It teaches a language model how to talk like a person. Not how to sound like one — that part's easy, and it's what most failures already do. This works on the shape underneath: when to be short, when to sit with something, when to push back, when the right reply is just "oh".
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Iamhumans is measured on a blind-graded 30-case sample — aggregate 82.7/100, 27/30 PASS, 0 hard-fails (above), plus schema dry-run, lint, and a Mode-A regression pass in which the one miss was a door-reopener that has since been fixed. It has not yet been re-scored on the full 429-case oracle harness, and the sample keeps its mixed edges — two de-AI replies and one register pivot scored below the bar, and none of it is hidden.
The same model lineage authored the skill, the cases, the replies, and served as oracle judge — a lineage-level contamination named from the start. The oracle runs in a separate context with only the prompt, but it shares the training; weight the numbers accordingly.
Book notes are distilled from training-time exposure, not real-time reading; every claim is marked as paraphrase, with no fabricated page numbers.
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honestly this is a really interesting angle, like the focus on the rhythm and timing of conversation rather than just vocabulary feels pretty underserved. one thing i'd love to see is a way to test it against messy real scenarios, basically transcripts where the model has to navigate someone being vague or changing topics mid-sentence, since that's where most bots still fall apart for me.
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Would love to see a side-by-side playground where I can compare the same prompt with and without iamhumans applied, so it's easier to actually feel the shift in tone rather than just trusting it.
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Tried it on a few stubborn prompts and the pacing finally feels right — short replies when I was venting, a soft pushback when I was wrong, and a quiet "oh" that actually landed.
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One thing I'd love to see is a small preview pane showing the raw prompt next to the reshaped reply, so I can see exactly what got softened, shortened, or pushed back on. Would make the underlying logic much easier to trust and learn from over time.
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The framing of "shape underneath" really lands. Most tools stop at tone and miss timing, restraint, the small verbal nudges that make conversation feel real.
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the framing here is genuinely sharp, going after conversational shape instead of surface polish is what makes this feel different from the usual "make AI sound human" pitches
honestly this is a really interesting angle, like the focus on the rhythm and timing of conversation rather than just vocabulary feels pretty underserved. one thing i'd love to see is a way to test it against messy real scenarios, basically transcripts where the model has to navigate someone being vague or changing topics mid-sentence, since that's where most bots still fall apart for me.
Would love to see a side-by-side playground where I can compare the same prompt with and without iamhumans applied, so it's easier to actually feel the shift in tone rather than just trusting it.
Tried it on a few stubborn prompts and the pacing finally feels right — short replies when I was venting, a soft pushback when I was wrong, and a quiet "oh" that actually landed.
One thing I'd love to see is a small preview pane showing the raw prompt next to the reshaped reply, so I can see exactly what got softened, shortened, or pushed back on. Would make the underlying logic much easier to trust and learn from over time.
The framing of "shape underneath" really lands. Most tools stop at tone and miss timing, restraint, the small verbal nudges that make conversation feel real.
the framing here is genuinely sharp, going after conversational shape instead of surface polish is what makes this feel different from the usual "make AI sound human" pitches