sutaburosu

// by <sutaburosu@gmail.com>
// License: Creative Commons CC0

#include <Arduino.h>
#include <FastLED.h>
#include <colorutils.h>

#define BRIGHTNESS     64
#define LED_PIN        2
#define COLOR_ORDER    GRB

mosgaragedev

Abrir teminal com direitos de administrador
git-bash installed with LxRunOffline
download last version https://github.com/DDoSolitary/LxRunOffline/releases
Alternative Download

cd ~/Downloads
curl -L -O https://github.com/DDoSolitary/LxRunOffline/releases/download/v3.5.0/LxRunOffline-v3.5.0-mingw.zip
ls LxRunOffline-v3.5.0-mingw.zip
unzip -j "LxRunOffline-v3.5.0-mingw.zip" "LxRunOffline.exe" -d "/c/Program Files/Git/usr/bin/"

Tprabath

@echo off
setlocal enabledelayedexpansion

REM === CONFIGURATION ===
if "%GLASSFISH_HOME%"=="" (
    echo [ERROR] GLASSFISH_HOME not set in environment.
    exit /b 1
)

set JAVA_HOME=C:\Program Files\Java\jdk-17

triangletodd

On the host

Ensure these modules are loaded

cat /proc/sys/net/bridge/bridge-nf-call-iptables

Disable swap

sysctl vm.swappiness=0
swapoff -a

FuzzysTodd

// This script can be used to deploy the "Storage" contract using ethers.js library.
// Please make sure to compile "./contracts/1_Storage.sol" file before running this script.
// And use Right click -> "Run" from context menu of the file to run the script. Shortcut: Ctrl+Shift+S

import { deploy } from './ethers-lib'

(async () => {
  try {
    const result = await deploy('HelloWorld', [])
    console.log(`address: ${result.address}`)

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.

Mr-xn

#!/usr/bin/env node
/**
 * Typora macOS 激活脚本
 * 原理：替换二进制中的 RSA 公钥，用自己的私钥签名生成 Activation Token
 */

const crypto = require('crypto');
const fs = require('fs');
const path = require('path');
const { spawnSync } = require('child_process');

BlakeGardner

# Last updated May, 2024 for Apple silicon Macs
# Install Homebrew if you don't already have it: https://brew.sh

# install nano from homebrew
brew install nano nanorc

# update your nanorc file
echo 'include "'"$(brew --cellar nano)"'/*/share/nano/*.nanorc"' >> ~/.nanorc

# close and re-open your terminal and you'll have syntax highlighting

talkingmoose

#!/bin/zsh

:<<'ABOUT_THIS_SCRIPT'
-----------------------------------------------------------------------

	Written by:William Smith
	Technical Enablement Manager
	Jamf
	bill@talkingmoose.net
	https://gist.github.com/talkingmoose/b3553675a613176b35c27b5c5028e0c2

Gipetto

oMLX local configuration

Captured 2026-09-02. This is a redacted reconstruction reference: it records explicit local settings, storage paths, installed-model metadata, and profiles. It intentionally omits credentials and request statistics.

System settings

Server and lifecycle

Setting	Value