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Joined 2 years ago
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Cake day: July 3rd, 2023

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  • I’ve been “collecting” content for many years now. I learned most of what I needed to know about ripping and transcoding over the years, such that each time I need to deal with a new video format, or a new application, it’s not too hard, because I’m building on everything I’ve already learned.

    And each time I was learning new things, it’s not like there was a risk that all my previous content might suddenly become unusable or inaccessible.

    Meanwhile, a couple years ago I was finally able to build myself a proper NAS. While I know my way around Linux somewhat, I never kept a Linux-based daily driver because most of the apps I use regularly are on Windows, and I’m not confident about running them stably in Linux, nor am I confident about equivalent native Linux apps. And I’m not confident about setting up and administering my own server. My past experiences have shown me that whenever you need to do anything complex and specific, it involves a lot of work.

    So at a coworker’s suggestion, I got a Synology NAS that turned out to be a breeze to setup. And then I figured out how to get Plex server on there (not available in the Synology package manager, but the “manual” process turned out to be simple enough)

    And it just WORKS! it’s not perfect, but it’s mostly painless to use. I was happy paying for the lifetime Plex pass at the beginning, because it handles all the routing and discovery that needs to happen to allow me to stream to my phone, or to my parents’ TV when I’m visiting them.

    My next NAS might not be by Synology due to their recent announcement about supported hard drives, but I’ll probably be looking for something that “just works” because I can’t be bothered to learn how to be a sysadmin, and risk losing my stuff because I’m making the kinds of mistakes one makes as they’re learning.

    Just like, if I owned a car, I wouldn’t be digging under the hood to “tweak the timing” or replacing brake discs. I’d be happy paying someone I trust to do that work, leaving me with a car that “just works”.





  • I don’t like the idea of restricting the model’s corpus further. Rather, I think it would be good if it used a bigger corpus, but added the date of origin for each element as further context.

    Separately, I think it could be good to train another LLM to recognize biases in various content, and then use that to add further context for the main LLM when it ingests that content. I’m not sure how to avoid bias in that second LLM, though. Maybe complete lack of bias is an unattainable ideal that you can only approach without ever reaching it.





  • You’re trying to apply objectivity to a very subjective area. I’m not saying it’s impossible, and you should by all means try it, but maybe it would be a good idea to try something that has a better chance, first, such as this:

    How about an open platform for scientific review and tracking? Like, whenever a new discovery or advance is announced, that site would cut through the hype, report on peer review, feasibility, flaws in methodology, the ways in which it’s practical and impractical, how close we are to actual usage (state of clinical trials, demonstrated practical applications, etc.)

    And it would keep being updated, somewhat like Wikipedia, as more research occurs. It needs a more robust system of review to avoid the problems that Wikipedia has, and I don’t have the solution for that, but I believe there’s got to be a way to do it that’s resistant to manipulation.