The real revolution is Deepseek v4 flash and similar models (GPT 5.6 Luna, muse spark 1.2, mimo, etc...) - Genuinely good performance for a tiny fraction of the cost of Fable and even GLM etc...

I think a lot of people would be very content if they never got smarter, and just kept getting even cheaper/faster. Of course, both things continue to happen on a seemingly monthly basis

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geniium 2 hours ago | parent | next [-]

I was using ChatGPT voice during cooking to reflect on variations of a dishes i was preparing for years.

It was so amazing to get advices and reflect that it struck me : I could use this model forever - it’s clever enough to help me tons and do lot of work for me - even if ai would stop evolving I would love it

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r_lee an hour ago | parent | next [-]

imo this is the problem some of these labs are gonna face, because open models will do this just fine and you as the consumer don't need to pay their training costs

especially considering imo most use falls under this instead of those kind of tasks where you'd need the SOTA

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josephg an hour ago | parent [-]

Yeah. Sometimes I wonder who the long term financial winners will be from the ai boom. It might be ram / gpu manufacturers. Or whoever cracks putting LLMs on asics.

	▲	a2ff6eeb0 an hour ago \| parent [-]
		It's going to be the shareholders of the first companies to crack AGI, and make human brains fully irrelevant economically. With the trillions of dollars that's going in through both investment and users, it's going to happen. I don't believe the human brain has fundamental magic that will make this impossible.

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adrinavarro an hour ago | parent | prev [-]

I share this feeling too. The latest models, even if not necessarily frontier, say Opus 5, Sol high and the likes, I could keep using these models forever even if they did not significantly improve beyond this point. I also believe we'll come up with new ways of using these very same models beyond the mainstream chat and agent interfaces, as the bottleneck is imho in harnesses/environments and not so much model intelligence anymore.

+1 regarding voice usage too, I use it in so many different ways it's hard to enumerate: while driving long distances (think of a custom made, interactive podcast) / as a way to collaboratively build specs or shape an idea / as a way to provide input while vibe coding / just as a normal voice assistant (straight in the ChatGPT app or as OpenClaw input via telegram voice notes). I can't overstate how much my routines have changed over the last couple of years.

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matteoraso 2 hours ago | parent | prev | next [-]

>I think a lot of people would be very content if they never got smarter, and just kept getting even cheaper/faster.

There's a lot of truth to this. I think we're starting to approach the point where increased intelligence has declining marginal returns, such that it might not even be worthwhile to improve models unless it can be done cheaply.

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lilbigdoot 2 hours ago | parent | prev | next [-]

If they could be cheap+fast and not try to do too much, that's a good spot for me. I don't use the smarter models as much because of cost and because they're still not good enough to let loose on a lot of problems. For assistance I prefer something that can very quickly spit out a specific piece I can review on the spot and keep going. I let smarter models handle things that I treat as external dependencies and don't care how they're written, but in my core domain I'm still mostly hand coding

	▲	nchmy an hour ago \| parent [-]
		I have a similar process - its just a pair programmer most of the time. I dont understand how people can have a fleet of agents working a bunch of waterfall specs..

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poincareball an hour ago | parent | prev | next [-]

Evidence actually supports that capabilities are leveling off, and cheaper/faster is not really coming. Just log-linearly more capability at smaller parameter counts as they saturate.

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sipjca a minute ago | parent | next [-]

what do you mean cheaper/faster is not really coming? the cost of the same level of intelligence steadily decreases year over year. computer hardware also advances at the same time enabling cheaper and faster serving (or move to local)

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bad_haircut72 an hour ago | parent | prev | next [-]

not an AI researcher - this is probably true for these "everything" LLMs but I think specialized models are gonna be the next big thing

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ACCount37 an hour ago | parent [-]

"Specialized models" are a bit of a doozy.

The biggest generalist models beat the most fine-tuned specialists, as a rule. You can bias an LLM away from literature knowledge and towards coding capabilities, but that buys you very little performance, and for too much effort.

Generality and intelligence seem to be entangled very heavily in LLMs.

	▲	CamperBob2 20 minutes ago \| parent [-]
		And yet, there's VibeThinker 3B to bring this long-held premise into question (if not to blast it to pieces.) It is practically illiterate by the standards of larger models, yet performs like models 100x its size on mathematical and logical reasoning tasks.

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ACCount37 an hour ago | parent | prev [-]

What "evidence"? Because we keep running out of benchmarks to distinguish frontier model performance. If capabilities are "leveling off", we're not seeing it yet.

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ksh09 an hour ago | parent | prev | next [-]

I'd be content if I could get the DS4 flash, luna, mimo level intelligence running on MY low-end hardware completely offline and bearable TPS, not otherwise.

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redox99 an hour ago | parent | prev [-]

Eh. I don't think Luna is good enough. I think that threshold is around Opus / Sol where it can do most of the tasks for me. But I still have many tasks which require either better intelligence or better UI design capabilities.

With how generous subscriptions are, what I actually want is GPT Astra, not cheaper Sol.