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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.

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#!/usr/bin/env python3
"""
microgpt2.py

A dependency-free, single-file GPT-style language model in pure Python.

Key properties:
- stdlib only
- explicit forward/backward kernels (no generic scalar autograd)
- flat float32 parameter buffers using array('f')

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"""
microgpt_dnb.py — Fork of Karpathy's microgpt with Dynamic Notes Bus

Original: https://gist.github.com/karpathy/8627fe009c40f57531cb18360106ce95
Paper:    https://arxiv.org/abs/2512.10054
Code:     https://github.com/logan-robbins/parallel-decoder-transformer

Karpathy's microgpt is the complete GPT algorithm in ~200 lines of pure Python.
This fork adds ~100 lines to show where the Dynamic Notes Bus (DNB), Shared
Notes Cross-Attention (SNC), and Planner Head fit inside the transformer to

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jq: JSONL ↔︎ JSON conversion

Prerequisites

• jq — https://jqlang.github.io/jq/ — "like sed for JSON data"

  There are several options available for installing jq. I prefer to use Homebrew: brew install jq

1. JSONL → JSON

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1. Create a file Image.js from the source below (it is almost a copy of Image.js from tiptap-extensions except that it has a constructor that accepts uploadFunc (function to be called with image being uploaded) and additional logic if(upload) { ... } else { ... previous base64 logic .. } in the new Plugin section.

import {Node, Plugin} from 'tiptap'
import {nodeInputRule} from 'tiptap-commands'

/**
 * Matches following attributes in Markdown-typed image: [, alt, src, title]
 *

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brew install caddy mkcert nss dnsmasq

mkcert -install

mkcert '*.app.test' '*.cdn.test'

# rename the certs and move them under /usr/local/etc/caddy/certs

cat <<EOF > /usr/local/etc/caddy/Caddyfile
*.app.test:443, *.cdn.test:443 {

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# mount volume PWD on host to /app in container.
shai@lappy ~/tmp/example-working-docker-compose-environment-vars [master *] ± % cat docker-compose.yml
version: "3"
services:
  some_server:
...
    volumes:
      - $PWD:/app

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# admin
from django import admin
from django.db import models
from widget import TextareaWithCounter

class MyModelAdmin(admin.ModelAdmin):
    formfield_overrides = {
        models.TextField: {'widget': TextareaWithCounter},
    }
    class Media:

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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

You can get the list of installed codecs with:

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<script>
	let s = 0;
	let m = 0;
	let active = false;
	let timer;
	
	function setActive(a) {
		active = a;
		if(!active) {
			clearInterval(timer)