It seems like Julia already had a pretty good solution to the two language problem as well as being designed from the ground up for numerical computation (Python has numpy, but it seems bolted on and clunky by comparison). Yes, there are issues with large executables (large runtime), non-optimal gpu kernels, etc, but it seems like many of Julia's nagging issues could have been fairly easily solved if it had received as much investment as some of the alternatives have received - Modular appears to have a lot of funding to develop Mojo, and frameworks like PyTorch and TensorFlow have the backing of mega tech corporations. At one point Swift for TensorFlow was going to be the next big ML language and was being funded and developed by Google.
I'm not sure there's a question here... more of a frustration that Julia seems to be a very good solution already (and is more mature than Mojo) and yet it seems to get passed over because companies decide to fund some new(er) shiny thing instead.
BTW: I think your matrix multiplication demo could achieve very similar performance (including the SIMD, parallelization and LoopVectorization) in Julia.
I'm not sure there's a question here... more of a frustration that Julia seems to be a very good solution already (and is more mature than Mojo) and yet it seems to get passed over because companies decide to fund some new(er) shiny thing instead.
BTW: I think your matrix multiplication demo could achieve very similar performance (including the SIMD, parallelization and LoopVectorization) in Julia.