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 →

Arvind Jain 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.

clear all ✕
1exchanges match
1on raw tape
0redirected or not addressed
Answered raw tape D 5 · C 5 · P 4 · Cm 4 4.60

Q So what's going on at Glean? What's hot right now?

A What Lean has actually, we're finding ourselves in the midst of very, very good timing. So when you think about AI, making it work in the enterprise, the two big things are context. Like how do you bring, you know, like all these agents that you want to automate the work that humans do, like they need that context, that data, that information that humans use to do the same work. Uh, and that's actually like something that we are really, really good at. So being the leader in context crafts is actually helping Create a massive demand for, for Glean. And then the second big trend in the industry is people keep complaining about, like they can't measure the return on investment. Where is the business value coming from? And so Glean comes in handy on that front, at least from a bottom line perspective, because we do really, really good in terms of helping a customer reduce their token usage, ah, in two different ways. One, we can pick the right model since we work with all the closed domain and open source models. We can pick the right model for the right task, which is, um, cheaper for them, but still gets the work done. And second, with our context graph, we can actually, when a model is trying to do some complex work, it doesn't have to spend, like, all this time just trying to assemble the raw materials to do that work. You know, with Glean, they get that context in one shot, s…

AI assessment note: “being the leader in context crafts is actually helping Create a massive demand”

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