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  • ExFed@programming.devtoTechnology@lemmy.world•Why not LLMs?
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    10 days ago

    It’s not that my brain is tired or even that I trust LLMs, I just don’t have time and either an LLM can create the thing I want or I do without. If the project fails, nothing of value is lost.

    At home, that’s the summary of so many of my hobby projects lately. At work, however, there’s one phenomenon that I don’t know if we have a name for yet: loss of job satisfaction because a large part of the joy of creative problem solving comes from the process of wrestling with a hard problem, making a breakthrough, and genuinely learning something. LLMs make it too easy. I don’t feel like I’m learning anything of value. I’m just regurgitating some version of the LLM’s solution back to my coworkers during stand-ups. And so, if anyone really pressed me on the details, I would have a hard time coming up with anything. For this latest release on a project that I’ve worked on for most of a year and given demos for in the past, we unleashed AI. I’m genuinely nervous to give an upcoming customer demo for this release because I don’t really feel like I understand the changes were shipping.

    At first, I thought it was just a problem I was experiencing alone. But then I started hearing others express very similar sentiments. At least one person mentioned that they chose to work on hobby projects without LLMs, precisely because they wanted to recapture that joy they lost from their career. Even though I’ve finally started knocking out quite a few ideas from the backlog that had accumulated over the years, it’s hard to really reach that same level of satisfaction.

    Is there a balance point where I can stay on top of the backlog while feeling like my brain hasn’t fallen out of my head?















  • LLMs are fundamentally limited, because ‘what’s the next word’ shouldn’t work.

    Yes, you’re right. However, for fear of coming off as an AI sycophant (I’ve yet to sacrifice my brain at the altar of our future AI overlords), LLMs aren’t the whole picture. Plenty of research is dedicated to essentially combining the best of each class of AI algorithms into a composite model of intelligence. For instance, “Neuro-Symbolic AI” is really just the result of giving an LLM (good at translation, search, synthesis, bad at symbolic reasoning) a symbolic inference engine like Prolog (good at symbolic reasoning, no native ability for translation/search/synthesis). I’ve been coding for over 20 years, and I’m impressed at its results for software development.

    This all is reminiscent of Moore’s Law; even though we keep running into the physical limits of CPU clock speeds, transistor size, etc. we keep finding clever ways to work around those limits.

    Of course I’m not saying we should; these models are, after all, models of intelligence, not wisdom.

    Edit: fix apostrophe splice