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 youāll 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 cutānāpaste it into its own post ā thereās no quota for posting and the bar really isnāt that high.
The post Xitter web has spawned so many āesotericā right wing freaks, but thereās no appropriate sneer-space for them. Iām talking redscare-ish, reality challenged āculture criticsā who write about everything but understand nothing. Iām talking about reply-guys who make the same 6 tweets about the same 3 subjects. Theyāre inescapable at this point, yet I donāt see them mocked (as much as they should be)
Like, there was one dude a while back who insisted that women couldnāt be surgeons because they didnāt believe in the moon or in stars? I think each and every one of these guys is uniquely fucked up and if I canāt escape them, I would love to sneer at them.
(Credit and/or blame to David Gerard for starting this.)
I still laugh every time I see that this is what qualifies as proper ātuningā and āsecurity controlsā for these things.
I had hoped that with the whole āagentā push that we would start seeing more sane usage, like having AI be a fuzzy logic step in a chain of formal logic and existing deterministic tools, but the cult still has people treating them like reliable second brains. Theyāre used as the baseline fucking orchestrator rather than anywhere they might make a bit of sense.
I think this is the best you can expect out of LLMs, and the relatively more successful āagenticā AI efforts are probably doing exactly this, but their relative success is serving as hype fuel for the more impossible promises of LLMs. Also, if you have formal logic and deterministic tools wrapping and sanity checking the LLM bitsā¦ I think the value add of evaporating rivers and firing up jet turbines to train and serve ācutting edgeā models that only screw up 1% of the time isnāt there because you can run a open weight model 1/100th the size that screws up 10% of the time instead. (Note one important detail: training costs go up quadratically with model size, so a 100x size model is 10,000x training compute.) I think the frontier LLM companies should have pivoted to prioritizing smaller size, greater efficiency, and actually sustainable business practices 4 years ago. At the very latest, 2 years ago, with the release of 4o OpenAI should have realized pushing up model size was the wrong direction (as they should have realized training Chain-of-Thought was not going to be the magic bullet).
And to be clear I still think this is really generous to the use case of smaller LMs.