Sam sammcj

LLM Wiki v2

A pattern for building personal knowledge bases using LLMs. Extended with lessons from building agentmemory 10K Stars ⭐️, a persistent memory engine for AI coding agents.

This builds on Andrej Karpathy's original LLM Wiki idea file. Everything in the original still applies. This document adds what we learned running the pattern in production: what breaks at scale, what's missing, and what separates a wiki that stays useful from one that rots.

What the original gets right

The core insight is correct: stop re-deriving, start compiling. RAG retrieves and forgets. A wiki accumulates and compounds. The three-layer architecture (raw sources, wiki, schema) works. The operations (ingest, query, lint) cover the basics. If you haven't read the original, start there.

The Complete Guide to Building Skills for Claude

The Great Quant Wars of 2025

"All things leave behind them the Obscurity... and go forward to embrace the Brightness..." — Dao De Jing #42

tl;dr;

Q: Who provides the best GGUFs now?
A: They're all pretty good.

Skip down if you just want graphs and numbers comparing various Qwen3-30B-A3B GGUF quants.

Ollama GPU Selector: Customize GPU Usage for Ollama

If you want to run Ollama on a specific GPU or multiple GPUs, this tutorial is for you. By default, Ollama utilizes all available GPUs, but sometimes you may want to dedicate a specific GPU or a subset of your GPUs for Ollama's use. The idea for this guide originated from the following issue: Run Ollama on dedicated GPU.

Steps:

Create a script let's call it ollama_gpu_selector.sh:
```
nano ollama_gpu_selector.sh
```

	# Standard zshrc stuff like path, etc.
	# ...

	# Enable using the two files above:

	# Functions
	source venv-auto-activate.sh

	# Venv security whitelist/blocklist
	alias venv-security='uv run -q --no-project ~/scripts/venv-security.py'

	import requests
	import json
	import argparse
	from rich.console import Console
	from rich.table import Table
	from rich.live import Live
	from rich.spinner import Spinner

	def search_for_part(description, show_all, no_sort):
	"""

	import asyncio
	import aiohttp
	import os
	from pathlib import Path
	import logging
	from bs4 import BeautifulSoup
	from typing import List, Dict
	from dataclasses import dataclass
	from datetime import datetime
	import time

	#!/bin/bash

	# Define the main export folder
	export_folder=~/Desktop/iMessages_Export
	mkdir -p "$export_folder"

	# Part 1: Generate the CSV file
	echo "Generating CSV file..."

	sqlite3 ~/Library/Messages/chat.db <<EOF

	#!/usr/bin/env ruby

	# https://asmirnov.xyz/vram
	# https://vram.asmirnov.xyz

	require "fileutils"
	require "json"
	require "open-uri"

	# https://huggingface.co/spaces/NyxKrage/LLM-Model-VRAM-Calculator/blob/main/index.html

	from txtai import Embeddings, LLM

	# Data to index
	data = [
	"US tops 5 million confirmed virus cases",
	"Canada's last fully intact ice shelf has suddenly collapsed, forming a Manhattan-sized iceberg",
	"Beijing mobilises invasion craft along coast as Taiwan tensions escalate",
	"The National Park Service warns against sacrificing slower friends in a bear attack",
	"Maine man wins $1M from $25 lottery ticket",
	"Make huge profits without work, earn up to $100,000 a day"