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🐚 Advanced Features in TuskLang - Bash Guide

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Advanced Features in TuskLang - Bash Guide

🚀 Revolutionary Advanced Capabilities

Advanced features in TuskLang transform your configuration files into intelligent, self-adapting systems. No more static configurations or complex integration code - everything lives in your TuskLang configuration with machine learning, real-time optimization, and intelligent automation.

> "We don't bow to any king" - TuskLang advanced features break free from traditional configuration constraints and bring modern AI-powered capabilities to your Bash applications.

🧠 Machine Learning Integration

@learn Operator

Machine learning configuration

ml_config:
  enabled: true
  model: "optimal_settings"
  algorithm: "reinforcement_learning"
  learning_rate: 0.01
  batch_size: 100
#@learn: optimal_setting
#@learn-model: optimal_settings
#@learn-algorithm: reinforcement_learning
#@learn-rate: 0.01
Bash implementation

learn_optimal_setting() {
    local setting_name="$1"
    local current_value="$2"
    local performance_metric="$3"
    
    # Load ML configuration
    source <(tsk load ml-config.tsk)
    
    # Update learning model
    update_ml_model "$setting_name" "$current_value" "$performance_metric"
    
    # Get optimized value
    local optimized_value=$(get_optimized_setting "$setting_name")
    
    echo "$optimized_value"
}

@optimize Operator

Optimization configuration

optimize_config:
  target: "response_time"
  constraints: ["memory_usage", "cpu_usage"]
  algorithm: "genetic_algorithm"
  population_size: 50
  generations: 100
#@optimize: response_time
#@optimize-target: response_time
#@optimize-constraints: ["memory_usage", "cpu_usage"]
Bash implementation

optimize_performance() {
    local target_metric="$1"
    local constraints="$2"
    
    # Load optimization configuration
    source <(tsk load optimize-config.tsk)
    
    # Run optimization algorithm
    local optimized_params=$(run_optimization "$target_metric" "$constraints")
    
    # Apply optimized parameters
    apply_optimized_parameters "$optimized_params"
    
    echo "Optimization completed: $optimized_params"
}

📊 Real-Time Monitoring

@metrics Operator

Metrics configuration

metrics_config:
  enabled: true
  backend: "prometheus"
  interval: 30
  retention: "30d"
  alerts: true
#@metrics: response_time_ms
#@metrics-backend: prometheus
#@metrics-interval: 30
Bash implementation

collect_metrics() {
    local metric_name="$1"
    local value="$2"
    local labels="$3"
    
    # Load metrics configuration
    source <(tsk load metrics-config.tsk)
    
    # Send metric to backend
    send_metric "$metric_name" "$value" "$labels"
    
    # Check alerts
    check_metric_alerts "$metric_name" "$value"
}

@monitor Operator

Monitoring configuration

monitor_config:
  services: ["web", "api", "database"]
  health_checks: true
  auto_restart: true
  notification: "slack"
#@monitor: web_service
#@monitor-health: true
#@monitor-auto-restart: true
Bash implementation

monitor_service() {
    local service_name="$1"
    
    # Load monitoring configuration
    source <(tsk load monitor-config.tsk)
    
    # Check service health
    local health_status=$(check_service_health "$service_name")
    
    if [[ "$health_status" != "healthy" ]]; then
        # Auto-restart if enabled
        if [[ "${monitor_auto_restart}" == "true" ]]; then
            restart_service "$service_name"
        fi
        
        # Send notification
        send_notification "$service_name" "$health_status"
    fi
}

🔄 Dynamic Configuration

@adapt Operator

Adaptation configuration

adapt_config:
  triggers: ["load", "time", "events"]
  strategies: ["scale", "optimize", "reconfigure"]
  learning: true
#@adapt: load_based
#@adapt-trigger: load
#@adapt-strategy: scale
Bash implementation

adapt_configuration() {
    local trigger="$1"
    local current_load="$2"
    
    # Load adaptation configuration
    source <(tsk load adapt-config.tsk)
    
    # Determine adaptation strategy
    local strategy=$(determine_adaptation_strategy "$trigger" "$current_load")
    
    # Apply adaptation
    apply_adaptation "$strategy"
    
    echo "Configuration adapted: $strategy"
}

@predict Operator

Prediction configuration

predict_config:
  model: "time_series"
  horizon: "24h"
  confidence: 0.95
  features: ["load", "time", "day_of_week"]
#@predict: load_forecast
#@predict-model: time_series
#@predict-horizon: 24h
Bash implementation

predict_future_load() {
    local forecast_horizon="$1"
    
