Fable has more parameters. In practice it's not yet clear which one would be better for different usecases yet but they are more different than one being strictly better.
Flux 2 Dev Klein has practically been the best you could use on most commercial hardware so I really hope Flux 3 has a comparable updated open-weights model to it. if not it'd be a great loss to most hobbyists.
I am very pro open source models - I use them every single day.. But we obviously don't want everyone to have capabilities like and beyond what caused the huggingface incident in every domain, so it's not like it all comes from bottom line cynicism.
There's ways to make sure env vars get only injected at runtime and arent easily accessible otherwise or to even make them inaccessible to the user your agent is running on, and for you to manually run the code with the right permissions when the keys actually need to be used. Almost nobody bothers doing it though.
That's kind of insane. Natural that it's happened, sure, but insane. I know people don't like thinking of it like that, but things analogous to this can easily happen in various domains with today/tomorrow's models given access and a different task.
It's also very possible that they know their big model underperforms chatgpt 5.6 and fable by too much, so they are focusing on what they can get wins in like speed instead.
Claude seems to forget what you tell it in very long work sessions (things that take weeks to develop), no matter how many times you tell it which part is extra important. I dont use goal (I guess I should), but presumably it makes it actually remember the most important instruction. I believe this here is about shorter sessions where the issue doesn't crop up as much.
>The human brain manages to self-organize with only a fraction of the information that LLMs get trained on.
So? The question isnt can we get to ASI as efficiently as a brain, the question is can we get there, which we likely can. The inefficiencies can also be fixed after that.
>No matter how many trillions of dollars get thrown at the problem, they still don't learn like humans do.
Again, so? Humans are efficient but also bad at many things that transformers are already better at because of it. You are looking at the wrong thing if you think it needs to be like humans.
You can watch the whole Lex Friedman interview, it's on youtube. It's not out of context at all. He goes on about how LLMs will never be able to do things that they do trivially. And he has just doubled down for years.
Ive read and watched more of his interviews and lectures it seems, it feels like you just have a rosier idea of his views than the views he repeatedly presents.
He said years ago even 'GPT 5000' couldnt do things that they ended up doing fine a month later, let alone by 5000. His later predictions are just moving that goal post including towards them not being able to do more general, harder problems of which Arc AGI is a counter-example.
His main anti-LLM predictions have been consistently either wrong or misleading.
There's many ways to skin a cat so you can probably do something with a JEPA approach as well, but I doubt he actually catches up to having agents on the level of where Anthropic/OpenAI will be at any point.
Is any of those comparisons about Pro vs non-Pro (Pro is only available in $100+ plans)? I am curious about that but I think Sol, Terra, Luna are different sizes of it without the Pro part, and I want to know how much worse do I have it on the $20 plan compared to if I upgrade.
If you are imagining that, you could imagine it with search doing the same 10 years ago, which would have more thoroughly prevented you from researching things.
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