[nnq]

> Julia's [was] designed with scientific computing in mind. Swift was designed for general purpose app development.

General purpose always wins. Or should always win. Because in reality nobody has any idea what "the purpose" is in the grand scheme of things.

Python succeeded because it was general-purpose enough. Javascript too. We don't want more narrow purpose languages that force us to change the language every time we change the fragment of the stack we work on.

We need an ultra-general purpose language with good support for both OOP and FP, non-retarded type-system, decent performance, and a good "compile to readable JS" story... to unify this damn mess of "diversity" that forces us to over-specialize in narrow niches and drowns us in complexity.

(No, otoh, I don't think "general purpose" should mean "infinite power" or "maximum expressivity". There's are reason why we're not all using Common Lisp and Scala...)

[msravi]

You really should give Julia a go. It's as or more expressive in the general purpose sense of things as Python. It's the best of MATLAB and Python in one neat package.

Edit: Plus, you can pass your data structures out to Python or C for processing. And you can use a whole host of visualization tools.

[nnq]

I did. And it seems awesome.

I just dont see people with "software engineering" background taking any serious liking to it. So it creates DIVIDE between the "software engineering folks" (that want regular-looking-OOP-and-basic-FP architecting feature for APIs and stuff) and "data science folks" who just want to focus on the algorithms.

The litmus test for a "truly general purpose language" to me would be:

(1) write some algorithmic code in it (with not much concurrency and parallelism)

(2) write some (purposefully heavily overengineered) GUI or web-app (full-stack) code in it in a team of 3+ including at least one guy who's both really junior and another guy who's really sloppy

(3) write something making heavy use of networking, concurrency and parallelism

If all three feel EQUALLY natural in a language, than you've got a truly general purpose language. If not, look for something else.

And I know, people hate general purpose solutions just as much as they hate "expert generalist" people, and they have good reasons too, as we've all (or most) been burnt bad by contact with both such "solutions" and with such self-labeled people in the past. But just because we generally suck at "general purpose" doesn't mean we should stop trying!

[msravi]

> Python succeeded because it was general-purpose enough.

> I just dont see people with "software engineering" background taking any serious liking to it.

I guess my point is that if you found Python general purpose enough, you'd likely find Julia general purpose enough too. If people with "software engineering background" take a serious liking to Python but not to Julia, then the reason probably isn't the language itself, but a combination of lack of popularity and a pre-conceived notion that the language is meant to be "scientific" not "general-purpose", that there aren't enough libraries, that the language might not survive, etc.

[goatlover]

And Just came across link to new Julia article thanks to HN: https://increment.com/programming-languages/goldilocks-langu...

[collyw]

Expressiveness of the language is one thing. Does it have the vast numbers of quality and well tried and tested libraries that Python does? That's generally more important than expressiveness.

[saosebastiao]

I don't think that's necessarily true but can't help but still agree with you. There are tons of wonderful DSLs and niche languages out there, but with terrible interfaces to other languages that want to do more with it. In my case that's mostly R and Minizinc, but I know there's plenty more with the same problem.

But it doesn't have to be that way. SQL is a domain specific language. Regex is a domain specific language. They take different API strategies: one goes for ubiquitous and standardized interfaces, the other goes for direct embedding. But they both prove that it's not necessary to write everything in a general purpose language.

[siddboots]

All programming languages have a limited scope of applicability that is a natural product of the design choices that went into them. The idea of a general purpose language that is exactly the right fit for every task at hand is a myth.

There's nothing wrong with designing a language that is particularly good at data analysis, and which emphasises those features at the expense of others.