You tell MarketOtter what you're launching. It researches the market, picks the channels, writes the content, builds the schedule — then holds you to it. Built by a founder who kept shipping products nobody heard of. It planned and ran its own launch.
I've been watching teams ship AI-generated code faster than ever, which is great until six months in, someone needs to fix a bug in a function that no human wrote and nobody fully understands.
The problem isn't that AI code is inherently unmaintainable. It's that when you're moving fast, it's easy to skip the review rigor that keeps any codebase healthy. AI-generated functions can work perfectly on day one and become a liability on day 180 when requirements shift.
I'm curious how people are actually handling this in practice. Are you enforcing stricter code review for AI contributions? Adding extra documentation? Refactoring generated code immediately into your team's style before it gets committed? Or does it depend entirely on what the AI was asked to generate in the first place?
What's been your experience what's worked and what's backfired?
I've been building with AI for a while now, and I've noticed something: the projects that work best aren't the ones where I let AI do the most. They're the ones where I've done the hard thinking first.
Here's what I mean. Last week I was mapping out a feature set, and I realized I'd spent two hours prompting Claude to help me decide what to build. It was... fine. But it felt hollow. When I stepped back, predefined exactly what problem we were solving and what constraints mattered, then asked AI to help execute against that? Completely different quality.
