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Definitely cool, and I like the application of ARKit. But using ML to solve a sudoku seems like overkill. I remember writing a constraint-based solver as the first assignment for an undergrad-level AI course back in uni. Surely this implementation is less efficient? Someone let me know if I am wrong.

If the aim was to simply learn new tech, though, then I get it. I am just wary of ML being a hammer used on anything even remotely resembling a nail.

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The author says ML wasn't used for solving Sudoku. Vision was only used for transducing the image of a sudoku puzzle to a puzzle structure in memory.

Oops, must have missed it. Thanks for the clarification.

It says they used a "traditional recursive algorithm", probably referring to the backtracking solution. In my experience it's fast enough to not matter for this sort of application (the other things that are going on are 1000x more complex).

There is a small fraction puzzles for which simple backtracking gets stuck on unproductive branches and essentially never finishes.

Do you have any examples? I'd love to improve the algorithm.

Someone on /r/programming pointed me here: http://apollon.issp.u-tokyo.ac.jp/~watanabe/sample/sudoku/in...

But the app seems to already handle those Ok without doing anything special: https://www.dropbox.com/s/arfd03kr8ieczk5/recursive-solver-k...

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