Machine text leaves traces. This looks for them.
Paste text. Every registered detector runs over it and reports its evidence — how the text is written, AI-disclosure labels it carries, hidden characters embedded in it — annotated in place. Each signal is shown with what produced it, and none of them is proof.
A learned stylometric score, plus word-frequency shape and excess-vocabulary markers. Probabilistic and wrong at a measured rate; the learned score abstains under 50 words, the other two under 30 — below that the app says it cannot judge, which is not the same as clearing the text.
Mandated AI-disclosure labels and model self-disclosure boilerplate. Declared provenance, not a covert mark, and scored below the strong-signal threshold on purpose.
Zero-width characters, bidi controls, exotic spaces, homoglyphs — carrier channels for hidden payloads. Deterministic: a hit is a hit, but it shows something was embedded, not that a machine wrote it.
Detection is measured against a research pipeline: public corpora of AI-generated, watermarked and human text, generation harnesses for SynthID, KGW and related schemes, and user-contributed labels. The families are reported side by side, never merged into a single claim about who wrote the text.