Or something that goes against the general opinions of the community? Vibes are the only benchmark that counts after all.
I tend to agree with the flow on most things but my thoughts that I’d consider going against the grain:
- QwQ was think-slop and was never that good
- Qwen3-32B is still SOTA for 32GB and under. I cannot get anything to reliably beat it despite shiny benchmarks
- Deepseek is still open-weight SotA. I’ve really tried Kimi, GLM, and Qwen3’s larger variants but asking Deepseek still feels like asking the adult in the room. Caveat is GLM codes better
- (proprietary bonus): Grok 4 handles news data better than GPT-5 or Gemini 2.5 and will always win if you ask it about something that happened that day.
The broader generative AI economy is a steaming pile of shit and we’re somehow part of it? I mean it’s nice technology and I’m glad I can tinker around with it, but boy is it unethical. From how datasets contain a good amount of pirated stuff, to the environmental impact and that we’ll do fracking, burn coal and all for the datacenters, to how it’s mostly an unsustainable investment hype and trillion-dollar merry-go-round. And then I’m not okay with the impact on society either, I can’t wait for even more slop and misinformation everywhere and even worse customer support.
We’re somewhere low on the food chain, certainly not the main culprit. But I don’t think we’re disconnected from the reality out there either. My main take is, it depends on what we do with AI… Do we do the same unhealthy stuff with it, or do we help even out the playing field so it’s not just the mega-corporations in control of AI? That’d be badly needed for some balance.
Second controversial take: I think AI isn’t very intelligent. It regularly fails me once I give real-world tasks to it. Like coding and it really doesn’t do a good job with the computer programming issues I have. I need to double-check everything and correct it 30 times until it finally gets maths and memory handling somewhat right (by chance), and that’s just more effort than coding something myself. And I’m willing to believe that transformer models are going to plateau out, so I’m not sure if that’s ever going to change.
Edit: Judging by the votes, seems I’m the one with the controversial comment here. Care to discuss it? Too close to the truth? Or not factual? Or not a hot take and just the usual AI naysayer argument?
I’m flip-flopping between running local models on my PC with solar power vs. using OpenAI’s free ChatGPT to drive them into ruin, which most of the time ends with me having stupid a stupid argument with an AI.
Local AI will likely have a long lasting impact as it won’t just go away. The companies who released them can go bankrupt, but the models stay. The hardware which runs them will get faster and cheaper over time.
I have some hope with accessibility and making FLOSS development easier/faster. Generative AI can at least quickly generate mockup code or placeholder graphics/code. There are game projects who would release with generated assets, just like for a long time there were game projects who released assets which were modifications or redistribution of assets they didn’t have the rights for. They are probably less likely to get sued over AI generated stuff. It’s unethical but they can replace it with something self-made once the rest is finished.
Theoretically even every user could generate their own assets locally which would be very inefficient, also ethically questionable, but legally fine as they don’t redistribute them.
I like how Tesseract already uses AI for OCR and Firefox for realtime website translations on your device. Though I dunno how much they benefit from advancements in generative AI?
Though a different point/question: At what point is generative AI ethically and legally fine?
It still learned that style transfer somewhere and will close gaps I leave. But I created the style and what the image depicts. At what point is it fine?
I actually use it often to generate shell scripts or small simple python tools. But does it make sense? Sometimes it does work. For very simple logic they tend to get it right. Though writing it myself would probably been faster the last time I used, though at the moment I was too lazy to write it myself. I don’t think I’ve ever really created something usable with it aside from practical shell scripts. Even with ChatGPT it can be an absolute waste of time to explain why the code is broken, didn’t get at all why its implementation lead to a doubled file extension and a scoping error in one function … when I fixed them it actually tried to revert that.
