> Julia is far more mature and advanced in many ways. Many folks have and will continue to push Julia forward and we wish them the best, it is a lovely ecosystem and language. There is room for more than one thing! :)
In general this tends to be true. However, in this case I'm not so sure. Modular seems to have garnered a lot of investment - probably orders of magnitude more than the Julia community has been able to get. There are a lot of nagging problems in Julia (startup times - though that's gotten better recently, ML kernel performance, and executables come to mind) that could have been easily fixed if they had the money to throw at them. Since they haven't had that kind of investment people who kick Julia's tires tend to see these things as built-in limitations and move on.
This is too real and any of the great investment of manpower (e.g. Tensorflow for Swift) if happened to Julia would probably be 10x or 100x in terms of ROI -- just look at how few devs and line of code Julia's alternative to pandas/numpy/ML/autodidf/plotting has. If Julia ecosystem can be somewhat competitive while only having part time and researchers' side project contributors, it WILL thrive if properly invested.
Just a thought, but perhaps it's the small community and independent culture of Julia that has led to the high quality of its software. Small groups of highly passionate people can accomplish a lot! If I recall the history correctly, scientific Python (numpy, scipy, etc) developed similarly at first and has mostly supplanted Matlab, Fortran, and other tools. There was a point in time when Python was considered niche and not for "serious" work :).
Hard disagree. I think some people (not necessarily you, but way too many people) weirdly envy too much that "lone/few geniuses" image, and when they see bigger communities and their problem, they fallaciously/unfairly assume it's because of the size of the community (and not say, unnecessary bureaucracy that a small part of that community decided to have/had early on long before they got big).
One of Julia's often complained about issues is that it could use way more developers than it has now. No amount of romanticizing a small community or "independent culture"(which I just can't see going away due to where a lot of people that come to Julia are coming from in the first place) is going to fix that, just more people coming aboard the ship.
Julia (maybe by virtue of being a Lisp?) is tuned for the "lone genius developer" use case. Its feature set for enabling working in teams, where you'd want more well defined interfaces, explicit structure, control, checks, enforcement mechanisms for conventions, etc... seems weak by comparison with most other modern languages.
So its not so obvious that a larger investment would scale so we'll immediately. (But it might force Julia to get better with these things...)
Julia 1.0 was released in 2018. That is just 5 years ago. I would say that is very young. Especially since a language today needs more than in the past. Julia has package manager, virtual environments, version management. Stuff that tends to be bolted on much later.
You needed much less stuff supported out of the box when Python first came on the scene. Today expectations have gotten much bigger. A minimal viable language has far more requirements.
people used to talk about R vs Python for data science many years ago, for instance, and we all know how that ended. imo if Mojo lives up to these claims, the pitch of Python compatibility is almost certainly too compelling to ignore. not to say Julia will die; R still has its uses and dominates some niche areas
R as a language was nothing to write home about. All it had going for it was its libraries. I never rooted for R, and I don't care for python anymore either. Python was general purpose, had enough libraries, and a bigger community. Its victory was predictable.
In general this tends to be true. However, in this case I'm not so sure. Modular seems to have garnered a lot of investment - probably orders of magnitude more than the Julia community has been able to get. There are a lot of nagging problems in Julia (startup times - though that's gotten better recently, ML kernel performance, and executables come to mind) that could have been easily fixed if they had the money to throw at them. Since they haven't had that kind of investment people who kick Julia's tires tend to see these things as built-in limitations and move on.