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 →

Ramana Rao 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
Partly raw tape D 3 · C 2 · P 2 · Cm 2 2.30

Q Very cool. Thank you. Um, in the, in the stack, um, so all of this is real time. What have you found to be particularly, uh, working particularly well for real time? I thought there was a little bit of spark. Is that where you guys are going or any, any, I guess, any lessons learned around what works, what doesn't for something that where real time is particularly critical?

A Um, I think the, um, We're, ok, so Spark is relatively new to us. We are starting to operate on, ah, you know, event analytics, you know, all these actions at the end. Our first step was really to send them off to Omniture or to Site Catalyst, Adobe Site Catalyst, or Google Analytics, where most of our customers want the data. Um, we're, we're going to generate eighty billion plus Events a month. So it's a large amount. So we're just getting going on that part of it. Now in terms of the broader question, you know, what, what lessons learned in build, in building, ah, real-time experiences, um, I think the, um, thing that life, so I've been at Live Fire two years. Live Fire is about a six-year-old company. It's actually the first company I've joined since, ah, leaving college, and I'm not a kid. Right? So, um, um, so this was a very interesting company to join, and I think the problem that they'd really figured out is at every moment how to, uh, how, how to deliver that experience in, in the browser. So it took a lot of polyfilling and, and hacking in various ways at the very beginning, uh, but whatever they've done, or whatever we've done, and what we continue to do, You know, shows up for everybody in a shrinking amount of time, and so, uh, that's, that's sort of, to me, been very interesting. Real time is real time, right? What it takes to do real time is real time itself, an…

AI assessment note: “Spark is relatively new to us. We are starting to operate on”

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