| """ | |
| 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 |
This is a compiled list of falsehoods programmers tend to believe about working with time.
Don't re-invent a date time library yourself. If you think you understand everything about time, you're probably doing it wrong.
| " Get the defaults that most users want. | |
| unlet! skip_defaults_vim | |
| source $VIMRUNTIME/defaults.vim | |
| filetype plugin indent on | |
| call plug#begin('~/.vim/plugged') | |
| Plug 'tpope/vim-fugitive' " Git support | |
| Plug 'junegunn/vim-peekaboo' " Peek content of the registers | |
| Plug 'junegunn/vim-easy-align' " A Vim alignment plugin |
| AA | |
| AAA | |
| Aachen | |
| Aachener | |
| Aachenerin | |
| Aachenerinnen | |
| Aachenern | |
| Aacheners | |
| Aachens |
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.
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.
| /** | |
| * Creates a basic clock and binds it to a specified element | |
| */ | |
| class Clock { | |
| timeTemplate: string = "00:00:00"; | |
| clockHandler: number; | |
| target: HTMLElement; | |
| /** | |
| * @return string, time |
| from argparse import ArgumentParser | |
| from pathlib import Path | |
| import cv2 | |
| import numpy as np | |
| import numpy.typing as npt | |
| def generate_collage( | |
| image: npt.NDArray, |
This guide will walk through how to decompile/reverse engineer IL2CPP games for modding usage.
Note: expect this entire process to take upwards of an hour. Have something ready to do on the side while waiting for processing to finish.
