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Tech CEOs are apparently suffering from AI psychosis
(techcrunch.com)
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this post was submitted on 28 May 2026
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This is something I still like to do by hand. Especially when I'm using a new API. That stage of reading and understanding the API docs and finding undocumented issues (there's always tons) is important. I'll usually end up wrapping the API I'm interacting with in my own that behaves the way I expect for what I'm doing then use that.
I tried the LLM stuff a while ago and it just really rapidly devolved into my having no idea what was actually happening.
My use case is that I often need to work with services, and before I'd actually have to spin up the service, and try making curl calls to it to see how it actually works. Now, I can just ask the LLM to produce a curl or js call example, and it saves me a ton of time. It'll show exactly which params I need to pass, and the structure of the request and the response. Obviously, I still have to test all this end to end, but I can do that in the dev environment where all the services are running, instead of having to orchestrate everything on my machine while developing.
When I do get LLM to implement stuff, my approach is to just break things up into focused features, and then tell the agent to make a branch per feature, that makes it pretty easy to review the pull requests, because I know what the features is intended to do, and there isn't a mountain of code to go through.
That's fair, having a bunch of individual atomic features that you get around to eventually is better than full slop merging by far.
You have to stay in the loop or you'll get lost
Exactly, and that's why the human is the bottleneck in the whole process that can't be removed. Writing code isn't what takes the bulk of time when doing software development. It's figuring out what the code is already doing, what needs to be written, and how that's going to interact with the existing features. We get paid to think, and the LLM can't replace that.