5 ms·
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
by nchmy 2mo ago
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
- lilbigdoot 2mo agoIf 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 2mo agoI 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..
- ipsod 2mo agoI more have an agent that I drive to create features, and then a fleet of agents that turn those ad-hoc implementations into refined, integrated code.
- matteoraso 2mo ago>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.
- ColdStream 2mo agoI have argued for a while that this was an S-curve it was just a case of figuring out which part of it we were in. I am more confident nowadays that we are heading towards the upper plateau but there might still be some head room on that.
- insanitybit 2mo agoI'm really not convinced that these models are even that much more intelligent, as opposed to simply being more token aggressive. I do not find Fable that much smarter than Opus 4.6, and no Opus model seems to have improved things much at all. Benchmarks seem gamed at this point, real world experience just doesn't match up.
- someothherguyy 2mo agoi wish i was experiencing these things that everyone else is. my experience is mostly frustration and rewrites of anything that requires more than what would take me an hour to do myself, unless it is pure translation / boiler plate work. the leaps are there at getting to more "shaped" code (code that is correct for linters, static checking, etc), but i don't see the models exhibiting much intelligence. i really can't think of a time using LLMs for building anything where they did something that would make me go, "wow, that is really impressive, i wonder how it came up with that." just brute force search and pattern matching still. even the interesting results in academic work seem to be more of a function of effort (proofs by exhaustion, fitting puzzle pieces in a search space, etc) than anything else. not to say people aren't using large language models to do impressive things, but the agents themselves do not seem very intelligent to me. it feels like some engineering teams are aware of this fact and are driving agents using strict rule checks (like hooks on steroids), so they can drive some shape of output that aligns with what they require.
- geniium 2mo agoI 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
- r_lee 2mo agoimo 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
- josephg 2mo agoYeah. 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 2mo agoIt'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.
- poincareball 2mo agoEvidence 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.
- bad_haircut72 2mo agonot an AI researcher - this is probably true for these "everything" LLMs but I think specialized models are gonna be the next big thing
- ACCount37 2mo ago"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 2mo agoAnd 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.
- ACCount37 2mo agoWhich are the kinds of tasks computers have been historically quite good at. It's impressive that it does what it does, don't get me wrong. But if you expect it to replace the likes of GPT 5.6 Luna, let alone Sol? Nah.
- CamperBob2 2mo agoComputers have historically been good at answering word problems fed to them verbatim?
- ksh09 2mo agoI'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.
- redox99 2mo agoEh. 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.
- intrasight 2mo ago> content if they never got smarter, and just kept getting even cheaper/faster. I'm definitely not getting smarter. But my tolerance is 1 drink so I'm definitely cheaper. Also as a result, I spend more time training and so I am faster. And yes, I am more content
- jimmydoe 2mo agoCurrent AI is smart enough to help us, but the creators of AK want it to be smart enough to replace us.
- dsrtslnd23 2mo agoI think it really depends - for a lot of things outside of coding and general knowledge tasks even the best models (fable 5 etc.) are not good enough yet: e.g. CAD, PCB design (though getting there on PCB design), ...
- nbardy 2mo agoThe real revolution is both. The cost and capability of frontier intelligence will go up AND the cost of "good enough" intelligence will go down.
- KunYuan 2mo agoA computer that costs $10,000 is impressive. A computer that costs $100 and reaches billions of people changes the world. Maybe AI will follow the same path.
- RALaBarge 2mo agoI ask DS4F to make a plan, then check it with grok/fable, build the code, check it with grok/fable, ship
- bunderbunder 2mo agoBut they don't really have that option. They're trapped in a Red Queen's race. The world keeps moving on, and so the models need to be retrained so that they can keep up with new information. Otherwise you'll get stuck with a model that only works well with information that existed prior to a dataset horizon that's receding into the past at a constant rate. At the same time, they have to keep iterating on the training process itself. AI generated text and code is slowly spreading across the internet. Model collapse is a real concern; they wouldn't be spending quite so much energy on buying and scanning rare books if it weren't. But for coding in particular expanding their corpus of old text is not really a good option because of the previous problem - no good training your LLM to write 1980 vintage K&R C that won't even compile on a modern compiler.
- cj 2mo ago> they don't really have that option I imagine it must somehow be possible to update a model's understanding of recent events without training a completely new model from scratch?
- anthonypasq 2mo agoyeah its called web search
- ninahaberl 2mo agoModels don't need to keep retraining just to stay current. Harnesses give them access to the internet, internal systems use RAGs, and so on. I think lower-cost models will get the largest piece of the pie, as with almost everything that has ever been sold. Just look at cars: US consumers buy the F-150, EU consumers buy the freaking Dacia Sandero the most :))) Ferrari/Lambo numbers are microscopic
- zdragnar 2mo agoEven with a harness, models don't reach out for new information they don't know about. For some tech, I have to have a local model draft a plan, then I have to adjust the plan to update it with the new API and references for where to find it. Even if I include that updated information in the prompt for the plan, the model says "what the user says is wrong, they probably meant this instead" and goes off in its own direction with old APIs anyway.
- _s_a_m_ 2mo ago"good performance", well no, just no