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You know you're not writing for LinkedIn? So platitudes about drifting away, watching your project "succeed" by being really popular, is not relevant to the main concerns pushed by this piece. Particularly brushing off the non deterministic score calculation.
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I'm a bit disappointed to see "The critiques here are sharp", a Claude tell, in a response which (to me) is trying to subtly argue that hackerrank is not overly reliant on LLMs.

I'm not sure if your intent was to come across as having written this yourself, but it did not have the effect of improving my perception that this approach is flawed.

I was also disappointed that you didn't address the variability in scores. I'm inferring that you believe the larger model takes care of the main observation in the post, but I don't really see you directly addressing the points.

Maybe it's just me.

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There is variability in scores and that's expected given we are eventually using a LLM to score. At least, when I used it 7 months ago, the only way I could avoid it was by keeping the cutoff score low (as low as 10 or 20).

Reading this thread, I'm hoping to minimize the variability even further (even though I know it can't be fully removed).

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Do you read all ~50,000 then? Just with the ranked ones first?

Or are you using it to screen? I'm confused.

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There are some with very low scores that were ignored (like < 20).

Rest of the ones with good scores (at least more than 40K), was reviewed manually.

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>>It 's not an ATS.

>>No human can read that many resumes well. So I built something to rank them, helping me decide which resumes to read first

Translation: it's an ATS.

>>the system was designed to rank resumes, not reject them

>>Only resumes at the very bottom of the distribution were filtered out

Translation: it was designed to reject the CVs

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Saw this comment at the top with 0 replies and thought “How is that possible??” and then saw the “0 minutes ago” timestamp. Only on HN can you stumble into the comments section just moments after a CTO, founder, author, etc. left unfiltered remarks about the exact topic of the post. Never change HN.
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Depends how "unfiltered" you consider LLM output to be.
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Thank you for your fantastic work!
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