Health & wellbeingCalora
Make everyday nutrition easier to understand.
AI-assisted nutrition and calorie tracking across a Flutter app and a connected web workspace.
Make everyday nutrition easier to understand.
Food logging can become a chore when entering meals takes more effort than reviewing useful information. The product needs to make the daily habit approachable without hiding the underlying records.
What I built
I developed a mobile-first tracking experience backed by NestJS and MySQL, with a Next.js web layer. AI-assisted input supports the nutrition workflow while structured records remain central to the application.
Meal & calorie tracking
Daily food records provide the foundation for reviewing nutrition habits in the mobile experience.
AI-assisted input
Assistance is integrated into the logging workflow to reduce repetitive work around nutrition information.
Connected product surfaces
Flutter, Next.js and the backend share one product model across mobile and web responsibilities.
Under the surface.
Follow a real product workflow through its application boundaries. Select any step to explore the implementation.
From natural input to a structured food diary.
Entry states connect asynchronous parsing to transactional nutrition records, with source-specific review behavior.
Meal input
The mobile experience accepts a meal description or photo. Entry creation preserves the source so the application can track how the record was produced.
AI parsing is an input method. Explicit entry states and transactional summaries keep the user’s diary understandable as asynchronous work completes.
Conceptual flow based on the implementation. Animated connections illustrate data movement, not live traffic.
Built around the constraints.
Design around a repeatable daily habit. Keep nutritional records structured and editable instead of making AI output the sole source of truth.
Discuss the engineering ↗Keep exploring.
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