This gist shows how to create a GIF screencast using only free OS X tools: QuickTime, ffmpeg, and gifsicle.
To capture the video (filesize: 19MB), using the free "QuickTime Player" application:
matsglj matsglj
dergachev
/ GIF-Screencast-OSX.md
Last active
September 17, 2026 20:02
OS X Screencast to animated GIF
matsglj
/ Create iOS Icons.jsx
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August 29, 2015 14:14
— forked from twonjosh/Create iOS Icons.jsx
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| // Photoshop Script to Create iPhone Icons from iTunesArtwork | |
| // | |
| // WARNING!!! In the rare case that there are name collisions, this script will | |
| // overwrite (delete perminently) files in the same folder in which the selected | |
| // iTunesArtwork file is located. Therefore, to be safe, before running the | |
| // script, it's best to make sure the selected iTuensArtwork file is the only | |
| // file in its containing folder. | |
| // | |
| // Copyright (c) 2010 Matt Di Pasquale | |
| // Added tweaks Copyright (c) 2012 by Josh Jones http://www.appsbynight.com |
Estimated time: 10 minutes
stecman
/ AutoCrop.lua
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July 8, 2026 17:13
Lightroom plugin to calculate image crops using OpenCV
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| -- LR imports | |
| local LrApplication = import("LrApplication") | |
| local LrApplicationView = import("LrApplicationView") | |
| local LrBinding = import("LrBinding") | |
| local LrDevelopController = import("LrDevelopController") | |
| local LrDialogs = import("LrDialogs") | |
| local LrExportSession = import("LrExportSession") | |
| local LrFileUtils = import("LrFileUtils") | |
| local LrFunctionContext = import("LrFunctionContext") | |
| local LrLogger = import("LrLogger") |
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| int[][] result; | |
| float t, c; | |
| float ease(float p) { | |
| return 3*p*p - 2*p*p*p; | |
| } | |
| float ease(float p, float g) { | |
| if (p < 0.5) | |
| return 0.5 * pow(2*p, g); |
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.