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Apple and Google both want assistants to act inside your app, and they picked completely different ways to do it: Swift App Intents on one side, Kotlin AppFunctions on the other. I didn't want to write the same feature twice in two languages I don't ship in, so I built a source generator that writes both from one C# declaration.
A web app inside a .NET MAUI app, served from the device, updated from your own signed server whenever you like, and talking to GPS, Bluetooth, Wi-Fi, the camera and more through typed C# and TypeScript clients. Background jobs, pushes and geofences reach the page, or a background script when no page is open. Hot reload runs inside the real app, and a terminal simulator stands in for the device so you can test Wi-Fi drops, denied permissions and GPS walks from a browser.
Four Office editors, a free-form notebook, ribbons on everything, Find across all of them, presenting mode for decks, and a camera you call instead of a screen you build. All of it in the box, on .NET MAUI and Blazor, no WebViews.
Somebody is waiting on bytes that were supposed to have arrived. Usually nothing was carrying them — HttpClient stops the second iOS suspends your app, and Android will reclaim the process while you are not looking. Shiny.Net.Http hands the bytes to the platform instead — NSURLSession, a foreground service, a connectivity-driven managed loop, Service Worker Background Sync — under one API. Plus why I sign Azure Blob and S3 uploads by hand instead of taking the vendor SDKs, what the Android service does when the network drops, and why a Live Activity has to animate a time range instead of a percentage.
A seekable keyframe engine for .NET MAUI with CSS @keyframes semantics in XAML, 42 motion icons compiled from one spec into a drawn scene on MAUI and real CSS keyframes on Blazor, and a ShinyButton that finally makes press-wait-see-if-it-worked a one-liner.
Sixty-odd controls, one token contract, two renderers, zero WebViews. Shiny Controls hits 1.0 for .NET MAUI and Blazor — and there is a live playground you can click through before you install anything.
Ten new packages: a dependency-light, AOT/trim-clean HTTP/1.1, HTTP/2 and HTTP/3 server that runs anywhere .NET runs — including inside a .NET MAUI app on iOS and Android. Routing, middleware, DI scopes, WebSockets, SSE, TLS with managed self-signed certs, an OpenAPI document built with zero reflection, source-generated typed endpoints, XML/MessagePack/protobuf without reflection, gRPC and gRPC-Web, a WebDAV mount, mediator handlers and DocumentDb documents as endpoints, tunnelling so a phone is reachable from the internet, and an MCP server you can point Claude at.
A MAUI UI July post: two drop-in MAUI controls — FaceRecognitionView and FaceEnrollmentView — that put live face recognition and a guided enrollment wizard on screen in one line of XAML, plus the CameraView frame analyzer, a GraphicsView overlay with an even-odd scrim, speaker recognition, and Shiny.DocumentDb + sqlite-vec as the on-device vector database.
A .NET MAUI app built straight on Microsoft.Extensions.AI — an IChatClient onto OpenAI, ChatOptions.Tools, and UseFunctionInvocation running the loop. The chat UI is a control; the hands are Shiny — contacts, reminders, calendar, GPS, health, music, a local document DB, your own services, and the app's own navigation. All opt-in, all switchable, all AOT-clean.
Three new Microsoft.Extensions.AI packages turn device services into tools an LLM can call — Shiny.Contacts.Extensions.AI, Shiny.Notifications.Extensions.AI, and Shiny.Locations.Extensions.AI. Opt-in, read-only by default, AOT-safe, and they all compose into one IChatClient.
One CameraView for .NET MAUI (iOS, Android, Windows, macOS AppKit) and Blazor WebAssembly — live preview, photo/video capture, 11 live filters, and a frame-analysis pipeline that scans barcodes, boxes faces, detects motion, runs OCR, and parses invoices, receipts, driver's licenses, health cards, credit cards, and passports into strongly-typed records.
One IHealthService for Apple HealthKit and Android Health Connect — now with 30+ data types across activity, body, vitals, nutrition, reproductive/cycle tracking and workouts, real-time observation, and a Microsoft.Extensions.AI tool surface so an LLM can read and write your health data.
