A next-generation todo app that brings task management into a new dimension. This innovative application combines AI-powered task optimization, energy-aware scheduling, and adaptive learning to create a unique and effective productivity tool.
- Dynamic task evolution based on completion patterns
- AI-powered adaptive scoring system
- Intelligent time slot suggestions
- Visual timeline view with real-time positioning
- Energy level indicators for optimal task scheduling
- Dynamic color coding based on required energy
- Smart time slot suggestions based on energy patterns
- Completion streak tracking for motivation
- Tasks learn from your completion patterns
- Adaptive scoring affects task visibility and priority
- Performance tracking with completion accuracy
- Intelligent recurring task management
- Glassmorphic design with blur effects
- Smooth animations and transitions
- Dual view modes: Grid and Timeline
- Interactive progress indicators
- Node.js (v14 or higher)
- npm or yarn
- Clone the repository:
git clone https://github.com/yourusername/quantum-todo.git- Install dependencies:
npm install
# or
yarn install- Start the development server:
npm run dev
# or
yarn dev- Open http://localhost:3000 in your browser
- Next.js - React framework for production
- TypeScript - Type safety
- Chakra UI - Modern UI components
- Framer Motion - Animations
- Zustand - State management
- date-fns - Date utilities
Tasks are assigned energy levels (1-10) that help you match tasks with your daily energy patterns:
- 8-10: High energy tasks (best for morning)
- 5-7: Medium energy tasks (good for afternoon)
- 1-4: Low energy tasks (suitable for evening)
The app learns from your task completion patterns:
- Tracks completion time accuracy
- Adjusts task suggestions based on performance
- Builds completion streaks for motivation
- Provides optimal scheduling recommendations
Visual representation of your day:
- Real-time task positioning
- Energy-based color coding
- Current time indicator
- Drag-and-drop task rescheduling
Contributions are welcome! Please feel free to submit a Pull Request.
This project is licensed under the MIT License - see the LICENSE file for details.