daveguy

Dude straight up lied to everyone. Most blatantly corrupt incompetent fraud in US history. Just because he's narcissist enough to not care doesn't make it better. And yeah, lots of mistakes to fix that led to McConnell / Dumpty. Mainly erosions of separation of powers. At least the other presidents you mentioned tried to stick to what they campaigned on and didn't try to light fire to the government for personal gain.

I thought this would be a push for a nationwide open weights effort / initiative, but I was surprised to see this:

> Distillation ... reflects a long tradition of learning from, building upon, and improving existing technologies, a tradition that has helped drive innovation since the rise of the open-source software movement. By contrast, unlawful efforts to extract value from closed models raise legitimate concerns. Those concerns should be addressed through targeted legal and commercial frameworks rather than sweeping restrictions on techniques that play an important role in AI innovation.

Sounds like they are saying "please protect our IP theft" that created closed weight frontier models in case we arbitrarily decide to close our models. But don't get rid of distillations in general so that we can all also keep benefiting from open models. We don't want to lose the ability to benefit from the work of others as we launder IP into closed models.

Surprised Linux Foundation kept their name on it with that.

The entire reason we are in this war is the current administration's ineptitude. Anyone who voted for this president should be ashamed they fell for a con.

Thank you for the clarification. I figured I was missing something in your meaning. Although "if it comes so cheap that..." means it will probably come cheap for other providers, and the margins wouldn't be there in the end because a price increase to take advantage of those big clients / rush times might lose the clients. I think we agree that their chances of becoming profitable don't look great.

100% this. OpenAI created a for-profit subsidy (that is now an SBC and only controlled 26% by the original non-profit). Guess when they did that? After GPT-2 (Feb 2019) and before GPT-3 (June 2020). Not remotely surprising they started the move to for-profit as soon as they had something they thought could be profitable. Then in 2025 they switched to an SBC, dropped the non-profit control well below 50% and completely got rid of the profit cap. Just in time for the IPO talk. It was a mask the whole time.

You do realize "subsidizing" means charging less for something than it costs to provide, right? So they'll lose a dollar on every sale, but they'll make up for it in volume? E2E is usually where the profit comes from. If they're subsidizing getting regular users on board (pro/plus/max), and they're subsidizing to get businesses on board (massive deploys), where can the profit possibly come from without a pricing rug pull?

3 years ago is about when the BS "AI can do anything!" hype started. So, it is not a misinterpretation, but an accurate assessment of the hype cycle. Exactly the kind of things managers are bumbling into because "AGI any day now!" for the last 3-4 years. GPT 3.5 was released May 2022. I'm curious how many AI project in industry have crashed and burned during this hype-Summer before the Winter. Speaking of which, whatever happened to that "software dark factory" you were gushing over a while back?

But immitating others is about the only thing genAI does. Sometimes "others" is a 'programmer', sometimes "others" is an 'artist', but regardless, it still does it poorly.

Not OP, but I'd settle for something that actually learns, instead of being a static pile of linear algebra. Pretending it learns because you change the input (context) doesn't count.

> That Ford story was really misleading. It wasn't about modern LLMs, and the way it was reported implied that Ford had fired and then hired people but if you read closer that wasn't necessarily the case at all - it sounded more like they were re-hiring people who had retired because they needed expertise that had left the company.

Oh come on, Simon. Are your glasses so rose coloured they are opaque? They fired, and then re-hired because the AI couldn't do the job. They had to re-hire the quality folks. So, no, it wasn't LLMs, it was quality control -- which is a much more well established domain for automated systems. LLMs are a complete shitshow compared to industrial process automation.

Side question -- do you worry about being so pro-LLM when the promises of LLMs are so clearly falling short?

Be sure to short the shit out of that company after you leave, assuming something in your employment contract doesn't prevent it (doubtful). Especially if they are software development focused.

Sorry, but labs working hard doesn't make non-polynomial problems suddenly polynomial.

The revolution is way overblown.

Yes, definitely a case of bad-think.

Certainly not double-plus-good like AI.

Dangerous.

Well, it didn't take long for this post to be disappeared from the front page.

Edit: ahhh, I see why. In the list of ways to keep your sanity through the AI mania -- "I no longer visit Hackernews, Reddit, or really anywhere where I am going to be drip-fed nonsense."

The context is the input to the LLM model. It seems like you need to study up on how LLMs work, instead of spouting hype.

Seriously? Supid is literally incapable of learning. That is the underlying model. It does not change, therefore it does not learn. LLM models are quite literally stupid. No one gets a new model no matter how much they yell at it in the context.

You are incorrect. "memory.md" and other context manipulations do not change the underlying model.

Both of those things only happen once by the LLM model provider and not every time a prompt is issued.

I think just the possibility of consequences that they can give a damn about. An LLM doesn't have feelings no matter how much human-like text it can simulate. There is literally nothing going on between prompts for any given model. They are incapable of worry or any other emotion. Wipe the context clean and the LLM is completely unaware there was ever a problem.

> Why did you drop the first half of the sentence in your quote?

Because this entire discussion is about the release of a new model, and models are fixed. Sure you can try to modify all the scaffolding around it, but the model is the model. It doesn't matter what you're trying to improve. You can only improve the peripheral aides. And the peripheral aides can't fundamentally fix the problems with llm models when they can't learn new relationships or facts.

You will always have to wait for a new model (like this one we are talking about) for improvements to the model.

The models do not get better until a new one is released. And we are already at diminishing returns. So sorry. Also sorry you don't know the difference between a model and a context, harness, router, or cache.

None of what you mentioned changes the model. Because it's a fixed model. The weights are constant. It does not learn. It only knows what gets repeatedly fed to it and those fixed relationships represented by the weights. You can pretend like that's not true, but unfortunately for VCs it is true.

End of discussion.

Neural plasticity is real, and something LLMs are incapable of. So sorry.

> Often yes. In this case, it's more like they get upset when someone says something factually wrong, and then defensively changes the goalposts.

Oh give me a break. Show me one example of 1) any knob twisting that makes the underlying model better. or 2) any example of the AI providers twisting those knobs to do anything other than degrade performance for their own bottom line or safety.

The current post says: "it would be expected for a better model to use different amounts of brevity if it gets better at determining the appropriate amount."

When no, the model cannot "get better". It doesn't determine any appropriateness of response realtime except for the weights baked into it from the beginning and whatever context it can muster. If you cram enough guidance that it doesn't decide to ignore maybe you can make it more brief. But it (the model) can do none of those things.

LLM models are literally stupid by design.

Right. They can do all those things. And none of that will make it smart or able to learn new things. The underlying model is just an llm. But judging from the downvotes, it seems AI folks get upset when someone talks honestly about their precious piles of matrix multiplication.

It was edited. Original talked about the model learning. Glad they managed to clarify. Because the models are quite literally stupid.

The models don't get better, except when a new one is released. Their performance depends solely on the model training before release and how well you curate the context you feed it. That's it. Contrary to popular belief these things are not intelligent.

Can we turn off the whining bitchy little comments like yours along with the political commentary? That would be great.

Did you honestly think the mechanazi / porn generator AI wouldn't receive negative comments? Even "hardcore nerds" recognize garbage. Sorry 'bout your luck.

I doubt you can turn that off. That's how they upgrade it up from a tool to an engagement optimized addiction platform.