    # Load prediction configuration
    source <(tsk load predict-config.tsk)
    
    # Collect historical data
    local historical_data=$(collect_historical_data)
    
    # Generate prediction
    local prediction=$(generate_prediction "$historical_data" "$forecast_horizon")
    
    echo "$prediction"
}

🎯 Real-World Examples

Complete Advanced Configuration

advanced-config.tsk

advanced_config:
  ml:
    enabled: true
    models:
      - name: "optimal_cache_size"
        algorithm: "reinforcement_learning"
        target: "cache_hit_rate"
      - name: "optimal_thread_count"
        algorithm: "genetic_algorithm"
        target: "throughput"
  optimization:
    enabled: true
    targets:
      - "response_time"
      - "memory_usage"
      - "cpu_usage"
    constraints:
      - "max_memory: 8GB"
      - "max_cpu: 80%"
  monitoring:
    enabled: true
    metrics:
      - "response_time_ms"
      - "requests_per_second"
      - "error_rate"
      - "memory_usage_mb"
    alerts:
      - condition: "response_time > 1000ms"
        action: "scale_up"
      - condition: "error_rate > 5%"
        action: "restart_service"
  adaptation:
    enabled: true
    triggers:
      - "high_load"
      - "low_performance"
      - "time_based"
    strategies:
      - "scale_horizontally"
      - "optimize_cache"
      - "adjust_threads"

AI-Powered Web Server

ai-web-server.tsk

web_server_config:
  name: "AI-Powered Web Server"
  version: "2.0.0"
#@learn: optimal_worker_count
#@optimize: response_time
#@metrics: requests_per_second
#@monitor: web_server
#@adapt: load_based
#@learn-config:

 target: "response_time"


 features: ["concurrent_connections", "cpu_usage", "memory_usage"]


 algorithm: "neural_network"


 learning_rate: 0.001
#@optimize-config:

 target: "response_time"


 parameters: ["worker_processes", "worker_connections", "keepalive_timeout"]


 algorithm: "bayesian_optimization"


 max_iterations: 100
#@metrics-config:

 backend: "prometheus"


 interval: 15


 metrics:


   - "requests_per_second"


   - "response_time_p95"


   - "error_rate"


   - "active_connections"
#@monitor-config:

 health_checks:


   - endpoint: "/health"


     interval: 30


     timeout: 5


 auto_scaling:


   min_instances: 2


   max_instances: 10


   scale_up_threshold: 80%


   scale_down_threshold: 20%
#@adapt-config:

 triggers:


   - "high_load: cpu_usage > 80%"


   - "low_performance: response_time > 500ms"


   - "time_based: hour >= 9 && hour <= 17"


 actions:


   - "scale_up: add_worker_processes"


   - "optimize_cache: increase_cache_size"


   - "adjust_timeouts: reduce_keepalive_timeout"

Intelligent Database Configuration

intelligent-db.tsk

database_config:
  name: "Intelligent Database"
  type: "postgresql"
#@learn: optimal_connection_pool
#@optimize: query_performance
#@metrics: query_execution_time
#@monitor: database_service
#@predict: query_load
#@learn-config:

 target: "query_performance"


 features: ["connection_count", "active_queries", "cache_hit_ratio"]


 algorithm: "gradient_boosting"


 validation_split: 0.2
#@optimize-config:

 target: "query_performance"


 parameters: ["max_connections", "shared_buffers", "work_mem"]


 algorithm: "particle_swarm"


 population_size: 30
#@metrics-config:

 backend: "influxdb"


 interval: 10


 metrics:


   - "active_connections"


   - "query_execution_time"


   - "cache_hit_ratio"


   - "deadlocks_per_minute"
#@monitor-config:

 health_checks:


   - query: "SELECT 1"


     interval: 15


     timeout: 3


 alerts:


   - condition: "active_connections > 90%"


     action: "increase_connection_pool"


   - condition: "query_execution_time > 1000ms"


     action: "optimize_queries"
#@predict-config:

 model: "lstm"


 features: ["hour", "day_of_week", "historical_load"]


 horizon: "1h"


 update_frequency: "15m"

🚨 Troubleshooting Advanced Features

Common Issues and Solutions

1. Machine Learning Issues 

Debug machine learning

debug_ml_features() {
    echo "Debugging machine learning features..."
    
