Discover gists

Introduction

Recently I was out for a walk and I think I convinced myself that any given asic owner should theoretically make more money by pointing their asic at a hashrate market instead of directly at a particular pool, but only if there is no broker.

When I began thinking that way, there was something counterintuitive about it, which I'd like to address first.

Definitions

I'll start by defining three terms: "Owner" refers to a person who owns an asic and lists it for rent on a hashrate market. "Renter" refers to a person who visits a hashrate market and pays to temporarily rent someone else's asic. "Broker" refers to a person who runs a hashrate market — e.g. whoever bought the domain name, assuming it has a website.

name	extract-car-images
description	Use this skill when you need to build or explain a macOS-only tool that extracts image assets from an Apple asset catalog `.car` file.
version	1.0.0

Extract CAR Images

Use this skill when the user needs the core capability of exporting all image resources from an Apple .car asset catalog. The intended environment is macOS with Objective-C tooling available. This skill is about reproducing the capability, not reproducing any specific project structure.

Installing USB Advanced Card Systems, Ltd ACR122U NFC Reader / Writer (Debian, Mint, Ubuntu)

Intro

I've found a lot of conflicting information on getting one of these up and running on Debian/Ubuntu/Mint, this is a guide to what I did to get mine working. This was tested on Orange Pi 1.0.2 Bookworm aarch64 and Linux Mint 21.3 x86_64.

The OrangePi (and possibly other SBC devices) does not provide enough power to the USB port to activate the reader. You will need to get a powered USB hub.

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.

🧠 Make Your AI Coding Assistant 500x Smarter

Stop Burning Tokens — Use Knowledge Graphs + MCP to Give AI a Map of Your Code

ESPN's hidden API endpoints

Football

College Football

Latest News: http://site.api.espn.com/apis/site/v2/sports/football/college-football/news

Latest Scores: http://site.api.espn.com/apis/site/v2/sports/football/college-football/scoreboard

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>"

The QS-Zigbee-S10-C03 is a Smart Switch module for Garage Doors based on the BK7238 microcontroller (Available here: https://s.click.aliexpress.com/e/_c4teQDSR )

To save you some time, these are the pin configurations:

P1 -> The builtin button
P8 -> The Relay
P9 -> The door sensor
P24 -&gt; The blue led

	function strpos (haystack, needle, offset)
	local pattern = string.format("(%s)", needle)
	local i = string.find (haystack, pattern, (offset or 0))

	return (i ~= nil and i or false)
	end