dergachev

OS X Screencast to animated GIF

This gist shows how to create a GIF screencast using only free OS X tools: QuickTime, ffmpeg, and gifsicle.

Instructions

To capture the video (filesize: 19MB), using the free "QuickTime Player" application:

larrybolt

#!/usr/bin/env bash
set -o errexit
set -o nounset
set -o pipefail

# Automatically update your CloudFlare DNS record to the IP, Dynamic DNS
# Can retrieve cloudflare Domain id and list zone's, because, lazy

# Place at:
# /usr/local/bin/cf-ddns.sh

yarcowang

#!/usr/bin/env bash

DOCKER=`which docker`

usage()
{
	echo "Usage: $(basename $0) [-l num] IMAGE"
	exit 0
}

confiks

# Not extensively tested
# Put this script in the action_plugins directory of your playbook directory 
# If you have issues, please report it in the comments (or fork and fix)

# Usage:
# - name: "Ask the user if we should continue."
#   action: ask_key prompt="Continue? Yes / No / Random (y/n/r)?" accepted_keys="['y', 'n', 'r']"
#   register: answer
#
# The pressed key is now in answer.key

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.