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Life Tracker is a full-stack personal management platform that unifies three life domains — habits, finances, and health — into one intelligent dashboard instead of juggling three separate apps. It produces real-time analytics that turn raw daily inputs into actionable growth insights: streak heatmaps, expense breakdowns by category, macro nutrition tracking, and workout logging. Built for anyone who wants to manage their personal growth with the same rigor they would apply to a business — data-driven, systematic, and honest about patterns.
- Spreadsheet-style habit grid with daily logging
- Streak calculation and longest streak records
- GitHub-style contribution heatmap visualization
- Custom habit creation with flexible frequencies
- Monthly expense logging with categories
- Visual breakdowns by spending category
- Monthly reports with trend comparisons
- Running totals and budget awareness
- Nutrition logging with macro tracking (protein, carbs, fat, calories)
- Workout session logging with exercise tracking
- Health analytics dashboard
- Historical data visualization with Recharts
| Technology | Role |
|---|---|
| React | Frontend framework |
| TypeScript | Type safety |
| Tailwind CSS | Styling |
| Framer Motion | Animations and transitions |
| Supabase | Backend, auth, real-time database |
| PostgreSQL | Relational data storage |
| Row Level Security | Per-user data isolation |
| Recharts | Data visualization and charts |
| Google OAuth | Authentication |
| Email Auth | Alternative authentication |
| Vite | Build tool |
- Three-module architecture (habits, finance, health) shares a unified Supabase backend with isolated PostgreSQL tables per domain, linked by user ID
- All charts use Recharts with custom-styled components matching the dashboard design system — no third-party chart library overrides needed
- Authentication supports both Google OAuth and email/password with Supabase Auth handling session management
- Row Level Security enforces strict data ownership — every query is automatically scoped to the authenticated user at the database level, not the application level
1. Spreadsheet-style habit grid architecture A habit grid where each column is a day and each row is a habit requires a data model that can efficiently query completions across arbitrary date ranges. Solved with a completions table indexed by user, habit, and date that renders into the grid on the fly.
2. Nutrition estimation from food entries Users enter food in natural language rather than selecting from a fixed database. The system maps common entries to macro profiles while allowing manual override for accuracy.
3. Three-module unified UX design Three completely different domains (habits, money, health) needed to feel like one product, not three separate apps stitched together. Solved through a shared design system, consistent navigation patterns, and a unified dashboard that surfaces highlights from all three modules.
4. Analytics aggregation with heatmaps Generating heatmaps requires aggregating daily completion data into a week-by-week grid. The aggregation runs client-side on data fetched from Supabase, keeping database queries simple while the transformation logic handles the rendering format.
Prince Patel — AI-Powered Product Developer
- 🌐 Portfolio: prince-patel-portfolio.vercel.app
- 🐙 GitHub: github.com/Pro-Prince
- 𝕏 X: @Pro_Prince_1
- 💼 LinkedIn: linkedin.com/in/prince-patel476
This project is licensed under the MIT License.