Btw, Mojo's development is a masterclass in language development and community building, it's been fun watching Chris go back to fix technical debts in existing features rather than proceeding with adding new features.
I haven’t tried it in a long time, but as it’s a Python superset, I tried to drop it into my jupyter notebook docker container and you had to agree to license terms and register your email and install a modular package that contained a bunch of extra things.
If you want to get widespread adoption for a python superset, you would probably want to get it included in the official jupyter docker images as people who do this sort of programming like to use a jupyter repl, but they just made it so difficult.
I’m no open source zealot and I’m happy to pay for software, but I think the underlying language needs to be a lot more open to be practical.
Also to MLIR while Lattner was at Google:
> MLIR was born—a modular, extensible compiler infrastructure designed to bring order to the chaos. It brought forth a foundation that could scale across hardware platforms, software frameworks, and the rapidly evolving needs of machine learning. It aimed to unify these systems, and provide a technology platform that could harmonize compute from many different hardware makers.
But unification is hard. What started as a technical project quickly turned into a battleground: open-source governance, corporate rivalries, and competing visions all collided. What could have been a straightforward engineering win became something much more complicated.
https://www.modular.com/blog/democratizing-ai-compute-part-8...