It's built on top of MLIR so you can make compiler optimizations in a library instead of the compiler. I believe Reactant.jl has been using MLIR for optimizing higher level abstractions in Julia. Imagine a Lisp with that capability.
At this point "developers" these days sound a lot more like consumers than those who actually do research on a tool that solves a problem. Ocaml is barely mentioned in the news and rarely HNers here use that language, but it is Jane Street that maintains and uses it.
Judging by hype isn't a great way of evaluating a language. I am not going to check the entire Nvidia stack, from CUDA, to CUTLASS to cuDNN and even on PyTorch's side just to solve a runtime error that could have originated from either place when Mojo solves all of that.
No need for snarky comments. These days tools/frameworks/languages/libraries/etc are popping all the time. Do you expect people to research every simple signal they catch in the wild?
So far I've been enjoying the language (pythonic, comptime, tile support) and plan to spend time using it to work with gpus to learn more, the puzzles are also pretty cool.
This is by Chris Lattner, known for LLVM and Swift.
When mojo 1.0 was announced, I looked into porting my current rust project (a very opinionated type-driven DICOM library/anonymizer that has a spec implemented in haskell) and it wasn't ready for that sort of work yet vs continuing with rust. Which is fine! The numerics side looks great. I'm glad they have reached 1.0 and open sourced.
I remember being really interested a few years ago when it was billed as a superset of Python. I know they pivoted away from that, but I might play with it now that the compiler is also open source.
Strategically speaking, I think this only makes sense as an anti-NVIDIA play.
Don't reward them with your support just because they are forced to open source something.
I do not see your point.