woduq1414

woduq1414 / ko-microgpt

Public

Interactive Microgpt Visualizer Website

ko-microgpt.vercel.app
226
28
100% credibility
Found Feb 23, 2026 at 19 stars 10x -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
JavaScript
AI Summary

Interactive educational webpage visualizing a tiny GPT model's process for generating Korean names, from dataset to inference.

How It Works

1
🔍 Discover the Korean name AI explorer

You find a cool interactive webpage on GitHub that reveals how a tiny AI creates Korean names, complete with a demo video.

2
🌐 Launch and pick language

Open the page, choose Korean or English, and start scrolling through fun, visual lessons.

3
📚 Explore data and building blocks

See clouds of names break into phoneme pieces with unique numbers, feeling the magic of preparation.

4
⚡ Watch AI thinking and learning

Interactive demos light up embeddings, attention focus, loss calculations, and training steps, making the inner workings crystal clear.

5
🎲 Generate new names yourself

Adjust a slider to create unique Korean names, watching probabilities and sampling in action.

6
🧠 See how it scales to real AI

Compare this mini version to massive GPTs, grasping data, layers, and techniques behind the curtain.

🎉 Unlock AI name-making secrets

You now fully understand how AI crafts Korean names from data to dazzling results, ready to share the wow!

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Star Growth

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AI-Generated Review

What is ko-microgpt?

ko-microgpt is an interactive JavaScript web visualizer for a tiny GPT model trained to generate Korean names, inspired by Karpathy's microgpt. It turns complex transformer concepts like tokenization, embeddings, attention, training traces, and inference into scroll-based demos you interact with directly in the browser. Run it locally with npm install && npm run dev for the frontend, or python scripts for model inference—perfect for demystifying LLMs without heavy setups.

Why is it gaining traction?

Its hook is the smooth, educational scroll experience with animated flows for forward/backward passes, standing out from static READMEs or dry notebooks. Korean name data makes it culturally specific yet universally relatable for LLM internals, deployable as interactive GitHub Pages. Developers dig the hands-on controls like temperature sliders for sampling or step-by-step training traces.

Who should use this?

ML hobbyists dissecting transformers step-by-step, Korean NLP tinkerers prototyping char-level models, or educators building interactive tutorials. Ideal for frontend devs exploring JS-based viz tools or anyone prepping interactive GitHub READMEs/profiles with model insights.

Verdict

Worth forking for learning—solid viz beats theory dumps—but at 13 stars and 1.0% credibility, it's early-stage with basic docs. Clone, tweak the model data, and contribute to mature it.

(178 words)

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