.NET Framework for Backgrounding & Device Hardware Services

Shiny is a cross-platform .NET framework simplifying background processing and device hardware service integration across iOS, Android, macOS, Windows, Linux, and Blazor WASM. It offers a structured, testable API with dependency injection and logging, abstracting platform complexities like permissions, main thread traversal, and persistent storage.

How It Works

A modular design handles device functionalities: background jobs, HTTP transfers, data sync, Bluetooth LE, location, contacts, and notifications. It leverages native platform APIs (e.g., BGTaskScheduler on iOS, WorkManager on Android) for background execution. Key features include resumable transfers, conflict resolution, and AOT-compatible serialization, providing a consistent, testable abstraction over diverse native capabilities.

Quick Start & Requirements

Installation via NuGet packages. Requires .NET runtime compatibility and adherence to Native AOT/trimming practices. No specific hardware like GPUs or CUDA are prerequisites. Links to official documentation and demos are available.

Highlighted Details

• AI Integration: Optional *.Extensions.AI packages expose Shiny modules as Microsoft.Extensions.AI tool functions for LLM agents. Features AOT-compatible schemas and configurable read/write capabilities.
• Background Transfers: Supports resumable uploads/downloads with built-in Azure Blob Storage and AWS S3 request builders.
• Data Synchronization: Bidirectional JSON record sync over HTTP includes batched outbox, conflict resolution, and exponential backoff retries.
• Broad Platform Support: Covers iOS, Android, Mac Catalyst, macOS, Windows, Linux, and Blazor WASM.

Maintenance & Community

Community support channels, documentation, and change log links are provided. Core contributor, sponsorship, or roadmap details are absent from the provided text.

Licensing & Compatibility

The license type is not explicitly stated in the provided README text, requiring further investigation for commercial use or closed-source integration.

Limitations & Caveats

AI tool integrations necessitate pre-granted underlying platform permissions. Potential platform-specific nuances may exist despite the cross-platform abstraction. The framework is .NET-centric, lacking direct support for other language ecosystems.