Xiaofan Hu bom-d-van

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.

The Cloudflare outage was a good thing

Cloudflare, the CDN provider, suffered a massive outage today. Some of the world's most popular apps and web services were left inaccessible for serveral hours whilst the Cloudflare team scrambled to fix a whole swathe of the internet.

And that might be a good thing.

The proximate cause of the outage was pretty mundane: a bad config file triggered a latent bug in one of Cloudflare's services. The file was too large (details still hazy) and this led to a cascading failure across Cloudflare operations. Probably there is some useful post-morteming about canary releases and staged rollouts.

How large are large language models? (2025)

This aims to be factual information about the size of large language models. None of this document was written by AI. I do not include any information from leaks or rumors. The focus of this document is on base models (the raw text continuation engines, not 'helpful chatbot/assistants'). This is a view from a few years ago to today of one very tiny fraction of the larger LLM story that's happening.

History

GPT-2,-medium,-large,-xl (2019): 137M, 380M, 812M, 1.61B. Source: openai-community/gpt2. Trained on the unreleased WebText dataset said to 40GB of Internet text - I estimate that to be roughly 10B tokens. You can see a list of the websites that went into that data set here domains.txt.
GPT-3 aka davinci, davinci-002 (2020): 175B parameters. There is a good breakdown of how those parameters are 'spent' here [How d

Network Device Management with container runtimes

This document provides a hands-on guide to understanding how runtimes interacts with network devices and namespaces, focusing on the new "Network Devices" feature described in the OCI (Open Container Initiative) runtime specification. The feature is expected to be released in the version 1.3.0 of the OCI specification.

In high-level container orchestration systems like Kubernetes, the management of network namespaces and interfaces is handled by the [Container Runtime Interface

How to create a patch for a repository with submodules

You checkout whatever repository you want.

Then you make all the changes that are required to make it all work in any submodule as needed. You don't commit in the submodules.

Make sure you have committed everything in your own repository and git diff shows that there are only changes in your submodules and you "parent" repository is still clean.

git --no-pager diff --no-color --submodule=diff &gt; patch.diff

Anti-hype LLM reading list

Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.

Foundational Concepts

	"""
	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

	# DuckDB Vector Tiles with mapgl Example
	# This script demonstrates how to serve vector tiles from DuckDB using ST_AsMVT()
	# and display them in a mapgl map using httpuv

	library(mapgl)
	library(duckdb) # Requires the latest DuckDB version (>= 1.4.0)
	library(httpuv)
	library(sf)
	library(duckspatial)
	library(tigris)

	// defer.h
	// john@crashoverride.com
	// © 2025 Crash Override, Inc.
	// Licensed under the BSD 3-Clause license

	#pragma once
	#include <stdint.h>

	typedef struct n00b_defer_ll_t n00b_defer_ll_t;

	#!/usr/bin/env -S bash -c "docker run -p 8080:8080 -it --rm \$(docker build --progress plain -f \$0 . 2>&1 \| tee /dev/stderr \| grep -oP 'sha256:[0-9a-f]*')"

	# syntax = docker/dockerfile:1.4.0

	FROM node:20

	WORKDIR /root

	RUN npm install sqlite3