I’m ready to turn an idea I’ve been sketching for months into a fully-functional AI meal-planning application. The core of the product is clear: it must serve truly personalized meal recommendations for every user, factoring in tastes, caloric goals, schedule constraints and available ingredients. From there the app should instantly convert those suggestions into a clear, shareable shopping list so users can head to the store (or an online cart) without extra clicks. Dietary coverage starts simple—vegetarian and non-vegetarian options both need first-class support. I’m open to adding further filters such as vegan or gluten-free once the foundation is solid, so structuring the data model with extensibility in mind is important. I picture an intuitive mobile experience backed by a robust recommendation engine. If you’re comfortable pairing something like Python / TensorFlow (or a comparable ML stack) with a cross-platform front-end—React Native, Flutter, or a solid web PWA—I’d love to see how you’d architect it. Key deliverables • Working AI model that outputs daily/weekly meal plans based on user profiles • Automatic generation of consolidated shopping lists, grouped by aisle/category • Clean, responsive UI with user onboarding, preference editing and plan review • Admin dashboard or simple backend panel so I can tweak recommendation rules without redeploying code • Documentation and hand-over session so I can continue iterating after launch I’m aiming for an MVP that is polished enough to land early adopters yet lean enough to roll out quickly. Let me know how you would break down milestones, the tech stack you prefer, and what you’d need from me to get started.