feat: vendor midstream and sublinear-time-solver libraries

Add ruvnet/midstream (AIMDS real-time inference) and
ruvnet/sublinear-time-solver (sublinear optimization algorithms)
as vendored dependencies under vendor/.

Co-Authored-By: claude-flow <ruv@ruv.net>

vendor/midstream/AIMDS/crates/aimds-response/examples/basic_usage.rs

@@ -0,0 +1,79 @@
+//! Basic usage example for aimds-response
+
+use aimds_response::{ResponseSystem, FeedbackSignal};
+
+#[tokio::main]
+async fn main() -> Result<(), Box<dyn std::error::Error>> {
+    // Initialize tracing
+    tracing_subscriber::fmt()
+        .with_max_level(tracing::Level::INFO)
+        .init();
+
+    println!("=== AIMDS Response Layer - Basic Usage ===\n");
+
+    // Create response system
+    println!("Creating response system...");
+    let response_system = ResponseSystem::new().await?;
+
+    // Simulate threat detection
+    println!("Detecting threat...");
+    let threat = create_sample_threat();
+
+    // Apply mitigation
+    println!("Applying mitigation...");
+    let outcome = response_system.mitigate(&threat).await?;
+
+    println!("✓ Mitigation applied successfully!");
+    println!("  Strategy: {}", outcome.strategy_id);
+    println!("  Actions: {}", outcome.actions_applied.len());
+    println!("  Duration: {:?}", outcome.duration);
+    println!("  Success: {}", outcome.success);
+
+    // Learn from outcome
+    println!("\nLearning from outcome...");
+    response_system.learn_from_result(&outcome).await?;
+
+    // Generate feedback
+    let feedback = vec![FeedbackSignal {
+        strategy_id: outcome.strategy_id.clone(),
+        success: outcome.success,
+        effectiveness_score: outcome.effectiveness_score(),
+        timestamp: chrono::Utc::now(),
+        context: Some("basic_usage_example".to_string()),
+    }];
+
+    // Optimize strategies
+    println!("Optimizing strategies...");
+    response_system.optimize(&feedback).await?;
+
+    // Display metrics
+    let metrics = response_system.metrics().await;
+    println!("\n=== System Metrics ===");
+    println!("Learned patterns: {}", metrics.learned_patterns);
+    println!("Active strategies: {}", metrics.active_strategies);
+    println!("Total mitigations: {}", metrics.total_mitigations);
+    println!("Successful mitigations: {}", metrics.successful_mitigations);
+    println!("Optimization level: {}", metrics.optimization_level);
+
+    if metrics.total_mitigations > 0 {
+        let success_rate =
+            metrics.successful_mitigations as f64 / metrics.total_mitigations as f64 * 100.0;
+        println!("Success rate: {:.2}%", success_rate);
+    }
+
+    println!("\n✓ Example completed successfully!");
+
+    Ok(())
+}
+
+fn create_sample_threat() -> aimds_response::meta_learning::ThreatIncident {
+    use aimds_response::meta_learning::{AttackType, ThreatIncident, ThreatType};
+
+    ThreatIncident {
+        id: "example-threat-001".to_string(),
+        threat_type: ThreatType::Attack(AttackType::SqlInjection),
+        severity: 8,
+        confidence: 0.92,
+        timestamp: chrono::Utc::now(),
+    }
+}