This repository implements an educational interactive web application ("MICROGPT LAB") that visualizes the inner workings of a small transformer language model (MicroGPT) trained to generate Korean and English names. The backend consists of Python scripts for model training, inference, and data export, while the frontend is a React 19 + Vite application with scroll-based interactive chapters covering data tokenization, embeddings, attention mechanisms, training dynamics, and sampling. The project adapts Karpathy's microgpt concept to Korean Hangul names, using custom autograd for educational clarity. It features a neo-brutalism design system, bilingual support (Korean/English), and GSAP-powered animations. Architecturally, the repo is a hybrid of an ML pipeline and a web application: the model code in `model/` trains a 1-layer, 16-dim transformer on name lists and exports weight snapshots and training traces as JSON files to `app/public/data/`; the frontend (`app/`) loads these snapshots and runs the model forward pass in JavaScript for real-time visualization. Key entry points include `model/ko_main.py` for training/inference, `app/src/App.jsx` for the main page orchestration, and `app/src/components/sections/` for each chapter's content. Notable components include a reusable utility library (`chapterUtils.js`) with mathematical helpers for vector/matrix operations and heat map coloring, a well-structured localization module (`localization.js`), and several interactive demo components (attention, training, inference) that demonstrate non-trivial web visualization craftsmanship. The project is actively maintained with a recent commit, has no open issues, and uses modern tooling (React 19, Vite 7, Tailwind CSS 4). However, there are some code quality concerns such as large single-file components, hardcoded paths, and pickle-based serialization.