hexagon VS NumPy

Hexagon is a Microservices Toolkit that aims to provide all you need to develop applications that run on Cloud environments.

Ktor is a project that's not primarily focused on application performance but developer performance. It seems to be sth different than what you want to achieve. I don't think a lot frameworks... or any other framework writes its own low level io stuff from scratch, they mostly use https://netty.io/ because there's not too much room for improvement and it's complete, battle tested and so on. I lately came across https://github.com/hexagonkt/hexagon which seems to have a very solid code base based on modules modules modules, is kotlin first and they have an adapter for netty. The usage of the naked http server seems to be as easy as it can be https://hexagonkt.com/port_http_server/port_http_server/ and they are even listed on techempower benchmarks https://github.com/hexagonkt/hexagon/ .

https://github.com/hexagonkt/hexagon A microservices toolkit written in Kotlin.

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]