enescang

enescang / microgpt-visualizer

Public

Interactive visualization of a minimal GPT implementation with autograd engine.

microgpt.enescang.dev autograd educational gpt microgpt transformer
88
13
100% credibility
Found Feb 13, 2026 at 30 stars 3x -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
TypeScript
AI Summary

An interactive web app visualizing a tiny GPT model's components, from tokenization and embeddings to training and text generation.

How It Works

1
🌐 Discover MicroGPT Visualizer

You find this fun web app that lets you peek inside how AI like GPT thinks and learns from scratch.

2
🔤 Play with the tokenizer

Type a name and watch it turn into numbers the AI understands, like a secret code for letters.

3
📊 Explore embeddings

Hover over colorful charts to see how the AI represents each letter and its position as patterns of numbers.

4
⚡ Follow the forward pass

Pick a letter and position, then trace how it flows through the AI's brain step by step to predict the next one.

5
🚀 Train your tiny AI

Hit train and watch the graph drop as your AI learns name patterns from thousands of examples in real time.

6
✨ Generate new names

Turn up the creativity dial and generate fun, realistic names your AI dreamed up all by itself.

🎉 AI magic unlocked

You've built intuition for how GPT works — from random numbers to generating clever names like a pro!

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Star Growth

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AI-Generated Review

What is microgpt-visualizer?

This TypeScript React app delivers an interactive visualization of a minimal GPT model, breaking down tokenization, embeddings, forward passes, training loops, and text generation step by step. Developers get a browser-based playground to watch a tiny transformer learn character-level name prediction on a real dataset, powered by a custom autograd engine for backprop demos. It's perfect for demystifying how GPTs process sequences without needing Python or heavy ML frameworks.

Why is it gaining traction?

Unlike static tutorials or dense Jupyter notebooks, it offers real-time interactive visualizations—like heatmaps for embeddings, live loss charts during training, and probability bars for inference—making transformer internals tangible. The step-through forward pass and per-neuron MLP activations stand out, echoing Karpathy's micrograd style but in a deployable web format via Vite. Early adopters praise the github actions interactive setup for quick local spins and shareable github pages demos.

Who should use this?

ML engineers onboarding juniors to transformers, CS educators building interactive github tutorials on autograd engines, or frontend devs exploring interactive visualization python alternatives in TypeScript. It's ideal for anyone debugging sequence models or prototyping interactive visualization of word embeddings without R, Julia, or Tableau overhead.

Verdict

Grab it for hands-on GPT education—train in-browser and inspect every layer—but temper expectations with its 1.0% credibility score from 12 stars and nascent docs. Solid prototype for learning; fork and contribute to push maturity.

(198 words)

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