A complete guide to what quantization is, how it works, and how it's used to compress large language models

Modern computer vision architectures, from CNNs to Transformers, predominantly rely on the stacking of heuristic modules: spatial mixers (Attention/Conv) followed by channel mixers (FFNs). In this...

Browser Harness | Self-healing harness that enables LLMs to complete any task. - browser-use/browser-harness

Native Ghidra Decompiler for r2. Contribute to radareorg/r2ghidra development by creating an account on GitHub.

Edit videos with coding agents. Contribute to browser-use/video-use development by creating an account on GitHub.

A generalist model achieving state-of-the-art on segmentation, depth, and surface normal tasks.

Taste-Skill (High-Agency Frontend) - gives your AI good taste. stops the AI from generating boring, generic, "slop" - Leonxlnx/taste-skill

The mathematical science of neural network training

A single CLAUDE.md file to improve Claude Code behavior, derived from Andrej Karpathy's observations on LLM coding pitfalls. - forrestchang/andrej-karpathy-skills

We are thrilled to announce Day-0 support for DeepSeek-V4 across both inference and RL training. SGLang and Miles form the first open-source stack to serve and train DeepSeek-V4 on launch day — with s...

Our mission is to ensure that AGI benefits all of humanity. Sam Altman shares five principles that guide our work.

How do neural networks trained over sequences acquire the ability to perform structured operations, such as arithmetic, geometric, and algorithmic computation? To gain insight into this question,...