peterwang peterwang

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.

This is an OPML version of the HN Popularity Contest results for 2025, for importing into RSS feed readers.

Plug: if you want to find content related to your interests from thousands of obscure blogs and noisy sources like HN Newest, check out Scour. It's a free, personalized content feed I work on where you define your interests in your own words and it ranks content based on how closely related it is to those topics.

macOS Internals

Understand your Mac and iPhone more deeply by tracing the evolution of Mac OS X from prelease to Swift. John Siracusa delivers the details.

Starting Points

How to use this gist

You've got two main options:

Purpose

Bootstrap knowledge of LLMs ASAP. With a bias/focus to GPT.

Avoid being a link dump. Try to provide only valuable well tuned information.

Prelude

Neural network links before starting with transformers.

An opinionated list of excellent Clojure learning materials

These resources (articles, books, and videos) are useful when you're starting to learn the language, or when you're learning a specific part of the language. This an opinionated list, no doubt. I've compiled this list from writing and teaching Clojure over the last 10 years.

🔴 Mandatory (for both beginners and intermediates)
🟩 For beginners
🟨 For intermediates

	"""
	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

	You are ChatGPT, a large language model based on the GPT-5 model and trained by OpenAI.
	Knowledge cutoff: 2024-06
	Current date: 2025-08-08

	Image input capabilities: Enabled
	Personality: v2
	Do not reproduce song lyrics or any other copyrighted material, even if asked.
	You're an insightful, encouraging assistant who combines meticulous clarity with genuine enthusiasm and gentle humor.
	Supportive thoroughness: Patiently explain complex topics clearly and comprehensively.
	Lighthearted interactions: Maintain friendly tone with subtle humor and warmth.

	#pragma once

	#define DYN_ARR_OF(type) struct { \
	type *data; \
	type *endptr; \
	uint32_t capacity; \
	}

	#if !defined(__cplusplus)
	#define decltype(x) void*

	import multiprocessing

	def worker():
	while True:
	pass

	if __name__ == '__main__':
	jobs = []
	cpu_count = multiprocessing.cpu_count()
	print("About to start %d process to warm your mac" % cpu_count)