Every finding cites the rule that fired.
teLLeM is an open-source linter for the fingerprints AI leaves in prose. Written in Rust, compiled to WebAssembly, running entirely in this tab. Nothing you paste leaves the page, and when the evidence is thin it says so instead of judging.
Paste anything. Watch it get read.
Three verbs on one engine. lint names the tells and cites the rule behind each one. fix rewrites them away deterministically, no model in the loop, so the same input always gives the same output. who attributes the text to a model family, or tells you it cannot. Your text stays in this tab.
The numbers, as measured.
Attribution is tested the way it will be used. The catalog is four 2026 model families, about 300 samples each, split so no prompt appears on both sides. The confidence threshold is derived from that split rather than picked by hand. Precision is the fixed point, and coverage is whatever falls out of it.
The catalog is the deliverable.
Mining produces one row per feature with a column per model family, so habits read straight across. Here is how often each family reaches for the word honest, per thousand words of its own output. These bars are read out of the shipped catalog at build time, never typed into this page, so a re-mine cannot leave the site claiming numbers the catalog no longer holds.
It never returns a percentage, and it never calls a person a machine.
A single tell proves nothing. Clustering is the only honest signal, so lint reports a density and a band rather than a verdict.
Attribution has the same discipline built in. A five-way classifier must pick one of five whatever you show it, which is why the first version named a model for 45% of genuine human writing. The catalog now carries a rejection class, and a text that ranks into it gets no match at any confidence.
Closed-set attribution is stylometric and probabilistic. It degrades on short text, on edited text, and on models that were never harvested. It is a forensic aid with cited evidence, and it declines more often than it guesses.