@malstrom @pseudonym that’s an interesting claim. I don’t know enough about LLM research to make a judgement. I do know that LLMs trained on synthetic (other LLM-generated) data tend to perform worse, but have we reached the limits of what LLMs are capable of? In my limited understanding, if an LLM can “learn” fundamental programming “concepts” (the same way they can “learn” concepts across human languages — I could be wrong in my understanding here), they should (might?) be able to transfer/apply those concepts to not-before-seen domains (maybe with a bit of “reasoning” prodded in).
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xrisk@social.treehouse.systems
@xrisk@social.treehouse.systems
Posts
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If you replace a junior with #LLM and make the senior review output, the reviewer is now scanning for rare but catastrophic errors scattered across a much larger output surface due to LLM "productivity." -
If you replace a junior with #LLM and make the senior review output, the reviewer is now scanning for rare but catastrophic errors scattered across a much larger output surface due to LLM "productivity."@pseudonym is the problem the increased volume of code that the LLM is producing (as compared to the junior dev) — what you are calling “productivity gains"? because I can see this same argument being made for code produced by humans as well.