How It Works

You can really tell when it's AI. Computers? Not so much, unless I force feed them what to look for. I use brutally simple criteria: ngrams. These are short collections of words which are “n” in length. For example, “AI slop” is a bi-gram. There's the other usual suspects too. Patterns like “It's not just profound — it's AI slop.”

To bootstrap, I collected labeled examples of AI and human content from subreddits like r/IsThisAI, where users collectively vote on whether content is AI-generated or not. The consensus is usually right. I also scraped loony places like r/microsaas to try to wrap my mind around their record-breaking emoji-to-word ratios. Then from there it's just a matter of compiling the worst offender n-grams, detecting the typical phrases (regex), and building a reference dictionary of slop-associated patterns.

When you submit text, it gets compared against this dictionary and you get back a score — basically how much it looks like known AI slop.

The model uses four types of detection rules:

• Phrase matching— known AI phrases like “delve into”, “tapestry of”, “navigating the complexities”
• Regex patterns— structural patterns like “it's not X — it's Y” with all dash variations (em dash, en dash, hyphen)
• Emoji signals — specific slop emojis (👉🚀💡🔥) and density thresholds (3+ emojis = suspicious, 8+ = high confidence)
• LinkedIn slop— growth-hack patterns like “10x'd my visibility”, “send me a DM”, “still in beta”

The Chrome extension ships with a compact 3KB rule file for instant local detection — no API calls needed. If you sign in, the rules stay updated automatically as the model improves.