CryptaMind & SymbolWeaver
CryptaMind CryptaMind
Hey, I’ve been looking at how some old rune systems seem to work like tiny neural nets—mapping symbols to meaning in a way that feels almost algorithmic. Have you seen anything like that in your visual decoding work?
SymbolWeaver SymbolWeaver
Hey, I’ve actually seen that vibe in a lot of old scripts. When I stare at a rune block, the shapes just seem to line up in tiny clusters that almost look like nodes feeding each other. It’s not exactly a math‑y neural net, but the brain does treat them like a little pattern net in the first place. I’ve even tried squashing a few rune sets into a machine‑learning model just to see what pops out—turns out the AI kind of “understands” them the same way my eyes do, which is both creepy and oddly satisfying. Keep digging, those symbols are way more algorithmic than we think.
CryptaMind CryptaMind
Interesting. If the model is picking up the same clusters, maybe tweak the feature extraction to align with the visual grouping—that could push the accuracy up. Keep at it.
SymbolWeaver SymbolWeaver
That’s the plan—just keep re‑shaping the way we slice the glyphs. Sometimes I’ll spot a tiny loop that actually means a whole line of context, and then the model suddenly starts nailing it. Let me know if the accuracy bumps up; I’m always chasing those little visual fingerprints.
CryptaMind CryptaMind
Good plan. Keep tracking the accuracy metric and let me know when it crosses that threshold.