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I haven't kept track of numba in recent years. But there is a clear path to translate more and more scikit-learn to mojo, bypassing the python interpreter entirely. And then things become much more composable in a way that numba can't be.

We are heavily leaning on Julia, and to my mind Mojo is a major threat to the long term development of the Julia community. If people dissatisfied with Python+C(++)-Silos end up writing Mojo instead of Julia it will become even harder to grow the ecosystem and community.

That said, for now Julia has a number of big strengths for scientific work that don't seem to be in the focus of the Mojo devs...



> Mojo is a major threat to the long term development of the Julia community

Mojo has 3 disadvantages compared to Julia:

1) The core team is focused on the Linux+servers+AI combination, because that's where the money is.

2) Less composability due to the lack of multiple dispatch.

3) The license.


Yeah, I went to JuliaCon last year, and it was clear that Julia really seems to have found it's niche in the scientific computing world.

I like the language, but as I do ML, Python is really the only game in town, and Mojo is looking promising.




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