that is to say, you have to be a good dev first and foremost, and no matter how much harnessing you do there is gonna be a limit to the quality of output by the user (otherwise why not just use a no-code tool instead)

(the ironic thing is tho, people starting out with just llms will probably never progress to 'good dev' in the first place...)

▲ sampullman 19 hours ago | parent [-]

This is the one thing I'm curious about, what's the next generation of devs going to look like? Just a bunch of people who treat the codebase as a black box, pump out slop, and call it a day when the feature works?

I imagine there will be value in being able to understand every line of code for a while, because many (most?) things don't seem amenable to long term vibe coding. But how will that knowledge be obtained? Maybe there will be a much smaller pool of devs with the patience to actual learn bespoke software development.

Or, AI will actually get good enough that vibe coding is better in every case than mindful development. I guess the main factor is how long it takes for that to happen.

▲ andrekandre 13 hours ago | parent [-]

  > Just a bunch of people who treat the codebase as a black box, pump out slop, and call it a day when the feature works?

my guess is yes, because that is what i am seeing today in front of me with new grads

  > Or, AI will actually get good enough that vibe coding is better in every case than mindful development. I guess the main factor is how long it takes for that to happen.

i think there is a limit to this; you could train an llm to never dereference a null pointer or something like that, and you could probably minimize other security related stuff, but in the end its a token predictor, so you stuck with probabilities... i wouldn't vibe anything that had any security/legal/user related things but thats just me.

	▲	m4x 5 hours ago \| parent [-]
		Most of us effectively function as next-token predictors, in my experience. Some of us are trained not to dereference null pointers, but there's always a non-zero probability that we do so anyway. To reduce that risk I use the same tools an LLM would, i.e. code review, automated testing and verification, etc. There isn't obviously much difference between humans and LLMs in that sense. Like you I would not vibe code something important, but I think that's primarily because I want to have a deep understanding of a system before I accept responsibility for it. An LLM would still be useful during implementation and likely to increase the quality of the result. It seems very likely that AIs will become capable enough that they can take complete responsibility for development, deployment and maintenance of the systems we're used to building. Further, it seems likely they'll end up much more capable than humans have been. I think that's an eventuality we need to be prepared for. A well-utilised AI tool can probably already build a much safer and more robust security/legal/user related system than you or I can, in any given time budget. There's a question around what happens as humans are required less for the "well utilised" part, and who is responsible when humans are not really involved anymore.