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> It's a very dangerous gamble. Today incredible value is available for nearly everyone. But it may stop without any warning, for reason outside our control. W
by apublicfrog 5mo ago
> It's a very dangerous gamble. Today incredible value is available for nearly everyone. But it may stop without any warning, for reason outside our control.
What stops you from running the best open weighted LLMs currently available on consumer grade hardware for the rest of time? They're good enough for 95% of use cases, and they don't have a used by date. From what I can see, the "danger" is not having the next tier that comes out, but the impact of that is very low.
- giobox 5mo ago> they don't have a used by date For quite a lot of use cases, the current systems arguably do get worse over time if not continually updated. The knowledge cutoff date will start to hurt more and more as the weights age in a hypothetical scenario where you are stuck with them forever. Coding, one of the most popular usescases today, would not be great if it say only understood java to a version from years ago etc. https://en.wikipedia.org/wiki/Knowledge_cutoff https://en.wikipedia.org/wiki/Knowledge_cutoff
- rrvsh 5mo agoNobody is unaware of the knowledge cutoff, and sharing the Wikipedia article is not helping anyone. Your point is easily rebutted by taking whatever open weights/source model has an outdated cutoff and training or fine tuning it on more data, which is again always going to be viable given a modicum of compute
- tcp_handshaker 5mo agoYou could learn how to code...a whole generation did it before...
- throwyawayyyy 5mo agoOne solution is not to advance anything of course. I'm not even joking, is there going to be a successor to React? I suspect not, with the vast amount of training data for React now, it's going to look silly to move to something else with less support. What is the last new popular programming language, rust? Will there be another one? I suspect not. Same reasoning. The irony of all this AI acceleration talk is it'll work best if we don't accelerate the underlying tech at all.
- WarmWash 5mo agoThere probably won't be new stuff so much as trends in how stuff is done, and updates around optimizing those trends.
- Spooky23 5mo agoAlot of the language work is scratching the itch of engineers and developers. I think you’re correct and react is the new COBOL.
- jvm___ 5mo agoWill programming languages evolve into less human oriented written code and more just calls to a trusted AI. Or will human readable code be less and less of a thing as AI learns it's own, more terse language to talk to other AI's.
- hadlock 5mo agoName/post content combo on point
- apsurd 5mo agoHumans are notoriously bad at predicting the future. Toward that end, your prediction is laughable. React is the end all be all of UI… lol
- melagonster 5mo agoProgrammers won't be allow to exist in future. Vibe coding is the final resolution people can apply.
- digitaltrees 5mo agoYes. I am seeing a big push to use vanilla js for single file html apps that are easy to build, deploy and distribute because they have no build step. I could see component libraries emerging that make it easier build from chat interfaces with less ceremony
- byzantinegene 5mo ago
- nullc 5mo agoSmall models are more useful for "doing stuff" than "knowing stuff" to begin with. Add in an agentic harness and a small model can happily read more current information on demand (including from e.g. a local wikipedia snapshot).
- henry_kang 5mo agoThis feels increasingly true. A lot of useful AI work is shifting from “knowing more” to “working with more context”, files, recordings, repos, screenshots, browsing history, etc. Once that happens, memory and orchestration start mattering much more than raw model size.
- mrtesthah 5mo ago>Coding, one of the most popular uses cases today, would not be great if it say only understood java to a version from years ago etc. This LLM trained only and entirely on pre-1930s texts was able to code Python programs when given only a short example: https://talkie-lm.com/introducing-talkie https://talkie-lm.com/introducing-talkie
- moffkalast 5mo agoHa yes I used to think this was not a notable issue, but just today I was getting qwen 3.5 to fix my network drivers and it immediately freaked out like: "kernel 6.17, what the fuck? that doesn't exist yet!". It almost had a mental breakdown over that detail and derailed the conversation towards checking what's wrong with the kernel version reporting lol.
- AlienRobot 5mo agoI genuinely don't understand how can this possibly be a problem long term. It feels very obvious that the solution is to have a smaller model that can be trained exclusively on Java information to augment the older model. If the architecture doesn't support it currently, then that's what the architecture will look like in the future. Otherwise you'd be arguing that, to serve users who want to an up-to-date LLM on topic X, you have to train the model on the entire ABC all over again. It's simply ludicrous to have a coding LLM that needs to be retrained on the latest published poems and pastry recipes to generate Java.
