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A cross-platform file change monitor with multiple backends: Apple macOS File System Events, *BSD kqueue, Solaris/Illumos File Events Notification, Linux inotify and fanotify, Microsoft Windows and a stat()-based backend.

  • event-notifications
  • change-monitor
  • fswatch
  • kqueue

The Boehm-Demers-Weiser conservative C/C++ Garbage Collector (bdwgc, also known as bdw-gc, boehm-gc, libgc)

STL compatible C++ memory allocator library using a new RawAllocator concept that is similar to an Allocator but easier to use and write.

Free open-source modern C++20/23 framework to create console (CLI), forms (GUI like WinForms) and unit test (xUnit) applications and libraries on Windows, macOS, Linux, iOS, Android, FreeBSD, Haiku, and SerenityOS.

G3log is an asynchronous, "crash safe", logger that is easy to use with default logging sinks or you can add your own. G3log is made with plain C++14 (C++11 support up to release 1.3.2) with no external libraries (except gtest used for unit tests). G3log is made to be cross-platform, currently running on OSX, Windows and several Linux distros. See Readme below for details of usage.

⚔️ A cross-platform, open source, and super fast MUD client with scripting in Lua

  • mudlet
  • lua
  • cross-platform
  • mud

DSP IIR realtime filter library written in C++

  • signal-processing
  • filters
  • filter-plugin
  • iir

NOTE: development in stuntrally3 repo. 3D racing game based on VDrift and OGRE with own Track Editor. The main repository with Stunt Rally 2.x sources and data.

An Event-Driven, Asynchronous, Behavioral State Machine Library for ROS (Robotic Operating System) applications written in C++

  • state-machine
  • ros
  • ros-packages
  • navigation

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.