u/Amine69hahaparsen

Hello again Reddit! I just released ProsperoGallery, a payload that allows you to transfer your PS5 screenshots and videos to your other devices easily, without the need of PSN: https://github.com/AmineWallah/ProsperoGallery

It is heavily inspired by NXGallery, which is the Nintendo Switch equivalent.

The main features are supposed to be fast and easy transfer, support for HDR media as well as video playback without necessarily downloading the file in question. As for the setup, you only have to install the payload on the github repo, open the newly installed entry in your console's Media tab, and scan the QR code on your screen or open the displayed link.

Everything should work as intended, but for any bugs you might encounter make sure to open an issue on github!

If you like this project, giving my github repo a star would be appreciated :)

Introducing osunator, an AI model (an LSTM, specifically) that aims to generate non-robotic replays of a given map. (demo below, comparison vs human replay on the github repo)

This is a passion project and a proof of concept that human behavior-cloning in rhythm games is possible. It does not aim to provide perfectly played scores (unlike Neuro Sama for example, which is based on reinforcement learning), as it prioritizes humanness and indistinguishability

From an ethical standpoint, my tool's purpose is only to be a research artifact and not a cheating tool, and it is built to stay that way. Which is why it doesn't provide any means to submit generated scores and doesn't have any hosted deployment (the only way to make this run is by self hosting). For now the replays also possess a few fingerprints that differentiate it from human ones (more on the github repo)

Let me know what you think of it!

I'm also running a proper blinded test (watch pairs, guess which is human) for a writeup, DM me if you're interested in participating :)

https://github.com/AmineWallah/osunator

The project is basically a machine learning pipeline that produces a model capable of extracting a .midi file from provided audio, trained on the NSynth dataset.

Even though the model underperforms when comparing it to commercial ones (the dataset isn't really adapted for this use case and many other factors contribute to this) I'm happy with how it came out considering that I started my ML journey around 2 weeks ago and finished my first course 3 days ago (IBM's machine learning with python on coursera)

Here's a link to the github repo, any critics are welcome!!!!

https://github.com/AmineWallah/nsynth2midi