Every credible wiki has moved away from fandom at this point. All that’s left is the abandoned shells of the former wikis they refuse to delete and kids who don’t know better.

I’d guess the 3 key staff members leaving all at once without notice had something to do with it.

What’s the deal with Alpine not using GNU? Is it a technical or ideological thing? Or is it another “because we can” type distro?

Cohere’s command-r models are trained for exactly this type of task. The real struggle is finding a way to feed relevant sources into the model. There are plenty of projects that have attempted it but few can do more than pulling the first few search results.

There should be no difference because the video track hasn’t been touched. Some software will display the length of the longest track rather than the length of the main video track. It’s likely that the the audio track was originally longer than the video track and because of the offset it’s now shorter.

You can use tools like ffmpeg and mediainfo to count the actual frames in each to verify.

Koboldcpp should allow you to run much larger models with a little bit of ram offloading. There’s a fork that supports rocm for AMD cards: https://github.com/YellowRoseCx/koboldcpp-rocm

Make sure to use quantized models for the best performace, q4k_M being the standard.

I only have 60 down and 12 up so I cap about 80% of the time with a short uncapped window late at night.

The ~400USD price tag is really impressive, but the big thing with these folding phones is the reliability of the hinge. It will be interesting to see how it fares when proper reviews come in.

Tun0 is the interface that most vpns are using so I assume proton is the same.

9/11 killed more in one day than mass shootings have in the last 20+ years. https://www.statista.com/statistics/811504/mass-shooting-victims-in-the-united-states-by-fatalities-and-injuries/

QKSMS has less active development but I don’t think that’s an issue as it works well as is. I haven’t dug too deeply into the more advanced stuff but I’ve yet to have any issues with it.

I might have to get a vr headset…

Almost everything has been done already. Most new app ideas are just gimmicks thrown onto existing concepts.

I’m not sure why it would be any different from how this is treated with search engines. Both scrape massive amounts of openly available data and make it available in some form. Any training data or information that a model could potentially spit out is already available through a search engine’s index.

Flatpak is good for diversity. Users don’t need to worry about whether the obscure distro they want to use has the software they want in its repos. If a distro supports flatpak it will work with most popular software out of the box.

You’re never going to be banned from a store. Individual games with anticheat might however. Even then bans are pretty rare outside of games that are explicitly hostile towards Linux like destiny 2.

French laws don’t recognize software patents so videolan doesn’t either. This is likely a reference to vlc supporting h265 playback without verifying a license. These days most opensource software pretends that the h265 patents and licensing fees don’t exist for convenience. I believe libavcodec is distributed with support enabled by default.

Nearly every device with hardware accelerated h265 support has already had the license paid for, so there’s not much point in enforcing it. Only large companies like Microsoft and Red Hat bother.

a thorough investigation is planned beforehand in order to find out how Huawei was able to produce an advanced smartphone so quickly without relying on global supply chains

There’s no way a country of 1 billion people which already manufactures most of the world’s electronics could have possibly produced complex electronics.

In the case of Machine learning the term has sort of been morphed to mean “open weights” as well as open inference/training code. To me the OSI is just being elitist and gate keeping the term.

That isn’t neccesarily true, though for now there’s no way to tell since they’ve yet to release their code. If the timeline is anything like their last paper it will be out around a month after publication, which will be Nov 20th.

There have been similar papers for confusing image classification models, not sure how successful they’ve been IRL.