[CoreyFieldens]

I'm really interested in Mojo not for its AI applications, but as an alternative to Julia for high performance computing. Like Julia, Mojo is also attempting to solve the two-language problem, but I like that Mojo is coming at it from a Python perspective rather than trying to create new syntax. For better or for worse, Python is absolutely dominating in the field of scientific computing, and I don't see that changing anytime soon. Being able to write optimizations at a lower level in a Python-like syntax is really appealing to me.

Furthermore, while I love Julia the language, I'm disappointed in how it really hasn't taken off in adoption by either academia or industry. The community is small and that becomes a real pain point when it comes to tooling. Using the debugger is an awful experience and the VSCode extension that is recommended way to write Julia is very hit-or-miss. I think it would really benefit from a lot more funding that doesn't actually seem to be coming. It's not a 1-to-1 comparison, but Modular has received 3 times the amount of funding as JuliaHub despite being much younger.

[pjmlp]

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.

[chrislattner]

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 ;-)

More on GPE if you're curious: https://llvm.org/devmtg/2018-10/slides/Hong-Lattner-SwiftFor...

[ElectronCharge]

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…