Same; the most infuriating parts - Claude caused the issue and Claude misdiagnosed the issue, making me spend more time than if I was debugging it myself.
This happens all the time to me. The model emits thousands of lines of high-quality code, making vast progress very quickly, but on the way does a few very, very stupid things a human being would never have been silly enough to do.
Then it takes hours or days - sometimes weeks - to find and fix the AI-induced problems. If you very, very tightly constrain the AI by using structured processes and unit tests you can work wonders with it, but you do start to wonder to what extent this is better than if you had simply coded it yourself.
You may be wise beyond your years and are sitting on a goldmine of consulting opportunity if you can actually get tasks done faster without ai given the exact same inputs
Can you share what other "sucky tools" would be on the same level? I understand they do kind of suck holistically but to explain it better to laymen how would you describe the whole thing?
I think the issue is that obtaining empirical proof of AI or manual coding being more efficient is very difficult, since true costs and outcomes aren’t known for months and often years. AI _is_ faster at producing short term results though, so naturally, given the state of the industry, everyone is piling both money and time into AI driven workflows and manual work is met with suspicion, if not outright discouragement. Engineers might hate AI, but they need their paychecks.