They already failed once with Swift for Tensorflow, so I am currently curious if there will be some lessons learned from that effort.
For the time being, my chips are still on the Julia horse.
For the time being, my chips are still on the Julia horse.
More on GPE if you're curious: https://llvm.org/devmtg/2018-10/slides/Hong-Lattner-SwiftFor...
…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…
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.