Thank you very much

Thank you! I think I need develop a new app.

Thank you very much

Thanks! FB number: FB16403173

Thanks for your answer! There is more information: • training/validation: 10,000,000 • macOS: 15.3 • Xcode: 16.2 • Memory: 48GB I think it may be a problem of insufficient memory, because the amount of data is large. So if so, I want to know how to solve this problem (such as choosing to occupy hard disk space instead of memory).

Thank you very much

Thanks for your help. I found the right solution and trained my model correctly in about 24 hours. However,if you could help me with this question, it would be greatly appreciated. I noticed that my post-training model’s accuracy didn’t meet my expectations with 1,000,000 training and verification data. So, I increased the data to 10 times the original, resulting in 10,000,000 training and verification data.With the Boosted Tree algorithm,how long will it take to train this model on my machine?

@zentention There are already hints of Swift Assist in Xcode 16.2 Beta (for instance, “Swift Assist” has been added to the change record, except for “Source Control,” indicating that all changes in Swift Assist will be recorded). However, it’s not yet available for use. Swift Assist will be released in Xcode 16.2 RC, and I’m eagerly anticipating its release.

Thank you very much. If possible, I would like to know whether "low" means that it can interact with underlying frameworks like Metal, and why metal and msl are used.

Thank you very much

You also can see: https://developer.apple.com/forums/thread/756978

Although I appreciate your response, there may be some misunderstandings.I hope to detect whether the user is in the room (not a specific room, but any room) or in a non-room environment (such as outdoors, airplanes).

Although I appreciate your response, there may be some misunderstandings. I am interested in understanding how to identify whether the user is currently in a room.

It seems that I need to continue to use RealityKit, thank you!

Thanks! @Vision Pro Engineer also gives how to use the code to implement it.