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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.

@kupietools
kupietools / Docker Desktop v 4.0.0 thru 4.22.1 direct download links
Last active July 12, 2026 15:17
List of Direct Download links for Docker Desktop from version 4.0.0 released 2021-08-31 thru 4.22.1 released 2023-08-24, as archived on archive.org
@mehmetsefabalik
mehmetsefabalik / nginx-https-local.md
Last active December 2, 2025 18:27
Enable https on your local environment with nginx

enable https on your local environment

install mkcert and create certificates

brew install mkcert
mkcert -install
@SehgalDivij
SehgalDivij / middleware.py
Last active September 10, 2024 20:08
Middleware in django to log all requests and responses(Inspired by another Github gist I cannot find the link to, now)
"""
Middleware to log all requests and responses.
Uses a logger configured by the name of django.request
to log all requests and responses according to configuration
specified for django.request.
"""
# import json
import logging
from django.utils.deprecation import MiddlewareMixin