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Source channel @githubtrending · Post #14985 · Jul 22

#c_lang#cuda#cuda_driver_api#cuda_kernels#cuda_opengl You can use the CUDA Samples from NVIDIA to learn and test CUDA Toolkit 12.9 features by downloading them from GitHub or as a ZIP file. These samples show how to use CUDA for GPU programming, including utilities, concepts, libraries, and performance optimization. You build them with CMake on Linux, Windows, or Tegra devices, and can run tests automatically with a provided Python script. This helps you understand CUDA programming, debug GPU code, and optimize your applications for better performance on NVIDIA GPUs. It’s a practical way to develop and improve GPU-accelerated software efficiently. https://github.com/NVIDIA/cuda-samples

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@githubtrending · Post #15171 · 09/27/2025, 11:30 AM

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#ruby#backup#network#nms#rancid Oxidized is a free tool that automatically backs up network device configurations from over 130 device types, replacing older tools like RANCID. It runs efficiently by adjusting how many tasks it uses based on your setup and offers a web API to manage backups and see changes. It can track who made changes using syslog and integrates with Git to show detailed version history. You can install it on many systems, configure it easily with YAML files, and use various sources and outputs for flexibility. This helps you keep your network device settings safe, organized, and easy to review or restore when needed. https://github.com/ytti/oxidized

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