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 →

Gavriel Cohen no published score: only 1 usable exchange 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.

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1exchanges match
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Answered raw tape D 5 · C 5 · P 4 · Cm 4 4.60

Q I'm curious, we guys talk for two hours. Anything you can share about, like, surprising, uh, use cases or whatever. It's like, you talk about the tech step, but just like, how does he think about it? And maybe open your mind and say, oh, you know, can you let it both?

A I think for me, his use case really helped for me to kind of crystallize what direction we want to build in. Going back, I would say a month ago, there were these two overlapping, but kind of distinct directions when we're thinking about adoption of claw type of agents, autonomous agents in a business setting in a company. Uh, so there's the one where it's the team manages agents. So it's agents that you build an agent factory or, or agents that automate workflows. I gave a talk about that, the agent factory that we built, and we had started to build that already for ourselves, and we've been building it for a while. It's still kind of under construction. So that's one to see factory. It's the team as a team working together to build the agents and managing them as a team. And then you've got the other side, which is personal agents in, in a work setting, in a business setting, but individual people who have their agent, their assistant that's helping them do their job, and it's more one-to-one. And we were going, working on both of those use cases, both internally, we were using agents in both ways within our team, uh, and started to work with design partners that were interested in both use cases. So we were working with a team where they wanted to give each person in their legal team their own personal assistant. And then we're working with another, uh, design partner that w…

AI assessment note: “his use case really helped for me to kind of crystallize what direction”

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