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Source channel @githubtrending · Post #15535 · Mar 3

#shell ESP-CSI uses Wi-Fi Channel State Information (CSI) on all ESP32 chips to sense breathing, chewing, walking, or other movements without contact or extra hardware. You get rich data like signal phase and amplitude for smart monitoring, activity detection, positioning, and AI apps, with easy OTA upgrades, BLE help, and examples for quick starts like radar or cloud uploads—saving costs while boosting your IoT projects' accuracy and reliability. https://github.com/espressif/esp-csi

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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