Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Karpathy-style LLM knowledge base for Obsidian. Clone, run Claude Code, start building your second brain.
| Date | Stars |
|---|---|
| 2026-07-31 | 313 |
| 2026-08-06 | 313 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
<p align="center">
<img src="assets/cover.png" alt="LLM Wikid" width="700">
</p>
# LLM Wikid
An AI-maintained knowledge base that lives in Obsidian. Based on [Karpathy's LLM Wiki pattern](https://x.com/karpathy/status/1890540708772143562).
You dump raw sources into a folder. An AI agent reads them, compiles structured wiki pages with cross-references, runs bias checks, and maintains a master index. Every question you ask gets filed back in. The wiki compounds the more you use it.
Works with any agent that can read markdown and run shell commands: Claude Code, OpenClaw, Hermes, Codex, or your own setup.
## Quick Start
```bash
git clone https://github.com/shannhk/llm-wikid.git my-wiki
cd my-wiki
# open as an Obsidian vault
# File > Open Vault > select the my-wiki folder
# start your agent
claude --dangerously-skip-permissions # Claude Code
# or open with OpenClaw, Hermes, Codex, etc.
```
The agent reads `CLAUDE.md` and knows everything. That file is the schema that controls the entire system.
## How It Works
This is **not RAG**. RAG re-derives answers every time by chunking documents and running vector search. This system compiles knowledge once into structured wiki pages, keeps them current, and pre-builds cross-references. At ~100 articles / ~400K words, Karpathy found this outperforms RAG for Q&A.
```
┌──────────────────────────────────┐
│ CLAUDE.md │
│ the schema that controls │
│ everything the agent does │
└──────────┬───────────────────────┘
│
┌──────────┐ ┌────────▼────────┐ ┌──────────────┐
│ raw/ │ │ INGEST │ │ wiki/ │
│ │─────>│ │─────>│ │
│ clips │ │ 0. sort clips │ │ concepts/ │
│ ideas │ │ 1. resolve URLs │ │ entities/ │
│ tweets │ │ 2. extract media│ │ sources/ │
│ articles │ │ 3. classify │ │ index.md │
│ papers │ │ 4. compile pages│ │ log.md │
│ │ │ 5. cross-link │ │ │
└──────────┘ │ 6. bias check │ └──────┬───────┘
│ 7. update index │ │
└─────────────────┘ ┌───────▼───────┐
│ Obsidian │
┌──────────┐ ┌─────────────────┐ │ graph view │
│ question │─────>│ QUERY │ │ + Dataview │
│ │ │ │ └───────────────┘
│ │ │ scan TLDRs │
│ │<─────│ read relevant │
│ answer │ │ synthesize │
│ (filed │ │ file back in │───> wiki grows
│ back) │ └─────────────────┘
└──────────┘
```
The compound loop: every answer filed back means the next query has more to work with. Your curiosity makes the system smarter.
## The Ingest Pipeline
When you run `/wiki-ingest`, the agent processes everything in `raw/` through these phases:
**Phase 0 - Sort clippings.** Anything in `raw/clippings/` gets moved to the right subfolder based on its URL. Tweet goes to `raw/bookmarks/`, article to `raw/articles/`, PDF to `raw/papers/`. The clippings folder is just an inbox.
**Phase 1 - Resolve.** The agent detects what each file contains and fetches the full content:
- YouTube URL: extracts transcript via `yt-dlp`
- X/Twitter URL: fetches tweet text, thread, metrics via X API
- Web URL / Reddit: scrapes full page via `scrapling`
- PDF: reads directly
- Plain text: already resolved
The resolved content replaces the file in-place. The original URL stays in frontmatter for provenance.
**Phase 1.5 - Media extraction.** Images get downloaded to `raw/assets/images/` and analyzed. Videos get their transcripts pulled. A tweet that says "here's my stack" with an image oExcerpt of 13,371 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:2e28304236fbf2c3, llm:description: 'Karpathy-style LLM knowledge base for Obsidian. Clone, run Claude Code, start building your second brain.' (repo description)
matched fp:2e28304236fbf2c3, llm:description: 'Karpathy-style LLM knowledge base for Obsidian. Clone, run Claude Code, start building your second brain.' (repo description)
matched fp:2e28304236fbf2c3, llm:description: 'Karpathy-style LLM knowledge base for Obsidian. Clone, run Claude Code, start building your second brain.' (repo description)