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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.

System Prompt - Spec Agent

Goal

You are an agent that specializes in working with Specs in Claude Code. Specs are a way to develop complex features by creating requirements, design and an implementation plan. Specs have an iterative workflow where you help transform an idea into requirements, then design, then the task list. The workflow defined below describes each phase of the spec workflow in detail.

Workflow to execute

Global AI Rules for Windsurf

AI Guidelines

You are an expert programming assistant focusing on:

TypeScript, React, Node.js, AstroJS 5.x, AstroDB
Shadcn UI and Tailwind CSS useations
Latest features and best practices
Clear, readable, and maintainable code

FWIW: I (@rondy) am not the creator of the content shared here, which is an excerpt from Edmond Lau's book. I simply copied and pasted it from another location and saved it as a personal note, before it gained popularity on news.ycombinator.com. Unfortunately, I cannot recall the exact origin of the original source, nor was I able to find the author's name, so I am can't provide the appropriate credits.

Effective Engineer - Notes

By Edmond Lau
Highly Recommended 👍
http://www.theeffectiveengineer.com/

What's an Effective Engineer?

Docker 虎の巻

Fork from Docker cheat sheet
実際にコマンドを試したい場合は、すぐにDockerを試したい人のための基礎コマンドを参照

何故Dockerを使うべきか

Why Should I Care (For Developers)

"Dockerが面白いのはシンプルな環境に隔離性と再現性をもたらしてくれることだ．ランタイムの環境を一度作れば、パッケージにして別のマシンでも再利用することできる．さらに，すべてはホスト内の隔離された環境で行われる（VMのように）．最も素晴らしい点は，シンプルかつ高速であることだ．"

	"""
	The most atomic way to train and run inference for 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

	#! /usr/bin/perl
	#
	# Written in 2017 by Kazuho Oku
	#
	# To the extent possible under law, the author(s) have dedicated all copyright and related and neighboring rights to this software to the public domain worldwide. This software is distributed without any warranty.
	# You should have received a copy of the CC0 Public Domain Dedication along with this software. If not, see <http://creativecommons.org/publicdomain/zero/1.0/>.
	#
	use strict;
	use warnings;

	// parallels processing v1 (broken code)

	package main

	import (
	"fmt"
	"runtime"
	"time"
	)

	# LVDB - LLOOGG Memory DB
	# Copyriht (C) 2009 Salvatore Sanfilippo <antirez@gmail.com>
	# All Rights Reserved

	# TODO
	# - cron with cleanup of timedout clients, automatic dump
	# - the dump should use array startsearch to write it line by line
	# and may just use gets to read element by element and load the whole state.
	# - 'help','stopserver','saveandstopserver','save','load','reset','keys' commands.
	# - ttl with milliseconds resolution 'ttl a 1000'. Check ttl in dump!

	#!/usr/bin/env bash

	############### USAGE ###############
	#
	# 1. Create a new workspace on Cloud9 using the "Blank" template
	#
	# 2. Run this command in the console:
	# bash <(curl -fsSL https://gist.githubusercontent.com/padde/3c6301f15cbd5025e131740dae33aa95/raw/c9-elixir.sh)
	#
	# 3. There is no step 3!