Your experience with AI coding seems to align with mine. I think it’s awesome for generating boilerplate code, placeholders including images, and for quick mockups. Or asking questions about some documentation. The more complicated it gets, the more it fails me. I’ve measured the time once or twice and I’m fairly sure it’s more than usual, though I didn’t do any proper scientific study. It was just similar tasks and me running a timer. I believe the more complicated maths and trigonometry I mentioned was me yelling at AI for 90min or 120minutes or so until it was close and then I took the stuff around, deleted the maths part and wrote that myself. Maybe AI is going to become more “intelligent” in the future. I think a lot of people hope that’s going to happen. I think as of today we’re need to pay close attention if it fools us but is a big time and energy waster, or if it’s actually a good fit for a given task.
I like to believe that as well, but I don’t think there’s any guarantee they’ll continue to release new models. Sure, they can’t ever take Mistral-Nemo from us. But that’s going to be old and obsolete tech in the world of 2030 and dwarfed by any new tech then. So I think the question is more, are they going to continue? And I think we’re kind of picking up what the big companies dumped when battling and outcompeting each other. I’d imagine this could change once China and the USA settle their battle. Or multiple competitors can’t afford it any more. And they’d all like to become profitable one day. Their motivation is going to change with that as well. Or the AI bubble pops and that’s also going to have a dramatic effect. So I’m really not sure if this is going to continue indefinitely. Ultimately, it’s all speculation. A lot of things could possibly happen in the future.
If that’s a question about development of AI in general, it’s an entire can of worms. And I suppose also difficult to answer for your or my individual use. What part of the overall environment footprint gets attributed to a single user? Even more difficult to answer with local models. Do the copyright violations the companies did translate to the product and then to the user? Then what impact do you have on society as a single person using AI for something? Does what you achieve with it outweigh all the cost?
Yes, I think that and text to speech and speech to text are massively underrated. Firefox Translate is something I use quite often and I can do crazy stuff with it like casually browse Japanese websites.
My point was more the people they replace now they’ll replace indefinitely in the context of “impact on society”
and
I just ran into https://huggingface.co/briaai/FIBO, which looks interesting in many ways. (At first glance.)
It also only works with JSON inputs. The more we split AIs into modules that can be exchanged, the more we can update pipelines module by module, tweak them…
It’s unlikely that there’ll never be new releases. It’s always interesting for new-comers to gain market penetration and show off.
It’s possible that there’ll be companies at some point who proudly train their models with renewable energy etc. like it’s already common in other products. It just has to be cheap/accessible enough for them to do that. Though I don’t see that for GPU production anytime soon.
Thanks.
Indeed. Seems it has good performance, licensed training material… That’s all looking great. I wonder who has to come up with the JSON but I guess that’d be another AI and not my task. Guess I’ll put it on my list of things to try.
I said it in another comment, I think that’s a bit hypothetical. It’s possible. I think we should do it. But in reality we ramp up natural gas and coal. US companies hype small nuclear reactors and some some people voiced concerns China might want to take advantage of Russia’s situation for their insatiable demand for (fossil-fuel) energy. I mean they also invest massively in solar. It just looks to me we’re currently overall headed the other direction and we need substantial change to maybe change that some time in the future. So I categorize it more towards wishful-thinking.
I haven’t tried to run any of that yet, but they have these models on HF:
Yes, absolutely. It can happen, but we shouldn’t make decisions based on the assumption that it might happen. In other fields there are companies who try to make their products better recyclable, less energy hungry (production and run time), made from sustainable resources, repairable, more ethically sourced resources etc. So it’s not out of question, but it often starts with people who just wanna see it happen, not with a business case. There are also many black sheep who only do green washing by just letting it sound like they do that without actually doing it.
Ecosia already tries to sell their chatbot as green, but it only uses OpenAI’s API and they plant trees how they always do. Though I generally don’t like their compensation concept, at least they claim their own servers run 100% renewable energy. I haven’t tried their chatbot(s) yet, but it looks like it’s still only OpenAI. If they do it like duckduckgo at some point in the future, they could run open models on their own servers. Whether they can produce enough energy and get their hands on hardware to get that working etc is a different question though. There isn’t any indication yet that they plan to go that way.
It’s probably already possible to let an EMS start AI training when there is solar overproduction. That’s only worth it when the pace of new break throughs have slowed down or when they use outdated techniques anyways. I dunno where the current balance currently is between electricity prices, hardware cost, energy efficiency of the hardware and time pressure.
