That I don’t know. I haven’t been looking into one-board computers for a while. The one I bought ~10 years ago was running out of juice when I was trying to run Kodi on it last year. Wifi shouldn’t be a problem IMO, I’ve been using mine as torrent downloader and hosted a few university projects (dynamic web apps) on it. The graphics might. I would guess that as long as you find one with decent specs (so probably not the 10$ one) it should work. I’m sure there’s someone who is doing exactly that and either could answer what to buy/look for or wrote a blog about it

I haven’t gotten to it for myself but I think Kodi+something like RaspberryPI is your solution

EDIT: or just install a Linux on some one-board computer or old laptop and check how HDMI-CEC is working. You’ll need to install virtual keyboard too

I don’t use flatpak but I didn’t have to configure Lutris or Steam in any way for that.

If you look around goverlay you’ll find that there are two ENVs that you can set up in X/wayland service to have MangoHud automatically in every context it can render in.

So first try running with MANGOHUD=1 env exported. If that doesn’t help, try mangohud steam.
If I understand correctly, flatpack run in some kind of container, so it’s possible that you might need to set the env or the command, so it happens inside the container

Yes, but that’s only because a generation found some random, specific motion that scored better. Not because it analyzed that doing a skip should be possible

Yes, but that’s kind of my point

We see it learn something with insane precision but most often it is almost an effect of over-training. It probably would require less time to learn another layout but it’s not learning the general rules (can’t go through walls, holes are bad, we want to get to X), it learns the specific layout. Each time a layout changes, it would have to re-learn it

It is impressive and enables automation in a lot of areas, but in the end it is still only machine learning, adapting weights to specific scenario

It’s cool but my question is (I did not see this addressed in the article nor video but might have missed it) did it learn to win the game in general terms or only this one example? I mean, if the layout of the board was changed, would it still solve it?

I’m not big on gambling. But I feel I could bet that their software/firmware is so bad that someone could still hack the network via the bricked printer

The difference between private and public companies is the single biggest threat to us all

Nah. One does not build a company to provide a service but to earn money. “Well-being of the company” only matters if you are sure you can sell it for more if you grow it more

So it’s not like: when I affect the hue (some attribute) of my half, the other half will change too? That has always been my understanding of it

I don’t think that’s a solution, unless one of these installs out of the box some other version of a driver or module. And all three are rather using older versions. Maybe Mint could be a little bit more up to date.

I would propose: execute steam with PRESSURE_VESSEL_SHELL=instead (PRESSURE_VESSEL_SHELL=instead steam in a terminal or launcher where you type what to execute) and once you have xterm run the game with "$@" > $HOME/tmp/sniper.log 2>&1. That will redirect the output from the process to the file which you can use after reboot to search what is the problem you are having. If there’s not much inside, retry with export WINEDEBUG=+all; "$@" > > $HOME/tmp/sniper.log 2>&1. The latter will run very, very slowly but should print debug of everything that happens in proton

From what I can find it seems that it is only available on Steam. And a lot were able to run it without problems

I agree that this is important news but what does it have to do with technology?

I’ve found this. Personally I would not say the difference is worth having another name, maybe for the sake of differentiating between the ratios.

But it seems that indeed 4K is not 2160p 🤷