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1–22 of 22 · Most stars

An efficient, small mobile key-value storage framework developed by WeChat. Works on Android, iOS, macOS, Windows, POSIX, and OHOS.

TinyXML2 is a simple, small, efficient, C++ XML parser that can be easily integrated into other programs.

Small C++ program to display images in a (modern) terminal using RGB ANSI codes and unicode block graphics characters

  • terminal-graphics
  • ansi-colors
  • cpp
  • ascii-graphics

Horde3D is a small 3D rendering and animation engine. It is written in an effort to create an engine being as lightweight and conceptually clean as possible.

Crinkler is an executable file compressor (or rather, a compressing linker) for compressing small 32-bit Windows demoscene executables. As of 2026, it is the most widely used tool for compressing 1k/4k/8k intros.

A small desktop robot that brings an AI coding assistant to life, physically acting out when it reads files, thinks, writes code, and finishes work.

A lightweight high performance tensor algebra framework for modern C++

  • tensors
  • tensor-contraction
  • multidimensional-arrays
  • simd

An implementation of Actor, Publish-Subscribe, and CSP models in one rather small C++ framework. With performance, quality, and stability proved by years in the production.

  • cpp
  • cplusplus
  • cplusplus-17
  • actor-model

Small C++ header to display async animations, counters, progress bars, and status messages

  • cpp
  • progress-bar
  • counter
  • animation

A safe and fast high-level and low-level character input/output library for bare-metal and RTOS based embedded systems with a very small binary footprint.

SimpleAI is a small C++ AI behaviour tree based library with a QT5 based remote debugger (and with optional LUA bindings) released under MIT.

C++11/20 library that provides static_vector, small_vector, static_map, small_map, flat_map, static_flat_map, small_flat_map, static_unordered_map, small_unordered_map, etc.

  • cpp11
  • header-only
  • small-vector
  • flat-map

Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.