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Joined 3 years ago
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Cake day: June 12th, 2023

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  • Just so you know, operating spinning drives this way is a bad idea. If the platters are spinning and the drive tips over, the rotation of the drives resists the movement. This gyroscopic force is enough for the platters to touch the heads which are flying a tiny distance above the platter. Obviously this is a bad thing and will damage the drives.

    A quick fix is to just lay them flat or fix both of them together so they have a more stable base to stand on. Putting it in an enclosure is even better.


  • One thing I’ve also noticed is people doing code reviews using ai to pad their stats or think they are helping out. At best it’s stating the obvious, wasting resources to point out what doesn’t need pointing out. At worst it’s a giant waste of time based on total bullshit the ai made up.

    I kinda understand why people would think LLMs are able to generate and evaluate code. Because they throw simple example problems at them and they solve them without much issue. Sometimes they make obvious mistakes, but these are easily corrected. This makes people think LLMs are basically able to code, if it can solve even some harder example problems, surely they are at least as good as beginner programmers right? No, wrong actually. The reason the LLM can solve the example problem, is because that example (or a variation) was contained within its training data. It knows the answer not by deduction or by reason, it knows the answer by memorization. Once you start actually programming in the real world, it’s nothing like the examples. You need to account for an existing code base, with existing rules, standards and limitations. You need to evaluate which solution out of your toolbox to apply. Need to consider the big picture as well as small details. You need to think of the next guy working with the code, because more often than not, that next guy is you. LLMs crumble in a situation like this, they don’t know about all the unspoken things, they haven’t trained on the code base you are working with.

    There’s a book I’m fond of called Patterns of Enterprise Application Architecture by Martin Fowler. I always used to joke it contained the answer to any problem a software engineer ever comes across. The only trick is to choose the correct answer. LLMs are like this, they have all these patterns memorized and choose which answer best fits the question. But it doesn’t understand why, what the upsides and downsides are for your specific situation. What the implications of the selected answer are going forward. Or why this pattern over another. When the LLM answers you can often prompt it to produce an answer with a completely different pattern applied. In my opinion it’s barely more useful than the book and in many ways much worse.


  • Well there’s the stuff I personally dislike. Like the Elon cringe skits she does, or the super weird uncanny valley face filter.

    But the biggest issue is she didn’t stay in her realm of expertise. She might know a lot about certain things, but then also talks about other stuff with the same level of authority. No caveats, no this is my opinion, she present it as fact. But the fact is she is really really wrong about a lot of shit. And just mixing and matching shit you know and shit you don’t know is a big no-no in science communication.

    One of the most egregious thins she did was make a video about trans folk and talked about it like it’s a fad or even a disorder. She was not only factually wrong, she was spouting anti-trans propaganda. When called out she kept the video up and didn’t do anything like a follow up, correction or apology. She has some really boomer views about a lot of things and then presents it like it’s fact. Another panned video was the one about neurodivergence (autism) and there are more like that. There are multiple hour+ video essays about how she is wrong in these cases and they are worth a watch imho.

    The annoying thing is, I don’t really know what she actually does know. Because she mixes everything and doesn’t stay within her knowledge base, now everything is suspect. So even the videos about physics where I think she does know what she’s talking about, I can’t trust. And even in physics it seems like she’s very hit or miss, I spoke to somebody at a party once that did his PhD on one of the physics topics she covered in a video. He said she was like 10 years behind the times and was wrong about several key facts. Some of these were just wrong because of simplification, which might be excused given the format, but others were plain wrong. Now I don’t know enough about the subject to make a judgement, but the dude I spoke to seemed to know what he was talking about.

    Science communication is really really hard and it’s a skill not a lot of people have. Look at how big the teams of researchers at for example Kurzgesagt are and even they mess up once in a while. But when they get called out, they go back and delete the video or better yet post a follow up or recently even a replacement video. And they qualify things with sources and caveats, mentioning which parts are fact, consensus, speculation and opinion. They also make it very clear at the beginning of the video what a viewer can expect. That way we can qualify the information and know what in what light to put the information presented. Now I realize Kurzgesagt may be one of the best channels when it comes to short form YouTube video science communication out there and it isn’t fair to hold everyone to that standard. But there needs to be at least some level of due diligence involved imho.

    I’m sure I left out some other stuff, there is a lot to find if you look for honest critique. I’m sure there’s also a lot of unwarranted hate out there, but also a lot of stuff that’s warranted.