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Some of my recent conversations has convinced me enough that most of these Neolabs are going to hire
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Recommended resources to learn Mechanistic Interpretability (explaining how LLMs think)
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there is something very beautiful about gpt-oss-120b that makes it most hackable model for numerous
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Farzapedia, personal wikipedia of Farza, good example following my Wiki LLM tweet.
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I decided to write down some of my thoughts in blog posts. In this post: https://t.co/EjCdoPI14u, I
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Quick reflections on this great Karpathy post on LLM wikis:
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Efficient AI Lecture 6(Quantization Part II) gave me a much more complete picture of quantization.
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Karpathy's "second brain" concept in 60 seconds:
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Introducing FloatyDo:
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The post explains our design rationale, how missions actually run, and breaks down a real 16.5-hour
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in the current context window, given the information that you have, would it make sense for my kerne
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Ten years ago, Anil Jain and I wrote making a case for electric cook tops. The current context adds
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MAMBA models train like transformers and generate like RNNs.
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HACKATHON - Ready to build the next generation of verified autonomous AI?
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we just published the definitive guide to writing good prose with your AI
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We've been using RL to train LLMs for superforecasting.
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Took me 4 months to write this in my spare time, I hope everyone will find it helpful, going to post
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still completely overwhelmed by the huge, positive engagement this post generated!!
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I think this is one of the most important articles we've published at @AsimovPress. If you read care
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Couldn’t find a unified knowledge base on quantization, so we wrote one 📘
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Introduce SWE-Vision: A Minimal Agent for Advancing Visual Intelligence
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some positive + ethical examples where you have to lower inhibition -
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A client hands you a single, cluttered photo of their living room and asks what it would look like r
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