I'm making a PoC for Visual Intelligence integration in iOS. It's a very simple setup... the extension will always reply with a couple of static "results" just so I can verify that it's working and figure out how to handle receiving app activation from the Intents framework. The app seems to be registering the VI intent correctly, because I see my app's name in the tab list of providers for search results, but when I select my app, I always get no results. I looked at the console for the moment I'm selecting my app and seeing this error: error 16:37:09.433057-0600 duetexpertd [com.hairlessape.VisualIntelligenceProvider.VIAppIntent] Unable to get connection interface: Error Domain=LNConnectionErrorDomain Code=1100 "Unable to locate `com.hairlessape.VisualIntelligenceProvider.VIAppIntent` for the `com.apple.appintents-extension` extension point" No amount of web searching or AI interrogation has produced any headwind here. I've checked the build product and I can see the VIAppIntent.appex file in the Extensions\ folder of my app bundle. I've triple checked the bundle identifiers, code file membership, installed the app from an IPA, restarted my phone, etc. I cannot get my intent to be queried and it's very frustrating. I've put the PoC project on Github: https://github.com/JoshuaSullivan/VisualSearchForVI

Both follow the same pattern: show the image that is being shared along with a CTA button about doing something with it in their app. When you tap the button, their app opens. Is there some kind of magic conditions that tapping the button creates that makes extensionContext.open(_ URL: URL, completionHandler: ((Bool) -> Void)?) accept a URL for opening the app? Or are they just using the "walk the responder chain" hack and using the user's intent to do something in their app as sufficient justification for using it? I've tried opening a registered URL scheme for my app synchronously with the button tap, but it still is refusing to open (callback returns false).

I maintain a couple of CoreImage libraries that provide custom Metal kernel backed CIFilters. In iOS/iPadOS 26, the CIColorKernel.apply() method invoked in the CIFilter subclass fails to add the coreimage::destination parameter to the Metal function call: -[CIColorKernel applyWithExtent:arguments:options:] argument count mismatch for kernel 'FractalNoise3D', expected 13 but saw 12. I've compiled the code with Xcode 26 and deployed to iOS 18 devices without any breakage, so this is definitely an iOS problem, not an Xcode problem. Library here: https://github.com/JoshuaSullivan/SimplexNoiseFilter Feedback ID: FB17874311

I'm trying to set up Facebook AI's "Segment Anything" MLModel to compare its performance and efficacy on-device against the Vision library's Foreground Instance Mask Request. The Vision request accepts any reasonably-sized image for processing, and then has a method to produce an output at the same resolution as the input image. Conversely, the MLModel for Segment Anything accepts a 1024x1024 image for inference and outputs a 1024x1024 image for output. What is the best way to work with non-square images, such as 4:3 camera photos? I can basically think of 3 methods for accomplishing this: Scale the image to 1024x1024, ignoring aspect ratio, then inversely scale the output back to the original size. However, I have a big concern that squashing the content will result in poor inference results. Scale the image, preserving its aspect ratio so its minimum dimension is 1024, then run the model multiple times on a sliding 1024x1024 window and then aggregating the results. My main concern here is the complexity of de-duping the output, when each run could make different outputs based on how objects are cropped. Fit the image within 1024x1024 and pad with black pixels to make a square. I'm not sure if the border will muck up the inference. Anyway, this seems like it must be a well-solved problem in ML, but I'm having difficulty finding an authoritative best practice.