Slop or not – can you tell AI writing from human in everyday contexts?
I’ve been building a crowd-sourced AI detection benchmark. Two responses to the same prompt — one from a real human (pre-2022, provably pre prevalence of AI slop on the internet), one generated by AI. You pick the slop. Three wrong and you’re out. 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…
In plain words
Slop or Not is a crowd-sourced benchmark game that tests whether players can distinguish human-written text from AI-generated content. Players compare pairs of responses to identical prompts—one from a human (pre-2022 Reddit, Hacker News, or Yelp posts) and one from AI models by Anthropic or OpenAI. The dataset contains 16,000 human posts paired with AI generations across six models at three capability tiers, with all votes logged for research. The creator plans to release the full dataset and publish findings on AI detection difficulty across different platforms.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
I’ve been building a crowd-sourced AI detection benchmark. Two responses to the same prompt — one from a real human (pre-2022, provably pre prevalence of AI slop on the internet), one generated by AI. You pick the slop. Three wrong and you’re out. 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?
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