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Cake day: July 9th, 2023

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  • I used to work summers as an apprentice electrician. The amount of crazy wiring I saw in old houses was (heh) shocking. Sometimes it was just that it was old. Real old houses sometimes just had bare wire wrapped in silk. … And a few decades later that silk was frayed and crumbling in the walls and needed replacing.

    My current house was wired at a time when copper was more precious, so it was wired up and down through the house, with circuits arranged by proximity, not necessarily logic. When a certain circuit in my house blows the breaker, my TV, PC and one wall of the master bedroom all lose power. The TV and PC are not in the same room either.




  • I wanted to disagree with you, but checking the data almost all of the best action flicks I could have sworn were fairly recent actually came out in the early-mid noughts. Seems like after The Matrix blew up the genre, nobody ever figured out how to put it back together.

    Even if I wanted to quibble and argue for the best my personal favorite action flicks within a precise “2 decade” window… it’s a depressingly short list:

    • 2004

      • Hellboy (technically a comic movie, but I’m keeping it because Doug Jones and Ron Perlman just rocked)
      • Kill Bill Vol. 2 (Vol 1 missed the cutoff)
    • 2006

      • Crank
    • 2007

      • Hot Fuzz
    • 2009

      • The Bourne Ultimatum
      • District 9
    • 2017

      • Baby Driver

    … Almost every single other action flick I thought of came out between 1998 and 2004. (Also, 2000 was a weirdly good year for action fans in retrospect)

    Sigh. I’m gonna go bemoan the world getting lame and shake my cane at the kids out on my lawn.

    Edit: JOHN WICK! How TF did I forget those? But yeah, I’m pretty sure that’s it now.




  • Have you ever been in an old house? Not old, like, on the Historic Register, well-preserved, rich bastard “old house”. Just a house that has been around awhile. A place that has seen a lot of living.

    You’ll find light switches that don’t connect to anything; artwork hiding holes in the walls; sometimes walls have been added or removed and the floors no longer match.

    Any construction that gets used, must change as needs change. Be it a house or a city or a program, these evolutions of need inevitably introduce complexity and flaws that are large enough to annoy, but small enough to ignore. Over time those issues accumulate until they reach a crisis point. Houses get remodeled or torn down, cities build or remove highways, and programs get refactored or replaced.

    You can and should design for change, within reason, because all successful programs will need to change in ways you cannot predict. But the fact that a system eventually becomes complex and flawed is not due to engineering failures - it is inherent in the nature of changing systems.




  • Oh, for sure. I focused on ML in college. My first job was actually coding self-driving vehicles for open-pit copper mining operations! (I taught gigantic earth tillers to execute 3-point turns.)

    I’m not in that space anymore, but I do get how LLMs work. Philosophically, I’m inclined to believe that the statistical model encoded in an LLM does model a sort of intelligence. Certainly not consciousness - LLMs don’t have any mechanism I’d accept as agency or any sort of internal “mind” state. But I also think that the common description of “supercharged autocorrect” is overreductive. Useful as rhetorical counter to the hype cycle, but just as misleading in its own way.

    I’ve been playing with chatbots of varying complexity since the 1990s. LLMs are frankly a quantum leap forward. Even GPT-2 was pretty much useless compared to modern models.

    All that said… All these models are trained on the best - but mostly worst - data the world has to offer… And if you average a handful of textbooks with an internet-full of self-confident blowhards (like me) - it’s not too surprising that today’s LLMs are all… kinda mid compared to an actual human.

    But if you compare the performance of an LLM to the state of the art in natural language comprehension and response… It’s not even close. Going from a suite of single-focus programs, each using keyword recognition and word stem-based parsing to guess what the user wants (Try asking Alexa to “Play ‘Records’ by Weezer” sometime - it can’t because of the keyword collision), to a single program that can respond intelligibly to pretty much any statement, with a limited - but nonzero - chance of getting things right…

    This tech is raw and not really production ready, but I’m using a few LLMs in different contexts as assistants… And they work great.

    Even though LLMs are not a good replacement for actual human skill - they’re fucking awesome. 😅


  • What I think is amazing about LLMs is that they are smart enough to be tricked. You can’t talk your way around a password prompt. You either know the password or you don’t.

    But LLMs have enough of something intelligence-like that a moderately clever human can talk them into doing pretty much anything.

    That’s a wild advancement in artificial intelligence. Something that a human can trick, with nothing more than natural language!

    Now… Whether you ought to hand control of your platform over to a mathematical average of internet dialog… That’s another question.










  • Lots of little quality of life things. For instance, in Kotlin types can be marked nullable or not. When you are passing a potential null into a non-nullable argument, the compiler raises an error.

    But if you had already checked earlier in scope whether or not the value was null, the compiler remembers that the value is guaranteed not to be null and won’t blow up.

    Same for other typechecks. Once you have asserted that a value is a given type, you don’t need to cast it everywhere else. The compiler will remember.