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@mems
mems / README.md
Last active October 26, 2026 18:47
Fullres images of a https://www.fashionanthology.com/ galleries
@adamawolf
adamawolf / Apple_mobile_device_types.txt
Last active October 26, 2026 18:01
List of Apple's mobile device codes types a.k.a. machine ids (e.g. `iPhone1,1`, `Watch1,1`, etc.) and their matching product names
i386 : iPhone Simulator
x86_64 : iPhone Simulator
arm64 : iPhone Simulator
iPhone1,1 : iPhone
iPhone1,2 : iPhone 3G
iPhone2,1 : iPhone 3GS
iPhone3,1 : iPhone 4
iPhone3,2 : iPhone 4 GSM Rev A
iPhone3,3 : iPhone 4 CDMA
iPhone4,1 : iPhone 4S
@Gummibeer
Gummibeer / index.html
Last active October 26, 2026 18:01
Tailwind Admin Dashboard Template
<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
<title>Tailwind Admin Dashboard</title>
<link href="https://unpkg.com/tailwindcss@^1.0/dist/tailwind.min.css" rel="stylesheet">
</head>
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<meta http-equiv="X-UA-Compatible" content="ie=edge" />
<title>Weather App</title>
<style>
h1,
h2,
@cemizm
cemizm / aqara_fp300.py
Last active October 26, 2026 17:50
Aqara FP300 Presence Sensor
# Docs / install guide (custom ZHA quirks + FP300 example):
# https://meshstack.de/post/home-assistant/zha-custom-quirks/
#
# Upstream PR: https://github.com/zigpy/zha-device-handlers/pull/4504
# Tracking issue: https://github.com/zigpy/zha-device-handlers/issues/4487
"""Quirk for Aqara lumi.sensor_occupy.agl8."""
import asyncio
from typing import Any, Final
@SamuraiButlerStaple
SamuraiButlerStaple / Moon-Sweepers-PC-Trainer-Tools.md
Created October 26, 2026 14:51
Moon Sweepers PC Trainer Tools for Windows with automation profiles, resource workflows, upgrade presets, saved configurations and a custom desktop dashboard.

Moon Sweepers Windows download

Moon Sweepers desktop utility

Moon Sweepers project badges

Moon Sweepers PC Trainer Tools

A Windows desktop utility page for Moon Sweepers focused on reusable profiles, resource workflows, automation-oriented configuration and progression presets.

@nvckai
nvckai / gametime-badge.md
Last active October 26, 2026 18:31
Discord Game Time Badge Spoofer

Discord Game Time Badge Spoofer (5000 Hours)

How to use

  1. Open Discord client, press Ctrl + Shift + I to open DevTools (Discord client only, Browser is not supported)
  2. Go to Console tab
  3. Type allow pasting and press Enter
  4. Paste the snippet below and press Enter

Snippet

@naim149
naim149 / GH-600-public-study-guide.md
Created May 24, 2026 15:08
GH-600 Public Study Guide

GH-600 Public Study Guide

Markdown-only workbook for Exam GH-600: Developing in Agentic AI Systems.

Last updated: May 24, 2026.

This version is organized around the official GH-600 domains. Each domain explains the concepts, shows the GitHub implementation artifacts, and includes examples you should be able to read in YAML, Markdown, CLI output, PR timelines, and audit logs.

Public sharing note: this guide is not an exam dump and does not contain real exam questions or answer choices. It is a structured study workbook built from official Microsoft and GitHub documentation, with practical examples written for learning and review.

name explain-diff-html
description Use when the user asks for a rich explanation of a code change, diff, branch, or PR. Produces HTML output.

Explain Diff

Please make me a rich, interactive explanation of the specified code change.

It should have these sections:

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.