how clanker
are you?

// a surprisal Turing test

Drop anyone's X handle or reddit u/name — we pull their recent posts and grade how clanker (AI-like) they write. Language models predict the next token; the more predictable the writing, the more clanker.

→ or diagnose yourself — finish 5 sentences

⚠ demo mode: inference isn't funded yet — scores come from a deterministic stand-in, not the real models.

pulling recent posts…

• ⋯ finding the account on X
• ⋯ pulling their 5 most recent posts (retweets & replies excluded)
• ⋯ scoring word-by-word against the model panel

the robots are reading their timeline. they have opinions.

 ▌

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

for fun, from public posts — not a judgment of the person.

method: each word you typed is scored by its surprisal under each model — −log p_model(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.)