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Mcptube

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

67 AI Score
Show_hn other Added Apr 14, 2026

Details

Sector
other
Total Funding
$0
Last Round
$0

About

I watch a lot of Stanford&#x2F;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&amp;A as an MCP server. It got traction (34 stars, my first open-source PR, some notable stargazers like CEO of Trail of Bits).<p>But v1 re-searched raw chunks from scratch every query. So I rebuilt it.<p>v2 (mcptube-vision) follows Karpathy&#x27;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.<p>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.<p>Coming soon: I am also building SaaS platform. This platform supports playlist ingestion, team wikis, etc. I like to share early access signup: <a href="https:&#x2F;&#x2F;0xchamin.github.io&#x2F;mcptube&#x2F;" rel="nofollow">https:&#x2F;&#x2F;0xchamin.github.io&#x2F;mcptube&#x2F;</a><p>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 (<a href="https:&#x2F;&#x2F;github.com&#x2F;0xchamin&#x2F;mcptube" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;0xchamin&#x2F;mcptube</a>)

AI Score Reasoning

Mcptube is a timely technical play leveraging the Model Context Protocol (MCP) to bridge the gap between AI agents and video-based knowledge. While early-stage and facing significant platform risk from YouTube's TOS, the sophisticated vision-based indexing approach and high-quality initial interest from industry leaders suggest strong potential in the developer tool space.

Source

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