> Mistral is the only AI company in the world building the full stack required to answer that question: open-weight models, the infrastructure and the compute capacity they run on
Didn't know Mistral sold affordable inference hardware. /s
I encourage you to actually read the OP if you haven't, because I think it gives the most obvious example for that. That kind of app makes much more sense (for the user) to be a web app, especially when you don't intend to become a repeat customer.
Yes, for the article example I completely agree that a travel itinerary should never be an app. But it shouldn't be a web page either. It should be a document saved on your device. So that you always have offline access to it when traveling.
It's a mystery to me why everyone tries to use OnCalendar here, when "n amount of times within a certain timeframe" can be done much more easily with OnActiveSec, in this case that'd be OnActiveSec=6h.
Somehow, in the AI world, "local-first" means a local harness talking to a remote model, almost never "local harness talking to local model". But then "open source model" apparently also means "you can download the weights if you agree to our license" and almost never "you can see, understand and iterate on what we did", so the definitions already drifted a lot between the two ecosystems.
I'm not sure I understand the question. Regardless of what provider you choose - be it cloud based or local - you have to provide setup information such as host, authentication, etc. So it "defaults" to nothing; you have to select something.
Local first means running Atomic with local models is not an afterthought. It’s a first class citizen that works just as seamlessly as running with a cloud provider - assuming you’ve done the work to provision the local models and their connections yourself.
I'm not sure what the dunk is supposed to be here .. Atomic supports the exact same feature set with local models as it does for OpenRouter. Is your gripe just that Openrouter is the first option in the dropdown?
Yes. Why even call it local-first when local isn't first? Not to mention, for some reason they decided to only support Ollama instead of giving you the option to connect to any OpenAI-compatible server, which would make this work with any other inference server such as llama.cpp and vLLM as well as Ollama. (and also most SaaS inference providers, including OpenRouter, so the custom integration would not be necessary either, https://schizo.cooking/schizo-takes/9.html)
Did you think local-first meant how a dropdown is sorted?
OpenAI-compatible is indeed one of the provider options for Atomic. Ollama and openRouter are separate options to allow for easier selection of models from these specific providers.
The online documentation does not suggest that using a generic OpenAI-compatible server is an option, and it once again lists the non-local option first.
> OpenAI-compatible is indeed one of the provider options for Atomic. Ollama and openRouter are separate options to allow for easier selection of models from these specific providers.
Why is this necessary over just presenting the result of `/v1/models`?
You can say it's just the ordering of a dropdown, but to me it seems pretty clear that this thing is developed with the idea that you'll most likely use a SaaS provider.
It has supported local LLMs from the beginning, it was not something that was just tacked on. I don't know what else to tell you. Your assumptions are just wrong.
You mean you will read all code with dependencies and compile it yourself to make sure?;) good for you. but good luck creating a popular e2e messenger then.
If you release it as GPL or AGPL, it should be pretty difficult to obey those terms while using the code for AI training. Of course, they'll probably scoop it up anyway, regardless of license.
The legal premise of training LLMs on everything ever written is that it’s fair use. If it is fair use (which is currently being disputed in court) then the license you put on your code doesn’t matter, it can be used under fair use.
If the courts decide it’s not fair use then OpenAI et al. are going to have some issues.
Quite possibly. If they care a great deal about not contributing to training LLMs then they should still be aware of the fair use issue, because if the courts rule that it is fair use then there’s no putting the genie back in the bottle. Any code that they publish, under any license whatsoever, would then be fair game for training and almost certainly would be used.
Didn't know Mistral sold affordable inference hardware. /s
reply