this post was submitted on 09 Feb 2026
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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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The post Xitter web has spawned so many “esoteric” right wing freaks, but there’s no appropriate sneer-space for them. I’m talking redscare-ish, reality challenged “culture critics” who write about everything but understand nothing. I’m talking about reply-guys who make the same 6 tweets about the same 3 subjects. They’re inescapable at this point, yet I don’t see them mocked (as much as they should be)

Like, there was one dude a while back who insisted that women couldn’t be surgeons because they didn’t believe in the moon or in stars? I think each and every one of these guys is uniquely fucked up and if I can’t escape them, I would love to sneer at them.

(Credit and/or blame to David Gerard for starting this.)

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[–] lagrangeinterpolator@awful.systems 8 points 9 hours ago (1 children)

A machine learning researcher points out how the field has become enshittified. Everything is about publications, beating benchmarks, and social media. LLM use in papers, LLM use in reviews, LLM use in meta-reviews. Nobody cares about the meaning of the actual research anymore.

https://www.reddit.com/r/MachineLearning/comments/1qo6sai/d_some_thoughts_about_an_elephant_in_the_room_no/

[–] CinnasVerses@awful.systems 6 points 5 hours ago

I like this reply on Reddit:

I do my PhD in fair evaluation of ML algorithms, and I literally have enough work to go through until I die. So much mess, non-reproducible results, overfitting benchmarks, and worst of all this has become a norm. Lately, it took our team MONTHS to reproduce (or even just run) a bunch of methods to just embed inputs, not even train or finetune.

I see maybe a solution, or at least help, in closer research-business collaboration. Companies don't care about papers really, just to get methods that work and make money. Maxing out drug design benchmark is useless if the algorithm fails to produce anything usable in real-world lab. Anecdotally, I've seen much better and more fair results from PhDs and PhD students that work part-time in the industry as ML engineers or applied researchers.

This can go a good way (most of the field becomes a closed circle like parapsychology) or a bad way (people assume the results are true and apply them, like the social priming or Reinhart and Rogoff's economic paper with the Excel error).