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 →

Rita Kozlov 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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Answered raw tape D 5 · C 4 · P 4 · Cm 4 4.30

Q Yeah. Anything you'd add, Rita, uh, in terms of like what you see here from your eyes as a PM?

A Yeah, I think as Sunil mentioned, I think the, how do people call agents or interact with agents, the entry point story there is really interesting. Which is where I'm really excited about email. What else would I add? I don't know. I'm actually kind of curious your perspective for seeing this for the first time. What stands out to you as useful? Um, I think one of the hard and where I'm actually kind of curious your take, I asked myself this question the other day and then I asked it on Twitter and it didn't get very many responses. But one of the things that I think is telling of where we are in the agent's conversation more broadly is there's not really a canonical hello world for an agent. Like I feel like Like the hello world of Ella Lambs' rag, right? Like everyone has built a rag at this point. And we're at this interesting kind of uncanny valley with agents where we don't, it's, I think the hard thing about coming up with it is there's nothing that you trust it enough to do that you're like, I'll give it the keys to the car and I'm fine with it running off and doing right. So like something that feels very agentic to me, for example, is like, Okay, don't just suggest restaurants for me. Go make me a reservation. But then, you know, I need to start giving it a lot of credentials and my credit card information. And so I think that that's kind of the next interesting bits …

AI assessment note: “one of the things that I think is telling of where we are”

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