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Joined 8 months ago
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Cake day: November 19th, 2023

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  • I managed a CentOS system where someone accidentally deleted everything from /usr, so no lib64, and no bin. I didn’t have a way to get proper files at the time, so I hooked the drive up to my Arch system, made sure glibc matched, and copied yum and other tools from Arch.

    Booted the system, reinstalled a whole lot of yum packages, and… the thing still worked.

    That’s almost equivalent to a reinstall, though. As a broke college student, I had a laptop with a loose drive, that would fall out very easily. I set it up to load a few crucial things into a ramdisk at boot, so that I could browse the web and take notes even if the drive was disconnected, and it would still load images and things. I could pull the cover off and push the drive back in place to save files, but doing that every time I had class got really tiring, so I wanted it to run a little like a live system.


  • What we have done is invented massive, automatic, no holds barred pattern recognition machines. LLMs use detected patterns in text to respond to questions. Image recognition is pattern recognition, with some of those patterns named things (like “cat”, or “book”). Image generation is a little different, but basically just flips the image recognition on its head, and edits images to look more like the patterns that it was taught to recognize.

    This can all do some cool stuff. There are some very helpful outcomes. It’s also (automatically, ruthlessly, and unknowingly) internalizing biases, preferences, attitudes and behaviors from the billion plus humans on the internet, and perpetuating them in all sorts of ways, some of which we don’t even know to look for.

    This makes its potential applications in medicine rather terrifying. Do thousands of doctors all think women are lying about their symptoms? Well, now your AI does too. Do thousands of doctors suggest more expensive treatments for some groups, and less expensive for others? AI can find that pattern.

    This is also true in law (I know there’s supposed to be no systemic bias in our court systems, but AI can find those patterns, too), engineering (any guesses how human engineers change their safety practices based on the area a bridge or dam will be installed in? AI will find out for us), etc, etc.

    The thing that makes AI bad for some use cases is that it never knows which patterns it is supposed to find, and which ones it isn’t supposed to find. Until we have better tools to tell it not to notice some of these things, and to scrub away a lot of the randomness that’s left behind inside popular models, there’s severe constraints on what it should be doing.





  • Cost is obviously a big factor. Almost every printer can change to any nozzle size and layer height for just the cost of the nozzle. Print volume is a major limitation, depending on your use case. The filaments it can print will probably be the same across any relatively low cost printers, with the only significant change being direct drive vs. Bowden.

    Bed leveling is huge, and makes probably the most difference in print quality on low cost printers these days. If there’s an easy way to tension the belts, that’s a plus. If there isn’t a power switch on the front (or even if there is), a emergency stop switch can be a help, like if the nozzle is running into the bed.

    Maintenance varies from printer to printer, generally you’re aiming for tight but not too tight on any belts or rollers. If the pulleys on the motors aren’t preinstalled, use something like loctite blue to fix them in place better.

    Also make sure if you plan to buy a printer that it’s got a decent amount of community around it. Running into the same problems with a bunch of other people is a big plus as a beginner, so popular printers are better.

    Teaching Tech made a calibration guide website that I’ve had a lot of good experiences with.








  • I’ve known a lot of math people, and /on average/ I think they’re more capable of programming useful code than the other college graduate groups I’ve spent a lot of time working with (psychology, economics, physics) /on average/.

    That said, the best mathematicians I’ve known were mostly rubbish at real programming, and the best programmers I’ve known have come out of computer engineering or computer science.

    If you need a correct, but otherwise useless implementation, a mathematician is a pretty good bet. If you need performance, readability, documentation, I’d look elsewhere most of the time.


  • The major strategy on CWR is pretensioning, but there are also multiple kinds of expansion joints used in different circumstances. I’m not saying it’s impossible to do the same with a vacuum chamber, but I am saying there’s no simple reliable answer, and certainly no answer so obvious and bulletproof that it doesn’t even require testing before you could start construction.

    Elon Musk either didn’t know or didn’t care that his company wasn’t doing the required engineering and testing to make a real functioning hyperloop.