Personally I tried using LLMs for reading error logs and summarizing what's going on. I can say that even with somewhat complex errors, they were almost always right and very helpful. So basically the general consensus of using them as assistants within a narrow scope.

Though it should also be noted that I only did this at work. While it seems to work well, I think I'd still limit such use in personal projects, since I want to keep learning more, and private projects are generally much more enjoyable to work on.

Another interesting use case I can highlight is using a chatbot as documentation when the actual documentation is horrible. However, this only works within the same ecosystem, so for instance Copilot with MS software. Microsoft definitely trained Copilot on its own stuff and it's often considerably more helpful than the docs.

I do a lot with AI but it is not good enough to replace humans, not even close. It repeats the same mistakes after you tell it no, it doesn't remember things from 3 messages ago when it should. You have to keep re-explaining the goal to it. It's wholey incompetant. And yea when you have it do stuff you aren't familiar with or don't create, def. I have it write a commentary, or I take the time out right then to ask it what x or y does then I add a comment.

They never actually say what "product" do they make, it's always "shipped product" like they're fucking amazon warehouse. I suspect because it's some trivial webpage that takes an afternoon for a student to ship up, that they spent three days arguing with an autocomplete to shit out.

To quote your quote:

I got the product launched. It worked. I was proud of what I’d created. Then came the moment that validated every concern in that MIT study: I needed to make a small change and realized I wasn’t confident I could do it. My own product, built under my direction, and I’d lost confidence in my ability to modify it.

I think the author just independently rediscovered "middle management". Indeed, when you delegate the gruntwork under your responsibility, those same people are who you go to when addressing bugs and new requirements. It's not on you to effect repairs: it's on your team. I am Jack's complete lack of surprise. The idea that relying on AI to do nuanced work like this and arrive at the exact correct answer to the problem, is naive at best. I'd be sweating too.

Just ask the ai to make the change?

I cannot understand and debug code written by AI. But I also cannot understand and debug code written by me.

Let's just call it even.

Not immediate failure—that’s the trap. Initial metrics look great. You ship faster. You feel productive.

And all they'll hear is "not failure, metrics great, ship faster, productive" and go against your advice because who cares about three months later, that's next quarter, line must go up now. I also found this bit funny:

I forced myself to use Claude Code exclusively to build a product. Three months. Not a single line of code written by me... I was proud of what I’d created.

Well you didn't create it, you said so yourself, not sure why you'd be proud, it's almost like the conclusion should've been blindingly obvious right there.

So there's actual developers who could tell you from the start that LLMs are useless for coding, and then there's this moron & similar people who first have to fuck up an ecosystem before believing the obvious. Thanks fuckhead for driving RAM prices through the ceiling... And for wasting energy and water.

Same thing would happen if they were a non-coder project manager or designer for a team of actual human programmers.

Stuff done, shipped and working.

“But I can’t understand the code 😭”, yes. You were the project manager why should you?

AI is really great for small apps. I've saved so many hours over weekends that would otherwise be spent coding a small thing I need a few times whereas now I can get an AI to spit it out for me.

But anything big and it's fucking stupid, it cannot track large projects at all.