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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.

Summary

MuMu Player Pro for macOS (by NetEase) executes a comprehensive system data collection routine every 30 minutes while the emulator is running. This includes enumerating all devices on your local network, capturing every running process with full command-line arguments, inventorying all installed applications, reading your hosts file, and dumping kernel parameters -- all tied to your Mac's serial number via SensorsData analytics.

None of this is disclosed in MuMu's privacy policy. None of it is necessary for an Android emulator to function.

Environment

App: MuMu Player Pro for macOS (v1.8.5)
Bundle ID: com.netease.mumu.nemux-global

Super fast, keyboard-optimized, client side Hugo search

This is a fork of and builds upon the work of Eddie Webb's search and Matthew Daly's search explorations.

It's built for the Hugo static site generator, but could be adopted to function with any json index.

To see it in action, go to craigmod.com and press CMD-/ and start typing.

⚠️ This is not maintained. Feel free to check comments and/or forks for more current options.

Background

This was created years ago; at the time I'd been a Shibboleth admin for nearly a decade but we needed something that could handle OIDC/OAuth and that explicitly supported OpenJDK. After a lot of investigation, I really liked Keycloak/Red Hat Single Sign-On. More details here: Gluu vs keycloack vs wso2 identity management

Comparison

(Items in bold indicate possible concerns)

Google Public NTP [AS15169]:

time.google.com

time1.google.com

time2.google.com

time3.google.com

	#!/usr/bin/env osascript -l JavaScript

	const App = Application.currentApplication();
	App.includeStandardAdditions = true;

	const kCFPrefsFeatureFlagsDir = '/Library/Preferences/FeatureFlags/Domain';
	const kCFPrefsFeatureEnabledKey = 'Enabled';

	const kUIKitDomainPrefsTemporaryPath = '/tmp/UIKit.plist';
	const kUIKitRedesignedTextCursorKey = 'redesigned_text_cursor';

	#!/usr/bin/bash
	#
	# Cut a fragment of a video with the minimal possible re-encoding.
	# If the new start point is not a key frame it reencodes the video
	# from that point until the frame before a new keyframe. The remaining
	# part is copied as passthrough and both fragments are concatenated
	#
	# In order to make the video streams compatible we use the same codec
	# and bitrate. This works fine with h264. No idea about other codecs

	#!/usr/bin/env python

	import math
	import sys
	from moviepy.editor import AudioClip, VideoFileClip, concatenate_videoclips



	# Get average RGB of part of a frame. Frame is H * W * 3 (rgb)
	# Assumes x1 < x2, y1 < y2

	2019-01-01 open Assets:CN:Cash
	2019-01-01 open Assets:CN:Alipay ; 支付宝余额
	2019-01-01 open Assets:CN:Wechat ; 微信零钱
	2019-01-01 open Assets:CN:DebitCard:CMB ; 招商银行储蓄卡
	2019-01-01 open Assets:CN:DebitCard:BOC ; 中国银行储蓄卡

	2019-01-01 open Assets:CN:FinTech:LCT ; 微信理财通
	2019-01-01 open Assets:CN:FinTech:ZZY ; 招行朝朝盈
	2019-01-01 open Assets:CN:FinTech:YEB ; 支付宝余额宝

	import os
	import signal
	import socket
	import sys
	import time


	def be_a_worker(sock):
	"""将当前进程变成一个 worker