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PaperclipPerfector  2d ago (text post)   10748 thread views

Culture War Roundup for the week of September 14, 2026

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I may yet write a top level post about bad epistemics and Ed Zitron. Somehow the man has about 100,000 subscribers on substack despite there being entire webpages assembled of his false predictions.

There's still quite a few people waiting for the bubble pop. We're way past the point of it being a reasonable opinion I agree, yet there are so many who still believe.

The embittering part of it all is that I wish I could agree with the accelerationists, it'd be so beautiful, and I'm not even full Yudd in that I think there's a very good chance Alignment ends up being tractable and even might go well automatically. But I just can't stomach ten or twenty percent chance that everyone dies. It can still be beautiful even with a pause, we can still cure cancer and fight back against aging and probably end all toil. But it'd be so nice to not have to feel trepidation about this.

One of the things that safety-pilled me was thinking about just how much work there still is to do maximising the human benefit of existing models - society still hasn't adapted to a world where an AI assistant with the intelligence of a smart intern is available at a consumer-SaaS price point. As a trivial example, there should be an insanely great agent harness (think Apple-tier) which allows my grandmother to enlist an AI agent to deal with the medical bureaucracy without my having to set things up for her. Part of the reason why this hasn't been built is that the underlying models are improving too fast and too unpredictably to make building it a good business.

A world in which training of new models stopped tomorrow would still see more tech-driven social change in the next decade than we have seen in any previous decade of human history.

Part of the reason why this hasn't been built is that the underlying models are improving too fast and too unpredictably to make building it a good business.

Do we not have, uh like, modular software design? Encapsulation? Maybe instead of hiring weirdo rationalists to philosophize about sentience and safety these companies can pick up a couple of 90s-era C++ developers?

Do we not have, uh like, modular software design? Encapsulation?

Nah, we just let the AI care about that now.

It's too busy making ObjectFactoryFactoryClasses to deal with such things, I suppose...

I still don't fully understand that meme. I get that it's the most spaghetti/rigid-definition Java code that was popular in the late '90s but I've never seen it quite that bad before.

If anything else C# is even worse, since you look at the docs to try and figure out how to use something... and the minute you do, you see

public static IQueryable<TResult> SelectMany<TSource,TCollection,TResult>(
    this IQueryable<TSource> source,
    Expression<Func<TSource,int,IEnumerable<TCollection>>> collectionSelector,
    Expression<Func<TSource,TCollection,TResult>> resultSelector);

which is fucking awful in a way the worst natural-language sentence doesn't seem to be. Though maybe it's a better Rorschach test for programmers; if even the insane people give up when they see that shit maybe it's time for an ADHD diagnosis.

C# is also kind of bad, yes -- to the extent that I had a point beyond lazy shitposting, it was that the LLMs seem to have picked up on a particular... style of OOP which is not only overly verbose, but also misses the basic idea of the thing. All of the factories are more of a symptom, people do the same thing even when trying to be object oriented in Python. Rigid adherence to something that they read somewhere while not really understanding the reasons, I guess? The "Clean Code" series is likely the source of a lot of it. And yes I have seen it in the wild (from humans) all the fuckin time -- you can actually coax an LLM not to be too brutal with it more easily than most human perps, but so far as I can tell it's absolutely their default mode. (due to training data prevalence I should think)

Which sure, fine -- but shouldn't the default architecture for any tools that implement LLM call and response be pretty fuckin agnostic towards what it's plugged into? Like, we are sending some questions and getting back answers; "the answers keep getting better!" seems like an awfully lazy excuse for not making the tools also get better!

Rigid adherence to something that they read somewhere while not really understanding the reasons, I guess?

Ok, so they simulate a human being perfectly then.

Like, we are sending some questions and getting back answers; "the answers keep getting better!" seems like an awfully lazy excuse for not making the tools also get better!

It's because we don't fucking know how to make the tools better. Half of it is because we have tunnel vision on just how hard the models can go, and the other half of it is because we don't even know if we should bother making the tools better because the current approach seems to be working pretty well so far (and to pitch that in a way that doesn't just sound like you're advocating for faster horses) and the entire industry (and certain companies, and their workers) have come down with a case of clinical depression because of it.

At this point I'd rather just switch to the WIS side of software development, rather than the INT side, because that to me seems like a losing battle. But maybe that's the wrong approach. I dunno.

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