Sounds about right. There are some valid and good use cases for “AI”, but the majority is just buzzword marketing.

The main thing (by far) degrading a battery is charging cycles. After 7 years with say 1,500 cycles most batteries will have degraded far beyond “80%” (which is always just an estimate from the electronics anyway). Yes, you can help it a bit by limiting charging rate, heat and limit the min/max %, but it’s not going to be a night and day difference. After 7 years with daily use, you’re going to want to swap the battery, if not for capacity reduction then for safety issues.

I think I have a simple function in my .zshrc file that updates flatpaks and runs dnf or zypper depending on what the system uses. This file is synced between machines as part of my dotfiles sync so I don’t have to install anything separate. The interface of most package managers is stable, so I didn’t have to touch the function.

This way I don’t have to deal with a package that’s on a different version in different software repositories (depending on distribution) or manually install and update it.

But that’s just me, I tend to keep it as simple as possible for maximum portability. I also avoid having too many abstraction layers.

That’s mostly down to Teams though (being the bloated web app that it is), and not the underlying operating system.

Technically, wired charging degrades the battery less than wireless charging, mainly because of the excessive heat generated by the latter. The same way slower wired charging generates less heat. Lower and upper charging limits also help (the tighter the better).

But I personally don’t bother with it. In my experience, battery degradation and longevity mostly comes down to the “battery lottery”, comparable to the “silicon lottery” where some CPUs overclock/undervolt better than others. I’ve had phone batteries mostly charged with a slow wired charger degrade earlier and more compared to almost exclusively wireless charging others. No battery is an exact verbatim copy of another one. Heck, I had a 2 month old battery die on me after just ~20 cycles once. It happens.

Sure, on average you might get a bit more life out of your batteries, but in my opinion it’s not worth it.

The way I see it with charging limits is that sure, your battery might degrade 5% more over the span of 2 years when always charging it to 100% (all numbers here are just wild estimates and, again, depend on your individual battery). But when you limit charging to 80% for example, you get 20% less capacity from the get go. Unless of course you know exactly on what days you need 100% charge and plan your charging ahead of time that way.

Something I personally could never be bothered with. I want to use my device without having to think about it. If that means having to swap out the battery one year earlier, then so be it.

When talking about the kernel, Windows actually skipped 3 major versions iirc from the top of my head. Windows 8 was Windows (NT) 6.2, and Windows 10 skipped that version number to, well, 10.

Why when a simple alias will do?

I also experienced less “hiccups” since switching to Linux with KDE but I’d like to know on what combination of hardware and Windows you experienced anywhere close to an average of 1s response time to “any input”.

The article links an article from March '24 talking about the introduction of these devices that contains this part:

The scanner that Adams and police officials introduced during Thursday’s news conference in a lower Manhattan station came from Evolv, a publicly traded company that has been accused of doctoring the results of software testing to make its scanners appear more effective than they are.

So they could never be trusted but were still allowed to proceed.

Bitwarden keeps working just fine.

Best case is that the model used to generate this content was originally trained by data from Wikipedia so it “just” generates a worse, hallucinated “variant” of the original information. Goes to show how stupid this idea is.

Imagine this in a loop: AI trained by Wikipedia that then alters content on Wikipedia, which in turn gets picked up by the next model trained. It would just get worse and worse, similar to how converting the same video over and over again yields continuously worse results.