I think unlike Gluon/CuTe/ThunderKittens (which distinguish themselves from Triton by being lower level giving you more control, thus being less performance portable and harder to write), Helion distinguishes itself from Triton by being higher level and easier to write.
IMO, this is something that makes sense for PyTorch to release, as "neutral ground" in the industry.
What's the point of Triton compared to Gluon? What's the point of PyTorch compared to Triton?
One of the main values of Triton is that it significantly expanded the scope of folks who can write kernels - I think Helion could expand the scope even more.
As a contrary opinion, I quite like workplace. I definitely feel that, for me, it provides a much more effective way of staying up to date on what's happening compared to mailing lists and such.
I don't understand how one stream of notifications is different from email - at least with workplace there's some ranking going on.
Speaking as someone doing research in this field, I have an unbelievably hard time imagining this to be the case.
The ML community is generally extremely open, and people know what the other top people are working on. If an AGI was developed in secret, it would have to be without the involvement of the top researchers.