This a fun experiment testing using a neural network as the storage engine for git

When you git commit, the network gets trained on your data, then when you checkout, the data is generated back by the neural network (+ a small classifier model on top that fixes most of the errors from the lossy generation)

I first made a cli version (it's linked in the post if you are curious), and now made this demo that runs entirely on the browser (ie. the whole source control and ai models run on directly on the site, no backend at all - which also means you loose the info when you reload)

There are 2 modes on the demo, Image and Text, you can save either an image (there are 4 presets, but you can also paste an image from your clipboard, or drag/drop one from your computer as well), or text (there's some default JS code, which you can run, and you can try to run again after checkout, to test the fidelity of the code)

By default, lgit trains the proper network size to fully learn your code, but if you want to save space, or make it faster, you can play with the parameters and see if you get your data back or not

Hope you enjoy it. Any feedback is welcome

Done with Dana in C# inspired by ASCII sharks XD