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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.

@tallguyjenks
tallguyjenks / fcpx_markers_import.py
Last active May 22, 2023 00:32 — forked from tin2tin/fcpx_markers_import.py
Grab Marker time stamps and text from Final Cut Pro X XML export and format for youtube timestamps in an output text file
#!/usr/bin/env python
import sys, datetime
from xml.etree.ElementTree import parse
xmlfile = raw_input()
if xmlfile == "":
exit()
# Converts the '64bit/32bits' timecode format into seconds
def parseFCPTimeSeconds (timeString):
anonymous
anonymous / rsyncupdate.sh
Created April 12, 2016 21:25
cd /tmp
wget http://rsync.samba.org/ftp/rsync/src/rsync-3.1.1.tar.gz -O - | tar xz
cd *rsync*
./configure
make
sudo make install
make clean