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Augustin Delaporte GuGuss

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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.

@stonehippo
stonehippo / RPi-Dashing-howto.md
Last active October 6, 2021 13:52
Setting up a Raspberry Pi as a dashboard server with Dashing

Setting up a Raspberry Pi as a dashboard server with Dashing

Why the heck did I do this?

I wanted to set up one of my Raspberry Pi's as a data dashboard, pushing sensor data to a web interface that's easy to digest. I decided to use Shopify's Dashing framework. Dashing is based on Sinatra, and is pretty lightweight.

Dashing does require Ruby 1.9.3 to run. In addition, it makes use of the execjs gem, which needs to have a working Javascript interpreter available. Originally, I tried to get therubyracer working, but decided to switch over to Node.js when I ran into roadblocks compiling V8.

One warning: The RPi is a very slow system compared with modern multi-core x86-style systems. It's pretty robust, but compiling all this complex software taxes the system quite a bit. Expect that it's going to take at least half a day to get everything going.