NVIDIA's official CUDA Samples repository is a large catalog of self-contained, compilable example programs demonstrating the CUDA platform across C++ and Python. The C++ collection spans hundreds of samples organized into ten categories: fundamentals (vector add, matrix multiply), utilities (deviceQuery), concepts and techniques (reduction, scan, convolution, histograms), feature showcases (CUDA graphs, cooperative groups, Tensor Core GEMMs, dynamic parallelism, NVRTC JIT), CUDA libraries (cuFFT, cuRAND, cuBLAS, cuSOLVER, CUB), domain-specific applications (Black-Scholes, N-body, optical flow, fluid simulation, volume rendering, Monte Carlo), performance studies, libNVVM/PTX generation, platform-specific Tegra/embedded examples, and CUDA Tile C++ microbenchmarks. A parallel Python collection uses the modern cuda.core API with CuPy/NumPy interop and framework integrations (PyTorch, TensorFlow, RAPIDS). Each sample is typically a standalone CLI or interactive graphics program with its own main() entry point; most include a CPU 'gold' reference implementation and automated pass/fail verification, making the collection both educational and regression-tested.