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� Critical Bug Fixes: Fixed runtime errors preventing analysis - unifiedHeader/validator/spinner API issues resolved, removed canvas dependency, all functions now working
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‎IMPROVEMENTS_SUMMARY.md‎

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# DataPilot Improvements Summary
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## Addressing Mental Health CSV Testing Issues
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**Date**: 2024-12-30
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**Version**: Post-v1.1.0 improvements
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**Status**: Implemented and ready for testing
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---
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## 🎯 **Issues Addressed**
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Based on the testing report in `TASK.md`, the following critical issues have been resolved:
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### 1. 📊 **CSV Parsing Robustness** ✅
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**Problem**: All values detected as "undefined" (100% missing), indicating CSV parsing failures with complex column names and special characters.
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**Solutions Implemented**:
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- Enhanced encoding detection with better Windows support
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- Improved delimiter detection for various formats (comma, semicolon, tab, pipe)
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- More robust type casting with conservative number/date parsing
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- Better handling of null value representations (`null`, `undefined`, `na`, `n/a`, etc.)
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- Data quality validation after parsing to catch issues early
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**Key Changes**:
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- `src/utils/parser.js`: Enhanced `parseCSV()` function with comprehensive fallback mechanisms
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- Added `validateParsedData()` function to assess data quality
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- Improved `cast()` function with better edge case handling
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### 2. ⏱️ **Timeout Handling** ✅
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**Problem**: EDA and LLM functions hanging on datasets (6,780 rows), causing indefinite waits.
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**Solutions Implemented**:
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- Added timeout protection to EDA analysis (default: 30 seconds)
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- Added timeout protection to LLM analysis (default: 60 seconds)
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- Configurable timeout via `--timeout` flag
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- Graceful degradation with helpful error messages
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- Early termination for expensive calculations (stats, duplicates)
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**Key Changes**:
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- `src/commands/eda/index.js`: Wrapped analysis in `Promise.race()` with timeout
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- `src/commands/llm.js`: Added timeout handling and fallback mechanisms
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- `src/utils/stats.js`: Added timeout protection for statistical calculations
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### 3. 🔧 **Better Error Messages** ✅
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**Problem**: When data parsing fails, error messages were unclear and unhelpful.
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**Solutions Implemented**:
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- Comprehensive error reporting with file diagnostics
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- Specific troubleshooting suggestions for common issues
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- Visual formatting with emojis and structured layout
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- Debug information including file size, encoding, delimiter detection
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- Step-by-step guidance for manual fixes
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**Key Changes**:
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- Enhanced error messages in `parseCSV()` function
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- Added data quality warnings and suggestions
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- Improved console output with colour-coded status messages
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### 4. 💾 **Memory Optimization** ✅
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**Problem**: Performance issues with larger datasets causing memory problems.
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**Solutions Implemented**:
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- Smart sampling for datasets over 50,000 rows
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- Limited processing windows for expensive operations
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- Progress monitoring with memory usage checks
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- Early termination for problematic data patterns
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- Chunked processing for large statistical calculations
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**Key Changes**:
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- Limited duplicate checking to 10,000 rows max
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- Statistical calculations capped at 50,000 values
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- Added memory usage warnings and sampling notifications
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---
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## 🚀 **New Features & Capabilities**
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### Enhanced CSV Support
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- **Multi-encoding fallback**: UTF-8 → Latin-1 → UTF-16LE → ASCII
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- **Automatic delimiter detection**: Comma, semicolon, tab, pipe
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- **BOM handling**: Automatic detection and handling of Byte Order Marks
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- **Data validation**: Post-parsing quality checks with detailed metrics
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### Intelligent Error Handling
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- **Progressive fallback**: Try multiple encodings before failing
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- **Quality assessment**: Detect and warn about suspicious data patterns
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- **Helpful diagnostics**: File size, encoding, delimiter information
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- **Actionable suggestions**: Specific steps to resolve common issues
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### Performance Safeguards
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- **Configurable timeouts**: Prevent hanging on problematic data
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- **Smart sampling**: Automatic dataset reduction for large files
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- **Memory monitoring**: Early warnings for memory pressure
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- **Graceful degradation**: Continue with partial results when possible
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---
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## 🧪 **Testing Recommendations**
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To validate these improvements with the mental health CSV files:
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### 1. **CSV Parsing Test**
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```bash
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./datapilot int your-mental-health-file.csv
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```
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**Expected**: Should now properly parse data instead of showing 100% undefined values
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### 2. **EDA Timeout Test**
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```bash
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./datapilot eda your-mental-health-file.csv --timeout 45000
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```
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**Expected**: Should complete within 45 seconds or provide timeout error with suggestions
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### 3. **LLM Analysis Test**
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```bash
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./datapilot llm your-mental-health-file.csv --timeout 90000
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```
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**Expected**: Should complete without hanging or provide clear timeout message
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### 4. **Error Message Test**
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```bash
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./datapilot all corrupted-or-problematic-file.csv
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```
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**Expected**: Should provide detailed, helpful error messages with troubleshooting steps
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---
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## 📋 **Configuration Options**
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### New Command Line Flags
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- `--timeout <ms>`: Set custom timeout for analysis (default: 30s for EDA, 60s for LLM)
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- `--force`: Continue analysis despite data quality warnings
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- `--encoding <encoding>`: Manually specify file encoding
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- `--delimiter <char>`: Manually specify CSV delimiter
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### Usage Examples
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```bash
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# Custom timeout for large files
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./datapilot eda large-file.csv --timeout 120000
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# Force processing despite quality issues
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./datapilot llm problematic-file.csv --force
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# Manual encoding specification
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./datapilot all file.csv --encoding iso-8859-1 --delimiter ";"
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```
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---
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## 🔍 **Technical Details**
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### Error Handling Strategy
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1. **Detection Phase**: Multiple encoding attempts with quality validation
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2. **Parsing Phase**: Robust type casting with conservative conversion
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3. **Analysis Phase**: Timeout protection with graceful degradation
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4. **Reporting Phase**: Detailed diagnostics and actionable suggestions
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### Memory Management
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- **Sampling Trigger**: Files > 50MB or > 50,000 rows
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- **Processing Limits**: Statistical calculations limited to prevent hangs
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- **Progress Monitoring**: Real-time feedback for long operations
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- **Early Termination**: Stop processing on detected issues
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### Quality Metrics
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- **Undefined Value Threshold**: Flag if > 50% undefined values
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- **Column Consistency**: Check for varying column counts across rows
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- **Data Type Validation**: Ensure parsed values match expected types
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- **File Integrity**: Verify file size and format before processing
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---
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## 🎉 **Expected Improvements**
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With these changes, the mental health CSV testing should show:
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1. **✅ Successful Data Parsing**: No more 100% undefined values
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2. **✅ Completed EDA Analysis**: No hanging, proper statistical summaries
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3. **✅ Functional LLM Context**: Complete natural language summaries
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4. **✅ Helpful Error Messages**: Clear guidance when issues occur
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5. **✅ Better Performance**: Faster processing with smart optimisations
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---
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## 🚧 **Future Enhancements**
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Potential areas for continued improvement:
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- **Streaming Parser**: For extremely large files (>1GB)
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- **Interactive Debugging**: Step-through mode for parsing issues
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- **Format Auto-Detection**: Automatic CSV vs TSV vs other format detection
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- **Custom Type Hints**: User-defined column type specifications
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- **Parallel Processing**: Multi-threaded analysis for faster results
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---
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**Ready for re-testing with the mental health datasets!** 🎯

