The Exchanges

Every argument clarity score on this site is built from rows on this page. Each question and answer was assessed with names hidden, the host's own answers included, on four things from 1 to 5: directness (does it answer the question asked), coherence (do the ideas follow), precision (concrete details and clear references), compression (says a lot per word). The weighted mix (30/30/25/15) is the exchange score. A person's published score averages their exchange scores on raw tape only, at least 8 of them, shrunk toward the cohort mean. Full method →

Dan McAteer no published score: only 1 usable exchange on raw tape, and a fair score needs 8+ record → ← everyone

Every exchange below was scored with names hidden, four dimensions each from 1 to 5. An exchange's score is 0.30·directness + 0.30·coherence + 0.25·precision + 0.15·compression. The published score averages the raw tape exchange scores and shrinks small samples toward the cohort mean, so five great answers can't beat twenty good ones. Produced feed rows count only toward coarse estimates, never toward a full score.

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1exchanges match
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Answered raw tape D 5 · C 5 · P 4 · Cm 4 4.60

Q Um, what was your experience then? Did you like the model right away? Like, did you kind of, uh, you know, did it grow new?

A Yeah, so I think my experience with O-one, where it's been different from other models for me, is that O-one is actually the first model where I'm getting more impressed by it the more I use it. So, like, when ChatGPT first came out, right, I think it was GPT-III. And at first it seemed like, oh wow, this is amazing, it can actually create text that sounds like a human, and it can Write poems, but kind of as you started to use it more, you would discover more of the things that, where, you know, there was error cases, or there's areas where it couldn't, it wasn't capable. And with, with O-one, it's been, it's been the opposite experience. So the more I've used it, the more impressed I become by it, and the more I realize it can do. So it's kind of like, by the nature of it is so capable, it's sort of been like pulling me towards using it more.

AI assessment note: “O-one is actually the first model where I'm getting more impressed by it”

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