Nvidia just shared that they trained an LLM on 30+ years of internal docs so junior engineers can query decades of design knowledge instead of interrupting senior designers. That is exactly what a persistent, compiled knowledge base should do. But right now most individual researchers, developers, and knowledge workers are stuck re-reading the same papers, re-parsing the same docs, and re-discovering the same concepts in every new AI chat session. I built llm-wiki-compiler to give smaller teams and individuals the same advantage:
- Ingest papers, URLs, docs, and project notes
- The LLM compiles them into a structured markdown wiki with cross-links
- Query it later, and save useful answers back into the wiki
- The knowledge base compounds instead of resetting
- Plain markdown on disk: readable, inspectable, versionable, Obsidian-compatible It’s complementary to RAG, not a replacement. RAG is great for ad-hoc retrieval over huge data. This is for the curated, high-signal corpus you actually want to grow over time. Curious if anyone here has tried building a persistent research wiki instead of querying scattered sources every week. submitted by /u/riddlemewhat2
Originally posted by u/riddlemewhat2 on r/ArtificialInteligence
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