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A comment on Measuring the productivity impact of generative AI. Parent comment

Re:Measuring the productivity impact of generative AI by jonnycat on Thursday June 01, 2023 @12:19PM (#36153386)
I agree with the overall assessment, but there's a catch: even "highly skilled" workers are not skilled in "all the things". I've felt that ChatGPT hasn't increased my productivity in my core languages and frameworks, but it has helped immensely in areas where I'm not an expert.

To be concrete about it, I recently ported a project from SwiftUI on iOS (an ecosystem I am very comfortable with) to an Android app written in Kotlin (an ecosystem I mostly dread). I don't find ChatGPT very helpful with my day-to-day Swift stuff, but it was incredibly helpful with the Android work, from language syntax to idioms to outright translation of code.

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Re:Measuring the productivity impact of generative AI by cudgy on Thursday June 01, 2023 @02:01PM (#36154924)
What framework or approach did it recommend you use for the Android UI? Compose? XML layouts? From code?

I tried using the Bing chat interface, and it repeatedly pushed me in the direction of using Microsoft tools to accomplish tasks for a cross-compile mid tier solution. I had to explicitly tell it to exclude them.

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Re:Measuring the productivity impact of generative AI by rvieira on Thursday June 01, 2023 @04:29PM (#36156938)
That's exactly my experience.

In areas where I'm an expert, I find myself correcting ChatGPT constantly. But for a language I want to learn, it's been really helpful to get me started on a simple algorithm or debug an error message.

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Re:Measuring the productivity impact of generative AI by dgroshev on Friday June 02, 2023 @07:57AM (#36163952)
I can't help but think that it sounds like productised Gell-Mann Amnesia. If a system is useless and/or counterproductive in areas you're an expert in, but appears useful in areas that you aren't, shouldn't it be a red flag? How would you know if solutions it comes up with are bad, wrong, or just bad practices?
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