What about Python? They had 34 core developers in 2017 [0]. Python is used by millions of devs. They have to support a multitude of platforms as well.

Same goes for a lot of programming languages like Go: a pretty small core, the rest is external contributions. And they have to support all sorts of platforms/configurations as well (probably more than Flutter does).

[0] https://pythondev.readthedocs.io/core_devs.html

As core languages, the scope of both Python and Go is fairly well defined.

V8 is a matter of investment of billions in expertise and time.

I’m not sure what you mean by this but Python can be very performant when you want it to be. That is to say, when you build computation heavy parts in C (or Zig), and remember to avoid the various performance pitfalls like not using generators when you didn’t want to load all those millions of elements into your memory.

What sets professional Python aside from most other programming languages is that everyone who uses it knows that it’s terrible and how to deal with that. Which will sometimes be replacing parts (or all) or it.

To say that it’s inherently less performant than JS is frankly silly though.

Here's one source on Python very obviously not matching Node.js's perf on a simple microbenchmark: https://jott.live/markdown/nodejs_vs_python_

CPython really is inherently less performant than V8. CPython, until very recently, didn't have a JIT at all. It compiles scripts to bytecode, then runs the bytecode in a giant case statement. It doesn't have a tracing profiler-guided optimizer or an exotic garbage collector. CPython is way, way simpler than V8, but it's consequently slower. It's just the consequence of Google putting centuries of developer-years into an engine.

You can't just claim Python is fast because Python's C libraries are fast. Those libraries are fast despite Python. Torch is extremely fast, but it's fast from C and Lua too. There's valid reasons to want to compute in your programming language. Python is, ironically, probably popular because its slow speed encouraged users to write blazing fast C libraries rather than even try writing native Python, vs. settling for middling performance as in Java or .NET.

I shouldn't have to get out my hammer and tongs when NumPy doesn't implement the operator I need. Why can't Python be fast like Julia?

I fail to see how Python 3.8.2 could in anyway be relevant in 2024.

Relevant or not, python 3.13 shows the same thing:

Python 3.13.0: 799.6559143066406 ms

Node 18.20.4: 59.34080000221729 ms

So, basically: Python is performant when you don't use Python?

Yes. Is that so weird? JS is also performant when you don’t use JS considering you’re running it on C or Rust and are likely using the FFI if you’re doing any form of computation heavy work.

Yes, it's very weird. When I write JavaScript, I expect it to run reasonably fast because of the JIT - and it does. I've never felt the need to use FFI, and libraries that use it generally do so because they want to interact with pre-existing C or C++ code, not because it's the only way to get decent performance.

Keep in mind performant just means that it works in an effective or expected way. It doesn’t necessarily say much about performance.

Yes. Python is performant when all the performance-sensitive code is written as extension code.

I'm comparing the impact. Small tens of developers, affecting millions of people.

Sure and 1 guy built flappy birds played by tens of millions. The complexity is a completely different ball game IMO.

Millions of people can be affected in different ways.