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 →

Jeremy Howard 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 4 · C 3 · P 3 · Cm 2 3.15

Q What models are you guys using for this?

A We're using anthropic models. Um, but we're not just using system prompts. We've got a whole kind of different system for how we get it to work the way we want it to. Actually, actually, here's a, here's an interesting thing now. By having it go in these small steps and being, you know, we, we, it gives us bits of code and We can now immediately run that code, so I can hit a button, and it pulls the code out, you see. So by kind of having this integrated prompts and code all in the one place, I can immediately run it, and then I can say, like, it's very interactive, you can see. So we've, we've seen a lot of folks using this. Actually, I'll give you a sense. It's like, so we, we kind of had this test run, OSU, where we, we thought like, oh, this has been so useful for us. I suspect other people are gonna like this as well. So we decided to just let a few people try it, and we opened up, along with the course, this kind of preview a year ago, and we opened it up and we said, okay, is anybody else I want to try this idea, and we're going to teach you a bit about it, and within 24 hours we had a thousand sign-ups, so we immediately shut it down, and so we just did a thousand people a year ago, and we actually have, it's amazing, we have this, like, list now of hundreds and hundreds and hundreds and hundreds of bits of feedback from those first thousand people, you know, everything…

AI assessment note: “We're using anthropic models.”

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