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RaySpace draws on a century of ray-based geometry techniques, from Radon's uniqueness theorem and Carmack's raycasting to ambient occlusion and acoustic impulse responses.

RaySpace is a geometric approach to confusable character detection that compares font vector outlines directly using raycasting.

RaySpace found 250,000 unique confusable character pairs.

The first systematic, font-specific multi-character confusable dataset. 676 Latin bigrams tested against 133,000 single-character targets across 245 fonts using geometric SDF comparison. 571,753 pairs found, including 'rn' matching 'm' in 95 fonts and 'oy'

Different fonts make different characters confusable.

confusable-vision's scoring pipeline went from 8.5 hours to 3.5 minutes through three targeted optimisations: replacing sharp with pure JS bicubic resize, moving SSIM computation to a WASM kernel in worker threads, and switching PNG decoding from sharp to…

Unicode's confusable detection maps 6,247 characters to Latin targets, protecting English readers from homograph attacks.

I scanned 22,581 characters from 12 scripts against each other across 230 fonts. 248 confusable pairs between non-Latin scripts have no coverage in TR39, ICANN guidelines, or any existing detection system.

confusable-vision's Milestone 2b scanned 122,862 CJK, Hangul, Cuneiform, and Egyptian Hieroglyph codepoints against Latin a-z/0-9 across 230 macOS system fonts. 28 novel confusable characters found, all simple geometric primitives.

Unicode confusable characters create attack surfaces in LLM pipelines, but not the one everyone expects.

Can pixel-identical Unicode homoglyphs fool LLM contract review?

Confusable detection pipelines normalise characters to a fixed canvas before measuring similarity.

Three attack vectors for Unicode identifier spoofing, a survey of twelve detection systems, and a published benchmark corpus for testing your own defences.

confusable-vision renders every TR39 confusable pair across 230 macOS system fonts and measures visual similarity with SSIM. 96.5% of confusables.txt is not high-risk, but 82 pairs are pixel-identical in at least one font.

confusable-vision's Milestone 2 scanned 23,317 Unicode characters not in TR39 confusables.txt against Latin a-z/0-9 across 230 macOS system fonts. 793 novel high-risk pairs discovered, from 96 different scripts.

Why a deterministic rule engine beats an LLM for legal document generation — and where the AI actually helps.

The legal profession has adopted AI as a productivity tool when it should be adopting it as an infrastructure layer.

A survey of how 12 real-world systems apply Unicode confusable detection.

TR39 and NFKC map 31 characters to different targets.

AI-generated music deserves proper distribution.

Every multi-tenant app has to solve it.