Comment by dd8601fn

6 hours ago

Not everyone has this, but I always felt that a significant percentage of the value I bring is in immediately recognizing what you shouldn’t do.

I have a hard enough time explaining why “yet another bespoke application on some unmanaged azure resources” is a bad idea when they have more reasonable alternatives at hand.

Now these goofs can (very nearly) press a button and do it anyway, with no comprehension of the consequences. It’s high fives and pats on the back, until I’m cleaning up the mess.

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dd8601fn

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chermi  6 hours ago

This lands. Llms are bad precisely at following what not to do. They work best off of positive constraints.

I have a design principles + tech preferences doc I force llms "lint" their approach against. It's not perfect but it helps. I call it a bias field, pushes them toward hopefully the happy and harmonious (with the rest of the system) paths. Obviously this is only partial and imperfect enforcement, but if it's applied to everything consistently it naturally encodes some self-consistency and harmony.

  • datsci_est_2015  5 hours ago

    > I have a design principles + tech preferences doc I force llms "lint" their approach against. It's not perfect but it helps.

    I’ve done the same but it’s a moving target as models advance and I find half of my points are ignored until I’m prompting “No wtf why are you still trying to symlink the global Python executable just use the virtual environment that’s already activated”.

    Anyway, companies are pouring billions into improving AI tooling user experience so most of what I do manually I just anticipate to be a waste of time. There’s no way my hobby fiddling will outpace whatever gets released in the next couple months.

    In the meantime, real linting does work pretty well, if you can write a detector for whatever antipattern you find LLMs fall into (like multiline comments).

    • oceanplexian  4 hours ago

      > Anyway, companies are pouring billions into improving AI tooling user experience so most of what I do manually I just anticipate to be a waste of time.

      I get patches into the biggest AI projects all the time to make inference faster on my 3090 and all you have to do is read the contribution docs and open a small PR.

      It’s not a cabal of super geniuses. We are literally in the Wright Brothers era of AI.

      You can trivially outpace what big companies are doing it’s insane, why do you think so many innovations are coming out of scrappy Chinese labs? They are willing to engage instead of being defeatist about it.

      1 reply →

    • chermi  3 hours ago

      Oh I'm certain I'm not doing it optimally or close to best practices. It's just an example I had in my head related to my comment.

      Although, given opus 4.8->5.0 experience within claude code, I don't know if I'd agree they necessarily improve things with time. But that's just my experience. I should probably read their release notes, maybe just a tweak to my claude.md would help