Yeah - being open to surprises (code has tons of broken corner cases, security vulnerabilities, missing error handling, etc, etc) isn't exactly what comes to mi

Yeah - being open to surprises (code has tons of broken corner cases, security vulnerabilities, missing error handling, etc, etc) isn't exactly what comes to mind when writing real software. I'm not even sure in what use cases, even toy ones, vibe coding works. It's been a few months since I tried using Claude to even do simple things like generate a React-based prototype for part of a web page (the sort of use case I would expect it to do well on), and even there it wasn't a "haha isn't this cool" hands-off "vibe coding" experience - I had to intervene and to do my own google research to find out why it was failing and then tell it explicity what to do. I also have to wonder how many of these AI researchers fully realize the massive gap in complexity between what they are writing (typical ML model or prototype) and what real software development looks like. Let's see Karpathy "vibe code" a 100L-1M LOC system that needs to interact with a dozen undocumented legacy systems via proprietary interfaces, then come back and tell us how that went.