QuickBucks transforms task management into a rewarding experience by combining gamification with AI-powered insights. Users earn virtual currency for completing tasks while receiving intelligent productivity coaching.
- Smart Parsing: Convert natural language input into structured tasks
- Automatic Extraction: AI identifies task names, categories, priorities, due dates, and rewards from phrases like "Finish marketing report by Friday for ₹200"
- Context Understanding: Recognizes work, health, personal, and learning contexts
- Pattern Analysis: Identifies completion patterns and peak performance times
- Category Performance: Analyzes productivity across different task categories
- Task Size Optimization: Recommends optimal task sizing based on completion rates
- Behavioral Coaching: Provides personalized recommendations for improvement
- Overdue Task Management: Smart alerts and rescheduling suggestions
- Completion Rate Analysis: Tracks and provides feedback on task completion efficiency
- Time Pattern Recognition: Identifies most productive days and times
- Priority Optimization: Suggests better task prioritization strategies
- Earning Opportunities: Highlights high-value pending tasks
- Task Management: Create, organize, and track tasks with custom rewards
- Virtual Wallet: Earn currency for completed tasks
- Statistics Dashboard: Visual progress tracking with charts
- Dark/Light Theme: Consistent theming across all pages
- Data Export: Backup and restore functionality
- Responsive Design: Works on desktop and mobile devices
- Frontend: HTML5, CSS3, JavaScript (ES6+)
- Storage: LocalStorage with JSON serialization
- AI Engine: Client-side JavaScript algorithms for real-time processing
- NLP Library: Custom regex-based parser with contextual analysis
- Analytics Engine: Statistical computation using native JavaScript Math functions
- PWA: Progressive Web App with service worker caching
- Data Structures: Arrays, Objects, and Maps for efficient data manipulation
The AI functionality is implemented using advanced computational techniques:
- Regular Expressions (Regex): Pattern matching for date extraction (
/by\s+(today|tomorrow)/i) - Named Entity Recognition (NER): Identifies monetary values, dates, and task priorities
- Tokenization: Breaks down user input into analyzable components
- Semantic Analysis: Context-aware keyword classification using predefined dictionaries
- Statistical Classification: Bayesian-inspired category assignment based on keyword frequency
- Time Series Analysis: Tracks completion patterns over temporal dimensions
- Clustering Analysis: Groups tasks by similarity for optimization recommendations
- Regression Analysis: Predicts completion likelihood based on task attributes
- Completion Rate Algorithms: Calculates success metrics using statistical formulas
- Pattern Recognition: Identifies behavioral trends using moving averages
- Anomaly Detection: Flags unusual productivity patterns for user attention
- Predictive Modeling: Forecasts optimal task scheduling based on historical data
- User Profiling: Creates dynamic productivity profiles using completion metrics
- Adaptive Recommendations: Adjusts suggestions based on real-time performance data
- Temporal Analysis: Identifies peak productivity windows using chronological data
- Performance Optimization: Suggests task restructuring using size-completion correlation
- Open
index.htmlin a web browser - Add tasks using natural language or manual input
- Complete tasks to earn rewards
- View AI insights in the Statistics page
- Customize settings including theme preferences
- "You complete 85% of small tasks vs 60% of large ones. Consider breaking tasks >₹200 into smaller chunks."
- "Monday is your most productive day! Schedule important tasks then."
- "You're most productive in the morning. Try scheduling challenging tasks during this time."
The AI continuously learns from user behavior to provide increasingly personalized productivity recommendations.