himanshu
@himanshustwts • 29,338 subscribers
simulating @PhyseraAI • DMs open!
Videos
Today, I am excited to announce Animation Bench. Our first benchmark to evaluate frontier models on web animation reconstruction. Frontend generation is a common commercial use case for coding agents. In addition to static layout it contains transitions and interactions triggered by dynamic behaviours. We evaluated four frontier models on 48 tasks from real websites across three axes: 1. Visual Similarity 2. Motion Consistency 3. Layout Correctness. We find that motion consistency is often incomplete or missing.
himanshu26,326 views • 27 days ago
A Quest to Formalize Intelligence ft. ludwig 0:00:00 - INTRO 0:01:06 - What is he most excited about these days 0:03:31 - All arcs are isomorphic, Dropping out in 7th grade 0:09:31 - Does skipping formal education make you naturally broader? 0:10:11 - Growing up on 4chan, Mathematics is humbling 0:14:52 - Mentors, First programming job, Internet microcultures 0:21:47 - Culture in SF, Why he won't start a company 0:25:21 - Does AI make self-teaching easier or trap you in infinite explanations? 0:29:49 - The 20% doing 80% inside a company 0:33:24 - Company culture and Building subcultures 0:35:56 - Define understanding, Compression equals Prediction 0:45:44 - Understanding vs knowing, Michael Levin, Cognitive light cones 0:54:39 - Starting a Research lab, Sheaf theory and Grothendieck 0:58:43 - Mapping Levin's biology onto proof space with MCTS and Lean4 1:02:49 - Active inference, Distributed systems and Morphogenesis 1:12:28 - Signal vs noise on X, The unit distance proof, Flanderization 1:17:39 - Community Questions 1:21:39 - Shape rotator vs wordcel, Tenstorrent, How to hire great engineers 1:28:01 - Building mental space to do things that will be left to do 1:33:42 - Advice to a 20-year-old. Be a capable thinker, you have 20k days left
himanshu43,187 views • 5 months ago
Soham Parekh Unplugged! full podcast soon. Soham Parekh @groundzero_ai
himanshu75,863 views • 1 year ago
The Never Ending Lore of Harness w/Viv⚡️ Viv brought some amazing perspective around harness design, evals, file systems, RL Envs and so much. 0:00:00 - INTRO 0:03:30 - PhD at Temple University 0:12:42 - Lockheed Martin and MeekMill lore 0:16:18 - Building at LangChain, DeepAgents, R&D in Open Source 0:25:09 - Secret behind Harness Design, Filesystem as most Foundational Harness Primitive 0:42:25 - Trajectories for Continual Learning, Where and Where "NOT to" RL, Skills and Context Rot 0:56:30 - Harness Engineering: OpenClaw and Hermes, Why you might don't need a Claw, Custom Harnesses, Quick Fire (Nick Khami lore) 01:12:15 - Meta-Harness, Self-Improvement Loop, Simulation-as-a-Service, RL Envs (Computer-Use, Long-horizon) 01:28:30 - Advice to anyone 20yo / starting out college
himanshu31,636 views • 6 months ago
The Arcee AI Podcast is here! In this episode, Lucas Atkins and stochasm join us to discuss the story of Trinity models and everything frontier. I can say, this talk has been one of the most amazing and technical conversations we've had on Ground Zero. 0:00:00 - Intro 0:00:59 - Varun's transition from SWE to Pre-Training Lead 0:04:20 - Trinity Manifesto, Openclaw Ecosystem 0:12:15 - Arcee's Post-Training to Pre-Training Pivot 0:23:45 - Varun's first Pre-Training run (you can just do things!) 0:27:33 - Saturation in Pre-Training?, Mid-Training 0:37:00 - Tweaking the Training Architecture, Adam vs Muon, Evals 01:09:07 - Inference Engineering, Quick Fire, Post-Training Recipe 01:18:02 - Alpha in RL Envs, Harness Design 01:23:00 - American Open Source is trailing Chinese Competitors, Trinity Adoption 01:29:25 - Hiring at Arcee, Advice to 20yo
himanshu32,531 views • 6 months ago
Recently, PostTrainBench showed how well AI agents can post-train models. Meta Harness showed that the harness itself can improve. What happens if a harness is improvising itself and the improvised harness is post training language models, all in a loop? Viv on this:
himanshu29,325 views • 6 months ago
