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Source channel @githubtrending · Post #14919 · Jul 6

#cplusplus#aes#avx#avx_instructions#chrome#chrome_devtools#chromedriver#chromium#chromium_browser#content_shell#jpeg_xl#jpegxl#jxl#libjxl#linux#thorium#thorium_browser#thoriumos#web_browser#web_platform#webbrowser Thorium is a fast, optimized web browser based on Chromium, designed to work well on modern CPUs with advanced instruction sets like AVX and SSE4. It offers better performance than standard Chromium and Chrome, opening tabs and rendering pages quickly. Thorium includes enhanced privacy features such as DNS over HTTPS and Do Not Track enabled by default, plus support for modern media formats like HEVC and JPEG XL. It keeps the familiar Chrome interface and supports all Chrome extensions, making it easy to switch. Available on Windows, Linux, macOS, Android, and Raspberry Pi, it suits users wanting speed, privacy, and compatibility across devices[3][5][1]. https://github.com/Alex313031/thorium

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@githubtrending · Post #15271 · 11/05/2025, 12:30 PM

#cplusplus#arm#baidu#deep_learning#embedded#fpga#mali#mdl#mobile#mobile_deep_learning#neural_network Paddle Lite is a lightweight, high-performance deep learning inference framework designed to run AI models efficiently on mobile, embedded, and edge devices. It supports multiple platforms like Android, iOS, Linux, Windows, and macOS, and languages including C++, Java, and Python. You can easily convert models from other frameworks to PaddlePaddle format, optimize them for faster and smaller deployment, and run them with ready-made examples. This helps you deploy AI applications quickly on various devices with low memory use and fast speed, making it ideal for real-time, resource-limited environments. It also supports many hardware accelerators for better performance. https://github.com/PaddlePaddle/Paddle-Lite