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[–] 9 points 6 days ago (12 children)

I am genuinely curious if anyone knows if that has an effect or not. I wouldn’t think so per se, but if the AI interprets it as “double check your initial analysis for errors” it would actually work maybe?

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  • [–] 18 points 6 days ago (1 child)

    For image generation it does. They give negative prompts like "wonky", "creepy", and "ugly", and the image generator evaluates how well the generated image matches those prompts, and produces images opposite those parameters.

    Some poor artist in the training data not only had their work stolen to train the AI, but also had it labeled ugly and wonky.

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  • [–] 4 points 6 days ago

    That could also be human training as well. For example, an artist's work would be used as a "correct" sample, and the machine told to make some other image based on the correct samples, and people would mark results with those tags.

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  • [–] 4 points 6 days ago

    It won't affect the output meaningfully except by rerolling whatever training data ends up being associated with that or whatever. It may end up getting the model to "check" its work which just compares previous output to training data.

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  • [–] 3 points 6 days ago (8 children)

    Earlier LLMs it helped a bit.

    Now a days the harnesses know to spawn 'review' agents which will catch some mistakes but not all.

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  • [–] 3 points 6 days ago (5 children)

    You mean it will spawn agents to drive up the token costs and maybe fingers crossed catch some errors?

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  • [–] 1 point 6 days ago* (2 children)

    Correct

    I literally have Claude send every edit to another model to check and make sure it isn't word barfing. Every file edit is a call to another model to make sure that edit doesn't suck.

    Tokens++

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  • [–] 2 points 5 days ago (1 child)

    When does it become easier to just write the thing yourself?

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  • [–] 1 point 5 days ago

    It really really depends on what 'it' is.

    The LLMs are really very very good at pumping out scripts that can accelerate things like machine learning where it are wrangling data and doing proof of concepts.

    They are also pretty good at basic CRUD feature work which is what the majority of software devs are actually doing.

    The further out of the user's depth they go the more problematic they can be. They bake many many assumptions in and make hidden decisions that someone without domain expertise cannot easily intuit. Which means someone without experience can get into deep water and not realize and that is where a lot of the problems are.

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