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Source channel @githubtrending · Post #14747 · May 25

#python#deep_learning#intel#machine_learning#neural_network#pytorch#quantization Intel Extension for PyTorch boosts the speed of PyTorch on Intel hardware, including both CPUs and GPUs, by using special features like AVX-512, AMX, and XMX for faster calculations[5][2][4]. It supports many popular large language models (LLMs) such as Llama, Qwen, Phi, and DeepSeek, offering optimizations for different data types and easy GPU acceleration. This means you can run advanced AI models much faster and more efficiently on your Intel computer, with simple setup and support for both ready-made and custom models. https://github.com/intel/intel-extension-for-pytorch

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@githubtrending · Post #15173 · 09/27/2025, 12:30 PM

#cplusplus#c_plus_plus#cpp#datachannel#libdatachannel#libnice#p2p#peer_to_peer#peerconnection#rfc_8831#rfc_8834#rtcdatachannel#rtcpeerconnection#sctp#webrtc#webrtc_datachannel#webrtc_video#websocket libdatachannel is a lightweight, easy-to-use C/C++ library that lets you add real-time peer-to-peer data, media, and WebSocket communication to your apps across many platforms like Linux, Windows, macOS, Android, and iOS. It simplifies WebRTC by providing a smaller, simpler alternative to Google's library, with compatibility for browsers like Firefox and Chrome. You can use it to connect native apps directly to web browsers with minimal dependencies, supporting secure connections via GnuTLS, Mbed TLS, or OpenSSL. It also supports compiling to WebAssembly for browser use, making it flexible for cross-platform real-time communication development[1][4]. This helps you build fast, efficient apps for video, audio, or data sharing without heavy libraries. https://github.com/paullouisageneau/libdatachannel