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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.

obsidian copilot

your notes = your lifelong ai companion + intelligence augmentation

COMMENTS VERY WELCOME! this is a first pass to put these ideas in one place

tldr - combine obsidian + openinterpreter to create a bespoke pkm copilot experience. if you follow the "file over app" philosophy, this combination can be your lifetime AI companion

How to

<details>
  <summary>Click me</summary>
  
  ### Heading
  1. Foo
  2. Bar
     * Baz
 * Qux

ffmpeg

Converting Audio into Different Formats / Sample Rates

Minimal example: transcode from MP3 to WMA:
ffmpeg -i input.mp3 output.wma

You can get the list of supported formats with:
ffmpeg -formats

Convert WAV to MP3, mix down to mono (use 1 audio channel), set bit rate to 64 kbps and sample rate to 22050 Hz:

	# OpenClaw Implementation Prompts

	Each prompt below is a self-contained brief you can hand to an AI coding assistant (or use as a project spec) to build that use case from scratch. Adapt the specific services to whatever you already use — the patterns are what matter.

	---

	## 1) Personal CRM Intelligence

	```
	Build me a personal CRM system that automatically tracks everyone I interact with, with smart filtering so it only adds real people — not newsletters, bots, or cold outreach.

	# train_grpo.py
	import re
	import torch
	from datasets import load_dataset, Dataset
	from transformers import AutoTokenizer, AutoModelForCausalLM
	from peft import LoraConfig
	from trl import GRPOConfig, GRPOTrainer

	# Load and prep dataset

	# You can export a Copilot Chat session in Visual Studio Code
	# to a JSON file via `Ctrl+Shift+P \| Chat: Export Session...`
	# or by choosing `View > Command Palette \| Chat: Export Session...`

	# Inspired by https://github.com/Marijn-Bergman/copilot-chat-export-formatter

	import sys
	import json

	#301 Redirects for .htaccess

	#Redirect a single page:
	Redirect 301 /pagename.php http://www.domain.com/pagename.html

	#Redirect an entire site:
	Redirect 301 / http://www.domain.com/

	#Redirect an entire site to a sub folder
	Redirect 301 / http://www.domain.com/subfolder/

	// Modified version of this Stack Overflow response:
	// https://stackoverflow.com/a/48459005/1950503
	// (Removes ramda dependency, adds .trim() to string replacer, adds it to Jest global var instead of exporting)

	// To use:
	// Put `<rootDir>/path/to/customWhitespaceMatcher.js` in your Jest config under setupFiles
	// Call it in your tests like this:
	// expect(
	// customMatchers.whitespaceMatcher(receivedResult, expectedResult).pass
	// ).toBeTruthy();

	' Disables Vanguard from starting when you boot your computer
	Call CreateObject("Shell.Application").ShellExecute("cmd.exe", "/c ""sc config vgc start= disabled & sc config vgk start= disabled""", "", "runas")