I‘ d recommend playing around a bit with opencode and their free models, just to see what changed. Oh and you don’t have to use it to code, but a fun experiment would be to run it in a Linux container and see what it can do.

Fair point. But is it even more of a ADHD amplifier then a modern smartphone connected to social media?

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Sorry, I’m curious: what’s your workflow looking like when you’re dealing with LLMs?

Because I‘m just tinkering with them as a hobby and while I consider them erratic and certainly limited in many regards, I still find them useful. Even fun, but on the other hand I’m not forced to use them.

Somebody voted you down, let me upvote you for balance.

Well I say: put the development of AI in the hands of academia and put regulations in place for the building of data centers. Consider environmental impact, force the use of renewable energy and prevent hardware shortage on the market. Let companies and organisations host their own models. Ban the use of phones in schools and shift the weight of exams in colleges and universities to oral questionnaires that confirm the student actually knows this stuff.

Yeah lol. That could be done in Europe, I’m skeptical about the US.

Please send me some looted hardware if you are destroying data centers.

Exactly. I run some local models on my graphics card for fun and honestly, those things getting kinda good.

But I guess if you’re on Lemmy, have a hateboner for LLMs and are full in your media confirmation bubble, then you probably don’t care. A bit disappointing, I always thought the metaverse is full of people that are interested in new things. I really can’t blame them however, the techbros do a good job to push everyone away from their technology.

Sigh. This comic is as delusional as the rambling of Sam Altman about AGI.

AI is a tool and despite all its shortcomings not a disease. It’s important to wrestle it out of the hands of the techbros and give it back to the people.

But it’s going to stay and no yelling at the clouds will change it.

Sounds less then ideal. I honestly never used a current model of those big AI providers, except maybe a bit of ChatGPT 3.5 back in the day. My fiancé uses Claude is mostly satisfied. I played around with opencode and a local model and I‘m mostly impressed. I use it do admin my Linux gaming pc and to teach me Linux.

The local Qwen 3.6 works quite well, does good online research and actually proved me wrong when I thought it hallucinated some fact. Of course it’s no „real“ AI, but it’s useful.

Strange. You should think that’s a pretty basic task. Like you said, limited problem space. Every current LLM that is big enough and has the necessary guardrails and instructions should be able to handle it.

Interesting. In this case not only the free tier house LLM that’s included with with opencode is smarter, also the local model I run on 16GB graphics card can beat Gemini, lol.

Honestly, you’re example could already be done today by a local model like like Qwen 3.6 27b. There’s no need to run an expensive cloud model for such a simple task.

Wouldn’t even destroy jobs, there are always people needed to fry the burger.

Now do I want to talk Rona fucking robot while ordering burger? Hell no. But it could be done economically without any problems.

No offense, but when did you last use a LLM? Two years ago?

Granted they‘re talkative, but that’s it what they are, literal blah machines.

I mean fuck Altman and the rest of the tech bros, can’t wait until their bubble burst and they all crash. But the technology is going to stay, like it or not.

“AI tools are great, but only if they actually help, rather than cause unnecessary pain and pointless make-believe work,” he wrote. “Feel free to use them, but use them in a way that is productive and makes for a better experience.”

That’s a pretty nuanced view. I agree, but I’m not sure how many people of this community do.

Sure! How much experience do you have with LLMs?

I have a Radeon RX 7800 XT.

Qwen 3.5-9b is blazingly fast on it. However while it’s its impressive for its size, it has its limitations. Complex tasks with several steps are too much for it.

So now I run the 3.6-35B model with llama.cpp It’s too big for my VRAM so I had to split it: everything that doesn’t fit on the graphics’s card runs in the normal RAM. That slows everything down, but with the right flags I get a bit over 20 tokens/s.

If you have problems with speed and you’re using ollama I would replace it with something faster like llama.cpp.

I recommend Qwen3.6, either the 27B dense or the 35B MoE model. Both outstanding for local models.

They can also make you smarter if you use them right. Key is to use local models and not giving the techbros any money.

Brought to you by the power of Destillation. But I actually don’t mind, so we’re getting at least somewhat open models.

Various AI features for Ubuntu Linux are expected to land over the next year with a bias on local inferencing by default. Canonical engineers will be working on integrating agentic workflows into Ubuntu for those that want it. There are areas being explored for AI use on Ubuntu both for the desktop as well as for Ubuntu servers such as for assisting in interpreting system logs

Sounds actually reasonable. As long as it doesn’t get shoved down the users throat it could turn out fine. And sifting through logs is in fact a good task for LLMs in my opinion.

Take that, flat earthers!!!