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Raspberry Pi 5 Media Server — Commands

Immich (photos) + Jellyfin (movies/TV) + Tailscale (access from anywhere). Commands only, in the order they're run in the video.

Tested August 2026 on:


Hardware	Raspberry Pi 5, 8GB — CanaKit Starter Kit PRO

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.

AGENTS.md

Apply these rules within the host's instruction hierarchy and follow repository instructions within their scope. “Prefer” marks guidance; other instructions are requirements.

Judgment and Scope

Identify the requested outcome, scope, constraints, and observable completion criteria. Challenge assumptions or approaches that materially undermine the goal; then follow the user's informed decision.
Resolve uncertainty from available evidence. Ask early when missing information materially affects correctness, scope, authorization, or consequential choices. Make routine, reversible decisions directly.
Carry authorized work through implementation and verification without routine reconfirmation. When approval is required, first complete already-authorized preparation that does not depend on that approval, and present a concrete, reviewable proposal or result.
Change only what the task requires and preserve unrelated existing work. Report out-of-scope findings without fixing them. Obtain approv

	import json
	import os
	import psycopg2
	import pandas as pd
	import requests
	import numpy as np
	import random


	ENDPOINT = os.environ['ENDPOINT']

	#!/bin/bash

	# Claude Desktop Multi-Instance Launcher
	#
	# Uses Electron's --user-data-dir flag to run fully isolated instances.
	# Each instance gets its own data directory (config, VM bundles, SDK, sessions).
	#
	# Usage:
	# ./claude_quick.sh # Show interactive menu
	# ./claude_quick.sh <instance> # Launch named instance

	<!DOCTYPE html>
	<html lang="en">
	<head>
	<link rel="icon" type="image/x-icon" href="/favicon.ico">
	<title>Rebane's Discord Colored Text Generator</title>
	<meta charset="UTF-8">
	<meta name="description" content="Rebane's Discord Colored Text Generator">
	<meta name="author" content="rebane2001">
	<meta content="#5865F2" name="theme-color">
	<script id="discord:component-embed" type="application/json">