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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.

@mikhailov
mikhailov / ruby_gc.sh
Created March 11, 2011 10:33
Ruby’s GC Configuration
- http://www.coffeepowered.net/2009/06/13/fine-tuning-your-garbage-collector/
- http://snaprails.tumblr.com/post/241746095/rubys-gc-configuration
article’s settings: ("spec spec" took 17-23!sec)
export RUBY_HEAP_MIN_SLOTS=1250000
export RUBY_HEAP_SLOTS_INCREMENT=100000
export RUBY_HEAP_SLOTS_GROWTH_FACTOR=1
export RUBY_GC_MALLOC_LIMIT=30000000
export RUBY_HEAP_FREE_MIN=12500
# unicorn_rails -c /data/github/current/config/unicorn.rb -E production -D
rails_env = ENV['RAILS_ENV'] || 'production'
# 16 workers and 1 master
worker_processes (rails_env == 'production' ? 16 : 4)
# Load rails+github.git into the master before forking workers
# for super-fast worker spawn times
preload_app true