ImprobableTruth

The current benefit is that chat has no quota.

Why would you accept it when the benchmark's ranking is obviously nonsense. It literally has muse spark 1.3 above 6 astra, 5.6 sol and fable 5. Anyone who has played with any of these models for any amount of time would immediately realize that this is total bunk.

That's the ankle. The actual knee is hidden in the feathers of the body.

Its "pure capabilities" are definitely worse than Fable, but I find codex has a much more pleasant style and is in comparison much more generous with its limits.

It's probably GLM 5.3 flash, so weaker but cheaper.

These exist and are used. The issue is that because they're so much smaller, they're also much worse, so they tend to have lots of false positives while still being easy to circumvent.

The fundamental issue is that for these systems bigger is essentially always better (if affordable). So if we can squash something like Kimi K3 down to run on a 'normal' system, that just incentivizes devs to increase the model size until once again we're at the limit of what can be run.

This is not what the bitter lesson is about. It's not "don't develop better methods, just scale", it's that those methods which scale best win. LeCun's work is fundamentally about devising a method that scales better with data than LLMs. Agree with him or not about the feasibility of it, but this is fundamentally still a bitter lesson-pilled mindset.

This is some bizarre victim inversion. The providers of closed models are the ones who are trying to use regulation to stop their open model competition, not the other way around.

Now, what if you order a construction crew for building your house? What if you also hire an architect to design the plan? And an overseer who manages everything?

Surely at some point you would stop saying "I built this house", even if you ordered and financed it?

Sure, but "10x faster, but only applies on small greenfield, throwaway projects" is a major caveat. In fact, there's a good chance this doesn't disprove the original blog post, you could be way faster on small projects but slower on 'real' projects.

>Also give it a chance - as you said "good" models have only been available very recently and you wouldn't expect everyone to start using them instantly.

But I'm not expecting everyone to have built something like that, but surely among millions of users someone should have, especially the people proclaiming insane productivity gains? There are no super impressive open source projects done using AI and all the companies boasting about how all their code is AI written now don't show much improvement either.

Opus 4.5/4.6 are what many people consider the first 'good' models and it's from last year/start of this year.

But fine, let's say everything before gpt 5.5 was unusable crap. Then there should still be projects that would normally have previously taken ~2 years done in just two months. Where are they?

>I just did a ~6 month project in ~2 weeks using a frontier model.

Claims like this are hard for me to take seriously because 'good' models have been available since the start of the year. So, if they really 10x one's productivity, then people should be able to have gotten done 5 years worth of work since then, but I've never actually seen anybody show any project like this.

This is a very strange mindset. Even if you want to treat everything as sort of billable hours this doesn't really make sense because the average boss's boss's isn't paid anywhere near 144x. If a SWE spends 100 hours to save their boss's boss one hour, they're wasting a ton of money.

Anthropic had to settle with authors because they literally pirated books! Their behavior regarding distillation is genuinely beyond parody.