If executed well, Delaunay-based tetrahedral dithering can outperform the N-convex method and produce results that rival Knoll’s algorithm. The devil is in the detail however, as actually implementing a robust Delaunay triangulator is a non-trivial task, especially where numerical stability is concerned. The additional memory overhead required by the triangulation structure may also be a concern.
In Python, you actually can’t do that because *args is always a tuple:
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This is better in that there is far less boilerplate, but it doesn't solve everything. Async iteration was retrofitted onto an API that wasn't designed for it, and it shows. Features like BYOB (bring your own buffer) reads aren't accessible through iteration. The underlying complexity of readers, locks, and controllers are still there, just hidden. When something does go wrong, or when additional features of the API are needed, developers find themselves back in the weeds of the original API, trying to understand why their stream is "locked" or why releaseLock() didn't do what they expected or hunting down bottlenecks in code they don't control.