

Back when I owned an XPS, one of the driver options was ‘compressed screen updates’, which only updated the part that had changed. As far as I could tell, made no difference to battery life whatsoever - turning down the screen brightness even a notch did much more.
Daily driver laptop for nearly ten years, and the part that finally failed was the CPU fan, which wasn’t easy to obtain replacement parts for, so treated myself to a new laptop entirely. Mind you, the power connection was a PoS, would have been as well keeping that on an annual reorder for how often it failed. Pretty good laptop otherwise.




Interesting, but misguided, I think.
If you’ve selected Python as your programming language, then your problem is likely either to do some text processing, a server-side lambda, or to provide a quick user interface. If you’re using it for eg. Numpy, then you’re really using Python to load and format some data before handing it to a dedicated maths library for evaluation.
If you’ve selected Go as your programming language, then your problem is likely to be either networking related - perhaps to provide a microservice that mediates between network and database - or orchestration of some kind. Kubernetes is the famous one, but a lot of system configuration tools use it to manipulate a variety of other services.
What these uses have in common is that they’re usually disk- or network- limited and spend most of their time waiting, so it doesn’t matter so much if they’re not super efficient. If you are planning to peg the CPU at 100% for hours on end, you wouldn’t choose them - you’d reach for C / C++ / Rust. Although Swift does remarkably well, too.
Seeing how quickly you can solve Fannkuch-Redux using Python is a bit like seeing how quickly you can drive nails into a wall using a screwdriver. Interesting in its way, but you’d be better picking up the correct tool in the first place.