[shivaniShimpi_]

interesting, so the ai got the hard stuff right. password hashing, schema design, fine. it fumbled the stuff that isn't really "coding" knowledge, feels more like an operational intuition? backup folder sitting in web root isn't a security question, it's a "have you ever been burned before" question, and surgeon hadn't. so they didn't ask and the model didn't cover it, imo that's the actual pattern. the model secures exactly what you ask about and has no way of knowing what you didn't think to ask. an experienced dev brings a whole graveyard of past mistakes into every project. vibe coders bring the prompt

[nerptastic]

This is what I’m noticing. At my workplace, we have 3 or 4 non-devs “writing” code. One was trying to integrate their application with the UPS API.

They got the application right, and began stumbling with the integration - created a developer account, got the API key, but in place of the applications URL, the had input “localhost:5345” and couldn’t get that to work, so they gave up. They never asked the tech team what was wrong, never figured out that they needed to host the application. Some of the fundamental computer literacy is the missing piece here.

I think (maybe hopeful) people will either level up to the point where they understand that stuff, or they will just give up. Also possible that the tools get good enough to explain that stuff, so they don’t have to. But tech is wide and deep and not having an understanding of the basic systems is… IMO making it a non-starter for certain things.

[animuchan]

What I see in the workplace is, people specifically outsource decisions to LLM. It tries to flag and explain all sorts of landmines, it really sometimes does, but the prompt is "make it work" and "be relentless", and the operator is barely even looking at the (conversational) output of the LLM, just the code (or other file) they asked for.

This is another difference to a largely organic developer: the ability to refuse a massively damaging or stupid task.

[NoGravitas]

The competence profile of any LLM-based AI is extremely spiky - whether it does a particular task well or not is pretty independent of the (subjective) difficulty of the task. This is very different from our experience with humans.

[shivaniShimpi_]

slow was the safety net for sure but then there were errors too, there's a sweet equilibrium spot where ai + human oversight reaches that efficient + almost perfect situation. ofc with the right methods

[TeMPOraL]

Maybe this is what's missing in the prompt? We've learned years ago to tell the AI they're the expert principal 100x software developer ninja, but maybe we should also honestly disclose our own level of expertise in the task.

A simple "I'm a professional surgeon, but sadly know nothing about making software" would definitely make the conversation play out differently. How? Needs to be seen. But in an idealized scenario (which could easily become real if models are trained for it), the model would coach the (self-stated) non-expert users on the topics it would ordinarily assume the (implicitly self-stated) expert already knows.

[shivaniShimpi_]

this is more on the lines of the marketing play that "developers are out of job" or "oh i created an app overnight", well sure you did but you also put yourself at a lot of risk, a lot of ai tools are just so risky at times, i'm tbh surprised why claude still asks for api keys to be provided in terminal. you could almost bet that the devs building it 100% know they should not do it, but there's always a way you can brainstorm with the ai, crosscheck with stackoverflow, reddit or anything like you did earlier. this is no substitute for the way programming worked, just a 100% faster and efficient engine for sure. it's such an under-explored area that there's def more eyes needed on it