MicroGPT Explained Interactively
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Understanding LLMs Through 200 Lines of Python
Andrej Karpathy's MicroGPT is a 200-line pure Python script that trains and runs a GPT from scratch — no libraries, no dependencies. The script contains the same algorithm that powers large language models like ChatGPT. Developer growingSWE has now created an interactive, visual walkthrough designed to make this code accessible to beginners.
What You Will Learn
- Tokenizer: Converting text to integer sequences. Type any name and watch it get tokenized in real time.
- Softmax: How raw logit scores get converted into a probability distribution over possible next tokens.
- Backpropagation: Step through gradient flow on a computation graph to understand how the model learns from its mistakes.
- Attention heatmaps: Visualize which tokens the self-attention mechanism focuses on during generation.
From Names to ChatGPT
The model trains on 32,000 human names and learns to generate plausible new ones like 'kamon', 'karai', 'anna', and 'anton'. The key insight: from ChatGPT's perspective, your entire conversation is just a document. The model's response is a statistical document completion — the same principle at work in this 200-line script.
The tutorial earned 182 points on Hacker News, complementing Karpathy's original MicroGPT post which scored 1,678 points. Together, they represent one of the most accessible entry points into understanding how modern LLMs work under the hood.
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growingSWE has created an interactive walkthrough of Andrej Karpathy's 200-line pure Python GPT implementation, letting you tokenize names, watch softmax convert scores to probabilities, step through backpropagation, and explore attention heatmaps.
A Show HN thread highlighted GuppyLM, a tiny 8.7M-parameter transformer with a 60K synthetic conversation dataset and Colab notebooks. The point is not state-of-the-art performance, but making the full LLM pipeline inspectable from data generation to inference.
A recent Show HN post highlighted GuppyLM, a tiny education-first language model trained on 60K synthetic conversations with a deliberately simple transformer stack. The project stands out because readers can inspect and run the whole pipeline in Colab or directly in the browser.
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