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 →

Uffe Koch 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
Partly produced feed D 3 · C 4 · P 3 · Cm 3 3.30

Q Okay, so 449, uh, U.S. dollars per month on average. And give me more of the backstory here. So what year did you launch the company?

A So, so actually I, I, yeah, I had my company, previous company where I was a consultancy and doing apps for other people. And, and I, we got really good at that, but, but the, the, I also kind of grew tired of, of, of always building on somebody else's visions and ideas. And I got tired of working with, with some really great customers and some less mature customers who, whose ideas were maybe not app ready or ready or maybe shouldn't have an app. Uh, so I, I, I wanted to build on my own product and, and build that up. And then I, I found an old friend with, which has a marketing background and she was in, in a kind of same situation as me. So we decided, uh, to, uh, go for it and, uh, actually use what, what we had, we had worked on both of us worked on a lot of projects, finding the good ideas and, and weeding out the bad ones.

AI assessment note: “I had my company, previous company where I was a consultancy and doing apps”

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