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 →

Hirsch Tapadia 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 communicating things like quota and quota target and percent of quota hit. Yeah. So help me understand how you're breaking this down. I'm just going off the screenshot of your homepage, right? So when you say like you have a couple of things, there's a progress bar, there's velocity per week, and there's scope remaining. What do you set first? Like, do you set a number of points per scope?

A Yeah, so we, we use the data that's already being leveraged in tools like Jira, for example. So somebody's defined, you know, call it an epic, right? Some body of work. And they've either created a bunch of tasks inside of that, or they've added story points, you know, whatever process that they use. And then what we've done is we've looked at your historical data to say, when something like that materializes, how does it typically go from start to finish? And then we start tracking work against that. And what we do is we look for deviations from that normal. And normally it takes maybe a day to review code. This one's taking four days to review code. Why is that? Right? Oh, the code's really complicated. It was rewritten a bunch of times. Seven people wrote it instead of one person. So, uh, it's hard to review. And each one of those has a historical factor that said, last time something like this happened, This thing got later and later, or it got earlier and earlier, and that gets worked into a forecasting model. This machine learning algorithm that we developed that then predicts three things. When do we think it's going to get delivered? How does that date change it? And how is the change changing? So is it getting later faster? Is it getting earlier slower? Basically what we're trying to explain to folks is, are we confident in what we're saying we're going to do, or are w…

AI assessment note: “we use the data that's already being leveraged in tools like Jira”

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