nlvm VS NumPy

That looks interesting. Unfortunately it looks like it hasn't been updated in a while? Is that because it's complete or a lack of interest?

For example, the approach mentioned at the bottom of the README of integrating via nlvm (https://github.com/arnetheduck/nlvm) sounded great but appears to be unpursued.

Being able to compile to C, C++, ObjC, and JavaScript natively (and LLVM using https://github.com/arnetheduck/nlvm ), along with an excellent FFI (including to and from Python) means you don't need to rewrite dependencies as you can use them directly. Nim is great at glue code - arguably better than Python.

Along with general language characteristics such as being high level and productive like Python, but with intricate "bare metal" control when you want it, really does make it suitable for writing almost everything.

Check out nlvm https://github.com/arnetheduck/nlvm

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]