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 →

Lance Martin 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 Well, we're on the topic of Drew. Drew is obviously another really good author. Uh, he's, so he's created, he's coined a bunch of, like, sort of context engineering lore. Um, any other, uh, commentary on, on stuff that, you know, you particularly like or disagree with?

A I'll show you something kind of funny. If you go to his post, um, so he and I did, did a meetup on this, and I, I kind of like this quote from Stuart Brand. It was kind of comical. If you want to know where the future is being made, look for where language is being invented and lawyers are congregating. So it, and it was talking about this, this idea of kind of why buzzwords emerge. And he actually was the one who turned me on to this idea that a term like condense engineering kind of catches fire because it captures an experience that many people are having. They don't come out of nowhere. And if you scroll down a little bit, He kind of talks about this. He's a whole post about kind of, I think it's how to build a buzzword. Um, but he talks a lot about this idea of, of kind of successful buzzwords are capturing a common experience that many of us feel. And I think that's kind of the, the genesis of context engineering is also largely because many of us build agents. Ooh, there's lots of ways they can be quite tricky. And, oh, context engineering is kind of what I've been doing. And you hear a number of people saying, And then you can, it kind of resonates and you say, oh, okay, yes, that describes my experience. So I think that's just an interesting aside on, on kind of how language emerges anthropologically kind of in, in, in different communities.

AI assessment note: “He actually was the one who turned me on to this idea”

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