Of course it was political retribution and not the whole unregistered securities and gambling market thing.

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.

I’d guess the 3 key staff members leaving all at once without notice had something to do with it.

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.

How many times is this same article going to be written? Model collapse from synthetic data is not a concern at any scale when human data is in the mix. We have entire series of models now trained with mostly synthetic data: https://huggingface.co/docs/transformers/main/model_doc/phi3. When using entirely unassisted outputs error accumulates with each generation but this isn’t a concern in any real scenarios.

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.

About a dozen methods they could use https://arxiv.org/pdf/2312.07913v2

New record for most buzz words in a headline.

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

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.

Cohere’s command-r models are trained for exactly this type of task. The real struggle is finding a way to feed relevant sources into the model. There are plenty of projects that have attempted it but few can do more than pulling the first few search results.