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Joined 1 year ago
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Cake day: June 16th, 2023

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  • If you had a Samsung fridge, and you willingly put a bomb in the fridge, would you blame Samsung when your fridge explodes?

    Microsoft gives you the freedom to install software that runs with the same level of privilege as the kernel itself. You’re the one that chose to install defective software, and then give it kernel level permissions. You put a bomb in your computer and now you’re blaming Microsoft after the bomb exploded.

    Microsoft didn’t make the decision to allow the faulty input, the person who installed the software did, when they gave it permission to run in kernel mode.


  • I don’t remember much about plex photos, but facial (and object) recognition, photo map, easy sharing through albums (without the other person needing an account), and being open source are some features I imagine plex photos does not have.

    it seems barebones still, because it is a very young app, and the UI is not great, especially on mobile.

    It is the best replacement for Google photos that I have seen though.


  • +1 to immich

    However, because of the fast dev cycle, it has a lot of breaking changes, and needs regular maintenance (most notably for me, postgres docker changes. Especially if you are not giving it it’s own postgres instance and using their provided docker compose.)

    You could stay pegged to a single version, but the mobile app also doesn’t have full backwards compatibility with server versions, which results in a slew of other problems (how do I do a fresh install of an older app version on a new device?)

    But if you are willing to keep up and perform semi-regular maintenance, immich is great, and the rapid dev cycle means more new features faster!







  • As others have said, Canadian McDonald’s now has the old Tim Hortons coffee, and the new Tim Hortons coffee tastes like they brewed it with water they collected from a puddle in the parking lot.

    Presumably McDonald’s has different suppliers in other countries though.






  • Was this AI trained on an unbalanced dataset (only black folks?)

    It’s probably the opposite. the AI was likely trained on a dataset of mostly white people, and thus more easily able to distinguish between white people.

    It’s a problem in ML that has been seen before, especially for companies based in the US where it is just easier to find a large amount of white people as opposed to people of other skin colors.

    It’s really not dissimilar to how people work either, humans are generally more able to distinguish between two people who are races that they grew up with. You’ll make more mistakes when trying to identify people of races you aren’t as familiar with too.

    The problem is when the police use these tools as an authoritative matching algorithm.



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