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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.

/*
* Copyright 2026 Kyriakos Georgiopoulos
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
@cbrnr
cbrnr / .SRCINFO
Last active April 30, 2026 13:25
rstudio-desktop-bin AUR PKGBUILD
pkgbase = rstudio-desktop-bin
pkgdesc = An integrated development environment (IDE) for R (binary from RStudio official repository)
pkgver = 2026.04.0.526
pkgrel = 2
url = https://posit.co/products/open-source/rstudio/
arch = x86_64
license = AGPL
depends = r>=3.3.0
depends = sqlite
depends = libxkbcommon
@ttscoff
ttscoff / linkding-cards.css
Last active April 30, 2026 13:24
Custom CSS to give linkding a dark, card-based layout
/*
Card-based layout for Linkding
Large images, fully clickable
Entire card selectable in bulk edit mode
Tags moved to expandable sidebar
Author: Brett Terpstra (https://brettterpstra.com)
GitHub: @ttscoff <https://github.com/ttscoff>
License: MIT
*/
"""
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
@jboner
jboner / latency.txt
Last active April 30, 2026 13:20
Latency Numbers Every Programmer Should Know
Latency Comparison Numbers (~2012)
----------------------------------
L1 cache reference 0.5 ns
Branch mispredict 5 ns
L2 cache reference 7 ns 14x L1 cache
Mutex lock/unlock 25 ns
Main memory reference 100 ns 20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy 3,000 ns 3 us
Send 1K bytes over 1 Gbps network 10,000 ns 10 us
Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD