chrisdavidmiles

#!/bin/bash
# Bulk DNS Lookup
# Generates a CSV of DNS lookups from a list of domains.
#
# File name/path of domain list:
domain_list='domains.txt' # One FQDN per line in file.
#
# IP address of the nameserver used for lookups:
ns_ip='1.1.1.1' # Is using Cloudflare's 1.1.1.1.
#

smosko

# 1. Paste this into Script Editor, replace username and password;
# 2. Export as "Application", check "Stay open" and "Run-only";
# 3. Add to Login Items in Settings/General to run automatically;
# 4. To hide the Dock icon, add to Content/Info.plist:
#     <key>LSUIElement</key>
#     <string>1</string>

on idle
	set idleTime to (do shell script "ioreg -c IOHIDSystem | awk '/HIDIdleTime/ {print $NF/1000000000; exit}'") as integer
	if idleTime is less than 58 then

iagooar

Omarchy Cheat Sheet for Devs

A comprehensive reference for Omarchy - an opinionated Arch Linux + Hyprland setup by DHH.

Essential Keyboard Shortcuts

System & Window Management

Shortcut	Function

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.