karpathy

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.

aparente

---

name: tufte-viz description: | Ideate and critique data visualizations using Edward Tufte's principles from "The Visual Display of Quantitative Information." Use this skill when: (1) Designing new data visualizations or charts (2) Critiquing or improving existing visualizations (3) Reviewing dashboards or reports for graphical integrity (4) Deciding between visualization approaches (5) Reducing chartjunk or improving data-ink ratio (6) Planning small multiples or high-density displays

4abhinavjain

# should works on any cloud-init enabled hypervisor (openstack.. )

# start from a normal ubuntu 20.04 install as minimal was not available for ARM64
# Since ARM64 machines has higher RAM, Shrinking is desired but not necessary. Instead we will increase tmpfs to 1700MB

# Getting root (if sudo -i doesn't work then set a root password beforehand using 'sudo passwd root'

sudo -i

# make sure we are on the highest kernel, so we can delete all the others ...

addohm

#!/usr/bin/env python3
"""vid — a friendlier CLI wrapper around ffmpeg for common video tasks."""

import argparse
import json
import os
import re
import shlex
import shutil
import subprocess

b0gdanw

Links from https://www.apple.com/final-cut-pro/trial/
Final Cut Pro release notes https://support.apple.com/en-us/HT201237
What's new in Final Cut Pro https://support.apple.com/en-us/HT207877
Release history https://web.archive.org/web/20221121093911/https://en.wikipedia.org/wiki/Final_Cut_Pro_X#Release_history

Final Cut Pro 10.1.3
https://secure-appldnld.apple.com/Final_Cut_Pro_X/031-05296.20140814.VRR4r/FinalCutProTrial10.1.3.dmg

Final Cut Pro 10.1.4
https://secure-appldnld.apple.com/Final_Cut_Pro_X/031-02980.20141202.Jgt44/FinalCutProTrial10.1.4.dmg

karpathy

"""
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

kemitchell

https://youtu.be/-C-JoyNuQJs?t=39m45s

When I put the reference implementation onto the website I needed to
put a software license on it.

And I looked at all the licenses that were available, and there were a lot
of them. And I decided that the one I liked the best was the MIT License,
which was a notice that you would put on your source and it would say,
"you're allowed to use this for any purpose you want, just leave the
notice in the source and don't sue me."