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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.

@andy-thomason
andy-thomason / Genomics_A_Programmers_Guide.md
Created May 14, 2019 13:32
Genomics a programmers introduction

Genomics - A programmer's guide.

Andy Thomason is a Senior Programmer at Genomics PLC. He has been witing graphics systems, games and compilers since the '70s and specialises in code performance.

https://www.genomicsplc.com

@stevenringo
stevenringo / reinvent-2017-youtube.md
Created December 3, 2017 23:01
Links to YouTube recordings of AWS re:Invent 2017 sessions

| Title | Description

@LouisAmon
LouisAmon / avro_to_dataframe.py
Created January 15, 2017 11:55
Read Avro file from Pandas
import pandas
import fastavro
def avro_df(filepath, encoding):
# Open file stream
with open(filepath, encoding) as fp:
# Configure Avro reader
reader = fastavro.reader(fp)
# Load records in memory
records = [r for r in reader]
@rxaviers
rxaviers / gist:7360908
Last active April 26, 2026 02:16
Complete list of github markdown emoji markup

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