My impression is that this is on purpose on their part. They’ve repeatedly stated that by 2026 they will open source the compiler, and I think they’ve wanted a slow adoption ramp in order to spend some more time getting it right first.
Possibly rose-tinted glasses on my part, but I’m optimistic for 2026. Chris Lattner has a pretty strong track record of getting these things right.
Yeah, and he's clearly trying to avoid what happened to Swift[1]. Although the danger of "corporate owner priorities dictate releasing half-baked/awful changes" risk is still there, Lattner himself has more influence within Modular (obviously, as co-founder and CEO) than he did at Apple, so it may work out better this time.
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.
It was just very difficult primarily because of the way the license limitations and install steps made it difficult to drop it into the existing python tooling ecosystem.
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.
> he's clearly trying to avoid what happened to Swift
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.
Possibly rose-tinted glasses on my part, but I’m optimistic for 2026. Chris Lattner has a pretty strong track record of getting these things right.