mintable VS NumPy

One free alternative to Tiller is Mintable. https://github.com/kevinschaich/mintable

Yeah I'll be quite honest my project is very low-quality code (I'm not a front end/UI person so the website is straight up vanilla JS and HTML aka ugly). I just stumbled upon this that looks like a better version (and open source) version of what I have: https://github.com/kevinschaich/mintable

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]