- lowbloodsugar 5mo agoLaughs in JDK8 code base.
- suika 5mo agoThe use cases in the future will be nothing like the use cases from today.
- apublicfrog 5mo agoMaybe. The use cases people primarily use LLMs for (documents, coding, design, research) existed decades ago with different tooling. Who knows if the future will have a slew of new problems that require new models or will continue to be similar?
- turtlebits 5mo agoFOMO. A new model comes out weekly and the HN crowd debates over the minutia of changes. Pockets are too deep, it will only change once everyone is out of money.
- 3eb7988a1663 5mo agoWhat is really amusing to me is how N months ago, the latest SOTA was incredible, but now utterly unusable. Feels like there is a model reality-distortion field in play where people can only acknowledge the flaws in retrospect.
- nightski 5mo agoHardware. Frontier labs are driving up demand so much that it's priced significantly above cost making it far less affordable. Just look at Nvidia's profit margins.
- lxgr 5mo agoThey’re really not good enough, unless you consider 64 GB of memory or more consumer grade.
- steve_adams_86 5mo agoI’m pretty happy with what a 32GB Mac Studio can do for a lot of tasks. They’re the things I’d throw a model like Haiku at, but still genuinely useful. We don’t have an answer to frontier models in the consumer range yet, but we’re not totally trapped. Side note though, it’s the speed that bothers me more than the reasoning. Qwen 3.5 is awesome, but my Claude subscription can tear through similar workloads an order of magnitude faster than my local LLM can when using Haiku. That’ll matter a lot to some people.
- datadrivenangel 5mo agoYeah this is the real killer. slower and more expensive is tough.
- ai_fry_ur_brain 5mo ago95% of usecases. What are you smoking.
- deleted 5mo ago[deleted]
- selcuka 5mo agoThere are very good open weight models (such as DeepSeek v4 Flash) that can run on consumer level hardware. Note that we are talking about 95% of everyone's use cases, not your specific use cases (which could require better models all the time).
- avazhi 5mo ago> What stops you from running the best open weighted LLMs currently available on consumer grade hardware for the rest of time? Uh… the hardware requirements? And stop acting like some dog shit 8B model the average Joe can run on a laptop is even close to being comparable to what Claude or even Codex can currently do. I have pretty good hardware and I’ve tinkered with the best sub-150B models you can use and they are awful compared to Anthropic/OAI/Grok.
- apsurd 5mo agoWhat if the harness and loops get sufficiently better though? CC is using haiku for code-base gripping and such, you don't see a local commodity model being "good enough" for the 80% case when matched with better harnesses and tool calls? honest question, i'm very interested in this, but too casual as of now to know any better.
- byzantinegene 5mo agovast majority of average users don't use llms for coding, and for those purposes, local llms with low param count are a far cry from SOTA models.
- avazhi 5mo agoI think the main issue is, as the other guy also alluded to, the parameter discrepancy. I know Mixture of Experts models are popular specifically becaue they save a lot of space and memory, but if your initial answer space is two orders of magnitude smaller on a local machine compared to the frontier cloud models, that knowledge gap just gets wider as the conversation continues, and the initial answer isn't even going to be as good to begin with. I don't know how to solve that parameter gap without hardware - there's only so much optimisation you can do, but at the end of the day parameterised knowledge takes up some minimum amount of bits that you can't excise without the actual knowledge and intelligence suffering.
- apublicfrog 5mo ago> And stop acting like some dog shit 8B model the average Joe can run on a laptop is even close to being comparable to what Claude or even Codex can currently do. I'm not, you've actually illustrated my point. LLMs in 2022 were very impressive. By 2024 the general public was finding them an acceptable replacement for many research driven tasks and massive shortcuts for other tasks (coding, image work, document preperation, etc). Those models are absolutely runnable on consumer hardware now, and we were extremely happy with the results. It's no different to how we used to think CRTs were amazing or early smartphones, but going back now they seem awful. We're long past "danger". If what we have is the best we'll ever have open source, we're already in an excellent position.
- root_axis 5mo ago> They're good enough for 95% of use cases They're not at all, not even close. Especially when you consider the use cases for people who are paying for LLM services today.