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- Early access to new features for Microsoft Fabric's Semantic Link.
- Microsoft.FeatureManagement provides standardized APIs for enabling feature flags within applications. Utilize this library to secure a consistent experience when developing applications that use patterns such as beta access, rollout, dark deployments, and more.
- Microsoft Fluent UI Blazor components library. For use with ASP.NET Core Blazor applications
- Benchmarking and monitoring automation, designed for cloud, built with .NET.
- Programmatically extract data and apply schemas to unstructured documents across text-based and multi-modal content using Azure AI Foundry, Azure OpenAI, Azure AI Content Understanding, and Cosmos DB.
- LISA is developed and maintained by Microsoft, to empower Linux validation.
- Document Generation Solution Accelerator using Azure OpenAI Service and Azure AI Search.
- This repository holds the source code of Microsoft.Testing.Platform (MTP), a lightweight alternative to VSTest, as well as MSTest adapter and framework.
- Visual Studio Code
- Samples that demonstrate running axe accessibility scans from Azure Pipelines
- Modular data acquisition framework
- Windows system utilities to maximize productivity
- This open-source curriculum is designed to teach the concepts and fundamentals of the Model Context Protocol (MCP), with practical examples in .NET, Java, TypeScript, JavaScript and Python.
- Fluent UI web represents a collection of utilities, React components, and web components for building web applications.
- The Microsoft community Windows Package Manager manifest repository
- Research and development (R&D) is crucial for the enhancement of industrial productivity, especially in the AI era, where the core aspects of R&D are mainly focused on data and models. We are committed to automating these high-value generic R&D processes through R&D-Agent, which lets AI drive data-driven AI. 🔗https://aka.ms/RD-Agent-Tech-Report
- Official repository for the Microsoft C/C++ extension for VS Code.
- Terraform Provider for Microsoft Fabric
- Investigating the role of pre-training dataset size and diversity on the performance of single-cell foundation models