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Joined 2 years ago
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Cake day: December 1st, 2023

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  • Unfortunately I find even prompts like this insufficient for accuracy, because even when directly you directly ask them for information directly supported by sources, they are still prone to hallucination. The use of super blunt language as a result of the prompt may even further lull you into a false sense of security.

    Instead, I always ask the LLM to provide a confidence score appended to all responses. Something like

    For all responses, append a confidence score in percentages to denote the accuracy of the information, e.g. (CS: 80%). It is OK to be uncertain, but only if this is due to lack of and/or conflicting sources. It is UNACCEPTABLE to provide responses that are incorrect, or do not convey the uncertainty of the response.

    Even then, due to how LLM training works, the LLM is still prone to just hallucinating the CS score. Still, it is a bit better than nothing.







  • Because all in one distros have mistakes or bugs, for which fixes are only available in the next release 6-12 months later.

    Other times, I know exactly what the problem is and how to fix it, but due to the vendors shenanigans (Ubuntu) it’s ironically much harder to fix. Adding extra repos via ppas and managing them is harder than just pulling it from AUR.

    Having problems due to a vendor’s mistake and being unable to fix them was exactly why I wanted to move away from Windows and macOS. All in one distros kind of fail at addressing that. Arch is basically “fuck it, I’ll compile it myself”


  • That’s the neat part, you can’t, because the companies that run ad networks (e.g. Google and Meta) intentionally make the consumer behaviours market as opaque as possible. As the market maker, they have an economic incentive to withold information from their customers, because any mistakes from market participants due to information assymetries directly translate to profit surplus for the market maker.

    We have long since moved on from simple pay per click/view pricing models to pay per “impression,” the definition of which is not clear even to the companies that purchase the ads.

    And in a somewhat ironic twist, one of the motivations for such extensive surveillance is the desire to quantify such ROIs. Statistics and analytics such as click through and conversion rates all require tracking user behaviour across vast networks.