Show HN: How clanker are you? A reverse Turing test

A developer released 'how clanker are you?', a reverse Turing test that measures how surprising a user's writing is to language models. Users finish eight sentences and receive a score based on token surprisal, with low surprisal indicating machine-like writing. The demo mode uses simulated scores due to lack of funding for real model inference.

how clanker are you? // a surprisal Turing test, reversed. Language models predict the next token. You, allegedly, do something more interesting. Finish eight sentences; we measure how surprising your writing is to the machines. low surprisal = the model saw you coming. high surprisal = congratulations, human. ⚠ demo mode: inference isn't funded yet — scores come from a deterministic stand-in, not the real models. ▌ 3–10 words. be yourself. or don't. interrogating the models… token by token. they can't hide their logprobs. you are clanker one square per word · green = human · red = clanker deeper stats method: each word you typed is scored by its surprisal under each model — −log pmodel word , from the model's top-20 next-token probabilities conditioned on your text so far. mean surprisal in nats over your words is how predictable you were; low = clanker. words outside the top-20 are floored, scores are normalized against each model's self-baseline, and your overall is your nearest least-surprised model. equivalently: the KL from your one-hot word choice to the model's distribution collapses to exactly this surprisal. demo mode: logprobs are currently simulated.