open sourced its datasets 06:55 Working with the neo labs – and why more than one 08:20 Post-training in-house: building "Associate 1" 09:44 The model serving matrix: 60 countries, fallbacks, SLAs 11:05 Deciding what stays in production 12:29 Simple open source switches and model routing 13:55 Moneyball: "If we win on this budget, we change the game" 14:53 Q&A: Training with sensitive data 17:16 Q&A: Competing for research talent 18:46 Q&A: Designing rubrics that actually challenge frontier models 20:19 Q&A: Where the pipeline breaks — data, research, or infra 22:59 Q&A: The tension in open sourcing a benchmark 25:02 Q&A: Biggest remaining open problems 27:10 Q&A: Competing with horizontal products

Want world class research capabilities, but don’t have the resources of a big lab? At our recent Sovereign AI event, Gabe Pereyra shared Harvey ’s “moneyball” approach. Here’s the playbook: 00:00 Introduction 00:37 Building a research lab on a budget 02:28 Legal Agent Bench, contracting, and the diligence dataset 03:57 Domain experts guiding synthetic data generation 05:23 Why Harvey open sourced its datasets 06:55 Working with the neo labs – and why more than one 08:20 Post-training in-house: building "Associate 1" 09:44 The model serving matrix: 60 countries, fallbacks, SLAs 11:05 Deciding what stays in production 12:29 Simple open source switches and model routing 13:55 Moneyball: "If we win on this budget, we change the game" 14:53 Q&A: Training with sensitive data 17:16 Q&A: Competing for research talent 18:46 Q&A: Designing rubrics that actually challenge frontier models 20:19 Q&A: Where the pipeline breaks — data, research, or infra 22:59 Q&A: The tension in open sourcing a benchmark 25:02 Q&A: Biggest remaining open problems 27:10 Q&A: Competing with horizontal products

Pat Grady

179,052 次观看 • 1 个月前