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There are a lot of people working in software that have limited knowledge of information theory or machine learning.

Unfortunately LLMs are magic to them.

When you lack a first principles understanding of how something really works, you will bolt on hacks and duct tape on the wrong approach.

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it's also the unfortunate practice of trying to use the new shiny thing everywhere and anywhere to signal that you're keeping with the times. It's funny how i've seen newer devs brush off things as 'traditional ml'

Which is nuts because one thing LLMs are actually pretty good up is generating "traditional ml" harnesses. You just have to describe the problem you are trying to solve and ask the LLM for algorithm suggestions.

It's also nuts because "traditional ml" generally requires a fraction of the resources of an LLM. If you need to auto label, tag, categorize, it's never been easier to setup that sort of system in any project.

And yet, most seem to be completely unaware of this and focus on LLM-related stuff almost exclusively.

Hardly a surprise when the curriculum is vastly different between institutions with cohort going through an accelerated boot camp and others being told low level computing is not useful. There are resources for people that are curious and want to broaden their knowledge, but it has to come from the individual.

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