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https://reddit.com/link/1hzq4oa/video/gy0967kz2lce1/player

I've decided to make 2025 the year I finally get into Godot properly, and I'm having a blast!

The short vid above is the results of Week 2 overall (first week on this project after doing a quick Pong clone and faffing about with 3d procgen).

This project is far more serious; it's the actual first steps towards a long-standing game concept. Featuring:

• A smooth RTS-style top-down camera system with scroll-on-edge, adjustable deadzones, adjustable zoom, and support for configurable camera effects (not shown here; currently jittery screenshake a'la explosions, smooth bob a'la heavy breathing, and platformer-style

https://preview.redd.it/jsfmsb3oi1ce1.jpg?width=1000&format=pjpg&auto=webp&s=43d01742f92ec628da78a50a8e8fa41b5f1dc1d5

This is a result of an extended post-film discussion I had with a friend about the parallels between Ellen's and Friedrich's fates and how he's effectively part of the problem. And also being very silly. I apologise.

I have observed for some time now a pattern in music that is confusing the hell out of me from a theoretical standpoint; I could use some input on it.

I like me some good sadness in my music sometime (and I mean both listening and making) and I noticed that one of the most effectively heartbreaking melodic patterns... seemingly shouldn't be? They seem to be mostly based on fourth and fifth-based movement, for example:

• B -> A -> E all downwards
• B -> E -> B -> B (rising to E then returning down to B)
• Nearly any riff based on add9 chords (though madd9s are cheating a bit)

What confuses me is that these are some of the most consonant intervals you can use short of octaves! Conv

I'm building a deep autoencoder-based model in Keras that analyzes biological data, and I've ran into a problem. I'm hoping someone here could provide me with some guidance.

The number of feature vectors in datasets I have to work with can be absolutely huge, and if I try to train my model - either on CPU or GPU - memory allocation fails. Now, I suppose I could brute-force it by getting better hardware, but I came up with another approach, and I'm wondering if it is feasible.

If I were to simply sample the features with replacement between training batches and train the model on only the subset of features, would the resultant NN weights still 'make sense', or will they become a completely