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.
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.
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| #!/bin/bash -u | |
| touch /tmp/clip | |
| wezterm start --class Floaterm nvim /tmp/clip || exit 1 # Vimが正しく終了しなかった時はコピーしない | |
| # head -c -1は末尾の改行を削ぎ落とすやつ | |
| head -c -1 /tmp/clip | xclip -selection clipboard | |
| notify-send -t 1000 copied |
Revision 3501
DTBN is a substrate binary encoding notaion in which data is described in terms of its structure, type, and location. It's meant as a foundation format for information packaging, distribution, on-disk storage, and interchange. DTBN is descriptor-oriented: it focuses on providing complete descriptions of the data it stores or references.
| name | govuk-style | |||||||
|---|---|---|---|---|---|---|---|---|
| description | Write and edit in GOV.UK / GDS house style — plain English, active voice, front-loaded content, sentence case, and no bold or italics for emphasis. Use when writing or editing reports, research write-ups, guidance, documentation, summaries, or any prose where clarity and accessibility matter. | |||||||
| user-invokable | true | |||||||
| args |
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