Denis Denisov denji

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.

Claude Code Environment Variables

Scope and methodology

This file documents environment variables visible in the checked-in source of this repository snapshot. It is not guaranteed to be exhaustive for the full product, because many imported files are not present here.

For each variable, this document gives:

the visible purpose in code
the rough subsystem it belongs to

FAQ on the xz-utils backdoor (CVE-2024-3094)

This is a living document. Everything in this document is made in good faith of being accurate, but like I just said; we don't yet know everything about what's going on.

Update: I've disabled comments as of 2025-01-26 to avoid everyone having notifications for something a year on if someone wants to suggest a correction. Folks are free to email to suggest corrections still, of course.

Background

Pages

As you can see on the AdoptOpenJDK blog

OpenJ9 builds

	#!/usr/bin/env python3
	"""
	Claude Code token usage analyzer.
	Analyzes ~/.claude/projects/ JSONL files for token usage patterns.
	"""

	import json
	import os
	import sys
	from pathlib import Path

	#!/bin/sh
	# ISC License

	# Copyright (c) 2026 Alexandre Gomes Gaigalas <alganet@gmail.com>

	# Permission to use, copy, modify, and/or distribute this software for any
	# purpose with or without fee is hereby granted, provided that the above
	# copyright notice and this permission notice appear in all copies.

	# THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES

	"""
	The most atomic way to train and run inference for a GPT in pure, dependency-free Python.
	This file is the complete algorithm.
	Everything else is just efficiency.

	@karpathy
	"""

	import os # os.path.exists
	import math # math.log, math.exp

	How to quantize 70B model so it will fit on 2x4090 GPUs:

	I tried EXL2, AutoAWQ, and SqueezeLLM and they all failed for different reasons (issues opened).

	HQQ worked:

	I rented a 4x GPU 1TB RAM ($19/hr) instance on runpod with 1024GB container and 1024GB workspace disk space.
	I think you only need 2x GPU with 80GB VRAM and 512GB+ system RAM so probably overpaid.

	Note you need to fill in the form to get access to the 70B Meta weights.

	package main

	// ▄─▄ ▄ ▄ ▄ ──▄ ▄─▄ ▄─▄
	// ▓─ ▓ ▓ ▓ ▓ ▓ ▀─▄ ▀─▄
	// ▀ ▀─▀─▀ ──▀ ▀─▀ ▀─▀
	// f w d --> 5 5
	//
	// simple rfc1928 proxy server
	//
	//

	#!/bin/bash
	set -e

	GO_VERSION="1.22.3"
	GO_URL="https://go.dev/dl/go${GO_VERSION}.linux-amd64.tar.gz"
	EXPECTED_CHECKSUM="8920ea521bad8f6b7bc377b4824982e011c19af27df88a815e3586ea895f1b36"

	# Log output of script
	exec > >(tee -i /home/ubuntu/install.log)
	exec 2>&1