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Comment by chermi | Hacker News Reader

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.

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> 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).

> 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.

> 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

Not my core competence and I’m not interested in making it a core competence while the field is still rapidly changing. I find harness engineering interesting, but not lucrative enough to push one of the many very important other things on my plate to the backburner.

I don’t think Python language developers are super geniuses either, and I’ve never submitted code for consideration.

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

I would be interested in see that. I have reems of rules I use with Claude.

>This lands.

Where does it land?

Did I use the saying wrong? My bad I confuse them sometimes

I think it's technically right use of the meaning, but the way it's used somehow feels weird and out of place. Like I'm trying to remember ever seeing it as an assertive statement about a concept, instead of a question ("how does this land?") or a reference to something specific (new release has landed).

I don't think people have identified that as an LLMism, but the way it feels out of place I wouldn't be surprised if someone did. Being a punchy two word sentence doesn't help here.

Wait I'm I being accused of being an llm?

Edit- it being weird and out of place is probably right because I probably used it wrong. I probably shouldve said this lands for me. That sounds more normal

Edit2 I am not a bot trying to improve to sound more human. I promise

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