Alternatives
Products that do what Mcptube – Karpathy's LLM Wiki idea applied to YouTube videos does
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,…
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Hey HN, I've been working on this side project for the past month. It generates a step-by-step tutorial guide for YouTube videos that you can follow along without watching long videos. Best suited for tutorial videos but can work for other videos aswell. No BS. Just straight to the point. The guides are generated from pure transcript so you don't have to worry about it being AI. It's my first project as a total beginner. Something I had to do inorder to get out of tutorial hell. Please let me know if you have any suggestions or if you face any problems or bugs. I would try to fix them to the…
2024 · stepify.tech
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I shipped a wiki layer for AI agents that uses markdown + git as the source of truth, with a bleve (BM25) + SQLite index on top. No vector or graph db yet. It runs locally in ~/.wuphf/wiki/ and you can git clone it out if you want to take your knowledge with you. The shape is the one Karpathy has been circling for a while: an LLM-native knowledge substrate that agents both read from and write into, so context compounds across sessions rather than getting re-pasted every morning. Most implementations of that idea land on Postgres, pgvector, Neo4j, Kafka, and a dashboard. I…
Apr 2026 · github.com
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Hi there! When Supabase announced their recent hackathon, I thought it was a good time to build something to learn more about so many of the new AI models and tech out there. From the different techniques of embedding documents to the future RAG. With the rise of short form content with TikTok and Youtube. A lot more knowledge is in videos than ever before. Finding specific answers within millions of videos can be difficult for any one person to go through. So the question is if there is Google that indexes text on website making it easier to find based on the context of on your question,…
2023 · avse.vercel.app
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I built this tool because I wanted a way to just take a bunch of URLs or domains, and query their content in RAG applications. It takes away the pain of crawling, extracting content, chunking, vectorizing, and updating periodically. I'm curious to see if it can be useful to others. I meant to launch this six months ago but life got in the way...
2024 · embedding.io
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All content is based on Andrej Karpathy's "Intro to Large Language Models" lecture (youtube.com/watch?v=7xTGNNLPyMI). I downloaded the transcript and used Claude Code to generate the entire interactive site from it — single HTML file. I find it useful to revisit this content time to time.
Apr 2026 · ynarwal.github.io
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Hi HN! Excited to share our MCP Server at Hyperbrowser - something we’ve been working on for a few days. We think it’s a pretty neat way to connect LLMs and IDEs like Cursor / Windsurf to the internet. Our MCP server exposes seven tools for data collection and browsing: 1. `scrape_webpage` - Extract formatted (markdown, screenshot etc) content from any webpage 2. `crawl_webpages` - Navigate through multiple linked pages and extract LLM-friendly formatted content 3. `extract_structured_data` - Convert messy HTML into structured JSON 4. `search_with_bing` - Query the web and get results…
2025 · github.com
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A little idea I got from playing with AI SWE Agents. Can AI help make sure we understand the code that our AIs write? PR Quiz uses AI to generate a quiz from a pull request and blocks you from merging until the quiz is passed. You can configure various options like the LLM model to use, max number of attempts to pass the quiz or min diff size to generate a quiz for. I found that the reasoning models, while more expensive, generated better questions from my limited testing. Privacy: This GitHub Action runs a local webserver and uses ngrok to serve the quiz through a temporary url. Your code…
2025 · github.com
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This is a small project i made years ago and updated to whisper last year, i still use it from time to time and thought it might be useful to others, or just put the idea out there for someone better than me to make a better implementation!
2024 · github.com
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Large Language Models (LLMs) are powerful, but they’re limited by fixed context windows and outdated knowledge. What if your AI could access live search, structured data extraction, OCR, and more—all through a standardized interface? We built the JigsawStack MCP Server, an open-source implementation of the Model Context Protocol (MCP) that lets any AI model call external tools effortlessly. Here’s what it unlocks: - Web Search & Scraping: Fetch live information and extract structured data from web pages. - OCR & Structured Data Extraction: Process images, receipts, invoices, and handwritten…
2025
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Hello HN Over the New Year's break, I created semanticvideosearch.com. This can search any video based on meaning and context. I would love to get your feedback on it. What should I change and what can be improved? The preprocessed videos can be search very quickly, while the youtube video links take some time (yt videos also have a upper duration limit due to compute issues). I intend to add search based on the frames of the video soon. I would love to know your thoughts on the demo and any suggestions for improvements. Thanks! PS: the inspiration to create this was to get the 2 mins of…
2023 · semanticvideosearch.com
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Hi everyone, I built this recently and found myself and my friends using it for various different things so I figured I'd share it here. It uses LLMs to answer queries from videos so you don't have to watch them yourself! Each video found is also included with a summary of its transcript for further exploration. Most importantly, it uses up-to-date videos and can answer question about real and recent things unlike ChatGPT. It is particularly useful for: 1. Getting up to date information about the news from different sources E.g. - What are the features of the recently released Apple Vision…
2023 · askyoutube.ai
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This is an open‑source Model Context Protocol (MCP) server that gives any LLM a sense of the passage of time. Most MCP demos wire LLMs to external data stores. That’s useful, but MCP is also a chance to give models perception — extra senses beyond the prompt text. Six functions (`current_datetime`, `time_difference`, `timestamp_context`, etc.) give Claude/GPT real temporal awareness: It can spot pauses, reason about rhythms, and even label a chat’s “three‑act structure”. Runs locally in <60 s (Python) or via a hosted demo. If time works, what else could we surface? - Location /…
2025 · github.com
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OpenBrief is basically a GUI for yt-dlp with some AI on top — paste a link, it downloads locally, and transcription and voice generation run with local AI on your machine. Summaries and chat over the transcript use an LLM, which is bring-your-own-key for now. It's open source and free.
May 2026 · github.com
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I have a long YouTube watch-later playlist and wanted Claude to help me triage it by summarizing each video. It couldn't. I later discovered YouTube is not accessible to Claude, ChatGPT and majority of AIs. So I built this MCP server to fix that. This is a local MCP server that gives any MCP-compatible client (Claude, ChatGPT, OpenClaw and others) 8 tools for working with YouTube content: - fetch full transcript, with or without timestamps - download transcript as a .md file - get video metadata (title, channel, views, duration, likes, description) - search within captions for a keyword or…
May 2026 · github.com
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Simon(sfarshid) and I spend a lot of time on GitHub. As data nerds we put together a quick tool to explore your repository’s data. How it works: - Data Loading: We use dlt to pull data (issues, PRs, commits, stars) from GitHub - Semantic Layer: Relta wraps the underlying dataset into a semantic layer so the LLM doesn’t hallucinate. - Text-to-SQL: A text-to-SQL agent transforms your plain-English question into a query using the semantic layer - Generative Charts: assistant-ui dynamically generates a chart based on the SQL query - Refinements: If the semantic layer can’t handle your question,…
2024 · github.com
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Analyze videos, generate metadata, then automate publishing
Jun 2026 · vidseeds.ai
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Live demo here: http://fonctionlabs.com:8000 Similarly to aka_sh (guess we were working parallelly on similar topics), I created with my brother a chainlit-based webapp, which summarizes Youtube videos in order to gain time. It works as an RAG-based LLM, and is very light in the sense that it does not use RAG libraries like langchain or llamaindex. You can use it with your own OpenAI API key. It also supports local models like Mistral, or Llamma. It is ofc open-source, and you can deploy with Docker if you choose. Some of the next steps are: - using whisper to be able to compute a…
2024 · github.com
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