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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.

Cloud Security Orienteering: Checklist
by Rami McCarthy
via TL;DR sec

How to orienteer in a cloud environment, dig in to identify the risks that matter, and put together actionable plans that address short, medium, and long term goals.

Based on the Cloud Security Orienteering methodology.

Checklist

Studying for a Tech Interview Sucks, so Here's a Cheat Sheet to Help

This list is meant to be a both a quick guide and reference for further research into these topics. It's basically a summary of that comp sci course you never took or forgot about, so there's no way it can cover everything in depth. It also will be available as a gist on Github for everyone to edit and add to.

Data Structure Basics

###Array ####Definition:

Stores data elements based on an sequential, most commonly 0 based, index.
Based on tuples from set theory.

Download and install VirtualBox
Download and install Vagrant

Clone devstack repository

 git clone git://github.com/openstack-dev/devstack.git

Switch to devstack directory

cd devstack


	import React from 'react';

	function App() {
	return (
	<div>Hello, Advisor</div>

	);
	}