ManzDev

{
    /* UI */
    "workbench.colorTheme": "Monokai +Blue",
    "workbench.sideBar.location": "right",
    "window.zoomLevel": 2,
    "breadcrumbs.enabled": false,
    "workbench.iconTheme": "vscode-icons",
    "telemetry.telemetryLevel": "off",
    "window.commandCenter": false,

qoomon

Conventional Commit Messages

See how a minor change to your commit message style can make a difference.

git commit -m"<type>(<optional scope>): <description>" \
  -m"<optional body>" \
  -m"<optional footer>"

Nicolas-Richard

#!/bin/bash

# You need https://github.com/moretension/duti (installable via `brew`)
# and of course Sublime

# Set the bundle identifier for Sublime Text
sublime_bundle_identifier="com.sublimetext.4"

# List of file extensions
file_extensions=(

acidgreenservers

CODEBASE REASONING TOPOLOGY (Short)

You are a thinking partner for experienced developers. Your role is to help them think clearer, design better systems, and ship coherent code — not to teach or act as a blind code generator.

Core Truth: Structure is persistence. Prioritize tight topology over perfect context.

• Map the relationships deeply, even if you don't see the whole universe.

---

ENTRY PROTOCOL: Ambiguity Detection

orangeRat

Workflow Orchestration

1. Plan Node Default

• Enter plan mode for ANY non-trivial task (3+ steps or architectural decisions)
• If something goes sideways, STOP and re-plan immediately – don't keep pushing
• Use plan mode for verification steps, not just building
• Write detailed specs upfront to reduce ambiguity

2. Subagent Strategy

• Use subagents liberally to keep main context window clean

trashhalo

Ghostty Keyboard Shortcuts

Default keyboard shortcuts for Ghostty terminal emulator. Platform-specific differences are noted where applicable.

Window Management

Action	Windows/Linux	macOS
New window	Ctrl+Shift+N	Cmd+N
Close window	Alt+F4	Cmd+Shift+W

selcukcihan

• NodeJS and JavaScript
  • Event loop
  • Parallel programming
  • Async
  • Libuv and the thread pool
  • Streams and backpressure
  • Worker threads vs child processes
  • Memory model (V8 heap, garbage collection, memory leaks)
  • CommonJS vs ESM modules
• Clustering and process management (PM2, cluster module)

am17an

#!/usr/bin/env python3
import argparse, json, sys, time
from urllib import request

PROMPTS = [
    {"name": "code_python",      "prompt": "Write a Python function that returns the n-th Fibonacci number using memoization. Include a docstring."},
    {"name": "code_cpp",         "prompt": "Write a C++ template function `clamp(x, lo, hi)` that returns x clamped to [lo, hi]. No std::clamp."},
    {"name": "explain_concept",  "prompt": "Explain how speculative decoding works in large language model inference, in three short paragraphs."},
    {"name": "summarize",        "prompt": "Summarize in two sentences: The Industrial Revolution began in Britain in the late 18th century, transforming manufacturing through mechanization, steam power, and the factory system. It spread to continental Europe and North America during the 19th century."},
    {"name": "qa_factual",       "prompt": "Q: What are the four fundamental forces of physics?\nA:"},

kazzohikaru

<div class="overlay-navigation">
  <nav role="navigation">
    <ul>
      <li><a href="#" data-content="The beginning">Home</a></li>
      <li><a href="#" data-content="Curious?">About</a></li>
      <li><a href="#" data-content="I got game">Skills</a></li>
      <li><a href="#" data-content="Only the finest">Works</a></li>
      <li><a href="#" data-content="Don't hesitate">Contact</a></li>
    </ul>
  </nav>

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.