Engineer says Claude Code has made his job "soul-sucking" as workers spend 12-hour days pressing enter

Our place measures PR counts so a ton of tiny ass PRs is actually the move.

A guy I don’t really like at work does all sorts of AI induced PRs. I think he thinks it makes him look good. It probably does, to management.

I got reprimanded for asking if a guy’s extremely weird code was something he wrote himself. He went crying to my boss about how I’m mean on PRs. Fuck you, William. I hope your house catches fire and you pass out while asking Claude for help.

An elegant solution. I wonder if it is more efficient than a 1TW data centre.

Seriously, though, we use AI in the worst way possible. I like writing code, I hate checking someone else’s code. Why aren’t AIs doing the checking and engineers doing the coding?

Forget “human in the loop” - let’s shoot for “reviewed by AI”. Deep down, we know the humans are barely paying attention when they check Claude’s homework.

The reality is that these tools are not going away. Thinking that will happen is not realistic.

They’re part of the deal now. People who refuse to use them will simply become obsolete. It starts in technology, but it will quickly move around.

Thinking you can just avoid it isn’t practical. It is no different from cars displacing horses and computers displacing typewriters.

Think of how absurd it would sound if you came across someone who refuses to use a computer and only a typewriter.

Nah, fuck AI. I will pivot careers. Anyone that claims to be a developer but generates all of their code is just a modern day script kiddie. Idgaf about efficiencies because the ethical implications of AI are far worse than the extra money I can make for a company.

I will never be an AI code reviewer, because that’s all prompting is. Like I said, I’ll pivot careers out of dev work before I do that. Pick up a trade and work until I’m 80 if I have to, but that’s still better than being one of Claude’s bitches.

Outside of the ethics of AI, the idea of sitting in front of a screen and being a “prompt engineer” takes everything I enjoy out of coding. If it was that or death, I’d choose death.

I have not found AI code to be so helpful. I actually use it a lot less and more strategically than back when it first became viable. It can save a lot of time in the right situation though.

AI in engineering is an accelerator.

Yes, and most engineers effect on the product can be graded on [-1, 1] scale. And I find that the -1s accelerates faster.

If teams are producing high-quality software

I had one of those. My seniors are getting dumber though, and we have twice as many prod bugs as a year ago. I had an outage because someone didn’t properly trim an input for fucks sake recently.

That’s how i always describe them. Over-eager junior devs that are trying to impress you with how fast they can get stuff done. Have a lot of book learning but don’t know when to apply certain things. Sometimes, i feel like it should really only be a tool for senior devs because it feels a lot like mentoring a team of noobs

I watched it remove a JSON library include from the code because it wasn’t used yet, and then in the very next prompt where we needed our again, it attempted to write a basic parser from scratch…

I guess the AI isn’t iterating over its “ideas” and code yet. I come up with overcomplicated and overcomplex code too. But my natural neural network keeps reevaluating the code while I think about it, write it down, test it, and often even come back to it later after I moved on to another part of the codebase.
It looks to me like AI is using the infamous waterfall method to write code. It does the “thinking” phase and does bugfix iterations when compilation or tests fail. But never does it question the design it came up with after it wrote it down. It doesn’t reevaluate whether the last round of fixes made tests work that test irrelevant implementation details. It doesn’t question whether there really need to be four methods sharing 90% of their body for doing the basically the same thing only subtly different.
I hold the suspicion that AI will be as slow as humans when it becomes as reliable because what makes us reliable is the redundancy in our thinking processes. The constant reevaluation in subtly different states of mind helps to catch design flaws and silly mistakes.

Reevaluation of finalized code is probably not something you can just put into memory. You would need to modify the harness to automatically inject a new prompt triggering reevaluation of the design and implementation after each step. This would likely increase token use and execution time by an order of magnitude. And if you do it by spawning fresh agents, there would be a high chance of having the AI go in circles, switching between a few different versions of the same shitty design or implementation. So you need to keep the context, which grows each iteration. So context rot may become a real problem. The AI might deviate from the initial goal just like some human devs tend to do…

And natural neural networks learn on the job. They adapt to the codebase and industry they are working with/in. claude.md and memory text files help a lot. But they are still sometimes ignored. And I don’t know why. Also, human rules are almost always somewhat fuzzy. Long lines are to be avoided - but unit tests and that one massive dict of dicts really are a lot more readable when ignoring that rule. There might be a lack of good training material on what makes code readable for humans.

Even without external libraries, it routinely comes up with solutions that are at least 3-4x the complexity and LOC than how I would implement it.

Possibly motivated to do more complex problems to spend more tokens?