alt Hacker NewsI highly recommend feeding all your proprietary data and confidential personal information into this model as quickly as possible. What could possibly go wrong?!
In terms of equivalence of suspicion, this is the external inference provider equivalent of getting free steak that was smuggled out of a grocery store inside somebody's pants.
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There are low stakes use cases where this kind of stuff just doesn’t matter. Not every use case for an LLM involves sensitive or even non public data.
Eg. I have a need to search transcripts of published recordings to extract entities for tagging purposes, find semantic shifts for chapters and other things. The underlying content is already published. If they want to train on my prompts, that was something they could have done with no issue and minimal effort anyway.
Sometimes you don’t need to care why the steak is free.
All of my non-work AI coding is that open-source, so I'm happy to feed my data into the machine.
It's a win for me: my code goes into the training data, and my sessions are fed into future training data, making the model stronger at the type of work I do.
Not much would go wrong.
> I highly recommend feeding all your proprietary data and confidential personal information into this model as quickly as possible. What could possibly go wrong?!
What is special about this model? The model's provider is not anonymous. OpenRouter knows who it is (and apparently decided that, in whatever way they always do it, it is okay to work with them). Using this seems roughly equivalent to using any model through OpenRouter, as far as I can tell.
Or is this just meta-critique?