- In a lot of fields, the "deliverable" could just be a git repo. - Results could / should be more reasonable to update as possible issues arise. - Peer review is pretty busted in a lot of fields, so a humongous number of papers get through that have pretty glaring issues.

The combination of being incentivized to spew out nonsense papers + academic publishing institutions liking to get more papers to sell to libraries + government funding being partly chosen by academics who are also part of the problem.

void func(var1, var2) int var1, char* var2. { ... }

perhaps some legacy from that? Probably not, but just first thing that popped into my head since it feels similar :shrug:

(Though it could turn into "nvidia pay lots of people to use LLMs to make non-portable, tightly coupled backends to _even more_ open source projects")

I've personal almost stopped reading papers in my area, which is in ML but not related to LLMs or CV. I do look for work related to whatever I'm doing, but it's kind of depressing how uncommon it is for (say) neurips papers to actually have anything useful...

(It's also kind of annoying how basically all funding agencies are only funding research into or using AI, but don't provide enough funding for lots of gpu time lol)

I know someone in a profession that does a lot of writing, and it blew my mind how clearly feedback was communicated by a superior. Made me wish tech people had better written communication skills. ;-;

I'm not a big fan of the US application setup. IDK how it is in Europe, but in the US, it feels like there's a lot of not-very-meritocratic "secret" stuff you need to know to up your chances.

when I applied to PhD programs (not in math) it was basically CV + personal statement + recommendation letters + short chats with interested faculty :shrug: Maybe it was because my CV was "strong" but the chats were more see if interests were aligned, rather than actually interviewing me.