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 →
Lee Fan 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.
Answered produced feed
D 5 · C 4 · P 4 · Cm 4 4.30
Q desk and a vintage French country bench, and those two categories do not go together. If you were to do an explicit search, One would do mid-century, modern, and a different category for country or cottage chic. But now you have to infer this collection, this, this cluster of traits of what people are interested in. Can you guys talk to me about some of the challenges of doing this?
A Um, first difficulty or challenges. Don't know what they don't know, right? For me, like, for example, very hard to describe if this is contemporary, classic, traditional, and also you don't know whether this is the ultimate thing you want to match upon. Maybe you should put in the two different rooms, right? So I think one powerful thing we help users to do is, chances are some users have the same struggle with. And the hard part is maybe that user is in, in, um, Scotland and that they use different language to express. That's why lens can be powerful because you don't know how to describe. Instead of asking user to input a text as a query, take your camera, point it to whatever you want to understand, and we will give you ideas of what are the related ideas.
AI assessment note: “first difficulty or challenges. Don't know what they don't know, right?”
Answered produced feed
D 4 · C 3 · P 3 · Cm 3 3.30
Q desk and a vintage French country bench, and those two categories do not go together. If you were to do an explicit search, One would do mid-century, modern, and a different category for country or cottage chic. But now you have to infer this collection, this, this cluster of traits of what people are interested in. Can you guys talk to me about some of the challenges of doing this?
A Um, first difficulty or challenges. Don't know what they don't know, right? For me, like, for example, very hard to describe if this is contemporary, classic, traditional, and also you don't know whether this is the ultimate thing you want to match upon. Maybe you should put in the two different rooms, right? So I think one powerful thing we help users to do is, chances are some users have the same struggle with. And the hard part is maybe that user is in, in, um, Scotland and that they use different language to express. That's why lens can be powerful because you don't know how to describe. Instead of asking user to input a text as a query, take your camera, point it to whatever you want to understand, and we will give you ideas of what are the related ideas.
AI assessment note: “first difficulty or challenges. Don't know what they don't know, right?”
