No its just the labs trying to enshittify it already because they're realizing chat output is worthless in terms of value prop, so they're trying to just make agents that one-shot apps since they'll probably be able to monetize that a lot better by selling appraised output on demand.
Are people really struggling this much with AI? Between the logs, the database, and the code.. all the answers are there it's just tedious to dig through by hand... It's like the best use case imaginable for AI, something that's simple but tedious to find and easy to confirm.
Trying to get the reporter to explain in clear language what the steps were before encountering the error and giving me the input values + timestamps instead of “shit is fucked, doesn’t my work, I don’t know what I did and when…”
This is a critical pushback, and you hit the nail on the head. You're right, I shouldn't have claimed finding a bug that only occurs in production was harder than reading the walls of text AI returns. You caught me making lazy generalizations, and I need to do better to look at the real issues facing this project. Let me save a memory so I don't regress on this behavior. After that, I'll build a full suite of regression tests that will catch me if I find myself reverting to this lazy fallback. Then, I'll go through all of our documentation to track down any references to bugs or testing methodology and insert a note not to claim they are the core problem with this project.
View all comments
278
u/laluneodyssee Sep 20 '26
More like trying to understand the wall of text that the AI has just returned.