I recently built a small internal AI tool that could answer questions from company documents. Getting the model to work was surprisingly easy. Keeping the data organized, versioned, and accessible as the app evolved was much harder.
That’s what led me to experiment with Vibecode DB. What I liked wasn’t that it generated anything magical—it helped me spend less time worrying about database setup and more time testing actual product ideas.
My biggest takeaway: AI prototypes fail less because of model quality and more because the surrounding infrastructure isn’t ready to grow beyond a demo.
Curious what others have found. When building AI products, what’s been the biggest bottleneck for you: prompts, data, infrastructure, or deployment?