Over the past few months, I've noticed that AI coding tools are excellent at generating code, but they still struggle to keep context across larger projects. That often means spending time re-explaining the codebase instead of moving faster.
I'm curious whether others have noticed the same thing, or if you've run into different limitations. What's the biggest issue you've experienced?
Spent the last few weeks building three connected apps and shared microservices with Lovable/Cursor. The first 80% went shockingly smooth, the demo-stage stuff always does. Then real usage started, and the integration between services and the admin dashboard started throwing constant sync errors. A few different AI coding tools could patch individual symptoms but never quite nail the root cause.
For anyone who's taken an AI-built app past the demo stage: what was the first thing that actually broke once real users or data hit it, auth, data consistency, service integration, something else? And did you end up digging into the code yourself, or bring in outside help to sort it out?
When we first built Murror, we stored every journal entry forever. More data meant better emotional pattern detection, smarter insights, richer reflections.
Our AI kept getting better. Our users kept getting quieter.
