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Comment by epolanski

1 day ago

I keep hearing about this "when things break" argument, but things broke all the time with humans writing code too, if anything it feels like it's getting patched faster.

Look I loved our field, functional programming, etc. But the field as we knew it is dead, and if you think that your work is anything but providing new tests/edge cases and occasionally providing some guidance, you're in denial.

> the field as we knew it is dead

As someone whose world used to revolve around this stuff, I agree completely. For all intents and purposes, what I thought was my career is gone. Sure, it's always been about solving problems but not like this. The problem is that the future hasn't been evenly distributed. This is going to be one long drawn out funeral

I'm thinking scbench.ai, and the fact that future token costs may become more prohibitive to just use it for everything.

For me, the middle ground is review and keeping a mental model, not never using LLMs. The "Anthropic are our friends and will never fuck us over!" seems pretty naive.

There are ultimately a lot of unknowns at the moment, and it is simply too soon to know if giving up understanding your code base is sensible. People (influencers) have only been saying not to bother reviewing for about a month.

Also, so many vibe coders don't seem to know that you need to keep third party elements patched.

  • I'm nervous that saying that there are "unknowns" is too passive. We all have to develop intelligent thoughts about what we see and what we want, not just wait for things to unfold, which might well mean: wait to get screwed in various ways.

People keep talking about AI slop but then I go maintain our largely human written code base and it's like worse than any slop I've seen from my AI usage. This is with code reviews, tests, and a supposedly strong engineering team.

That said AI feels a bit alien.

My experience with frontier models is that in many areas of software engineering they outperform the average software developer. In some they blow everyone away. In others they're not as good. But they are getting better. The problem is often not the model's capabilities but rather what you ask it to do and are you able to understand what it does and give it guidance when needed.

  • Slop comes from food that is just mushed together to the point that the ingredients become indistinguishable. How that applies specifically to AI training should be obvious. It’s not a generic putdown.

    Semantic diffusion? Not that I have high standards for hmm, curious but humans already X comments.