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.
This repository is a collection of C++ sample programs for deploying deep learning models on AXERA AI SoCs (NPU chips). It provides ready-to-run inference examples for a wide range of computer vision tasks including object detection (YOLOv5/6/7/8/9/10/11/26, YOLOX, YOLO-World, PPYOLOE+, RT-DETR, SCRFD), instance segmentation, oriented object detection (OBB), pose estimation, depth estimation, semantic segmentation, stereo matching, and more. Each example is a standalone command-line executable that loads a pre-compiled .axmodel, runs inference via the AX_ENGINE API, post-processes the outputs, and saves a result image. The code is organized per target chip (ax650, ax637, ax620e, ax615) with near-identical copies of examples across platforms, differing only in system initialization and minor chip-specific logic. The architecture is not a library or framework but a set of reference implementations demonstrating the integration of AXERA's NPU SDK. The primary data flow is: load image → preprocess (letterbox resize, color conversion) → push to engine → run synchronous inference → decode model outputs (e.g., anchor-based proposals, DFL, NMS) → draw bounding boxes/keypoints/masks → save output. External dependencies include the AX_ENGINE runtime, OpenCV for image processing, and standard C++ libraries. The build system uses CMake with per-chip toolchain files and GitHub Actions CI for cross-compilation verification. While the code is functional and covers a broad model zoo, it exhibits significant duplication across chip directories and hardcoded configuration (thresholds, image sizes, class names). The monolithic detection.hpp (~3500 lines) bundles many model-specific post-processing functions, increasing maintenance burden. There are no automated tests, and manual memory management is prevalent. Despite these quality issues, the repository serves its purpose as a practical reference for developers integrating AXERA hardware, and it stays current with recent model releases (YOLOv13, YOLO26, Depth Anything V2).
This is a large collection of over 1000 sample projects and templates for developing Microsoft Office Add-ins (Excel, Word, Outlook, PowerPoint, OneNote). The repository demonstrates a wide range of features: task panes, custom functions, authentication/SSO (MSAL, nested app auth, OAuth), Microsoft Graph integration, Blazor WebAssembly add-ins, shared runtime, event-based activation for Outlook, ribbon customization, keyboard shortcuts, offline storage, and legacy VSTO/COM samples. The codebase spans multiple technology stacks including JavaScript/TypeScript with webpack, ASP.NET Core MVC, Node.js, and Blazor. While many samples are functional and serve as good learning resources, the repository suffers from massive duplication of localization files (outlook_strings.js in dozens of languages) and outdated dependencies (jQuery 1.9.1, AngularJS, VSTO). The overall code quality is typical of sample collections – functional but not production-grade – with isolated reusable patterns for authentication and Graph API interaction.
This repository is a vast collection of over 1,800 sample projects demonstrating nearly every feature of the Universal Windows Platform (UWP) SDK. It is not a single application but an educational resource containing hundreds of independent, self-contained samples covering sensors, cameras, networking, graphics, file access, notifications, background tasks, device interactions, and many other Windows APIs. Each sample follows a consistent architectural pattern: a MainPage with a scenario selector and multiple Scenario pages that illustrate specific API usage in C++/CX, C++/WinRT, C#, and occasional JavaScript (largely archived). The samples are designed to be built and run with Visual Studio on Windows 10 and 11, serving as both a learning tool for developers and a reference for API behavior. The codebase is extensive and fairly well-organized by feature area, but it suffers from significant technical drift. Many archived samples use deprecated technologies such as C++/CX (legacy) and WinJS (JavaScript), while newer samples adopt modern C++/WinRT. This inconsistency means that some samples may not compile or behave correctly on the latest Windows SDK without updates. Additionally, the repository lacks a unified build system or automated testing, and it includes a large amount of boilerplate code (e.g., identical SampleConfiguration files across hundreds of projects). Despite these drawbacks, the repository remains actively maintained by Microsoft (last commit within 30 days) and provides genuine value for understanding UWP APIs, with several reusable utility components buried within the samples.