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YOalphabet proposes a 16-glyph script in which the four edges of a square encode a four-bit value; YOconlang pairs those glyphs with a constructed language built around fixed word patterns and subject–verb–object syntax. The project describes this design as suitable for “zero-inference” machine vision, but its repository does not provide independent benchmarks showing that it improves recognition speed or reduces bandwidth in real deployments.
What YOalphabet proposes
YOalphabet defines 16 glyphs, indexed from 0 through 15. Each glyph corresponds to a four-bit binary value, a decimal index, a geometric form, and an IPA sound. The project presents a 4-by-4 matrix ordered from decimal 0 to 15 as a reference or calibration layout. This is the project’s proposed convention, not a generally adopted script standard. Project repository
How the four edges encode a value
A glyph is drawn inside a square box. Starting clockwise at the right edge, the edges represent bits as follows:
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|---|---|---|---|
| Right | 0 | 1 | Draw the edge when bit 0 is set. |
| Bottom | 1 | 2 | Draw the edge when bit 1 is set. |
| Left | 2 | 4 | Draw the edge when bit 2 is set. |
| Top | 3 | 8 | Draw the edge when bit 3 is set. |
The value is the sum of the weights for the drawn edges. Internal diagonal strokes are added to balance low-density contours; the project describes them as part of the glyph design, in addition to the four-edge encoding.
#1 Best Overall
Glyphs and sounds
The repository assigns five vowels and eleven consonants across the 16 values. It associates each glyph with an IPA sound, but the available description does not establish a standard outside the project for how others should interpret or adopt those assignments. The encoding and sound mapping therefore describe the project’s own script proposal.
How YOconlang is structured
YOconlang is described as an a priori constructed language with five vowels and eleven consonants. Its roots do not inflect internally; grammatical categories, tense, and modality are expressed with separate particles. The repository specifies these word templates:
Rank #2
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| Word category | Template |
|---|---|
| Verbs, adjectives, and adverbs | CVC |
| Nouns | C1V1C2V2 |
| Pronouns and structural particles | CV or VC |
Syntax is rigid subject–verb–object (SVO), and the README describes a three-axis semantic kernel for domain, process, and target categories. These are specifications of the proposed language. They do not establish that it is ambiguity-free, exhaustive in meaning, easy to learn, or suitable as a universal language.
What “zero-inference machine vision” means here
The project argues that a fixed geometric layout could be parsed by checking four pixel-intensity locations, potentially avoiding the need for heavy convolutional neural networks. It also claims bandwidth reductions “up to 90%.” These are claims by the YOalphabet project, not independently verified results.
Rank #3
The inspected repository does not supply a reproducible benchmark, comparison baseline, sample size, or deployment conditions for the machine-vision or bandwidth claims. No independent performance figure is established there, so the “up to 90%” figure should not be treated as a measured outcome or a general expectation. The repository also does not establish deployment history or external evaluation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the repository makes available
The project lists digital resources including font files, a glyph converter, decoder code, and language reference files. Their presence documents project assets; it does not establish a physical product recommendation or prove that any particular camera, board, or device is required. The repository is the primary source for the design description, rather than independent validation of its proposed performance.
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Rank #4
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