Latin BlackHacked

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.

Architectural Comparison: Claude Flow V3 vs Claude Code TeammateTool

Date: 2026-01-25 Analysis: Side-by-side comparison of Claude Flow V3 swarm architecture (developed by rUv) and Claude Code's TeammateTool (discovered in v2.1.19)

Executive Summary

A detailed analysis reveals striking architectural similarities between Claude Flow V3's swarm system and Claude Code's TeammateTool. The terminology differs, but the core concepts, data structures, and workflows are nearly identical.

Compress

tar cf - <files> -P | pv -s $(du -sb <files> | awk '{print $1}') | gzip > <some .tar.gz file>

where:

`` is the root-mounted (i.e. starts with /) path to the files

First install all these prerequisites for compiling:

sudo apt install g++ autoconf libfontconfig1-dev pkg-config libjpeg-dev libopenjpeg-dev gnome-common libglib2.0-dev gtk-doc-tools libyelp-dev yelp-tools gobject-introspection libsecret-1-dev libnautilus-extension-dev

First download the encoding files (no need to compile these) to the current working directory

wget https://poppler.freedesktop.org/poppler-data-0.4.7.tar.gz

Quickstart

pdm install

These are equivalent (given the main.py code)

UVICORN_ROOT_PATH=/api/ pdm run uvicorn main:app
FASTAPI_ROOT_PATH=/api/ pdm run uvicorn main:app

	"$schema" = 'https://starship.rs/config-schema.json'

	format = """
	[](red)\
	$os\
	$username\
	[](bg:peach fg:red)\
	$directory\
	[](bg:yellow fg:peach)\
	$git_branch\

	#!/usr/bin/env python3
	"""
	Script to convert requirements.txt to dependencies list for pyproject.toml for uv project

	Usage:

	Execute the script with:

	python3 requirementstxt_to_uv_pyprojecttoml.py path/to/requirements.txt

	FROM ubuntu:18.04

	# Install wget
	RUN apt-get update
	RUN apt-get install -y wget

	# Add 32-bit architecture
	RUN dpkg --add-architecture i386
	RUN apt-get update

	FROM ubuntu:20.04

	USER root
	WORKDIR /root

	SHELL [ "/bin/bash", "-c" ]

	ARG PYTHON_VERSION_TAG=3.10.5
	ARG LINK_PYTHON_TO_PYTHON3=1

	---

	kind: pipeline
	type: docker
	name: linux-amd64

	trigger:
	branch:
	- myapp
	event: