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 →

Alex Kilkka 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 5 · C 5 · P 5 · Cm 4 4.85

Q So what, that's helpful to understand kind of the challenges. Maybe that's why you shut that down. You're doing consulting now, but what, what, what did you tell your program to go look for every morning? Did you only look at electronics at a price point higher than a hundred bucks? Like what were some of those factors?

A Sure. Um, so we, um, started off in electronics and Um, because that's where the initial discovery was. Um, and we set certain parameters, like we needed at least a five dollar price difference to make it even worthwhile. Um, and then we increased that over time. So, you know, we need to look for a certain spread, um, certain product categories. And what we actually did is we, um, scraped the top selling, um, in different categories for Amazon. So we look at like the top hundred electronics overall, and then Kind of dive into top a hundred, um, you know, like computer products, top a hundred, um, you know, music products, um, so forth, and just kind of crawl through the hierarchy. Um, and then as soon as we found the best selling products on Amazon, then we do lookups on eBay, um, and then pull in potential matches. And then we went through a whole process of screening them for fit.

AI assessment note: “we needed at least a five dollar price difference to make it even worthwhile”

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