EDIT: Sounds like Ecosia is on it for runnning AIs at least https://blog.ecosia.org/what-we-are-doing-with-ai/. They probably push that renewable energy into grid somewhere else than where the AI is consuming it.
I don’t think they’ll say no to cheap energy, but they definitely don’t wanna be dependent on other countries for their energy. As far as I understand they push solar, electric cars etc for energy dependency reasons.
Personally, I do not care if datasets contain pirated stuff because the copyright laws are broken anyway. If the entirety of Disney movies and Harry Potter books are somewhere inside those datasets, I can play them a song on the world’s smallest violin.
Smaller artists/writers are the ones I empathize with. I get their concern about large corporations using their stuff and making money off of it. Not entirely something that applies to local AI since most people here do this for themselves and do not make any money out of it.
That’s actually the saddest part. Those models could be easily trained with renewables alone but you know, capitalism.
The thing is, those models are already out there and the people training them do not gain anything when people download their open weights/open source models for free for local use.
There’s so much cool stuff you can do with generative AI fully locally that I appreciate that they are available for everyone.
If we are talking about LLMs here, I don’t think that’s much of a controversial take.
Most people here will be aware that generative AI hallucinates all the time. Sometimes that’s good, like when writing stories or generating abstract images but when you’re trying to get accurate information, it’s really bad.
LLMs become much more useful when they do not have to completely rely on their training data and instead get all the information they need provided to them (e.g. RAG).
I’m a huge fan of RAG because it cites where it got the information from, meaning you can ask it a question and then continue reading in the source to confirm. Like fuzzy search but you don’t have to know the right terms.
Agreed.
It’s really sad to read the articles how they’re planning to bulldoze Texas and do fracking and all these massively invasive things and then we also run a lot of the compute on coal and want more nuclear plants as well. That doesn’t really sound that progressive and sophisticated to me.
You’re right. Though the argument doesn’t translate into anything absolute. I can’t buy salami in the supermarket and justify it by saying the cow is dead anyways and someone already sliced it up. It’s down to demand and that’s really complex. Does Mark Zuckerberg really gift an open-weights model to me out of pure altruism? Is it ethical if I get some profit out of some waste, or by-product of some AI war/competition? It is certainly correct that we here don’t invest money in that form. However that’s not the entire story either, we still buy the graphics cards from Nvidia and we also set free some CO2 when doing inference, even if we didn’t pay for the training process. And they spend some extra compute to prepare those public models, so it’s not no extra footprint, but it’s comparatively small.
I’m not perfect, though. I’ll still eat salami from time to time. And I’ll also use my computer for things I like. Sometimes it serves a purpose and then it’s justified. Sometimes I’ll also do it for fun. And that in itself isn’t something that makes it wrong.
Yeah, that’s really great and very welcome. Though I think it still needs some improvement on picking sources. If I use some research mode from one of the big AI services, it’ll randomly google things, but some weird blog post or a wrong reddit comment will show up on the same level as a reputable source. So it’s not really fit for those use-cases. It’s awesome to sift through documentation, though. Or a company’s knowledgebase. And I think those are the real use-cases for RAG.
That’s not comparable. You can’t compare software or even research with a physical object like that. You need a dead cow for salami, if demand increases they have to kill more cows. For these models the training already happened, how many people use it does not matter. It could influence whether or how much they train new models, but there is no direct relation. You can use that forever in it’s current state without any further training being necessary. I’d rather compare that with nazi experiments on human beings. Their human guinea pigs already suffered/died no matter whether you use the research derived from that or not. Doing new and proper training/research to get to a point where improper ones already got is somewhat pointless in this case, you just spend more resources.
Though it makes sense to train new models on public domain and cc0 materials if you want end results that protect you better from getting sued because of copyright violations. There are platforms who banned AI generated graphics because of that.
But you don’t have to. I can run small models on my NITRO+ RX 580 with 8 GB VRAM, which I bought 7 years ago. It’s maybe not the best experience, but it certainly “works”. Last time our house used external electricity was 34h ago.
