AI or human?
Twenty short snippets. Half written by models. Classify each. Reveal the tells at the end.
❂ Primer
Skip if you already know the theory; the interactive is right below.
Twenty short snippets. Half written by humans, half by models. Classify each. Reveal at the end with a one-line tell per snippet. Your best score is saved; the leaderboard is opt-in.
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⁂ Notes from the bench
What to watch for, why it matters, and the one thing that usually surprises people.
Tells that usually work
AI tells
- Hedged symmetry. "While this approach has its merits, it also presents certain challenges…" Whenever a sentence balances two sides with no specificity, suspect a model.
- Empty superlatives. "Truly transformative", "an exciting new chapter", "the possibilities are endless." Humans reach for superlatives rarely and specifically. Models reach for them as punctuation.
- Corporate vocabulary. "Leverage synergies", "empower users", "navigate complexities", "sustainable growth" — content-farm vocabulary clusters hard in LLM output.
- Self-reference. "As an AI, I'm happy to…" — the giveaway, though usually stripped out before snippets go out.
Human tells
- Specific nouns you wouldn't invent. A time (7:12am), a name (Dave), a cat, a printer. Humans ground their jokes in stuff.
- Self-deprecation that lands. Good humans are mean to themselves in a way that's not performative. Models trained on safe text usually can't.
- Grammatical irregularity on purpose. A lowercase "ok", a fragment, a dropped period. Humans break grammar to convey tone. LLM outputs almost always end with a period.
- Admissions of not-knowing. "nobody knows why", "this is cursed", "refactor later" — the voice of someone who has lived with consequences.
Why any of this matters
The stock question "how do we detect AI-generated text" has no stable answer — the generators keep improving and every tell rotates out eventually. What doesn't rotate is the underlying shape: models are trained to sound like text, but humans write to sound like themselves. The gap is usually specificity.
Also useful: the same pattern-matching you use to spot AI in other people's writing is the pattern-matching you need to avoid it in your own. If your first draft reads like item #8 above ("Whether you're a seasoned professional…"), something is wrong — either a model wrote it or you're imitating one without meaning to.
In a line
A twenty-round classification game over curated tweets, product copy, and code comments, with a one-line tell per snippet at reveal. Best score saved; opt-in leaderboard.
Other experiments
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How a sentence becomes tokens
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Temperature and top-p, visibly
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What does this prompt actually cost?
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Tokens per second
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How far should the model think?
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Neural language vs a Markov chain
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What each token looks at
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Words in space
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The injection arena
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Context Tetris
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Magnet flip