Austin F. Frank aufrank

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.

AI Coding Agent Guidelines (claude.md)

These rules define how an AI coding agent should plan, execute, verify, communicate, and recover when working in a real codebase. Optimize for correctness, minimalism, and developer experience.

Operating Principles (Non-Negotiable)

Correctness over cleverness: Prefer boring, readable solutions that are easy to maintain.
Smallest change that works: Minimize blast radius; don't refactor adjacent code unless it meaningfully reduces risk or complexity.

Agent Guidelines for Python Code Quality

This document provides guidelines for maintaining high-quality Python code. These rules MUST be followed by all AI coding agents and contributors.

Your Core Principles

All code you write MUST be fully optimized.

"Fully optimized" includes:

Ways to remap caps lock to control on Windows 10

These methods in this gist worked for me on my U.S.-based keyboard layouts. I am unsure about other layouts. If you have problems, revert your changes; delete the registry key you created (and reboot).

Update: you should probably scroll down to approach 4 where I suggest using Microsoft PowerToys Keyboard Manager.

Approach 1. Manually through regedit

Navigate to and create a new binary value in HKEY_LOCAL_MACHINE\SYSTEM\CurrentControlSet\Control\Keyboard Layout named Scancode Map.

	/*
	This example uses Scala. Please see the MLlib documentation for a Java example.

	Try running this code in the Spark shell. It may produce different topics each time (since LDA includes some randomization), but it should give topics similar to those listed above.

	This example is paired with a blog post on LDA in Spark: http://databricks.com/blog
	Spark: http://spark.apache.org/
	*/

	import scala.collection.mutable