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Lessons From Training Composer At Cursor And Building Meta/Nvidia Compute Clusters | YC Paper Club 77 min 
The Case For Data Centers In Space 37 min 
Jensen Huang: The Mindset That Built NVIDIA 49 min 
STALKER 2, GTA 6, Клоунада PlayStation, DLSS 5 Слоп, Far Cry 7, ПЗДЦ Game Pass Wolverine The Witcher 44 min 
AI News: Microsoft Finally Reveals Their Plan! 31 min 
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How Owning Your Own AI Can Unlock Smarter, More Precise Agents 
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Name That Infrastructure (Ep. 15) #shorts 1 min 
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13 most EXPENSIVE Tech Fails of all time 24 min 
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They’re cooking our brains, for real! #science #pollution 2 min 
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Building Robotic Hands | Real Life Power Armor (Part 5/6) 24 min 
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GPT-6 Astra Is Finally Here (And It’s REALLY Good) 20 min At our latest YC Paper Club, researchers and builders presented on multi-GPU kernel optimization, intelligence per watt for local inference, AI-generated GPU kernels and benchmarking, heterogeneous inference infrastructure design, and GPU-accelerated game engines for reinforcement learning. Thanks to the following presenters: Stuart Sul (Stanford / Cursor), John (Stanford), Mark (PyTorch / GPU Mode / CoreAuto), Misha (Marlo), and Brennan (Stanford) Transcript: https://www.ycrootaccess.com/p/multi-gpu-kernels-intelligence-per Chapters: 0:00 – Francois Chaubard: The case for chip and kernel specialization 7:16 – Stuart Sul: Parallel Kittens - Systematic and Practical Simplification of Multi-GPU Al Kernels (https://arxiv.org/abs/2511.13940) 21:29 – Jon Saad-Falcon: Intelligence per Watt - Measuring the Intelligence Efficiency of Local and Cloud AI (https://arxiv.org/abs/2511.07885) 31:05 – Mark Saroufim: When Al Starts Writing Systems Code 47:04 – Misha Smelyanskiy: Why AI Inference Needs Heterogeneous Hardware 1:04:33 – Brennan Shacklett: Building a High-Throughput Game Engine that Runs ENTIRELY on the GPU (https://madrona-engine.github.io/shacklett_siggraph23.pdf) 1:15:35 – Wrap-up & what's next Apply to Y Combinator: https://www.ycombinator.com/apply Work at a startup: https://www.ycombinator.com/jobs
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