Show HN: Slop or not – can you tell AI writing from human in everyday contexts? (slop-or-not.space)
The dataset: 16K human posts from Reddit, Hacker News, and Yelp, each paired with AI generations from 6 models across two providers (Anthropic and OpenAI) at three capability tiers. Same prompt, length-matched, no adversarial coaching — just the model’s natural voice with platform context. Every vote is logged with model, tier, source, response time, and position.
Early findings from testing: Reddit posts are easy to spot (humans are too casual for AI to mimic), HN is significantly harder.
I'll be releasing the full dataset on HuggingFace and I'll publish a paper if I can get enough data via this crowdsourced study.
If you play the HN-only mode, you’re helping calibrate how detectable AI is on here specifically.
Would love feedback on the pairs — are any trivially obvious? Are some genuinely hard?
9 comments
[ 4.4 ms ] story [ 31.1 ms ] threadSome were hard though, yeah (at least if not looking longer than 5-10 seconds). Btw, it seemed more logical to me to just see a green/red card when you click, i.e. right choice or wrong choice. Getting red for the correct answer confused me a bit (but this might just be me).
Some were hard but spottable after re-reading the answers a good 10 times... ahah.
FWIW, I found the “medium” one’s hardest. Most of the “hard” ones have dead giveaways in the form of either punctuation or common AI text rhythms.
I know I've been finding it all over the internet, but this suggests to me I'm encountering it more than I thought.