    # Check ML configuration
    if [[ "${ml_enabled}" == "true" ]]; then
        echo "✓ Machine learning enabled"
        
        # Check model availability
        local models=(${ml_models[@]})
        for model in "${models[@]}"; do
            if [[ -f "/models/$model.pkl" ]]; then
                echo "✓ Model available: $model"
            else
                echo "⚠ Model missing: $model"
            fi
        done
    else
        echo "✗ Machine learning disabled"
    fi
    
    # Check optimization status
    if [[ "${optimize_enabled}" == "true" ]]; then
        echo "✓ Optimization enabled"
        echo "Target: ${optimize_target}"
        echo "Algorithm: ${optimize_algorithm}"
    else
        echo "✗ Optimization disabled"
    fi
}

2. Monitoring Issues 

Debug monitoring

debug_monitoring() {
    echo "Debugging monitoring features..."
    
    # Check metrics backend
    local backend="${metrics_backend:-prometheus}"
    case "$backend" in
        "prometheus")
            check_prometheus_connection
            ;;
        "influxdb")
            check_influxdb_connection
            ;;
        *)
            echo "⚠ Unknown metrics backend: $backend"
            ;;
    esac
    
    # Check health checks
    if [[ "${monitor_health_checks}" == "true" ]]; then
        echo "✓ Health checks enabled"
        
        local services=(${monitor_services[@]})
        for service in "${services[@]}"; do
            check_service_health "$service"
        done
    else
        echo "✗ Health checks disabled"
    fi
}

🔒 Security Best Practices

Advanced Features Security

Security validation

validate_advanced_security() {
    echo "Validating advanced features security..."
    
    # Check ML model security
    if [[ "${ml_enabled}" == "true" ]]; then
        echo "✓ Machine learning enabled"
        
        # Validate model sources
        local models=(${ml_models[@]})
        for model in "${models[@]}"; do
            if validate_model_signature "$model"; then
                echo "✓ Model signature valid: $model"
            else
                echo "⚠ Model signature invalid: $model"
            fi
        done
    fi
    
    # Check monitoring security
    if [[ "${monitor_enabled}" == "true" ]]; then
        echo "✓ Monitoring enabled"
        
        # Check metrics encryption
        if [[ "${metrics_encryption}" == "true" ]]; then
            echo "✓ Metrics encryption enabled"
        else
            echo "⚠ Metrics encryption not enabled"
        fi
    fi
    
    # Check adaptation security
    if [[ "${adapt_enabled}" == "true" ]]; then
        echo "✓ Adaptation enabled"
        
        # Validate adaptation rules
        validate_adaptation_rules
    fi
}

📈 Performance Optimization

Advanced Features Performance

Performance validation

validate_advanced_performance() {
    echo "Validating advanced features performance..."
    
    # Check ML performance
    if [[ "${ml_enabled}" == "true" ]]; then
        echo "✓ Machine learning enabled"
        
        # Check model inference time
        local inference_time=$(measure_model_inference_time)
        if [[ "$inference_time" -lt 100 ]]; then
            echo "✓ Model inference time: ${inference_time}ms"
        else
            echo "⚠ Slow model inference: ${inference_time}ms"
        fi
    fi
    
    # Check optimization performance
    if [[ "${optimize_enabled}" == "true" ]]; then
        echo "✓ Optimization enabled"
        
        # Check optimization convergence
        local convergence_time=$(measure_optimization_convergence)
        echo "Optimization convergence: ${convergence_time}s"
    fi
    
    # Check monitoring overhead
    if [[ "${monitor_enabled}" == "true" ]]; then
        echo "✓ Monitoring enabled"
        
        # Check metrics collection overhead
        local overhead=$(measure_monitoring_overhead)
        if [[ "$overhead" -lt 5 ]]; then
            echo "✓ Low monitoring overhead: ${overhead}%"
        else
            echo "⚠ High monitoring overhead: ${overhead}%"
        fi
    fi
}

🎯 Next Steps

- Plugin Integration: Explore advanced feature plugins - Custom Algorithms: Implement custom ML algorithms - Advanced Patterns: Understand complex AI patterns - Testing Advanced Features: Test advanced functionality - Performance Tuning: Optimize advanced feature performance

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Advanced features transform your TuskLang configuration into an intelligent, self-adapting system. They bring modern AI-powered capabilities to your Bash applications with machine learning, real-time optimization, and intelligent automation!

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