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Comment by alberth | Hacker News Reader

Take a look at Nim.

You get C performance, with the readability of Python.

Everyone need to stop comparing Nim with Python. Nim is closer to pascal, and the only thing it shares with Python is indentation base syntax. It doesn't have the expressivity and flexibility of Python. It's a nice language, but there is no good reason to compare it to Python.

Both are general-purpose, high-level programming languages. Why not compare them? Say, I am considering starting a new programming project, I need to chose a language. Nim looks fine, so does Python, so which one do I chose? I need to compare those languages to make a motivated choice, right?

Anyhow, what are we allowed to compare with Python?

> Nim looks fine, so does Python, so which one do I chose?

Assuming you need to use libraries for stuff, you choose the language that has the better ecosystem, more users and more StackOverflow support. As a company you choose the language that makes it easier to hire devs in, and move them around from a project to another. Only when execution performance trumps all other concerns you select the faster/stricter language, and only for mature projects not for a POC which might be thrown away soon.

So you use the strict/performant languages for mature, execution intensive code that doesn't need much library support and where you don't need to hire many devs.

I think the comparisons with Python are driven by how divisive it is to have leading whitespace be significant (though Haskell also has it without as much divisive discussion). There was some effort in Nim to have some of the standard library names mirror Python such as `strutils.startswith(str, "oops")`. That said that but for a few things like that, it is true that Pascal/Ada but made more terse are more an inspiration.

I know both languages well and personally I consider Nim to be more expressive and syntactically flexible than Python although it is admittedly a difficult thing to quantify (which "more"/your comment framing implies). As just a few examples to make the abstract more concrete, in Nim you can add user-defined operators. So, with https://github.com/SciNim/Measuremancer I can have let x = a +- b or let y = c ± d construct uncertain numbers and then say y/x to get a ratio with uncertainty propagated. Function call syntax is more flexible (and arises more often than uncertain value algebras), e.g. "f(x)", "x.f", "f x", and even "f: x" all work for any kind of routine call. The need to be specific about let/var constancy & types is slightly more verbose but adds semantic/correctness value (e.g. first use). Just generally Nim feels like "much more choice" than Python to me. In fact, the leading whitespace itself can usually be side-stepped with extra ()s which is not true in Python.

I am speculating, but I think what might make you feel Python is more expressive may come from ecosystem size/support effects such as "3 external libraries exist that do what I want and I only need to learn the names of how to invoke whichever". So, you get to "express" what you "want" with less effort (logic/coding), but that's not really PLang expressivity. Python is more dynamic, but dynamism is a different aspect and often leads to performance woes.

I really really want nim to catch up. It's a lovely language. I've used it for small projects and it is great but I couldn't use it for anything complex that requires third-party libraries. The ecosystem is just not there.

I wonder what is stopping community mindshare?

I bet same reason that you just outlined.

(Chicken and the egg problem)

Pretty much. When Python was at this stage, what alternatives did a developer have than to improve Python/write a library? Nim has to “compete” with a solution already being available.

Perl dominated before Python, and had a massive library of modules via CPAN.

Clearly Python eclipsed Perl. Just say'n.

Python was a curiosity/tiny niche for over a decade from the earliest 0.9 releases I used. I was advocating it to thoroughly deaf ears in the mid-90s with rejoinders of "just use Common Lisp".

Then several things happened. 1) Large businesses like Google openly saying they used Python in the early 2000s helped for things like web / Internet work. 2) Linux distros like Gentoo leaned on it heavily helped push it in systems/sysadmin. 3) the "Numeric -> NumPy/SciPy" transition helped a ton - spurred by things like `f2py` and exorbitant licensing costs of Matlab, the main competitor, which also had a lot of 1970s/early 80s language problems like 1-function per file and scoping craziness and not doing reference semantics for big matrices and junk like that. Then 4) the culture of open source in scientific software helped build out that aspect of the ecosystem as the other, 1/2/3 were growing fast. Then 5 by the late noughties/early teens universities were switching to it as a teaching language and finally 6 ML/data science as a specific area of the NumPy/SciPy world started taking off in hype cycles and even real applied work.

This is just a long-winded (but still woefully incomplete) / historical way of saying "software has network effects" (not just PLs, but OSes and applications as well), of course. As Jack Reacher likes to say "details matter in an investigation". ;-) I don't think Python was purely lucky, exactly, but with one or two those big reinforcement/feedback factors missing, it might not have become so dominant.

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