Anthropic released an api for the same thing last week.

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.

What’s the deal with Alpine not using GNU? Is it a technical or ideological thing? Or is it another “because we can” type distro?

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.

I really like the simplicity and formatting of stock pacman. It’s not super colorful but it’s fast and gives you all of the info you need. yay (or paru if you’re a hipster) is the icing on top.

Don’t buy a Chromebook for linux. While driver support usually isn’t an issue, the alternative keyboard layout is terrible for most applications. To even get access to all of the normal keys that many applications expect you need to configure multi-key shortcuts which varies in complexity based on your DE. In most cases it will also void your warranty because of the custom firmware requirement.

Koboldcpp should allow you to run much larger models with a little bit of ram offloading. There’s a fork that supports rocm for AMD cards: https://github.com/YellowRoseCx/koboldcpp-rocm

Make sure to use quantized models for the best performace, q4k_M being the standard.

I only have 60 down and 12 up so I cap about 80% of the time with a short uncapped window late at night.

The ~400USD price tag is really impressive, but the big thing with these folding phones is the reliability of the hinge. It will be interesting to see how it fares when proper reviews come in.

Tun0 is the interface that most vpns are using so I assume proton is the same.

This is why you should always selfhost your AI girlfriend.