I was responsible for the S4TF effort at Google. In my opinion, it validated that some of the ideas are good (e.g. Graph Program Extraction is the algorithm that torch dynamo uses internally), that an efficient compiled language has benefits etc. However, I also learned that it should not be based on Swift and should not be based on TensorFlow. Other than those two things, everything is great ;-)
I’m a huge Julia fan, you can take a look at my posting history. I love Julia’s syntax, and some of its language ideas.
…BUT…
For my personal tastes, Mojo’s lack of garbage collection, Rust-like memory safety, and attention to ahead-of-time compilation put it way ahead. The vast pool of Python developers who can easily pick it up if interested is a big plus.
Julia is aimed at a somewhat different space, but there’s also a huge overlap.
Let’s hope for good interoperability between the two, it seems fairly straightforward…
Lets see how it plays out, given that they are focused only on AI workloads, and somehow those VCs want their money back, which doesn't appeal to everyone.
I acknowledge that there is finally pressure in the Python community to tackle down performance, but don't see Mojo being the solution unless there is something that it will make it go wild.
Right now, I see that more likely with Facebook, NVidia, Intel and Microsoft efforts.
More on GPE if you're curious: https://llvm.org/devmtg/2018-10/slides/Hong-Lattner-SwiftFor...