This is why Google has been using their browser monopoly to push their “Web Integrity API”. If that gets adopted, they can fully control the client side and prevent all ad blocking.

Nvidia drivers have had way more issues with mobile chips than with desktop. GPU compute workloads (including things like Blender) are very well supported. Nvidia on Linux has dominated the compute market for a long time.

No, it’s significant because attackers can pump out way more emails while also making them customized to their targets and constantly changing to help avoid detectors.

Aside from the group suggestions, you could also use ACLs. https://wiki.archlinux.org/title/Access_Control_Lists

They almost certainly won’t. Every so often they make a big show of these raids and then quietly drop it later. Check out some of Jim Browning’s videos to see how the raids work out.

Arch Wiki for more general info. Official docs/man pages of whatever thing you are working with for details.

Greatly increasing taxes for the super wealthy and closing tax loopholes would be a good start.

With rootless containers, even root in the container is basically useless anyway because it truly runs as a fake ID on the host.

I’ve seen this repeated a lot, but I’m not really convinced running as root inside containers is a good/safe thing to do. User namespaces can provide some protection for the host, but that does nothing for the rest of the files inside the guest. For example, consider a server software with an arbitrary file write vulnerability. If the process is running as a low privilege user, exploiting the vulnerability might not really get you anywhere. If it’s running as root, it’s basically a free pass to root privilege and arbitrary code execution within the container.

H264 does work fine in the paid version. The lack of AAC support is sometimes an issue though. For footage in AAC+H264, I usually just run it through ffmpeg to transcode the audio to PCM and keep the video as-is.

I think they already have. I held off on Wayland on my main machine for a long time due to Nvidia issues. For example, I was getting rendering issues where some windows/popups would be totally invisible until I moused over them. Those issues are now gone, and I’ve been running Wayland for the last few months with no problems at all.

The system will be secure for personal use as before.

I wouldn’t be so sure of that. CPU side channels allow data to be leaked across security contexts. For example, from a user process to sandboxed JavaScript in a browser, from kernel space to user space, or from one containerized process to another. This is a problem even on a single user system without any VMs.

That’s much easier said than done. For game developers that already have games based on unity released or in development, changing to another engine is an expensive and time consuming development effort.

What are you comparing it to? I’m pretty sure vnstat is using the raw.interface counters. This would include all protocol overhead. I would expect it to be higher than, for example, an application level counter.

Who ever said signal is anonymous? Secure, private, encrypted - yes. But definitely not anonymous.

Things like taking screenshots and setting wallpaper actually do have a standard API. That stuff is just part of xdg desktop portals and not the core Wayland protocols. If, for example, a screenshot app uses the org.freedesktop.portal.Screenshot API then it should work with any compositor (as long as the compositor follows the API standards).

This isn’t their first rodeo either. https://haveibeenpwned.com/PwnedWebsites#MGM2022Update

If you are familiar with the concepts and are looking more for the specific details, you can probably go a long way with official docs (iptables, nftables, kernel), the arch wiki, man pages, and some hands-on.

https://docs.godotengine.org/en/stable/getting_started/first_2d_game/index.html

At a very high level, training is something like:

• generate some output
• give the output a score based on how much it looks like real human text
• adjust the parameters slightly to improve the score
• repeat

Step #2 is also exactly what an “AI detector” does. If someone is able to write code that reliably distinguishes between AI and human text, then AI developers would plug it in to that training step in order to improve their AI.

In other words, if some theoretical machine perfectly “knows” the difference between generated and human text, then the same machine can also be used to make text that is indistinguishable from human text.

Pinecil works OK for small things, but struggles on larger joints because of it’s low power and small thermal mass. Personally, I’d prefer one of the many Hakko/Weller clones for a cheap solution.