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 →

Haile Wusu 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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Answered raw tape D 5 · C 5 · P 4 · Cm 4 4.60

Q awesome. Um, a little bit to the, um, as, as a sort of, uh, response to the previous talk, um, how do you think about, um, build versus buyer for, for something like this? I mean, how much, um, have you guys done in-house in terms of building the model, so even the infrastructure to enable you to, to do that, uh, versus, you know, working potentially with outside vendors?

A Yeah, um, so in, in everything that, the, the entire structure of, uh, of this, uh, Of this, uh, this app, uh, Velocity was built in-house. I mean, really, uh, we built it from the, the, the ground up. I, I had the great pleasure of working with exceedingly talented, uh, people in, in particular, our chief, uh, chief architect, um, built, uh, this from the ground up on Rails. Um, With respect to the model, that's definitely something that we built in-house. Really, we, because we obviously have an interest in, and have actually licensed this, uh, license velocity out, we haven't used any pre-existing models for, for predictions. It was very important for us to have something that was, uh, obviously accurate, but also something that would be hard to mimic, to be frank.

AI assessment note: “With respect to the model, that's definitely something that we built in-house.”

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