@learn Operator in TuskLang - Go Guide

🧠 AI Power: @learn Operator Unleashed

TuskLang's @learn operator is your machine learning rebellion. We don't bow to any king—especially not to static, dumb configurations. Here's how to use @learn in Go projects to create adaptive, intelligent systems that learn and optimize themselves.

📋 Table of Contents

- What is @learn? - Basic Usage - Learning Patterns - ML Integration - Go Integration - Best Practices

🤖 What is @learn?

The @learn operator enables machine learning directly in your config. No more static values—just pure, adaptive intelligence.

🛠️ Basic Usage

[ai]
optimal_timeout: @learn("timeout_optimization", 30)
best_cache_size: @learn("cache_optimization", 1000)
optimal_threads: @learn("thread_optimization", 4)

🧠 Learning Patterns

Performance Optimization

[performance]
optimal_connections: @learn("connection_pool", 10)
best_batch_size: @learn("batch_processing", 100)
optimal_timeout: @learn("request_timeout", 5000)

Resource Management

[resources]
optimal_memory: @learn("memory_usage", 512)
best_cpu_cores: @learn("cpu_utilization", 2)
optimal_disk_io: @learn("disk_performance", 1000)

User Behavior

[user_behavior]
optimal_session_time: @learn("session_duration", 3600)
best_notification_freq: @learn("notification_timing", 24)
optimal_retry_attempts: @learn("retry_behavior", 3)

🔗 ML Integration

[ml_config]
model_path: @file.read("models/optimizer.pkl")
training_data: @query("SELECT * FROM performance_metrics")
prediction_interval: @learn("prediction_frequency", 300)

🔗 Go Integration

// Access learned values
optimalTimeout := config.GetInt("optimal_timeout")
bestCacheSize := config.GetInt("best_cache_size")
// Update learning data
config.UpdateLearningData("timeout_optimization", map[string]interface{}{
    "current_timeout": 45,
    "response_time": 120,
    "success_rate": 0.95,
})

Custom ML Integration

type MLPredictor struct {
    model interface{}
}
func (m *MLPredictor) Predict(features map[string]interface{}) (interface{}, error) {
    // Implement your ML prediction logic
    return 30, nil
}
func (m *MLPredictor) UpdateModel(data []interface{}) error {
    // Implement model training/updating
    return nil
}

🥇 Best Practices

- Start with conservative default values - Monitor learning performance - Validate learned values before applying - Implement fallback mechanisms - Document learning objectives clearly

---

TuskLang: Intelligent configuration with @learn.