Intel’s Q1 2025 earnings press release talked up their new AI-enabled chips. But these are not selling. [Intel] In the earnings call, CFO Dave Zinsner mentioned they had “capacity constraints in In…
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…
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.
Yeah I can totally understand that, Genie is databricks’ one and apparently it’s surprisingly decent at that, but it has access to a governance platform that traces column lineage on top of whatever descriptions and other metadata you give it, was pretty surprised with the accuracy in some of its auto generated descriptions though.
Yeah, the more data you have around the database the better, but that’s always been the issue with data governance - you need to stay on top of that or things start to degrade quickly.
When the governance is good, the LLM may be able to keep up, but will you know when things start to slip?
I use it to parse log files, compare logs from successful and failed requests and that sort of stuff. Other than that and searching, I haven’t found much use for it.
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.
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.
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 can change a whole fucking sentence to FUCKING UPPERCASE by just pressing vf.gU in fucking vim with a fraction of the amount of the energy that’s enough to run a fucking marathon, which in turn, only need to consume a fraction of the energy the fucking AI cloud cluster uses to spit out the same shit. The comparison is like a ping pong ball to the Earth, then to the fucking sun!
Alright, bros, listen up. All these great tasks you claim AI does it faster and better, I can write up a script or something to do it even faster and better. Fucking A! This surge of high when you use AI comes from you not knowing how to do it or if even it’s possible. You!
You prompt bros are blasting shit tons of energy just to achieve the same quality of work, if not worse, in a much fucking longer time.
And somehow these executives claim AI improves fucking productivity‽
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…
presumably everyone who has to work with you spits in your coffee/tea, too?
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.
Yeah I can totally understand that, Genie is databricks’ one and apparently it’s surprisingly decent at that, but it has access to a governance platform that traces column lineage on top of whatever descriptions and other metadata you give it, was pretty surprised with the accuracy in some of its auto generated descriptions though.
Yeah, the more data you have around the database the better, but that’s always been the issue with data governance - you need to stay on top of that or things start to degrade quickly.
When the governance is good, the LLM may be able to keep up, but will you know when things start to slip?
what in the utter fuck is this post
I use it to parse log files, compare logs from successful and failed requests and that sort of stuff. Other than that and searching, I haven’t found much use for it.
and now we’re up to inaccurate, stochastic
diff. fucking marvelous.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.
(100% natural rant)
I can change a whole fucking sentence to FUCKING UPPERCASE by just pressing
vf.gUin fucking vim with a fraction of the amount of the energy that’s enough to run a fucking marathon, which in turn, only need to consume a fraction of the energy the fucking AI cloud cluster uses to spit out the same shit. The comparison is like a ping pong ball to the Earth, then to the fucking sun!Alright, bros, listen up. All these great tasks you claim AI does it faster and better, I can write up a script or something to do it even faster and better. Fucking A! This surge of high when you use AI comes from you not knowing how to do it or if even it’s possible. You!
You prompt bros are blasting shit tons of energy just to achieve the same quality of work, if not worse, in a much fucking longer time.
And somehow these executives claim AI improves fucking productivity‽