this post was submitted on 07 May 2025
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Big brain tech dude got yet another clueless take over at HackerNews etc? Here's the place to vent. Orange site, VC foolishness, all welcome.
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I use it to speed up my work.
For example, I can give it a database schema and ask it for what I need to achieve and most of the time it will throw out a pretty good approximation or even get it right on the first go, depending on complexity and how well I phrase the request. I could write these myself, of course, but not in 2 seconds.
Same with text formatting, for example. I regularly need to format long strings in specific ways, adding brackets and changing upper/lower capitalization. It does it in a second, and really well.
Then there's just convenience things. At what date and time will something end if it starts in two weeks and takes 400h to do? There's tools for that, or I could figure it out myself, but I mean the AI is just there and does it in a sec...
I use it to parse log files, compare logs from successful and failed requests and that sort of stuff.
and now we’re up to inaccurate, stochastic
diff. fucking marvelous.The first two examples I really like since you're able to verify them easily before using them, but for the math one, how to you know it gave you the right answer?
they don’t verify any of it
That's literally a built-in VSCode command my dude, it does it in milliseconds and doesn't require switching a window or even a conscious thought from you
Gotta be real, LLMs for queries makes me uneasy. We're already in a place where data modeling isn't as common and people don't put indexes or relationships between tables (and some tools didn't really support those either), they might be alright at describing tables (Databricks has it baked in for better or worse for example, it's usually pretty good at a quick summary of what a table is for), throwing an LLM on that doesn't really inspire confidence.
If your data model is highly normalised, with fks everywhere, good naming and well documented, yeah totally I could see that helping, but if that's the case you already have good governance practices (which all ML tools benefit from AFAIK). Without that, I'm totally dreading the queries, people already are totally capable of generating stuff that gives DBAs a headache, simple cases yeah maybe, but complex queries idk I'm not sold.
Data understanding is part of the job anyhow, that's largely conceptual which maybe LLMs could work as an extension for, but I really wouldn't trust it to generate full on queries in most of the environments I've seen, data is overwhelmingly super messy and orgs don't love putting effort towards governance.
I've done some work on natural language to SQL, both with older (like Bert) and current LLMs. It can do alright if there is a good schema and reasonable column names, but otherwise it can break down pretty quickly.
Thats before you get into the fact that SQL dialects are a really big issue for LLMs to begin with. They all looks so similar I've found it common for them to switch between them without warning.
it’s really embarrassing when the promptfans come here to brag about how they’re using the technology that’s burning the earth and it’s just basic editor shit they never learned. and then you watch these fuckers “work” and it’s miserably slow cause they’re prompting the piece of shit model in English, waiting for the cloud service to burn enough methane to generate a response, correcting the output and re-prompting, all to do the same task that’s just a fucking key combo.
how in fuck do you work with strings and have this shit not be muscle memory or an editor macro? oh yeah, by giving the fuck up.
I have used a system wide service in macOS for that for decades by now.
presumably everyone who has to work with you spits in your coffee/tea, too?