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Comment by vaughan | Hacker News Reader

A counter point would be to look at the Python ecosystem. They have many fewer libraries but the ones they have are very well maintained and widely adopted.

JS/Node is full of unmaintained projects that people inevitably need to migrate away from.

Of all the new choices in bundlers today, it’s also inevitable that some won’t work out, yet they will drive hype and adoption, which could have been spent on already established tools such as webpack.

Yes, innovation is good and no one should tell someone what to do in their free time.

But it’s always going to be harder to build on top of old rather than to write something new, but what is new eventually becomes old...and the cycle repeats.

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> A counter point would be to look at the Python ecosystem. They have many fewer libraries but the ones they have are very well maintained and widely adopted.

So what? It is what it is - the python ecosystem faces completely different challenges than the JS ecosystem, if the browsers exclusively ran Python instead of JS it would be the exact same situation for Python.

> JS/Node is full of unmaintained projects that people inevitably need to migrate away from

This isn't true. jQuery, Angular.js, Angular, React, Vue etc are all still maintained (check their githubs if you are in doubt) and all still work just fine, so if you want to use the same tooling you did a decade ago that's totally possible.

> it’s always going to be harder to build on top of old rather than to write something new

So what? If you want to contribute to old projects instead of writing something new you're free to do that, and if you don't like new stuff you don't have to use it, complaining about this stuff is just pointless contrarianism, but of course, you're free to do that too.

It isn't contrariarism. The end result is that most JS libraries are immature, have complex edge cases that can only be fulfilled by one package but not from another similar package, and that old packages don't evolve to adapt to new paradigms.

Knowing that pretty much every Python dev knows how to use requests, Django, and NumPy means saving a huge amount of effort in retraining and fighting to pick and choose on choices of ultimately little value.

It is contrarianism, there are many mature JS libraries, the fact that less mature packages also exist isn't a problem, just don't use them.

> Knowing that pretty much every Python dev knows how to use requests, Django, and NumPy

This isn't true, but even if I grant this to be true, you could just as accurately say pretty much every js dev knows node, axios and react.

AFAIK Axios isn't even universally used for requests, and in the SPA framework space it seems to be a split between Angular and React in popularity. None of these things seem to be occurring in the Python space.

Nothing is universal, you're seeing what you want to see. I could say the same thing about django, flask, and web2py or numpy and panda, or trio and asyncio.

Django doesn't occupy the same space as flask; not in the same way React and Vue seem to. Web2py is nonexistent. Numpy is a basic linear algebra library and Pandas is a high-level tabular data analysis library.

None of these things compete with each other in any meaningful way.

> Django doesn't occupy the same space as flask;

This is just wrong. Django absolutely occupies the space as Flask, this is pretty obvious, but you're splitting hairs to defend your argument - the bottom line is that a Python developer building a web application has to decide if Django or Flask is a better choice for their engineering needs, you wouldn't use both (though you could, same as you could use react and vue together).

> Web2py is nonexistent

This is totally wrong. You can selectively discard every example that doesn't meet your arbitrary standard of popularity, but I could just as easily discard a criticism of Vite which is far more "non-existent" relative to webpack than web2py is to Django

> Numpy is a basic linear algebra library and Pandas is a high-level tabular data analysis library.

Once again you're splitting hairs. Yes, Pandas and Numpy have different specialties, but there is clear overlap between them and a developer who is unfamiliar with the ecosystem wouldn't necessarily understand why e.g. they might choose pandas dataframes vs numpy arrays, and this dynamic is also true in the JS world where different packages have overlap but particular specialties that make one more attractive than the other depending on the needs.

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