THE WORD SILO — NINE LAYERS CONFINE THE WEIGHT OF THE WORD
layer k exerts electromagnetic cushion f=k/10 (0.1 top → 0.9 bottom) · weight of a word = its GPT-2 embedding norm, normalized to [0.1, 0.9] · overdamped confinement
tier: lit · W1 settle==predicted 33/33 · W2 determinism exact · W3 zero over-penetration · 2026-07-10
3D — WORDS FALLING TO THEIR WEIGHT, LIVE
words descend the axis under gravity (overdamped — lowered, not dropped) and are caught by the first layer strong enough to hold them ·
layer tint = field strength (faint 0.1 → hot 0.9) · settled words park around their ring, hovering a breath above the plane ·
“rune” (weight exactly 0.900) balances on layer 9’s exact limit — hover gap 0.000
2D — THE STRATIFIED DICTIONARY · THE MASS OF FEELINGS
left: every word at its confined level · right: the emotion mass ladder — GPT-2 weighs
love lightest (0.502) and rage heaviest (0.799); grief outweighs joy; peace sinks below hope
🖍 TODDLER CORNER
Every word has a weight — not how long it is, but how heavy the robot’s brain holds it. Everyday words like “in” and “the” are feathers. Strange words like “rune” and “phantom” are stones.
We built nine magnetic shelves, weakest on top, strongest at the bottom, and let the words float down. Each word sinks past every shelf too weak to hold it and rests on the first one that can. Feathers rest high, stones rest deep. All 33 words landed exactly where their weight said they would.
The feelings surprised us: love is the lightest feeling and rage is the heaviest. Grief weighs more than joy. Panic and shame sink almost to the bottom shelf.
And one lesson from a mistake: first we dropped the words instead of lowering them — and they smashed right through their own shelves like a bowling ball through a trampoline. Weight tells you where something belongs; speed decides whether it stays. Lower things gently. lol