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Cake day: July 29th, 2023

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  • For a lot of this stuff at the larger end of the scale, the problem mostly seems to be a complete lack of accountability and consequences, combined with there being, like, four contractors capable of doing the work, with three giant accountancy firms able to audit the books.

    Giant government projects always seem to be a disaster, be they construction, heathcare, IT, and no heads ever roll. Fujitsu was still getting contracts from the UK government even after it was clear they’d been covering up the absolute clusterfuck that was their post office system that resulted in people being driven to poverty and suicide.

    At the smaller scale, well. “No warranty or fitness for any particular purpose” is the whole of the software industry outside of safety critical firmware sort of things. We have to expend an enormous amount of effort to get our products at work CE certified so we’re allowed to sell them, but the software that runs them? we can shovel that shit out of the door and no-one cares.

    I’m not sure will ever escape “move fast and break things” this side of a civilisation-toppling catastrophe. Which we might get.


  • Reposted from sunday, for those of you who might find it interesting but didn’t see it: here’s an article about the ghastly state of it project management around the world, with a brief reference to ai which grabbed my attention, and made me read the rest, even though it isn’t about ai at all.

    Few IT projects are displays of rational decision-making from which AI can or should learn.

    Which, haha, is a great quote but highlights an interesting issue that I hadn’t really thought about before: if your training data doesn’t have any examples of what “good” actually is, then even if your llm could tell the difference between good and bad, which it can’t, you’re still going to get mediocrity out (at best). Whole new vistas of inflexible managerial fashion are opening up ahead of us.

    The article continues to talk about how we can’t do IT, and wraps up with

    It may be a forlorn request, but surely it is time the IT community stops repeatedly making the same ridiculous mistakes it has made since at least 1968, when the term “software crisis” was coined

    It is probably healthy to be reminded that the software industry was in a sorry state before the llms joined in.

    https://spectrum.ieee.org/it-management-software-failures



  • Stuff like this is particularly frustrating because this is one of they places where I have to grudgingly admit that llm coding assistants could actually deliver… it turns out that having to state a problem unambiguously and having a way in which answers can be automatically checked for correctness means that you don’t have to worry about bullshit engines bullshitting you so much.

    No llm is going to give good answers to “solve the riemann hypothesis in the style of euler, cantor, tao, 4k 8k big boobies do not hallucinate” and for everything else the problem then becomes “can you formally specify the parameters of your problem such that correct solutions are unambiguous” and now you need your professional mathematicians and computer scientists and cryptographers still…




  • Given the state of renewables and energy storage, this feels a lot like the final opportunity for nuclear power in its current state to actually do anything at all, and the “move fast and break things” crowd have no idea about building physical things more complex than a datacentre which honestly, isn’t that challenging in comparison.

    openai will be a smoking crater well before site for the first plant will get selected

    Other things that might not last that long include the government of the country in which you’re trying to build massive piece of infrastructure that represents a significant ongoing maintenance burden and risk.





  • I’m being shuffled sideways into a software architecture role at work, presumably because my whiteboard output is valued more than my code 😭 and I thought I’d try and find out what the rest of the world thought that meant.

    Turns out there’s almost no way of telling anymore, because the internet is filled with genai listicles on random subjects, some of which even have the same goddamn title. Finding anything from the beforetimes basically involves searching reddit and hoping for the best.

    Anyway, I eventually found some non-obviously-ai-generated work and books, and it turns out that even before llms flooded the zone with shit no-one knew what software architecture was, and the people who opined on it were basically in the business of creating bespoke hammers and declaring everything else to be the specific kind of nails that they were best at smashing.

    Guess I’ll be expensing a nice set of rainbow whiteboard markers for my personal use, and making it up as I go along.



  • I’m reminded of people back in the day using map/reduce via hadoop to solve issues that could just as well be done with postgres or even sqlite and a sprinkling of sql, because that’s how google did it and no-one has any idea what “big data” really is.

    Similarly, turning simple network applications into a hideous armada of microservices on a distributed kubernetes cluster, because that’s how google did it and people outside of giant tech companies don’t really know what that sort of scalability is for.

    And here we are in the age of readily accessible neural network software. This too will pass, and we’ll get a new sledgehammer for walnut-opening in due course.