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 →

Jack Beckor no published score: no usable exchanges 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.

clear all ✕
1exchanges match
0on raw tape
0redirected or not addressed
Answered produced feed D 4 · C 4 · P 3 · Cm 3 3.60

Q Okay. How are you getting, like, where are you getting on this content from? Are you paying someone else to curate it for you or you guys have your own big internal team doing this? How do you get it?

A So the, we, so part of the things we do is make sure we get the right content. So that's part of what we do. Um, and we take that content and, and we use a mix of machine learning and human curation. To bring the whole thing together. So it's a whole different things, process, machine learning, human curation to bring a hack. We do it that way so we can scale the business. So, so right now we can produce, we can do a hack in about a couple of hours. Whereas someone else would have to read a whole book, do the whole thing, et cetera. So we have a process that enables us to, to scale that.

AI assessment note: “we use a mix of machine learning and human curation”

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