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Cake day: June 11th, 2023

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  • This is a really good question!

    I believe the general answer is, until the compressed file is indistinguishable from randomness. At that point there is no more redundant information left to compress. Like you said, the ‘information content’ of a message can be measured.

    (Note that there are ways to get a file to look like randomness that don’t compress it)


  • By default, an enencrypted boot drive is not sufficient to be able to decrypt a LUKs drive. If you have to type in your password to start the computer/unlock LUKs then you should be good.

    If you’ve setup a keyfile or TPM based decryption of LUKS, then your data is probably not safe (though a TPM based decryption could be if the OS is secure and secure boot is setup properly)

    In this case, if you have another server then you could setup a mutual tang/clevis system where each device gets the keys it needs from the other server on the LAN. Both would be LUKs encrypted. So if one is online the other gets the required key from the online one while booting. But if both are offline then no keys are available and you have to type in a LUKS password to boot. Something like https://www.ogselfhosting.com/index.php/2023/12/25/tang-clevis-for-a-luks-encrypted-debian-server/ but what they do with multiple servers is probably overkill



  • Max@lemmy.worldtoTechnology@lemmy.world*Permanently Deleted*
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    2 months ago

    The original is something like “it’s all Ohio” “always has been”. The globe shown is just a large landmass that looks like Ohio. They’re probably astronauts since you’d have to be in space to see it. The astronaut killing the other has the Ohio flag on them and is killing them for knowing too much.











  • This is a really fantastic explanation of the issue!

    It’s more like improv comedy with an extremely adaptable comic than a conversation with a real person.

    One of the things that I’ve noticed is that the training/finetuning that’s done in order to make it give good completions to the “helpful ai conversation scenario” is that it flattens a lot of the capabilities of the underlying language model for really interesting and specific completions. I remember playing around with gpt2 in it’s native text completion mode, and even with that much weaker model, it was able to complete a much larger variety of text styles without sliding into the sameness and slickness of the current chat model fine-tuning.

    A lot of the research that I read on LLMs is using them in the original token completion context, but pretty much the only way people interact with them is through a thick layer of ai chatbot improv. As an example for code, I imagine that one would have more success using an LLM to edit your code if the context that you give it starts out written like it is a review of a pull request for the code, or some other commentary of a form that matches the way that code is reviewed in the training data. But instead of having access to create that context directly, we have to ask for code review through the fogged window of a chat between an AI assistant and a person discussing code. And that form of chat likely isn’t well represented in the training data.