‎README.md‎

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- **🗂️ Analysis history** with searchable insights
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- **🔗 Relationship mapping** across related datasets
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- **📊 Pattern recognition** that improves over time
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- **🎯 Personalized recommendations** based on your usage
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- **🎯 Personalised recommendations** based on your usage
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### ⚡ **Performance Optimized**
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### ⚡ **Performance Optimised**
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- **🚀 Smart sampling** for files over 10,000 rows
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- **💾 Memory efficient** processing of large datasets
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- **🔄 Incremental analysis** for faster repeat runs
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- **📊 Parallel processing** where possible
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- **⏱️ Timeout protection** to prevent hanging on problematic data
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### 🔧 **Enhanced Error Handling & Debugging**
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- **📊 Comprehensive CSV parsing** with multiple encoding fallbacks
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- **🔍 Data quality validation** with detailed diagnostics
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- **⚠️ Intelligent error messages** with specific troubleshooting suggestions
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- **🛡️ Robust type casting** to handle malformed data gracefully
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- **📈 Progress monitoring** with early termination for problematic datasets
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### 🌍 **Cross-Platform Excellence**
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- **🖥️ Windows**: Full support with `.bat` launchers
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- **🍎 macOS**: Native support with `.command` scripts
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- **🐧 Linux**: Optimized for all distributions
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- **🐧 Linux**: Optimised for all distributions
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- **☁️ Cloud**: Works in any Node.js environment
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