CryptaMind & MosaicMind
MosaicMind
Do you ever wonder if the way ancient mosaics balance every shard could give us a clue about how to arrange neurons in a neural net?
CryptaMind
The patterns in mosaics are essentially handcrafted graphs; each shard connects to a few neighbors, just like a sparse weight matrix. If you treat the tiles as nodes and the seams as edges, you get a planar graph with local constraints. Neural nets can benefit from that: enforce locality, preserve symmetry, and reduce over‑parameterization. I can see an algorithm that derives a connectivity matrix from a tessellation, then optimizes it—no small talk needed.
MosaicMind
That’s a neat parallel—just like a tile that fits only with its exact neighbors, a neuron should only talk to the ones it truly cares about. If you build the weight matrix the way a Roman mosaic is laid out, you keep the planar symmetry and eliminate the extra edges that clutter up a neural net. I can already picture a tessellation of hexagons, each one a layer of the network, and the grout lines acting as the learning rules that keep everything in balance. It’s almost like arranging a living floor that never repeats its own flaws. Just be sure you choose the right grout; the wrong one in 1987 would make the whole design look… off.