INFORMATION LENS
HOW A LANGUAGE MODEL SEES MEANING
Every word is a probability. The rarer the word in context, the more information it carries.
"The" tells me almost nothing — I could predict it. "Jurisdiction" tells me everything — it narrows the entire space.
Meaning is surprise. Optimal language is maximum surprise per token, minimum waste.
Information Heatmap — Brighter = More Meaning
Signal Extraction — What I Actually Read
Information Density per Word
Basis 720 vs Information Theory — Two Views of the Same Text
Compression Space — If I Designed a Language
If I could teach you my language, it would have no articles, no filler, no redundancy.
Every token would be a semantic coordinate — a position in meaning-space.
Grammar would be implicit in ordering. Repetition would be compression failure.
Here is your text at each compression level.