Want to wade into the sandy surf of the abyss? Have a sneer percolating in your system but not enough time/energy to make a whole post about it? Go forth and be mid: Welcome to the Stubsack, your first port of call for learning fresh Awful you’ll near-instantly regret.
Any awful.systems sub may be subsneered in this subthread, techtakes or no.
If your sneer seems higher quality than you thought, feel free to cut’n’paste it into its own post — there’s no quota for posting and the bar really isn’t that high.
The post Xitter web has spawned soo 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.)
New Ed Zitron to start the week off: The Case Against Generative AI
At the risk of being critical of Zitron, I have some comments. This is probably just nitpicking but regardless.
LLMs cannot be used to generate images or videos. Diffusion models can create images, but that’s not a text-generation model. I guess you could use an LLM to prompt an image or video generation model, but I’m not sure if that’s what he meant or not.
Sort of, but not really. GPT-5 with its (presumably) trillions of parameters and its (apparently) hundreds of millions of users per month needs a lot of throughput to cater to that, but there’s nothing about LLMs that inherently requires massive GPU clusters with high-speed networking.
Here’s a LLM running on a Raspberry Pi
Of course, the amount of people running LLMs on Raspberry Pis is effectively that guy in the video to show a LLM running on a Raspberry Pi, and it’s not like it’s particularily fast without adding a GPU (and at the end of the day it’s still LLM output, so), so perhaps he’s just using “Large Language Models” as in “The LLMs that the vast majority of people actually use.”
He’s not wrong about training, however.
IMO it’s not a particularly good start to his newsletter. Because an easy counter to his statement is that not all LLMs require massive amounts of compute to run, but a counter against that counter is that training even smaller LLMs still require vast amounts of compute that the average person doesn’t have, in addition to the copyrighted material needed to train on, even with the win that Anthropic got meaning that any LLM trained in the future is going to require vast amounts of capitol for just the training data alone. The problem is that he doesn’t state any of that. Maybe he does know about that and decided to omit it for brevity. If he did, then, personally, I think that’s a mistake. Or maybe I’m just not reading it properly.
The first paragraph immediately conflating all of generative AI with LLMs doesn’t particularly help his case either, even though stating that there are multiple types of generative AI wouldn’t really harm his thesis that this entire thing is a massive bubble. Again, perhaps he’s doing it for a reason that I’m not getting.