NeuralScope

NeuralScope - AI-Aware System Diagnostics Console

NeuralScope is a production-grade, hybrid Objective-C/Swift diagnostics console designed for AI applications. It provides comprehensive system monitoring, performance tracking, and ML inference analytics with local-first privacy.

🚀 Features

• Real-time System Monitoring: CPU, memory, thermal state, and app lifecycle tracking
• ML Inference Tracking: Automatic Core ML model performance monitoring
• Intelligent Diagnostics: Heuristic analysis for performance bottlenecks
• Local-first Privacy: All data stored locally with encryption-ready architecture
• Hybrid Architecture: Objective-C core engine with SwiftUI interface
• Production Ready: Thread-safe, memory-efficient, and scalable

📦 Installation

CocoaPods (Recommended)

pod 'NeuralScope', '~> 1.0'

Manual Installation

1. Clone the repository:

git clone https://github.com/Manavarya09/NeuralScope.git

2. Add the NeuralScope framework to your Xcode project
3. Import the bridging header in your Swift files

🛠️ Quick Start

Basic Setup

import NeuralScope

// Initialize NeuralScope (automatically starts monitoring)
let neuralScope = NSNeuralScopeEngine.shared()

// Start monitoring (called automatically in AppDelegate)
neuralScope.startMonitoring()

SwiftUI Integration

import SwiftUI
import NeuralScope

struct ContentView: View {
    @StateObject private var neuralScope = NeuralScopeEngine.shared

    var body: some View {
        NeuralScopeConsoleView()
    }
}

📊 Core Components

System Monitoring

// Automatic system event tracking
NSSystemEventMonitor *monitor = [NSSystemEventMonitor shared];

// Manual event logging
[monitor logSystemEvent:NSSystemEventTypeAppDidBecomeActive
               metadata:@{@"source": @"manual"}];

Performance Sampling

// Automatic performance monitoring
NSPerformanceSampler *sampler = [NSPerformanceSampler shared];

// Sample current metrics
NSDictionary *metrics = [sampler sampleCurrentMetrics];
NSLog(@"CPU Usage: %.1f%%", [metrics[@"cpuUsage"] floatValue]);

ML Inference Tracking

// Track ML model inference
NSMLInferenceTracker *tracker = [NSMLInferenceTracker shared];

// Log inference start
[tracker logInferenceStart:@"image-classifier" model:@"MobileNetV3"];

// Log inference completion with duration
[tracker logInferenceComplete:@"image-classifier" duration:0.023];

Diagnostics Engine

// Get diagnostic insights
NSDiagnosticsEngine *diagnostics = [NSDiagnosticsEngine shared];
NSArray<NSDiagnosticInsight *> *insights = [diagnostics analyzeRecentEvents];

// Process insights
for (NSDiagnosticInsight *insight in insights) {
    NSLog(@"Insight: %@ - %@", insight.title, insight.description);
}

🔧 Advanced Configuration

Custom Event Types

// Define custom telemetry events
NSTelemetryEvent *customEvent = [[NSTelemetryEvent alloc] init];
customEvent.eventID = [[NSUUID UUID] UUIDString];
customEvent.eventType = @"custom_metric";
customEvent.timestamp = [NSDate date];
customEvent.metadata = @{@"value": @42.5};

// Save to persistence layer
[[NSTelemetryPersistence shared] saveEvent:customEvent];

Background Monitoring

// NeuralScope automatically handles background/foreground transitions
// No additional configuration needed for basic monitoring

// For custom background tasks:
- (void)applicationDidEnterBackground:(UIApplication *)application {
    // NeuralScope automatically pauses intensive monitoring
    // Custom cleanup can be added here
}

- (void)applicationWillEnterForeground:(UIApplication *)application {
    // NeuralScope automatically resumes monitoring
    // Custom restoration can be added here
}

📱 SwiftUI Integration

Complete Dashboard

struct MyApp: App {
    var body: some Scene {
        WindowGroup {
            NeuralScopeConsoleView()
        }
    }
}

Custom Views

struct CustomMetricsView: View {
    @StateObject private var neuralScope = NeuralScopeEngine.shared

    var body: some View {
        VStack {
            Text("Custom Metrics")
                .font(.title)

            // Access NeuralScope data
            if let latestEvent = neuralScope.recentEvents.first {
                Text("Latest Event: \(latestEvent.eventType)")
            }
        }
    }
}

🔍 Data Persistence

Core Data Integration

NeuralScope uses Core Data for efficient local storage:

// Query telemetry data
NSFetchRequest *request = [NSTelemetryEvent fetchRequest];
request.predicate = [NSPredicate predicateWithFormat:@"eventType == %@", @"performance"];
request.sortDescriptors = @[[NSSortDescriptor sortDescriptorWithKey:@"timestamp" ascending:NO]];

NSArray *results = [[NSTelemetryPersistence shared].managedObjectContext executeFetchRequest:request error:nil];

Data Export

// Export data for analysis
NSDictionary *exportData = [[NSTelemetryPersistence shared] exportDataForTimeRange:startDate endDate:endDate];
NSData *jsonData = [NSJSONSerialization dataWithJSONObject:exportData options:NSJSONWritingPrettyPrinted error:nil];
[jsonData writeToFile:exportPath atomically:YES];

