cablate

cablate / llm-atomic-wiki

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An extension of Karpathy's LLM Wiki pattern: atom layer, topic-branches, two-layer Lint. Distilled from running the pattern end-to-end.

karpathy knowledge-base knowledge-management llm markdown
17
6
100% credibility
Found Apr 17, 2026 at 17 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Shell
AI Summary

A template and scripts for building and maintaining a personal wiki by processing raw text materials into atomic facts and compiled pages using AI assistance.

How It Works

1
👀 Discover the idea

You hear about a smart way to turn your scattered notes and articles into a personal knowledge wiki, inspired by a famous AI expert.

2
📥 Get your kit

You make a copy of the ready-made folder structure and read the simple guides to get started.

3
📝 Add your materials

You drop your favorite articles, posts, transcripts, or notes into a special folder.

4
🧠 Magic extraction

You guide a friendly AI to pull out key facts from your materials, creating tiny, reliable building blocks called atoms.

5
📂 Sort into topics

You organize these fact blocks into folders by subjects that interest you, like skills or projects.

6
📚 Build wiki pages

The AI helps group facts into smooth, linked pages that read like a personal encyclopedia.

7
🔍 Keep it fresh

You run easy check-ups to spot issues and log changes, making maintenance a breeze.

🌟 Your knowledge shines

Now you have a browsable wiki of your wisdom, easy to update and query anytime, growing smarter with every addition.

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Star Growth

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AI-Generated Review

What is llm-atomic-wiki?

llm-atomic-wiki is a shell-script framework extending Karpathy's LLM pattern for turning raw sources like posts, replies, or transcripts into a browsable personal wiki. You feed materials to an LLM to extract atomic claims—single facts with metadata—organized into topic branches, then compile them into flat, linkable wiki pages. Run lightweight shell commands to auto-generate an index, lint for broken links or orphans, and append change logs, making maintenance deterministic and LLM-assisted.

Why is it gaining traction?

It fixes Karpathy's core pattern for scale with an atomic layer as source of truth, topic branches for navigation, and split lint—fast shell checks before LLM semantics—proven on 630 atoms into 83 pages across 11 branches. Developers hook into it for end-to-end distilled workflows, driving LLMs like via GitHub Copilot chat extension in VSCode without extension GitHub Copilot not found errors. Zero deps beyond bash, it beats flat wikis by keeping facts immutable and derivables rebuildable.

Who should use this?

Solo AI devs or researchers distilling course notes, forum threads, or lectures into a stable cognitive map. Those extending GitHub Copilot workflows for atomic knowledge bases, tired of RAG hallucinations on noisy raw data. Ideal for single-owner setups under 200 pages where quality trumps volume.

Verdict

Early with 17 stars and 1.0% credibility score, but real-world proof, bilingual docs, and tight methodology make it fork-worthy for Karpathy fans. Skip if you need multi-author scale or real-time updates—grab for personal LLM wikis now.

(198 words)

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