microgpt

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Summary

This is a brief guide to my new art project microgpt , a single file of 200 lines of pure Python with no dependencies that trains and inferences a GPT. This file contains the full algorithmic content of what is needed: dataset of documents, tokenizer, autograd engine, a GPT-2-like neural network architecture, the Adam optimizer, training loop, and inference loop. Everything else is just efficiency. I cannot simplify this any further. This script is the culmination of multiple projects (micrograd, makemore, nanogpt, etc.) and a decade-long obsession to simplify LLMs to their bare essentials, and I think it is beautiful 🥹. It even breaks perfectly across 3 columns: Where to find it: This GitHub gist has the fu…

What This Teaches

  • How Karpathy frames technical judgment, learning, research, or AI systems in long-form prose.
  • Useful as a high-signal idea source for research taste, project framing, and agent workflow design.

Why It Matters

Karpathy’s posts often crystallize reusable heuristics; this wiki should preserve the ideas without relying on chat memory.

Public Handling Notes

  • EXTRACTED: This page records public metadata and a source-grounded summary.
  • INFERRED: Full local preservation, when available, is for private/local use unless a license or explicit source policy makes public redistribution safe.
  • Do not treat this page as permission to republish unlicensed source text or code wholesale.