This is a cheat sheet for how to perform various actions to ZSH, which can be tricky to find on the web as the syntax is not intuitive and it is generally not very well-documented.
| Description | Syntax |
|---|---|
| Get the length of a string | ${#VARNAME} |
| Get a single character | ${VARNAME[index]} |
| #!/usr/bin/env bash | |
| # Define an array of Braille patterns for a spinner | |
| six_dot_cell_pattern=("⠙" "⠹" "⠸" "⠼" "⠴" "⠦" "⠧" "⠇") | |
| eight_dot_cell_pattern=("⣾" "⢿" "⡿" "⣷" "⣯" "⢟" "⡻" "⣽") | |
| # Set the pattern | |
| braille_spinner=("${eight_dot_cell_pattern[@]}") | |
| # Set the duration for each spinner frame (in seconds) |
Download lastest Android platform tools
Unzip the folder to platform-tools
./platformtools/adb <COMMAND>
| """ | |
| 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 |
| export const isAutoplaySupported = () => { | |
| // Detect if user can autoplay inline video | |
| // Works when user is on low-battery mode on IOS | |
| // Return promise from video.play | |
| const video = document.createElement('video') | |
| video.src = 'data:video/mp4;base64,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 |
| #!/usr/bin/env bash | |
| ### UPDATED FOR SEASON PACK FROM USENET SUPPORT IN | |
| ### CROSS SEED v6 ONLY!! v5 IS NOT SUPPORTED FOR USENET | |
| ### SEASON PACKS AND WILL ALWAYS FAIL TO FIND MATCHES | |
| ### TO ENABLE THIS FEATURE YOU _MUST_ SWITCH TO THE | |
| ### ON IMPORT COMPLETE EVENT TYPE IN YOUR SONARR SETTINGS | |
| ### SEE FURTHER DOWN FOR CONFIGURATION SECTION, LEAVE THE HYPHEN (-) | |
| ### IN FRONT OF THE CONFIGURATION OPTIONS AS SEEN IN THE EXAMPLES |
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.
