Comment by notRobot
17 hours ago
I agree that a consistent QA mindset is rare, but I'm not sure even if present if it's enough to replace an SDE.
I very recently looked at the codebase of a vibe-coded app made by someone with domain expertise but no software dev experience.
It was very clear to me that he had described it from his POV to an AI, and the AI had implemented features in a manner that technically worked, but made future maintenance or expansion extremely tricky, which is why he was now looking for a dev.
For example, in his data schema, for every item on a menu, instead of simply having an array property like so for ingredients:
items["latte"]["ingredients"] = ["water", "milk", "sugar", ...]
He had individual flags for every item for every possible ingredient it could have or not have:
items["latte"]["has_milk"] = true
items["latte"]["has_nutmeg"] = false
items["latte"]["has_cinnamon"] = false
items["latte"]["has_sugar"] = true
...
This technically worked and passed tests from his POV at an MVP level. But added a lot of complications when actually trying to build more features or when a new menu item had ingredients the founder hadn't thought to include in the schema beforehand.
I totally get how he ended up where he did though. While describing it to the AI, he probably said something like "store info on each menu item's ingredients, they might have milk or coffee or sugar", and the AI created individual flags for them and he didn't think to question it, because he didn't know what's "right" or "wrong", but then as he kept building the AI stuck with keeping individual flags instead of swapping it out with an array mechanism, and he couldn't have known the correct way to implement it.
Only a dev with experience would know how to describe the system to an AI model to get an output that works well, and how to assess the quality of its output beyond what can be assessed through the basic UI. This wasn't a QA failure, it was a design failure.
I have found this to be the case as well. As developers we are just really good stewards of the code because we obviously have knowledge to make sure that the code is engineered in a way that it can scale and grow without tech debt becoming unwieldy.
I found AI to be pretty bad with like a bare bones code base without solid patterns in place already. It works but it's just monolithic files galore. use effects hooks everywhere. Nasty state situations with poor data practices. Security vulnerabilities up the wazoo.
It's weird to have this conversion with them. Like yeah your code works but it's so tangled up it's hard to reason about where to start to begin to unwind it all sometimes.
It can be done but cleaning up someone else's slop is the exact reason why I hate AI. It was hard enough to review great code and be critical, honest, and fair but we knew it was an essential part of the process, helped build shared understanding, and was a way to learn from one another.
Whereas throwing in jumbled garbage to review just feels like a waste of our brain cells we spent decades earning by embracing the craft.