Measured runtime and rendering behavior
Trust boundaries and protection model
What improved engineering quality over time
Resulting system quality and product impact
Delivered a production-grade habit tracking system with:
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Real-time habit tracking platform with server-side streak computation and optimistic UI.
Hybrid server/client rendering with auth-aware preloading
Server-authoritative streak and completion logic
Realtime updates with optimistic interactions
Constraints and product-level risks
Most habit apps struggle with:
This project solves these by centralizing domain logic server-side and aligning data modeling with query patterns.
Implementation direction and execution strategy
Habit Tracker is a fullstack, real-time habit tracking system built with a hybrid server/client architecture.
It supports daily and weekly habits across boolean and numeric targets, with server-authoritative streak computation and analytics aggregation.
The goal was to build a system where business logic lives on the backend while maintaining an instant, optimistic UI experience.
System boundaries and layered decisions
Hybrid architecture:
Convex schema designed around:
All queries are user-scoped and date-scoped.
Streak and completion logic computed server-side:
No streak logic duplication inside UI.
Next.js App Router
Structured Data Layer
Domain Boundaries
Runtime Discipline
Trust Boundaries
App Router with selective client components and Suspense boundaries
(See architecture visual below.)