Machine learning is singularly well-suited for superresolution, because you can trivially downsample and train it on that inverse operation, so training at scale is really easy.
But pleasing hinting—even operating on raster rather than vector sources—can’t be trained in the same way, because it’s inherently more subjective; it may be possible to come up with an alternative approach to training, but I suspect it’ll still be much more prone to inducing significant errors.
And of course, performance in all these things is such that they’re not going to be shipped in browsers; from your link, that model is thousands of times more expensive than the (admittedly-inferior) alternatives. (What’s their disk space like? I’m not familiar with how big such ML models end up.)
But pleasing hinting—even operating on raster rather than vector sources—can’t be trained in the same way, because it’s inherently more subjective; it may be possible to come up with an alternative approach to training, but I suspect it’ll still be much more prone to inducing significant errors.
And of course, performance in all these things is such that they’re not going to be shipped in browsers; from your link, that model is thousands of times more expensive than the (admittedly-inferior) alternatives. (What’s their disk space like? I’m not familiar with how big such ML models end up.)