This GitHub Repo Has 37,000 Stars and Zero Lines of Code
Last Updated on July 30, 2026 by Editorial Team
Author(s): Anubhav
Originally published on Towards AI.
The most-starred course on GitHub was never written. Here’s the full roadmap it points to, and where to learn every step.
Andrej Karpathy has a GitHub repo with 37,500 stars and not one line of code in it.
After introducing the repo’s “17 steps” roadmap, the article explains why the curriculum is designed as a continuous arc (from bigram to transformer), emphasizing that you build the simplest working model first and earn complexity only when the previous version fails. It walks through five phases—(1) making the model work at all (bigram, micrograd/backprop, transformer basics, attention, tokenization), (2) making it work for real (systems considerations like GPU utilization, precision choices, distributed training, and dataset importance), (3) making it cheap to run (inference engineering like kv-cache and practical weight quantization), (4) making it an assistant (supervised fine-tuning with role-based prompts and reinforcement learning for preferred responses), and (5) making it real and usable (deployment and multimodal integration). The piece then ties everything back together as a buildable “never-written” course by mapping each chapter to the actual repositories/videos where the code lives, finishing with the takeaway that the roadmap—and its correct order—is what’s scarce and valuable.
Read the full blog for free on Medium.
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