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

说明与用途

作者创建了 LLM Explained， 一个通过动画、交互式小部件和浏览器内 Python 代码来教授 Transformer 概念的交互式课程。

问题

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

结果

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.

工作流

• 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.

证据

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?"

来源

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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