Show HN: Turn documents into lip-synced video readers (shashekhar.github.io)

A new approach to utilizing large language models (LLMs) for personal knowledge management has been proposed, transforming how individuals interact with documents and synthesize information. Unlike traditional retrieval-augmented generation (RAG) systems that rediscover relevant content for every query, this method allows an LLM to maintain a persistent wiki, incrementally building a comprehensive, interlinked collection of markdown files. As users provide new sources, the LLM updates the wiki by extracting key information, revising summaries, and flagging contradictions, creating a dynamic resource that reflects all previous interactions and readings. This innovation holds significant potential for the AI/ML community as it streamlines the process of knowledge accumulation and organization. The architecture includes three distinct layers: raw sources that are immutable, the LLM-generated wiki containing structured information, and a schema for guiding the LLM's maintenance. This setup reduces the maintenance burden on users, enabling them to focus on sourcing and exploring while the LLM handles the bookkeeping. By automating the consolidation of knowledge, users can effectively manage research, personal development, and collaborative projects, creating richer, more valuable knowledge bases over time.