Summary
Highlights
The video introduces Andrej Karpathy's method of using Large Language Models (LLMs) to create a personal knowledge base. The speaker, Audermus, with 6 years of experience in AI, explains how this system optimizes learning and research, similar to a personal wiki.
Audermus details Karpathy's tweet, highlighting the use of LLMs for personal knowledge base construction across various research topics. The process involves data ingestion from sources like YouTube, articles, and blogs, using tools like the Obsidian Web Clipper Extension. The system categorizes information into 'raw' files and processed 'wiki' concepts.
The video outlines the system's architecture: data from various sources (articles, GitHub repos, etc.) enters a 'raw' folder. An LLM then compiles these into a 'wiki' of digestible knowledge, explaining concepts and entities. Backlinks create a knowledge graph, and the system supports Q&A, generating responses in formats like Markdown, slides, or graphs. Obsidian acts as the IDE.
Audermus guides viewers through setting up their personal knowledge base. He provides a detailed prompt for Claude Code that, after answering specific questions about the project's purpose and data sources, generates a configuration for Obsidian. This includes a folder structure and initial settings.
The speaker demonstrates how to open the created folder in Obsidian and configure the Obsidian Web Clipper extension. This extension allows users to import web pages into their Obsidian vault, which automatically stores the content as raw data in a designated 'clippings' folder.
Audermus shows how to use Claude Code to process the raw web content. By typing 'injest' and referencing the article, the LLM analyzes the text, identifies key concepts and entities, and creates detailed wiki pages, linking them in the knowledge graph. This process enriches the personal knowledge base with structured information.
As a bonus, the video explains how to ingest YouTube videos by configuring a custom connector in Claude Code, referencing an MCP server (Media Cloud Platform). This enables the LLM to retrieve and process video transcripts, adding them to the knowledge base and linking relevant concepts.
Audermus clarifies the distinction between the LLM wiki method and Retrieval Augmented Generation (RAG). While both use external knowledge, RAG relies on semantic search and vectorial databases for efficient information retrieval, especially at scale. The LLM wiki, conversely, directly processes an index and is better suited for smaller, personal knowledge bases, though less efficient with larger datasets due to context window limitations.