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Discussion (10 Comments)Read Original on HackerNews
Code review can go much deeper now if you use AI to aggressively attack a PR combined with your human insight. Same for planning a feature:
Can I consolidate this logic to a shared function? Does the error surface to the user and are there any gaps? What preexisting functionality is affected by this PR? Can this query be made more efficient?
LLMs are great with focused questions, up and down abstraction layers and across all kinds of concerns. Stack up these focused concerns into a rich understanding of what you are doing or writing.
Understanding is the real output, code is the byproduct.
If students of the 70s or today pretended they didn’t exist up to a certain point when they needed them to move forward, like bioinformatics or something, they 100% would be better off. There is plenty of research on off loading thinning providing a worse understanding of the material - eg side rules proving a better understanding than calculators.
I think AI might be more of a problem, at least for some skills because it is not just doing a small unit of work. its more like the problem of doing more complex calculations. An example I have come across with calculators is students learning statistics without knowing how to calculate a variance, because they just put the numbers into a calculator. There is a failure there to learn the concept - they do not really know what a variance or SD is. I can imagine AI doing a lot of that.
You learn stuff by heart, so that when you mess up with the calculator you have a feel that stuff has gone wrong