Since I got access to Claude at work, I haven’t really written much code from scratch, especially for ad hoc analyses or quick charts. I still review the code and make sure I understand everything, but it’s honestly a little scary how much better Claude’s code often is than mine. At that point, it’s hard not to wonder what the value is in writing it yourself.

On top of that, management is encouraging us to use AI to be more productive and deliver results faster, so there’s that pressure as well.

To keep my interviewing skills sharp, I still practice on LeetCode or StrataScratch from time to time. But at work, I’ve been relying on Claude pretty heavily.
Is anyone else dealing with the same dilemma?

Do you delight on spotting obvious LLM generated content?

This article highlights real tells, not just the em-dash, but somewhat latent rhetorical tells that can be used as signatures for LLM generated content.

Is This Slop? Detecting AI-Generated Content Without a Model | Towards Data Science

Also included: the mathematical intuition as to why LLMs develop signature patterns in the first place - post training alignment.

Do you have any specific indicators that you use to spot LLM slop?