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

You write 8 text completions and open models score how predictable each word was too them. Predictable => clanker. You can share results with your friends.

The scoring checks every word you write against the model's logprobs. Right now I'm using Llama3.1, Deepseek v3 and Qwen3 to keep costs low. I tried to calibrate it so other models (chatgpt/claude) score 100% and interesting human responses score in the 10-30% range.

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

The metric used is per-word surprisal: -logprob of each word you type. This is just the same thing as per-word cross entropy or KL-divergence where the user distribution is one-hot. Calibrating it so text generated by frontier models scored poorly was a challenge at first. Originally ChatGPT was scoring around 54%. I'm still having trouble assigning high scores to the personalized Gemini and ChatGPT responses when I'm logged in because all my personal context gives surprising responses.

And yes, gibberish responses score very human :)

Thanks! That's a great idea - i'll top up my fable budget and get started :)

holy the AI brainrot is real lmao

apparently I'm 55% clanker. probably cause english isn't my first language and I learn mostly by reading. dead internet theory?

I like this alot thanks

Omg, I am a 100% clanker

Beep boop