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Transform YouTube videos into a compounding knowledge base with transcripts, vision analysis, and agentic search. Works as an MCP server for Claude, Copilot & more.

117 starsPython

Mcptube – Karpathy's LLM Wiki idea applied to YouTube videos

Name: Mcptube – Karpathy's LLM Wiki idea applied to YouTube videos
Availability: InStock
Author: 0xchamin

by 0xchamin·Apr 13, 2026·13 points·3 comments

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AI Analysis

●●SolidBig BrainNiche Gem

Knowledge compounds across videos instead of re-searching from scratch every query.

Strengths

•Scene-change detection with ffmpeg plus vision model frame descriptions is genuinely clever.
•FTS5 with two-stage agent (narrow then reason) beats naive vector search for this use case.
•Works as both CLI and MCP server—tested with Claude Code, Cursor, VS Code Copilot.

Weaknesses

•Video Q&A is a crowded category; Cursor and Continue already handle transcript search.
•No evidence of cross-video reasoning actually working better than single-video queries.

Post Description

I watch a lot of Stanford/Berkeley lectures and YouTube content on AI agents, MCP, and security. Got tired of scrubbing through hour-long videos to find one explanation. Built v1 of mcptube a few months ago. It performs transcript search and implements Q&A as an MCP server. It got traction (34 stars, my first open-source PR, some notable stargazers like CEO of Trail of Bits).

But v1 re-searched raw chunks from scratch every query. So I rebuilt it.

v2 (mcptube-vision) follows Karpathy's LLM Wiki pattern. At ingest time, it extracts transcripts, detects scene changes with ffmpeg, describes key frames via a vision model, and writes structured wiki pages. Knowledge compounds across videos rather than being re-discovered. FTS5 + a two-stage agent (narrow then reason) for retrieval.

MCPTube works both as CLI (BYOK) and MCP server. I tested MCPTube with Claude Code, Claude Desktop, VS Code Copilot, Cursor, and others. Zero API key needed server-side.

Coming soon: I am also building SaaS platform. This platform supports playlist ingestion, team wikis, etc. I like to share early access signup: https://0xchamin.github.io/mcptube/

Happy to discuss architecture tradeoffs — FTS5 vs vectors, file-based wiki vs DB, scene-change vs fixed-interval sampling. Give it a try via `pip install mcptube`. Also, please do star the repo if you enjoy my contribution (https://github.com/0xchamin/mcptube)