I worry about people who use this approach where they never look at the code. Vibe-coding IS possible but you have to spent a lot of time in plan mode and be very clear about architecture and the abstractions you want it to use.

I've written two seperate moderately-sized codebases using agentic techniques (oftentimes being very lazy and just blanket approving changes), and I don't encounter logic or off-by-one errors very often if at all. It seems quite good at the basic task of writing working code, but it sucks at architecture and you need occasional code review rounds to keep the codebase tidy and readable. My code reviews with the AI are like 50% DRY and separating concerns

▲

johnmaguire 4 hours ago | parent [-]

In a recent Yegge interview, he mentions that he often throws away the entire codebase and starts from scratch rather than try to get LLMs to refactor their code for architecture.

	▲	kami23 3 hours ago \| parent \| next [-]
		This has been my best way to learn, put one agent on a big task, let it learn things about the problem and any gotchas, and then have it take notes, do it again until I'm happy with the result, if in the middle I think there's two choices that have merit I ask for a subagent to go explore that solution in another worktree and to make all its own decisions, then I compare. I also personally learn a lot about the problem space during the process so my prompts and choices on us sequent iterations use the right language I need to use.
	▲	d1sxeyes 3 hours ago \| parent \| prev [-]
		Honestly, in my experience so far, if an LLM starts going down a bad path, it’s better just to roll back to a point where things were OK and throw away whatever it was doing, rather than trying to course correct.