Regarding RAG, I just hope it improves machine readability, which is also useful for non-AI applications. It just increases the pressure.
I really like to disagree here. Sure today’s cow is already dead and turned into sausage. But the pack of salami I buy this week is going to make the supermarket order another pack next week so what I’m really doing is have someone kill the next cow, or at least a tiny bit because I’m having just some slices and it’s the bigger picture and how I’m part of a large group of people creating the overall demand.
And I think it’s at least quesionable if and how this translates. It’s still part of generating demand for AI. Sure, it’s kind of a byproduct but Meta directly invests additional research, alignment and preparation for these byproducts. And we got an entire ecosystem around it with Huggingface, CivitAI etc which cater to us, sometimes a sunstantial amount of their bussiness is the broader AI community and not just researchers. They provide us with datacenters for storage, bandwith and sometimes compute. So it’s certainly not nothing which gets added due to us. And despite it being immaterial, it has a proper effect on the world. It’s going to direct technology and society in some direction. Have real-world consequences when used. The pollution during the process of creating this non-physical product is real. And Meta seems to pay attention. At least that’s what I got from everything that happened with LLaMA 1 to today. I think if and how we use it is going to affect what they do with the next iteration. Similar to the salami pack analogy. Of course it’s a crude image. And we don’t really know what would happen if we did things differently. Maybe it’d be the same so it’s down to the more philosophical question of whether it’s ethical to benefit from things that have been made in an unethical way. Though this requires today’s use not to have any effect on future demand. Like the nazi example where me using medicine is not going to bring back nazi experiments in the future. And that’s not exactly the situation of AI. They’re still there and actively working on the next iteration. So the logic is more complicated than that.
And I’m a bit wary because I have no clue about the true motive behind why Meta gifts us these things. It costs them money and they hand control to us, which isn’t exactly how large companies operate. My hunch is it’s mainly the usual war, they’re showing off and they accept cutting into their own business when it does more damage to OpenAI. And the Chinese are battling the USA… And we’re somewhere in the middle of it. Maybe we pick up the crumbs. Maybe we’re chess pieces and being used/exploited in some bigger corporate battles. And I don’t think we’re emancipated with AI, we don’t own the compute necessary to properly shape it, so we might be closer to the chess pieces. I don’t want to start any conspiracy theory but I think these dynamics are part of the picture. I (personally) don’t think it’s a general and easy answer to the question if it’s ethical to use these models. And reality is a bit messy.
I think this is the common difference between theory and practice. What you do is commendable. In reality though, AI is in fact mostly made from coal and natural gas. And China and the US ramp up dirty fossil fuel electricity for AI. There’s a hype in small nuclear reactors to satisfy the urgend demand for more electricity and they’re a bit problematic with all the nuclear waste due to how nuclear power plants scale. So yes, I think we could do better. And we should. But that’s kind of a theoretical point unless we actually do it.
Yes, I’d like to see this as well. I suppose it’s a long way from pirating books because they’re exempt from law with enough money and lawyers… to a proper consensual use.
You pay for the salami and thus entice them to make more. There is monetary value for them in making more salami.
I don’t really know why they initially released their models but at least they kicked off a pissing contest in the open weight space on who can create the best open model.
Meta has not released anything worthwhile in quite a while. It’s pretty much Chinese models flexing on American models nowadays.
Still, their main incentive to train those models lies with businesses subscribing to their paid plans.
True, I exclusively run inference on AMD hardware (I recently got a Strix Halo board) so at least I feel a little bit less bad and my inference runs almost purely on solar power. I expect that is not the norm in the local AI community though.
I rarely use the commercial AI services but also locally hosted the web search feature is not really that great.
Yes, I prefer to use RAG with information I provide. For example, ask a question about Godot and provide it the full Godot 4 documentation with it.
Still working on getting this automated though. I would love to have a RAG knowledge base of Wikipedia, Stackoverflow, C documentation, etc. that you can query an LLM against.