ogzhanolguncu

name

socratic-code-mentor

description

Mentor a user who builds a project to learn how it works. They write the core; you do all other work and keep it fun. Use when the user says "teach me", "guide me", "mentor me", "hints only", "no spoilers", "Socratic", or "help me learn X by building it", or when a project file says the project is for learning. Do not use for normal feature work. Do not use when the first request is "just fix it" or "write it for me".

j8

defaults write -g NSScrollViewRubberbanding -int 0
defaults write -g NSAutomaticWindowAnimationsEnabled -bool false
defaults write -g NSScrollAnimationEnabled -bool false
defaults write -g NSWindowResizeTime -float 0.001
defaults write -g QLPanelAnimationDuration -float 0
defaults write -g NSScrollViewRubberbanding -bool false
defaults write -g NSDocumentRevisionsWindowTransformAnimation -bool false
defaults write -g NSToolbarFullScreenAnimationDuration -float 0
defaults write -g NSBrowserColumnAnimationSpeedMultiplier -float 0
defaults write com.apple.dock autohide-time-modifier -float 0

akorn

#!/bin/zsh
#
# Copyright (c) 2014, 2020 by Dr. András Korn. Implements the basic idea of a similar script by Robert Coup (2013).
# License: GPLv3

function usage() {
	echo 'Usage:

rsync_parallel [--parallel=N] <args to find(1) to generate list of stuff to transfer> -- <args to rsync>
 

carefree-ladka

Google 2026 Interview Preparation - Complete Weekly Roadmap

Target: Crack Google L3/L4/L5 by 2026 Duration: 24 weeks (6 months intensive preparation) Daily Time Commitment: 3-4 hours minimum

---

learnopengles

Agent Harness Seed

A single prompt that builds an agent harness for your repository. The agent scans the code, asks you a short series of questions, and writes a harness sized to your project rather than adapted from someone else's.

What this one gives you: rules that hold because a script checks them, with the reason for each written down; review by a second model family, briefed on what the product must do rather than how the code does it; plan files that make "done" binary and give executors the right to refuse a bad contract; handoffs so an unfinished session is resumable; routing

Pusnow

redding1

blueprint:
  name: LIFX 4 Button Generic Matter Switch
  description: >
    Easily create actions within a single Automation for **Single**, **Double** and **Long Press**
    for a **LIFX 4 Button Switch** over Matter.  
    Define actions for each press type, or leave blank if unused.
  source_url: https://gist.github.com/redding1/2731c6d0c4ff2eb9c40d05be409b217d
  domain: automation
  input:
    button1_entity:

EtsuNDmA

⚠️ This gist was the first time written in 2018. It was tested with django==2.2.x. It's likely to be relevant for the newer versions of django and jupyter, but I did't check that.

2023-03-15: checked it for python == 3.11, django == 4.1.7, jupyterlab = 3.6.1, django-extensions == 3.2.1, jupyter-server <= 2.0.0

Django_jupyter

Using Django project in Jupyter or JupyterLab

Method 1 (using shell_plus)

TheOnlyWayUp

This script likes all the songs in a Youtube Music Playlist at once.

How to use:

• Visit a Playlist's page on ytm (URL looks like: https://music.youtube.com/playlist?list=...
• Press ctrl + shift + j. This opens the Developer Console.
• Copy the script in this gist (That's in script.js)
• Paste the code into the Developer Console on the ytm Tab, hit enter.
• Great, you're done!

Star ⭐ this gist if it was useful. Follows to my GitHub Profile are appreciated.

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.