BetterDummy VS NumPy

BetterDummy lets you create the virtual screens on the computer https://github.com/waydabber/BetterDummy

If your on a Mac you can use this software instead of hardware dongles with much higher resolutions https://github.com/waydabber/BetterDummy

BetterTouchTool has a SuperBrightness action in the newest beta which works on the same principle as Vivid, while BetterDummy added an XDR slider that works on the same principle as Lunar.

I've had great results with BetterDummy[0] for my M1 make with a non-4K external monitor.

It's free and open source with an optional Pro version. I've been using it longer than the Pro version has been around, so I'm not familiar with what advantages it may have over the free version.

[0] https://github.com/waydabber/BetterDummy

You might want to try BetterDummy to add a virtual display to the Mac if there is not a physical one already connected. Might solve the black screen issue.

I'm currently on MontereyI use this https://github.com/waydabber/BetterDummy and create a dummy to emulate higher res display

better dummy, just download it

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]