sikirumoshood

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.

So you want to write a sync system for a web app with offline and realtime support? Good luck. You might find the following resources useful.

Overview articles

Database in a browser, a spec (Stepan Parunashvili)

What problem are we trying to solve with a sync system?
The web of tomorrow (Nikita Prokopov)

Node.js Deployment

Steps to deploy a Node.js app to DigitalOcean using PM2, NGINX as a reverse proxy and an SSL from LetsEncrypt

1. Sign up for Digital Ocean

If you use the referal link below, you get $10 free (1 or 2 months) https://m.do.co/c/5424d440c63a

2. Create a droplet and log in via ssh

I will be using the root user, but would suggest creating a new user

	#!/usr/bin/env bash
	# lcc - Local Claude Code launcher
	# Points Claude Code at a local LLM served by llama.cpp on your GB10 device
	#
	# Usage:
	# lcc <modelname> — launch Claude Code with the specified model
	# lcc <modelname> [args] — pass additional arguments to claude
	# lcc — show help/launch with model if one is available
	#
	# Prerequisites:

	"""
	The most atomic way to train and inference a GPT in pure, dependency-free Python.
	This file is the complete algorithm.
	Everything else is just efficiency.

	@karpathy
	"""

	import os # os.path.exists
	import math # math.log, math.exp