oc2 VS NumPy

There's OpenComputers II for 1.18.2, but unfortunately it appears to have been "abandoned".

There is this mod called OpenComputers II, which is a spiritual successor of the original OpenComputers mod but for newer versions of Minecraft. OpenComputers II uses a library mod called Sedna to emulate a RISC-V CPU within Minecraft and run an actual Linux virtual machine inside the game. Well recently, Android has announced that they are adding support for the RISC-V architecture. Considering the fact that Android uses the Linux kernel (or at least a modified version of it), along with it being able to run Minecraft Bedrock natively (as we all know), running actual Minecraft in Minecraft might not be as far off as we think.

Hey pal, Open computers II

I've seen a few rust projects compiling for this RISC-V emulator inside Minecraft: https://github.com/fnuecke/oc2

Support for Open Computers 2 exposing everything that we expose to CC: Tweaked (minus the utility methods to convert between energy types)

The OC dev is developing OC2 for 1.16.5 and newer.

Waiting for this to get pushed through. https://github.com/fnuecke/oc2/pull/58

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]