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Comment by daft_pink | Hacker News Reader

I think Mojo’s lack of being a true open product and existing to drive profits at Modular has really held it back.

It’s just really impractical to use a licensed programming language in 2025.

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.

[1]: https://news.ycombinator.com/item?id=30416070

Yeah, Mojo's development has been pretty transparent. Chris publishes technical documents for most features and takes community feedback into account. A recent example is here: https://forum.modular.com/t/variable-bindings-proposal-discu...

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.

> 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.

https://www.modular.com/blog/democratizing-ai-compute-part-8...

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.

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