malloc wrote: ↑Thu Jul 09, 2026 9:54 pm
Some terrible non-AI news for a change: I spilled soup all over my chair and now it's ruined because the soup soaked into the upholstery. Honestly not sure what to do since I can't really afford a new chair, except maybe a cheap plastic one.
have you asked chatgpt lmao no but seriously i'd try spraying it with a hose, then using alcohol or lysoform or whatever, and finally letting it dry in the sun (it is summer where you're at, i hope). if you're looking to throw it away you might as well risk destroying it beforehand. and who knows, maybe the wood will survive it? [if it's wood in the first place, maybe its one of those gamer plastic-and-stainless contraptions, in which case rotting the textile is your only concern].
Travis B. wrote: ↑Thu Jul 09, 2026 8:52 pmSome people actually
like writing code, and conversely have little interest in bot-herding ─ and having an LLM write my code for me would take the fun out of it.
sure. and that's fine and good, but i thought we were talking about whether chatbots could actually produce usable code, not about fun. i'm saying they can, though mostly in relatively small snippets and with close human supervision: it still ends up taking a lot less worktime for a low-but-not-zero-competence-programmer to produce a tiny little applet, or automation, or useful scraper or whatever else, by using them.
zompist wrote: ↑Fri Jul 10, 2026 1:31 am
I used to work for a company that did statistical programming, SPSS. And we had a team of statisticians who devised the formulas and checked the code to make sure it was correct. That's in addition to the designers and QA people, all separate from the developers. And believe me, there are a lot of gotchas in statistical programming. (And in what we consider simple arithmetic, as bradrn's link shows.)
I won't assume you didn't do testing... but what did it involve? Testing against a known piece of statistical programming? Running some calculations yourself? Eyeballing the code?
did you really work on spss? that's cool, i've sat in front of that thing for probably months of my life. a lot of these little stats thingies are just GUI implementations of already existing libraries, so I only do fairly superficial testing against other libraries, or against spss itself, or against previously known results where i have 'em. the only one i can remember where i wrote the maths themselves was one that did a particularly idiosyncratic thing for which there was no library. that one i tested a bit more thoroughly against the excel sheet they used to use it for, checking what it does with commas, points, nulls, strings, and tables formatted in weird ways, though i've found a couple minor glitches afterwards mostly to do with a function taking so long to process a large table that another function apparently starts running before the first one is done (i don't know man, javascript is weird, ended up adding a timer)
soo what Ares said, yea
zompist wrote: ↑Fri Jul 10, 2026 3:46 am
I have no objection to your workflow here. I do have an architectural objection though: why are these libraries and tools so damn hard to use?
I know the answer, of course: they were designed by other coders, who understood their own tools and saw no need to provide extra tools on top to make it easier, or document them properly. Or they saw the need but no one gave them time to do it.
true but i might add: they were made by coders *for* other coders: your library can be exceptionally easy to use, and be better for it, but if it's still hidden behind "you have to install python and pip install my library and then after dependencies you can upload your data -not as a pandas df, you undertstand, but as a numpy array- and then you get to run the function" it's just going to be out of reach for like 90% of everyone. shouldn't be, but hey.