capyvara

using UnityEngine;
using UnityEditor;
using System;
using System.IO;

public class CSharpProjectProcessor : AssetPostprocessor
{
    [MenuItem("Assets/Clean MonoDevelop Files")]
    static void CleanMonoDevelopFiles()
    {

MattRix

using UnityEngine;
using UnityEditor;
using System.Collections;
using System.Collections.Generic;
using System.Reflection;

[CustomEditor (typeof(MonoBehaviour),true)]
[CanEditMultipleObjects]
public class MonoBehaviourInspector : Editor
{

winkels

using UnityEngine;
using System.Collections;
using System.Collections.Generic;

public static class InterfaceExtensions
{
	#region GameObject methods
	
	public static T GetInterface<T>(this GameObject gameObject) where T : class
	{

Pulimet

Hi All!

I've recently launched a tool that wraps many of the commands here with a user interface. This desktop application is currently available for macOS. There's a roadmap outlining planned features for the near future.
Feel free to request any features you'd like to see, and I'll prioritize them accordingly.
One of the most important aspects of this application is that every command executed behind the scenes is displayed in a special log section. This allows you to see exactly what’s happening and learn from it.

Here's the link to the repository:  https://github.com/Pulimet/ADBugger

App Description:
ADBugger is a desktop tool designed for debugging and QA of Android devices and emulators. It simplifies testing, debugging, and performance analysis by offering device management, automated testing, log analysis, and remote control capabilities. This ensures smooth app performance across various setups.

silvesthu

GPU Optimization for GameDev

Graphics Pipeline / GPU Architecture Overview

• 2011 - A trip through the Graphics Pipeline 2011
• 2013 - Performance Optimization Guidelines and the GPU Architecture behind them
• 2015 - Life of a triangle - NVIDIA's logical pipeline
• 2015 - Render Hell 2.0
• 2016 - How bad are small triangles on GPU and why?
• 2017 - GPU Performance for Game Artists
• 2019 - Understanding the anatomy of GPUs using Pokémon

awni

Making MLX Go Fast

This guide assumes you have some familiarity with MLX and want to make your MLX model or algorithm as efficient as possible.

The guide covers the following:

• Graph Evaluation
• Type Promotion
• Operations

karpathy

"""
The most atomic way to train and run inference for a GPT in pure, dependency-free Python.
This file is the complete algorithm.
Everything else is just efficiency.

@karpathy
"""

import os       # os.path.exists
import math     # math.log, math.exp