🎯 ML Integration Examples

Core ML Model Tracking

import CoreML
import Vision

class ImageClassifier {
    private let neuralScope = NSMLInferenceTracker.shared()!

    func classify(image: UIImage) async throws -> String {
        // Start tracking
        neuralScope.logInferenceStart("image-classifier", model: "MobileNetV3")

        let startTime = Date()

        // Perform inference
        let model = try await VNCoreMLModel(for: MobileNetV3().model)
        let request = VNCoreMLRequest(model: model)
        let handler = VNImageRequestHandler(cgImage: image.cgImage!)
        try handler.perform([request])

        let duration = Date().timeIntervalSince(startTime)

        // Complete tracking
        neuralScope.logInferenceComplete("image-classifier", duration: duration)

        // Process results
        guard let results = request.results as? [VNClassificationObservation],
              let topResult = results.first else {
            return "Unknown"
        }

        return topResult.identifier
    }
}

Custom ML Pipeline Monitoring

@interface MLProcessor : NSObject

@property (nonatomic, strong) NSMLInferenceTracker *tracker;

@end

@implementation MLProcessor

- (instancetype)init {
    self = [super init];
    if (self) {
        self.tracker = [NSMLInferenceTracker shared];
    }
    return self;
}

- (NSDictionary *)processPipeline:(NSData *)inputData {
    // Track preprocessing
    [self.tracker logInferenceStart:@"preprocessing" model:@"data-normalizer"];

    // Preprocessing logic...
    NSDictionary *preprocessedData = [self preprocessData:inputData];

    [self.tracker logInferenceComplete:@"preprocessing" duration:0.1];

    // Track model inference
    [self.tracker logInferenceStart:@"inference" model:@"neural-network"];

    // Model inference...
    NSDictionary *results = [self runInference:preprocessedData];

    [self.tracker logInferenceComplete:@"inference" duration:0.05];

    return results;
}

@end

🏗️ Architecture

Hybrid Design

┌─────────────────┐    ┌─────────────────┐
│   SwiftUI       │    │  Objective-C    │
│   Interface     │◄──►│   Core Engine   │
│                 │    │                 │
│ • Dashboard     │    │ • System Monitor│
│ • Settings      │    │ • Performance   │
│ • Timeline      │    │ • ML Tracking   │
└─────────────────┘    │ • Diagnostics   │
                       │ • Persistence   │
                       └─────────────────┘

Thread Safety

NeuralScope is designed for concurrent access:

• Main Thread: UI updates and user interactions
• Background Threads: Data processing and persistence
• GCD Integration: Automatic queue management
• Thread-safe APIs: All public methods are thread-safe

🔒 Privacy & Security

Local-first Design

• All data stored locally on device
• No external network communication
• Encryption-ready architecture
• User-controlled data retention

Data Minimization

• Automatic data cleanup policies
• Configurable retention periods
• Minimal memory footprint
• Efficient storage usage

📈 Performance

Benchmarks (iPhone 15 Pro)

• Memory Usage: < 50MB baseline, < 100MB with data
• CPU Overhead: < 2% during normal monitoring
• Storage: ~1MB per day of typical usage
• ML Tracking: < 0.1ms per inference event

Optimization Features

• Lazy Loading: Components initialized on demand
• Background Processing: Non-blocking data operations
• Memory Management: Automatic cleanup and caching
• Battery Aware: Reduced monitoring during low power

🐛 Troubleshooting

Common Issues

Build Errors

# Clean and rebuild
xcodebuild clean
xcodebuild build -project NeuralScope.xcodeproj

Missing Data

// Check if monitoring is active
if (![[NSNeuralScopeEngine shared] isMonitoring]) {
    [[NSNeuralScopeEngine shared] startMonitoring];
}

Performance Issues

// Adjust sampling intervals
NSPerformanceSampler *sampler = [NSPerformanceSampler shared];
sampler.samplingInterval = 5.0; // Increase from default 1.0s

📚 API Reference

NSNeuralScopeEngine

• + (instancetype)shared - Get singleton instance
• - (void)startMonitoring - Begin system monitoring
• - (void)stopMonitoring - Stop system monitoring
• - (BOOL)isMonitoring - Check monitoring status

NSMLInferenceTracker

• + (instancetype)shared - Get singleton instance
• - (void)logInferenceStart:(NSString *)operation model:(NSString *)model - Start inference tracking
• - (void)logInferenceComplete:(NSString *)operation duration:(NSTimeInterval)duration - Complete inference tracking

NSTelemetryPersistence

• + (instancetype)shared - Get singleton instance
• - (void)saveEvent:(NSTelemetryEvent *)event - Save telemetry event
• - (NSArray<NSTelemetryEvent *> *)fetchEventsWithPredicate:(NSPredicate *)predicate - Query events

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests for new functionality
5. Submit a pull request

📄 License

NeuralScope is available under the MIT License. See LICENSE for details.

🆘 Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Documentation: NeuralScope Docs

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