Vsubb

An agent-agnostic recipe for LLM-maintained software knowledge bases

Andrej Karpathy's "LLM Wiki" (2026) introduced the idea of using an LLM agent as the bookkeeper of a structured knowledge base. This kit operationalises that idea for a concrete purpose: keeping open-source scientific (read: underfunded) software projects maintainable as contributors come and go. Westner et al. (2025) report bus factors of 1–3 across major FOSS projects in neuroscience. "More docs and comments" is not a sustainable solution; bookkeeping is boring, so why not outsource it to an LLM under a standing rule the agent loads every session?

Usage: Hand this file to a coding agent and say "set it up." The agent does the rest. This file is consumed once; what persists is the small skeleton and the standing rule it installs.

In the wild: This kit was developed alongside [IDE4EEG](https://gitlab.com/fuw_software/ide

Vsubb

LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.