LLMs could be a bad blow to anonymously publishing software.
If you want to keep a project separate from your day job / real identity, you need a second subscription. $20/mo is not that big a deal, but $200 (Claude Max) sure is. If the hater are to be believed, we might see a hike in pricing as well.

@WandererUber the haters are %100 wrong on this one. deepseek/glm/kimi inference is already profitable at low prices

as they often are.

>profitable
for whom?

@WandererUber for people who have inference hardware

@WandererUber these are chinese so not jews, please relax

that's not the issue. If the model is not profitable for the lab making it, they must raise prices (and stop/delay weight releases)

@WandererUber these are all open models, anyone can run them. if a third party can run them at profit right now, they'll be able to do that in the future. glm and deepseek and kimi are worse than claude and codex, but not so much worse that people wouldn't swithc to them if claude would cost $1000 a month

I'm not even agreeing. This was just an additional thing that could put the kibosh on it

>if a third party can run them at profit right now, they'll be able to do that in the future.
I don't like that I have to spell everything out WHILE playing devil's advocate. This does not follow at all. They have training costs to recoup and if they don't keep pace they will fall behind. If they fall behind, Anthrobbicc would be able raise prices to e.g. $40 for pro, maybe $100

barrier to entry for writing code and not publishing it under your own name was virtually zero (compared to having a different identity anyway of course) and now is not.

@WandererUber but the open models are trained already, there's no more cost. i'm not talking about anthropic and openai and who ever trains. the open models are profitable right now. if the trainers go bankrupt, well, that's sad, but inference still stays profitable.

@lain @WandererUber token providers are straight up printing money with margins above 10x too, can't wait for capacity to improve enough to where it's more or less 1:1 to electricity cost like bitcoin

We had this discussion before and you are making an argument that I
-already agree with
-is only tangentially related to what I am saying here

>People can just stay on GLM5.1
they can also hand-code. If SOTA devving costs money and you have to pay again if you want it under a second identity, that's a detriment.
If GLM 5.1 is not profitable for the model trainer, there will not be GLM N.1 in future. Open Weights models *could* lag behind which would make SOTA be OAI / DarioCorp -dependent. I believe quite a few people would be deterred and/or chance it under their real account and catch a C&D from Nintendo for it

@WandererUber yeah, that's true. if current level training turns out to be not profitable, we won't get better models soon. but the current ones still are profitable. so i'm not sure what we are disagreeing on

@lain @WandererUber Diminishing returns that way would honestly be for the best, a lot of hate is due to aggressive hype fear fomo mongering and having it turn into a normal industry would make folk relax. and treat it as a normal technology

idk I just don't get why you are making the argument that GLM 5.1 inference is profitable for providers at all here.
The issue is something else. If you are seriously implying that GLM 5.1 is good enough that people would use it over $100 2028 Claude, then I guess we disagree about that.
It's as if I said "training models could be an issue to do anonymously if Nvidia puts phone # verification in their GPUs and you have to buy a second one" and you replied with "a 2023 chromebook will be cheap forever"
It's besides the point.

I genuinely think it has too high an "intelligence-to-get-it floor", for lack of a better term.
Sometimes I'm not even sure if the LABS get it (Anthropic publishing a post about a "mind reader" for Claude). A lot of previously smart-enough people are falling for the convincingness of the output and are incapable of checking for *correctness*

all that to say, it could be a long while until it's "treated as a normal technology"

@WandererUber @lain

probably coming off as a bit of the anti llm for the sake of anti llm side but-

idk how that happens tbh ik web ui models are kinda shit especially without harnesses (agentic stuff inmean) and really haven't ever vibe coded.

but I've used it as a reseaech assistant (with all the deep research bells and whistles google afforded me) and while certainly helpful (still doesn't replace normal searching for me, probably as I prefer searching first) l. Its obviously not a smart thing and makes stupid as hell errors or honestly fails to get the point (which is kinda obvious since they are fundamentally word generators)

One example is how It starts phrasing everything with basis on a fact which should have been treated irrelevant.

I tried to use it to write a slop paper and report (uni obligation) it was so bad that I basically didnit manually. Ig it is me actually having standards but it felr quite bad and focised on entirelynthe wrongnthings and had such terrible flow that babying itnto get something worthwhile was harder than actually writing it myself

code (surprisingly due to the NLP roots of the architecture) is probably a best case scenario in hindsight which is why they actually end up being surprisingly useful for thay

@lain @WandererUber but are they overseas chinese?

>Ig it is me actually having standards
yes I think so. I have largely the same experience as you do, although I did like research and building basic knowledge with Grok (not so much post-nerf)

LLM Arena found out people largely rate the incorrect emoji-laden,bullet-pointed slop torrent higher than the correct concise answer.
This is what I was talking about. A lot of decision makers simply do not have the intelligence to understand what is going on. It's like when people can't tell a pic is AI, but for text and they can't tell it's incorrect.

@WandererUber @lain ig we forget the fact that actual human 'professionals' are the ones writing linkedin posts and people like these are ones providing ratings and scores

@meeper @WandererUber i know you're engaging with it, but somehow you're not getting the results. i don't know why, but it can do a lot more than you found out.

I am once again asking for the video of you using it

@lain @WandererUber probably the parricukar tooic for the slop research paper.

for example it wqs surprisingly good at hindi poetry (which is actually quite complex to get a good meter in but soinds really good) and analyzed stuff based on the rules I didnt even know at the time.

and recent models (late 2025) suddently became useful for my etymology interest which would otherwise require me to dive into obscure books (which I do want to I digress)

>based on the rules I didnt even know at the time.
it always seems better if you have no clue
Gell-Mann amnesia but for AI

@WandererUber @meeper learning something is not the same as gell-mann amnesia

@WandererUber @lain Nah I mean like I had it evaluate something on a whim as I wanted to see what the statistical text machine would say about my work which I painstakingly put into a meter (I'm fammiliar with how it should sound since as a sikh I've been raised on that kind of poetey)

Its through it thay Iearned that it actually had a conplex set of rules (I did verify it according to wikipedia) and it was mostly correct, some word choice changes tn suggested felt off but the actual analysis of the meter was quite correct

that's an LLM-type answer because you gave a sequence of words that are likely to appear in similar conversations but are not actually a response to what I actually mean.

It's similar in the sense that one's incompetence in a field makes the AI seem more correct than it is. If one looks at the answers after spending a few days actually learning, suddenly they don't seem so intelligent anymore (they still were helpful because they gave you keywords to research the actual rules. The intricacies of its' explanations are probably incorrect though)
You immediately notice this with topics you know about, thus it is similar to Gell-Mann amnesia.

@WandererUber @lain and tbh there wasn't that much to verify, llms (as shown by their capability of being useful in the routine theorem proving kind of math) are not too bad at that kind analysis stuff when its clear and defined, which due to the particulars in this case is highly formulaic