[colechristensen]

A considerable amount of work for grad students is answering the question: "How the f#$% do I get this code to compile and run"

Some other researcher, often with limited skills in your native tongue, even more limited skills in software development best practices, wrote some code for a paper between 5 and 50 years ago and your PI has told you to use that code and some OTHER code together at the same time to validate some experiment he wants you to do.

In the past you would take days/weeks/months to get this to work, but with an LLM?

I'm envious of the grad students of today for the amount of nonsense which is bypassable.

[nxobject]

The other half is: "What combination of packages and task views do I actually need to not reinvent the wheel for this particular type of analysis?"

[freehorse]

And the third half is "what preprocessing and analysis methods I actually need".

Because I have never met a person who is great at that last part (methods theory) and sucks at the others (technical implementation; because the same work and effort leads one to train both). The issue is that AI solves all these problems at once, which will probably result in more academics understanding their methods and choices in preprocessing etc even less. At least this is what I have seen, and seen it getting worse.

I wish the problem was just finding the right packages. Web search, and mentoring/talking to colleagues are pretty good solutions to that. LLMs are more of a gamble here if one use them as an authoritative source, they may suggest the right package, or they may take you on a long trip to nowhere, depending on random factors.