Anthropic released an api for the same thing last week.

Every credible wiki has moved away from fandom at this point. All that’s left is the abandoned shells of the former wikis they refuse to delete and kids who don’t know better.

This is actually pretty smart because it switches the context of the action. Most intermediate users avoid clicking random executables by instinct but this is different enough that it doesn’t immediately trigger that association and response.

All signs point to this being a finetune of gpt4o with additional chain of thought steps before the final answer. It has exactly the same pitfalls as the existing model (9.11>9.8 tokenization error, failing simple riddles, being unable to assert that the user is wrong, etc.). It’s still a transformer and it’s still next token prediction. They hide the thought steps to mask this fact and to prevent others from benefiting from all of the finetuning data they paid for.

The role of biodegradable materials in the next generation of Saw traps

It’s cool but it’s more or less just a party trick.

Based on the pricing they’re probably betting most users won’t use it. The cheapest api pricing for flux dev is 40 images per dollar, or about 10 images a day spending $8 a month. With pro they would get half that. This is before considering the cost of the language model.

I feel like they should at least provide them with a laptop If they’re going to do unpaid promotion.

She immigrated when she was 15, 30 years before she made the Queen of Canada claim. You can’t deport someone after 30 years of citizenship for mental illness.

The model does have a lot of advantages over sdxl with the right prompting, but it seems to fall apart in prompts with more complex anatomy. Hopefully the community can fix it up once we have working trainers.

The names missing from the list say more about the board’s purpose than the names on it.

The issue is that they have no way of verifying that. We’d have to trust 2 other companies in addition to DDG.

All of Firefox’s ai initiatives including translation and chat are completely local. They have no impact on privacy.

The “why would they make this” people don’t understand how important this type of research is. It’s important to show what’s possible so that we can be ready for it. There are many bad actors already pursuing similar tools if they don’t have them already. The worst case is being blindsided by something not seen before.

The 8B is incredible for it’s size and they’ve managed to do sane refusal training this time for the official instruct.

They’re already lying to get passed the 13 year requirement so I doubt it would make any difference.

I’m sure the machine running it was quite warm actually.

Partnered with Adobe research so we’re never going to get the actual model.

This has more to do with how much chess data was fed into the model than any kind of reasoning ability. A 50M model can learn to play at 1500 elo with enough training: https://adamkarvonen.github.io/machine_learning/2024/01/03/chess-world-models.html

The “AI PC” specification requires a minimum of 40TOPs of AI compute which is over double the 18TOPs in the current M3s. Direct comparison doesn’t really work though.

What really matters is how it’s made available for development. The Neural engine is basically a black box. It can’t be incorporated into any low level projects because it’s only made available through a high-level swift api. Intel by comparison seems to be targeting pytorch acceleration with their libraries.