The most widely used algorithms for classical feature detection today are "whatever opencv implements"

In terms of tech that's advancing at the moment? ML techniques. https://co-tracker.github.io/ if you want to track individual points, https://github.com/matterport/Mask_RCNN and its descendents if you want to detect, say, the cover of a book.

I believe the SIFT algorithm is most commonly used. Harris struggles when features change in scale between images, whereas SIFT does not. Harris can be outfitted with a Laplacian pyramid to overcome the scale issue though.

Actually, it is a part of the ORB algorithm and ORB is especially used a lot in visual SLAM applications.