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 →

Tavi Tamiste 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.

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1exchanges match
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Answered produced feed D 5 · C 5 · P 4 · Cm 4 4.60

Q Okay. Interesting. Give me the backstory here. How'd you get the idea? When did you write the first line of code for this?

A So we got started around two years ago, uh, and I was I've been working with, uh, applied machine learning for about eight years now. Uh, and I, I've been working with large corporates and I've been trying to run my own AI consultancy business. And this, this idea actually got started from when I was sort of bootstrapping my own AI consultancy business. And we were doing a, a project for a city here in Thailand, Estonia, where they, um, wanted to measure cars on city streets, basically sounds easy enough, right? Uh, but what ended up happening was that that project from start to finish Took six months. So we had to find data science resources. We have to gather the data. We had to train the model. We had to put that model to run on some infrastructure somewhere. And then we had to visualize the data with some software. So it took six months to actually get something out of it. Uh, and now with FIMA, you can do it in 20 seconds, basically. The trouble of sort of building custom AI, AI applications is where this pain got started from. Now it's a lot easier to do it basically. But there is a, there is a very important part in this is that you can only do it in a specific niche. And in computer vision, we're doing it in the CCTV camera angle because you can at least not yet make an AI algorithm that can, you know, look at everything.

AI assessment note: “this idea actually got started from when I was sort of bootstrapping my own AI consultancy”

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