defined sobel filters in perception phase as sdf, now I can compute convolutions of any size.
Although after 11x11 performance starts to drop a little (I have 8 of them in each agent)

WIP on implementing rotation for SDF perception filters.
In my imagination, the SDF for a sobel x filter would look something like
x = pow(dot(normalize(dx, dy)), vec2(0, 1)) 2.) * 2. * sign(dx)
and the direction of the filter is determined by rotation of the unit vector

Experimenting with different types of channel attention, that would actually reduce the amount of computation needed, by throwing out most of the input.
The idea is to save on buffer reads on inference, by applying some function + gated activation to internal state, and using this result to determine if we should perceive anything

btw there is a whole NCA section on ALIFE 2023 (started today)

https://webgpufundamentals.org/webgpu/lessons/webgpu-wgsl-function-reference.html
handy cheat sheet of WGSL functions

translation operator (random input, fixed shift)

day 2

NCA particle simulation
https://transdimensional.xyz/projects/neural_ca/index.html

(github: https://github.com/PWhiddy/Growing-Neural-Cellular-Automata-Pytorch)

representing radiance field as 3D Gaussians
https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/

==========================
swiss.gl ⛰👾📽 intro
==========================

This library: http://swiss.gl seems like an interesting model viewing tool for colab (example: last cell here)

and overall should be handy in many situations

A video tutorial with the use of this library has just been released: https://www.youtube.com/watch?v=jKt1Jq4FMRg

demo from the tutorial: https://google.github.io/swissgl/demo/criticality.html
source code: https://github.com/google/swissgl/blob/main/demo/criticality.html