to clarify:

The developers of the Alpine Linux-based postmarketOS mobile distribution today that they’re now supporting the systemd init system alongside OpenRC and other alternative init systems.

and:

postmarketOS currently supports the Sxmo, Phosh, GNOME Shell on Mobile, and KDE Plasma Mobile UIs. While the Sxmo images will stay with OpenRC, the GNOME and KDE Plasma Mobile images will be built on top of systemd

this is the ad-free version, which is available with the exact same (if I'm correct) features on F-Droid for free, along with the source code on GitHub.

the versions on the Play Store (paid version and free version with ads) likely just help pay the developer for their work

and as others have said already, free software is free as in freedom, not free beer.

you could use Termux::API to get stats (battery percentage, notifications, calls, some other stuff) of your phone from your PC.

this video gives good examples

also didn't know you could pipe text into lolcat like that

oh ok

I do appreciate the fact that more private Piped links are provided instead of direct YouTube links (my bad btw), although direct ways of saying thank you (messaging developers, sending donations, providing feedback) would be more useful and productive

thanks for the advice. I'm also not a fan of those type of comments too lol

haven't compiled it in like months bc it keeps erroring out lol

please tell me you use ccache tho

yeah, the training environment was a basic bullet hell "game" (really just bullets being fired at the player and at random directions) to teach the neural network basic bullet dodging skills

• the white dot with 2 surrounding squares is the player and the red dots are bullets
• the data input from the environment is at the top-left and the confidence levels for each key (green = pressed) are at the bottom-left
• the scoring system is basically the total of all bullet distances

• this was one of the training sessions
• the fitness does improve but stops improving pretty quickly
• the increase in validation error (while training error decreased) is indicated overfitting
  • it's kinda hard to explain here but basically the neural network performs well with the training data it is trained with but doesn't perform well with training data it isn't (which it should also be good at)

this app is amazing

that's ok. we all make mistakes

another cave story user on fedi!! very cool

i know someone with that exact bg too lol

oh another restic user

yeah restic is a very good choice

it even works with incremental backups so you don't waste as much space as other solutions

I always find it interesting to see how optimization algorithms play games and to see how their habits can change how we would approach the game.

me too! there aren't many attempts at machine learning in this type of game so I wasn't really sure what to expect.

Humans would usually try to find the safest area on the screen and leave generous amounts of space in their dodges, whereas the AI here seems happy to make minimal motions and cut dodges as closely as possible.

yeah, the NN did this as well in the training environment. most likely it just doesn't understand these tactics as well as it could so it's less aware of (and therefore more comfortable) to make smaller, more riskier dodges.

I also wonder if the AI has any concept of time or ability to predict the future.

this was one of its main weaknesses. the timespan of the input and output data are both 0.1 seconds - meaning it sees 0.1 seconds into the past to perform moves for 0.1 seconds into the future - and that amount of time is only really suitable for quick, last-minute dodges, not complex sequences of moves to dodge several bullets at a time.

If not, I imagine it could get cornered easily if it dodges into an area where all of its escape routes are about to get closed off.

the method used to input data meant it couldn't see the bounds of the game window so it does frequently corner itself. I am working on a different method that prevents this issue, luckily.

despite being a good paint editor for Windows, it is unfortunately not open source or source released (I thought it was as well):

However, citing issues with the open source code being plagiarised by others that had rebranded the software as their own and bundled user content without their permission, the availability of the source code was restricted

In November 2009, the software was made proprietary, restricting the sale or creation of derivative works of the software.

the developer talks more about it here and here

wow this is great

oh that's cool

you're right that abuse would be the biggest issue, made worse if people host ads for many people. ideally people would naturally host few ads in a similar fashion to smaller instances (ideally) federating with few instances? also didn't realise that so many webrings still exist until I searched them up

damn. rest in peace man

what's ram usage like?

currently, yes, but this is more an investigation into how well a neural network could play a bullet hell game

very few bullet hell AI programs rely on machine learning and virtually all of the popular ones use algorithms.

but it is interesting to see how it mimics human behaviour, skills and strategies and how different methods of machine learning perform and why

(plus I understand machine learning more than the theory behind those bullet hell bots.)

qt as well