![The vik episode on Ground Zero! [youtube link in replies] 0:00:00 - Vik's story and Moondream 0:18:50 - The core thesis of small and scale 0:28:35 - The data problem 0:33:35 - Deciding the training architecture 0:43:20 - Post-Training and RL perf 0:46:06 - Post-Training recipes of Moondream 3 0:47:40 - Open Source and VLM development priorities 0:52:05 - AI War : America and China 0:55:07 - Moondream acquisition and Future 1:04:08 - Community Questions 1:16:40 - Trivia, Lores and Opinions on state of AI 1:28:07 - Advice to 20yo](https://image.24vids.com/tw-2000811146081919427/amplify_video_thumb/2000807924021780485/img/NboD8RDqEj-M079c.jpg)
The vik episode on Ground Zero! [youtube link in replies] 0:00:00 - Vik's story and Moondream 0:18:50 - The core thesis of small and scale 0:28:35 - The data problem 0:33:35 - Deciding the training architecture 0:43:20 - Post-Training and RL perf 0:46:06 - Post-Training recipes of Moondream 3 0:47:40 - Open Source and VLM development priorities 0:52:05 - AI War : America and China 0:55:07 - Moondream acquisition and Future 1:04:08 - Community Questions 1:16:40 - Trivia, Lores and Opinions on state of AI 1:28:07 - Advice to 20yo
himanshu43,246 views • 10 months ago
The Delta of Intelligence is Human Data! Curtis G. Northcutt is Director of AI Research at Handshake AI. In this episode, Curtis shares his unique perspectives on human data, RL envs, four quadrants of the AI market, journey of Handshake and much more. 0:00:00 - INTRO 0:02:08 - The Snowman Effect: From Rural Kentucky to 7 Years at MIT 0:09:50 - Confident Learning, Two Systems of Intelligence, Classical ML Has to Prevail 0:20:37 - Origin of CleanLab and Handshake acquisition 0:28:00 - How does GPT-5 become GPT-6? 0:31:20 - The Fastest growing Data Lab: What makes Handshake different? 0:35:32 - Difference between Good Data and Bad Data 0:38:17 - New kind of Data, IA and AI 0:42:04 - Scaling Coding Benchmarks and Efficiency in Long Horizon Tasks 0:49:12 - On Pre-Training, Misconception around Human Data 0:57:13 - Open Question: Taste and Personality, Quick Fire 01:05:15 - Advice to 20yo : Skill and Obsession
himanshu22,264 views • 5 months ago
The tensorqt in SF episode is here. This is special. Just being terminally online on this app - post ideas, meet mutuals, raise funds, and build a frontier company. This has been Francesco's journey building infra for autonomous research at Paradigma. 0:00:00 - INTRO 0:01:03 - Dropping out of PhD, Saturation in Academia, Building from EU 0:13:15 - Autoresearch, Idea behind Flywheel, ex-OpenAI Co-founder 0:19:18 - Evals for Autoreserch and Research Taste 0:25:00 - How a Research Lab uses Flywheel? 0:31:15 - Thesis and Autoresearch Adoption in Top 5 Labs 0:41:00 - Working around with Flywheel, Combinatorial Explosion 1:02:03 - Science as Public Graph, Deriving New Research Loop, Quick Fire 1:06:23 - Toughest Autoresearch Challenge as of Today, Future Directions 1:11:53 - Automating Science: Sakana and Deepmind, Trust Problem? 1:21:07 - Business Model, Automating R&D, Model Capabilities 1:27:20 - Dropout of college to live on frontier!
himanshu14,913 views • 6 months ago
How does GPT-5 become GPT-6? Everything on the internet was already trained on a year, year and a half ago. New data is being added but it's marginal and a lot of it is AI slop. There is interesting private data (like JP Morgan's data) but nobody is opening that up. You don't go from GPT-5 to GPT-6 in isolation because that's not how learning works. Every learning that has ever occurred comes from having information to learn. You can't learn no information. cc Ground Zero / pod with Curtis from Handshake AI.
himanshu10,684 views • 5 months ago
okay so this one of many vibetest comparisons between glm-5 and opus-4.6 (below) on design + visualization. prompt: a window in a paris apartment. you sit and watch the hours pass - dawn, morning rain, golden afternoon, violet dusk, city lights at night. click anywhere to move time forward. each hour sounds different. each hour feels different. single html file. glm-5👇
himanshu12,552 views • 8 months ago
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