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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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@venturevillagewall · Post #3604 · 12/20/2024, 05:23 PM

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Navigating AI Opportunities & Threats Explore how AI presents both challenges and opportunities for small businesses. While larger firms dominate the market, many entrepreneurs can thrive with modest revenue targets. AI could lead to the decline of SaaS businesses due to cost-effective alternatives. However, it allows individuals to quickly replicate and improve existing applications, catering to proven market needs. Though scaling to $500M ARR is tough in niches, a small investment can create meaningful products. Innovators can introduce multiple unique offerings, even in shadow of larger tech giants. #AI#SaaS#Business#Investment#Tech#Entrepreneurship#Revenue#ProductDevelopment#Innovation#Market#Growth#Web3#Startups#Opportunity#Threats#SmallBusiness#Investors#ExistingMarket#Applications#PMF#CostEfficiency