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 →

Blake Burch 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 Were you quitting PMG or, or, or where were you, like, where'd you experience this problem?

A We have a very unique kind of, uh, backstory where, uh, we were solving a lot of problems at PMG, um, with internal technology, um, where we were doing things like automating bids, automating budgets, ad creation, um, turning things on and off based on inventory files and stuff like that. And, uh, we were templatizing this to be able to run it across all of the like fortune a thousand clients that PMG worked with. And we realized that there were much larger use cases than just the marketing side. Um, for all the data and the automation that we were putting in place. And so Shipyard is actually the, uh, like child product of something that was built at PMG. Uh, we ended up splitting things off, um, and spinning the technology out on its own to focus on a totally different sort of, uh, ICP of your typical data engineer, analytics engineer, uh, and everything else there, because we felt like with the massive amount of growth in the data ecosystem, that was something that we wanted to make sure we could capitalize on and help those teams, um, be able to build workflows more effectively.

AI assessment note: “we were solving a lot of problems at PMG, um, with internal technology”

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