I Built LLM Explained: 43 Interactive Pages That Take You From Tokens to AI Agents

Описание и применение

Автор создал интерактивный курс LLM Explained, позволяющий изучать трансформеры и связанные технологии через визуализации, виджеты и исполняемый код в браузере.

Задача

Learners struggle to understand LLM concepts because terminology changes rapidly and explanations are often abstract.

Результат

The project provides a structured, interactive, and hands-on resource that demystifies LLM concepts, enabling learners to experiment and verify ideas directly in the browser.

Рабочий процесс

• Design curriculum map with 7 parts and a capstone.
• Create modules with picture-first, symbol-second principle.
• Add depth switch (Intuition, Mechanics, Maths).
• Implement scroll-driven animations and widgets (e.g., toggle √d_k, parameter counter, tokenizer).
• Embed optional in-browser Python cells via Pyodide.
• Add quizzes and further reading links.
• Publish site and source code on GitHub.
• Invite community contributions via issues and pull requests.

Доказательства

I built the resource I wished I had: LLM Explained. It's a free, interactive course that goes from "why did RNNs hit a wall?" all the way to "how does a production agent retrieve, reason, act and get evaluated?"

Источник

Every module has at least one widget you control.

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Optional in-browser Python (NumPy) cells reproduce the figures.

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