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 →

Tom Gibby 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 4 · Cm 4 4.60

Q Wow, ok, interesting. When did you launch? What's the backstory?

A Backstory is interesting. So we launched in 2016, but at the time we were actually focused more on messenger bots, like, you know, the whole chat bot craze from a few years ago. Um, so we were actually an original messenger launch partner. Um, we launched one of the first bots on Facebook messenger. It was actually the first bot for the music industry for one of the biggest DJs in the world. Um, we, there was a huge amount of buzz around that and we had a lot of, you know, brands and, and entertainment clients getting in touch. And we thought it was really interesting, but we started to have a few concerns about how cluttered the market was. Um, but also we actually thought that the benefits of automation for our clients, even though they were getting huge benefits from the tools they were building, we actually thought that the benefits would be far better to be used internally rather than externally. So we kind of pivoted, um, about a year or so later, and we kind of ran a like Betaworks experiment on the side to see if our customers would also get value from building their own automated work tools. On, uh, Workplace and Facebook. And because we were already a messenger partner, um, we could actually kind of plug our, our software directly into Workplace without actually having to rebuild too much of it.

AI assessment note: “So we launched in 2016, but at the time we were actually focused more on messenger bots”

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