gumbo-parser VS NumPy

It uses libcurl and gumbo (https://github.com/google/gumbo-parser). Gumbo is apparently written in pure C99 (interestingly Curl is written in the even older C89 standard). Will've been more amusing if article was written considering that and used C99.

> A standards compliant HTML5 parser is at the bare minimum millions of lines of code.

But https://github.com/google/gumbo-parser is only 34K lines?

oops... I saw a markup parser and automatically thought XML, but you are right! HTML is actually a whole different beast!

As it turns out, seems like nim also has an html parser [1], but I'm guessing something like Google's gumbo [2] could be more reliable, but you would have to write bindings for nim.

1: https://nim-lang.org/docs/htmlparser.html

2: https://github.com/google/gumbo-parser

Well, regarding HTML5, what I've found was libxml (does not support tag-soup HTML5), https://github.com/lexbor/lexbor, for which I was unable to find good documentation ( see https://lexbor.com/docs/lexbor/#dom), Apache Xerces (appears to not support tag-soup HTML5 as well), and Gumbo, which does not appear to be active and to support selectors and XPath (although there are libraries that add that).

I haven't used any of them, but there's a few wrappers available for Gumbo.

In NumPy with @, dot() or matmul():

In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication.

Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:

numpy: A library for scientific computing in Python

Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy.

I was looking at all the big projects like numpy, pytorch, flask, etc.

I wish the NumPy devs would more thoroughly consider adding full fluent API support, e.g. x.sqrt().ceil(). [Issue #24081]