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 →

Kevin Ben-Smith no published score: only 2 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 ✕
2exchanges match
2on raw tape
1redirected or not addressed
Answered raw tape D 5 · C 5 · P 4 · Cm 4 4.60

Q Okay, okay. Let's talk about Snit. What is Snipt? And, you know, then we'll talk about your origin story, but I just, let's, let's get a crisp. What is Snipt?

A Yeah. I always see two definitions of Snipt. So I'll give you one really simple, straightforward one, and then a second more nuanced, um, which I think will be valuable for the rest of our conversation. So the most simple one is just to say, look, we are an AI powered podcast app. So if you listen to podcasts, we're now providing this AI enhanced experience. But if you look at the more nuanced, uh, perspective, It's actually, we, we have a very big focus on people who, like your audience, who listen to podcasts to learn something new. Like your audience, you want, they want to learn about AI, what's happening, what's, what's, what's the latest research, what's going on. And we want to provide a, a spoken audio platform where you can do that most effectively. And AI is basically the way that we can achieve that.

AI assessment note: “we are an AI powered podcast app”

Redirected raw tape D 2 · C 4 · P 3 · Cm 3 3.00

Q And then when did you go, like, full-time on Snips?

A Yeah, so basically that was, that was afterwards. I mean, how that started was the friend of mine who got me into machine learning, uh, him and I, uh, like, he also got me interested into startups. He's had a big impact on my life. And the two of us would just, uh, jam on, on like ideas for startups every now and then. And his background is also in AI data science. And we had a couple of ideas, but given that we were working full times, we were thinking about, uh, so we participated in hack Zurich. That's, uh, Europe's biggest hackathon, um, or at least was at the time. And we said, Hey, this is just a weekend. Let's just try out an idea, like hack something together and see how it works. And the idea was, That we'd be able to search through podcast episodes, like within a podcast.

AI assessment note: “how that started was the friend of mine who got me into machine learning”

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