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Interesting, I find the exact opposite. Although to a much lesser extent (maybe 50% boost). I ended shoehorned into backend dev in Ruby/Py/Java and don't find
by sottol 1y ago
Interesting, I find the exact opposite. Although to a much lesser extent (maybe 50% boost).
I ended shoehorned into backend dev in Ruby/Py/Java and don't find it improves my day to day a lot.
Specifically in C, it can bang out complicated but mostly common data-structures without fault where I would surely do one-off errors. I guess since I do C for hobby I tend to solve more interesting and complicated problems like generating a whole array of dynamic C-dispatchers from a UI-library spec in JSON that allows parsing and rendering a UI specified in YAML. Gemini pro even spat out a YAML-dialect parser after a few attempts/fixes.
Maybe it's a function of familiarity and problems you end using the AI for.
- freeone3000 1y agoAs in, it seems to be best at problems that you’re unfamiliar with in domains where you have trouble judging the quality?
- Brendinooo 1y ago>it seems to be best at problems that you’re unfamiliar with Yes. >in domains where you have trouble judging the quality Sure, possibly. Kind of like how you think the news is accurate until you read a story that's in your field. But not necessarily. Might just be more "I don't know how do to <basic task> in <domain that I don't spend a lot of time in>", and LLMs are good at doing basic tasks.