Architectural variations: rank-1/low-rank projections, factorized embeddings, custom positional encodings, alternative norms
To understand my bandwidth usage I looked at how bubbletea rendering worked (ironically, bubbletea made massive improvements to their renderer days before I published this blog 2).
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Latin Extended scores highest because phonetic extensions are deliberately designed to resemble their Latin base forms. Mathematical Alphanumeric Symbols dominate the dataset (806 of 1,418 pairs) but score low because ornate mathematical letterforms (script, fraktur, double-struck) look nothing like plain Latin in a different font. Arabic scores lowest: the letterforms are structurally different from Latin even when confusables.txt maps them as confusable.