Here you can read past volumes of HelloGitHub Monthly by category. You are currently viewing the HelloGitHub C++ collection.
Open-Source Voxel Game Creation Platform.This is an open-source voxel game engine that enables you to rapidly create your own 3D block games with Lua. It has an embedded content store for directly downloading community mods and games, supports single-player, LAN, and multiplayer modes, and is runnable on Windows, macOS, Linux, and Android

Handwritten Note-taking Tool for Old Tablets.This is a cross-platform handwritten note-taking app optimized for low-cost devices, aiming to deliver an iPad-like writing experience on old tablets. It supports pressure-sensitive writing, multi-layers, PDF annotation, and handwritten OCR, and is compatible with Windows, macOS, Linux, Android, and iPadOS. The author aims to make paperless learning accessible to the public and breathe new life into those old devices that have been neglected in corners

Free and Open-Source 3D Engine.This is a free, open-source, and real-time 3D engine that can be used to develop AAA-level games, movie-quality 3D scenes, and simulation applications, and it supports Windows and Linux platforms.

Ghibli-Style 2D Animation Creation Software.This is an open-source 2D animation creation software deeply customized and evolved from the famous animation studio Ghibli. It provides digital animation production processes including scanning, coloring, compositing, special effects, and film output, and is compatible with Windows, macOS, and Linux platforms.

C++ Library That Lets You Make Games with Just One Header File.This is a single-header 2D game development library for C++ beginners, requiring no installation or linking of any libraries, and it comes with built-in features like window management, drawing, sprite animation, keyboard/mouse input, audio, and collision detection.

Open-Source Slicing Software Supporting Multiple 3D Printer Brands.This is an open-source 3D printing slicing tool with built-in calibration kits like flow rate adjustment, temperature tower, and retraction test. It supports mainstream printer brands such as Bambu Lab, Prusa, and Creality, and is compatible with Windows, macOS, and Linux platforms.

Open-Source High-Performance General-Purpose Physics Engine.This project is an open-source general-purpose physics simulation engine developed by Google DeepMind, focusing on simulating complex joint structures and interactions with the environment. It has a built-in visual interactive interface and supports describing scenes using MJCF, and is applicable to scenarios like robotics, embodied intelligence, biomechanics, animation, and machine learning

Local Voice AI Assistant Tailored for macOS.This is a local voice AI command-line tool optimized for Apple Silicon, integrating speech recognition, LLM, VLM, and text-to-speech functions. It performs end-to-end local inference without relying on cloud services, supports voice control of music playback, creating reminders, adjusting volume, etc., and can analyze screen or image content.

Windows Mouse Effect Enhancement Tool.This is a lightweight Windows desktop mouse/cursor effect tool that supports various mouse effects like click ripples, particle trails, hover glow, and floating text.

Lightweight In-Process Vector Database.This open-source in-process vector database from Alibaba can be used directly without independent deployment. It is built on the Proxima engine, offering localized and low-latency vector data management and semantic retrieval capabilities, and supports functions like hybrid search, data persistence, and re-ranking.
import zvec # Define collection schema schema = zvec.CollectionSchema( name="example", vectors=zvec.VectorSchema("embedding", zvec.DataType.VECTOR_FP32, 4), ) # Create collection collection = zvec.create_and_open(path="./zvec_example", schema=schema) # Insert documents collection.insert([ zvec.Doc(id="doc_1", vectors={"embedding": [0.1, 0.2, 0.3, 0.4]}), zvec.Doc(id="doc_2", vectors={"embedding": [0.2, 0.3, 0.4, 0.1]}), ]) # Search by vector similarity results = collection.query( zvec.VectorQuery("embedding", vector=[0.4, 0.3, 0.3, 0.1]), topk=10 ) # Results: list of {'id': str, 'score': float, ...}, sorted by relevance print(results)