I'm interested to study the development of an app for improving the accessibility of the mouse pointer. I would try to access the "intimate area" of the notch to understand if it could give me some precise biometric data to elaborate on. There is any kind of SDK or class for this? I don't know the difference between iPhone and Mac notches, I hope it could help me to improve the result of this research. Thank you

Hello, it's fundamental for me for the development of an application the possibility to make a fullscreen app (in Ventura) that could draw its interface also on the extra space of the menu bar with the notch. It's fundamental because this is an entire interface extension application, and I don't know how to implement it with a fixed or disappearing menu bar. I tried with an window.level = NSWindow.Level.mainMenu+1, but it's useless. Thank you

Hello, I have a macOS app that helps navigation through running apps published on the App Store. I would like to create a Safari extension for macOS that would allow the user to open directly one of the last used tabs. Activate a tab is not a problem following the documentation, but my question is: can I allow the opening of a tab that is not in the frontmost window of the current instance of Safari? Security policies about window.focus() function are restrictive also for code executed directly in the "background" script of the extension? P.S. For example, in a Sandbox application you cannot activate directly a window but just a running application as it was left, so I cannot select the interested Safari's window from the macOS app. Thank you!

Hello, I have an app on the Mac App Store for 1 year, I'm working on the second version but I have big problems with the lack of feedback from the users. At the same time, I would appreciate the collaboration with other users, so I'm thinking about the possibility to public the source code on a git repository where the developers can fork, create new branches and pull back in the original source code their improvements and ideas and we can bargain a price for the work done. I don't want to complicate too much the situation, but I would to implement a sort of "stock action" payment possibility where based on the daily profits you can decide when to withdraw your stocks earned when you think their value is enough mature. This is to compensate for the potential added value and the initial lack of money. The license that I developed is this one: MakeItHome License: Source Code Forking, Editing, and Merging License Version 1.0 - 27 October 2023 Permission is hereby granted, free of charge, to any person or organization obtaining a copy of the software code (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: 1. Forking and Editing: a. Users are allowed to fork the original repository on GitHub and create their branches. b. Users are allowed to edit, modify, enhance, or otherwise change the source code in any manner they see fit. 2. Merging: a. Users are allowed to merge their changes back into the original repository via pull requests on GitHub. b. Users are encouraged to contribute their improvements to the original repository for the benefit of the community. 3. Restrictions on Distribution: a. Compiled Binaries Prohibition: Users are expressly prohibited from distributing any compiled binaries, executables, or other forms of the Software in a compiled or executable format, even if the source code has been modified, altered, or otherwise changed. b. Exception: Users are allowed to use the compiled binaries internally for testing, validation, or other non-distribution purposes. However, external distribution, sharing, or dissemination of the compiled binaries is strictly prohibited. 4. No Warranty/Liability: THE SOFTWARE IS PROVIDED "AS IS," WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES, OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT, OR OTHERWISE, ARISING FROM, OUT OF, OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. Has this concept of collaborative working on a project, in your opinion, potentialities and legal applicability? Is this a good idea to create an involved community? Thank you

I'm trying to create an updatable model, but this seems possible only by creating from scratch a neural network model and then, using the NeuralNetworkBuilder, call the make_updatable method. But I met a lot of problems on this way for the solution. In this example I try to open a converted ML Model (neural network) using the NeuralNetworkBuilder: import coremltools model = coremltools.models.MLModel("SimpleImageClassifier.mlpackage") spec = model.get_spec() builder = coremltools.models.neural_network.NeuralNetworkBuilder(spec=spec) builder.inspect_layers() But I met this error in the builder instance line: AttributeError: 'NoneType' object has no attribute 'layers' I also tried to define a neural network using the NeuralNetworkBuilder but then what do I have to do with this object? I didn't find a way to save it or convert it. The result I want is simple, the possibility to train more the model on the user device to meet his exigences. However the way to obtain an updatable model seems incomprehensible. In my case, the model should be an image classification. What approach should I follow to achieve this result? Thank you

Hello, I'm trying to train a MLImageClassifier dataset using Swift using the function MLImageClassifier.train. It doesn't change the dataset size (I have the same problem with a smaller one), but when the train reaches the 9 completedUnitCount of 10, even if the CPU usage is still high, seems to happen a soft lock that doesn't never brings the model to its completion (or error). The dataset is made of jpg images, using the CreateML app doesn't appear any problem during the training. There is any known issue with CreateML training APIs about part 9 of the process? There is any information about this part of the training job? Thank you

Hello, on the App Store I have two simple Safari extensions both for iOS and macOS, one app for iOS for testing and feedback purposes (so, even if it's a complex app, nothing of "serious" for the moment) and one mature and complex app for macOS. The last one is localized for the most important 12 languages, the others one are in english only (but due their simplicity are enough). At this moment they are all free apps. The point is this one: overall in the macOS app I except a general interest for a mature and very interesting application, but seems that, independently from everything, the only real downloaders and users are the US ones. This is really demoralizing because this app needs to be known, but only the US audience is not enough. Only the 10% of downloaders are generally equally divided between UK, India, Canada, Brazil, China and Japan. So the keywords seem effectively findable. Is it normal this huge difference? Not-US audience is more suspicious? There is a particular psychology difference between US and other countries? Ok, probably most macOS users, with a recent hardware (this app require at minimum macOS 12.3 to work), are located in US. But the ratio US:rest of the world seems anyway unbalanced. Thank you, have patience if my question is dumb.