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Under the Hood - Build Every Layer of a Large Language Model from Scratch. A 20-project, build-it/break-it/measure-it manual by Ramchand Kumaresan.
An open, large-scale, interactive textbook.
A SOTA quantization algorithm for high-accuracy low-bit LLM inference, seamlessly optimized for CPU/XPU/CUDA, with multi-datatype support and full compatibility with vLLM, SGLang, and Transformers.
A vector index built on TurboQuant, written in Rust with Python bindings
Learning Deep Representations of Data Distributions
You like pytorch? You like micrograd? You love tinygrad! ❤️
An all-in-one LLMs Chat UI for Apple Silicon Mac using MLX Framework.
The 100 line AI agent that solves GitHub issues or helps you in your command line. Radically simple, no huge configs, no giant monorepo—but scores >74% on SWE-bench verified!
🪐 Markdown with superpowers: from ideas to papers, presentations, websites, books, and knowledge bases.
Learn how to develop, deploy and iterate on production-grade ML applications.
Open-source policy engine that blocks bad AWS spending patterns before they ship and remediates what's already burning.
Super simple build framework with fast, repeatable builds and an instantly familiar syntax – like Dockerfile and Makefile had a baby.
🆔 A python library for accurate and scalable fuzzy matching, record deduplication and entity-resolution.
🦝 📦 a package registry for anything, but mostly javascript 🦝 🦝 🦝
Learning operating system development using Linux kernel and Raspberry Pi
✨ React patterns, techniques, tips and tricks ✨
A Python module for learning all major algorithms
Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep lear…
Bootstrap yourself to write an OS from scratch. A book for self-learner.
A collection of shaders for MagicaVoxel to generate geometry, noise, patterns, and simplify common and repetitive tasks.
The Big List of Naughty Strings is a list of strings which have a high probability of causing issues when used as user-input data.