Hey friends, the world needs more serious AI researchers. Many AI/LLM beginners mentioned to me that they learn better from implementations than from papers/math, but existing open-source examples rarely go beyond basic nanoGPT-level demos. To help bridge the gap, I spent the last two months full-time reimplementing and [open-sourcing](https://github.com/tanishqkumar/beyond-nanogpt) a self-contained implementation of most modern deep learning techniques from scratch. The result is [beyond-nanoGPT](https://github.com/tanishqkumar/beyond-nanogpt), containing 20k+ lines of handcrafted, minimal, and extensively annotated PyTorch code for your educational pleasure. It contains a clean, working implementation + demo of everything from KV caching to linear attention to diffusion Transformers to AlphaZero to even a minimal coding agent that can make [end-to-end PRs](https://x.com/tanishqkumar07/status/1931709892236116293) autonomously. I'd love feedback on how to make it more helpful for people interested in transitioning into deep learning research. I will continue to add features and maintain the repo for the foreseeable future. The roaring 2020s are a surreal time to be alive, and we need all hands on deck.