MindBase: AI wiki that learns from your notes, now in beta

MindBase addresses a real gap in the knowledge-management landscape. Existing tools force users into one of two camps: either passive containers (Notion, Obsidian, Roam) where the human does all the organizing and the AI is bolted-on generation; or stateless RAG systems (NotebookLM, Perplexity, ChatGPT search) that re-derive answers from raw sources on every query, meaning no knowledge accumulates. By implementing Karpathy's LLM-Wiki pattern as a working product, MindBase proposes a third option where the LLM is a maintainer that actively writes and updates synthesized wiki pages as sources flow in. The design is stateful: each contribution triggers the LLM to read new material, cross-reference it with existing knowledge, flag contradictions, and update relevant wiki pages. Over time, the synthesized context.md file becomes the source of truth, richer and more consistent than raw sources alone.

The architecture is deliberately transparent and local-first: three physical layers on disk (sources, markdown wiki files, logs) with no proprietary database or cloud dependency. This appeals to users who want to own their data and understand their system's data flow. The product also distributes as an MCP server, plugging into the ecosystem of AI editors rather than trying to build its own IDE — Claude Code gets a flagship experience with structured sub-agents and slash commands, while other MCP-compatible editors (Cursor, Windsurf, Cline, Continue.dev) can access the same core tools with lighter UX. The team framed MindBase explicitly as early access with core data model and loop stable but some UI features still in migration, signaling they're testing the idea in the wild before consolidating the experience.