Introduction

This post provides a comprehensive walkthrough of designing a system for Meta Glass (smart glasses) like the Ray-Ban Meta smart glasses. This system design question tests your ability to design systems for edge devices with constraints like battery life, connectivity, real-time processing, and AI/ML integration.

Table of Contents

  1. Problem Statement
  2. Requirements
  3. Capacity Estimation
  4. Core Entities
  5. API
  6. Data Flow
  7. Database Design
  8. High-Level Design
  9. Deep Dive
  10. Conclusion

Problem Statement

Design a system for Meta Glass smart glasses that supports:

  1. Media Capture: Capture photos and videos with high quality
  2. Real-Time Processing: Process media in real-time (AI, filters, effects)
  3. Cloud Sync: Sync media and data to the cloud
  4. AI/ML Features: Voice commands, object recognition, live translation
  5. Live Streaming: Stream video to social media platforms
  6. Low Power: Optimize for battery life and power consumption
  7. Offline Functionality: Work with limited or no connectivity
  8. Privacy and Security: Protect user data and privacy

Describe the system architecture, including edge computing, cloud services, and how to handle constraints like battery life, connectivity, and real-time processing.

Requirements

Functional Requirements

Core Features:

  1. Media Capture
    • Capture high-resolution photos (12MP+)
    • Record videos (1080p, 4K)
    • Multiple camera support (front, back)
    • Capture metadata (location, time, orientation)
  2. Real-Time Processing
    • Apply filters and effects in real-time
    • Object detection and recognition
    • Face detection and recognition
    • Live translation (text overlay)
    • Voice command processing
  3. Cloud Sync
    • Automatic media upload to cloud
    • Sync settings and preferences
    • Backup and restore
    • Multi-device synchronization
  4. AI/ML Features
    • Voice assistant (Hey Meta)
    • Object recognition
    • Scene understanding
    • Live captions and translation
    • Smart photo organization
  5. Live Streaming
    • Stream to Facebook, Instagram, WhatsApp
    • Real-time video encoding
    • Adaptive bitrate streaming
    • Connection management
  6. Connectivity
    • WiFi connectivity
    • Bluetooth connectivity
    • Cellular connectivity (optional)
    • Offline mode support

Non-Functional Requirements

Performance:

  • Photo capture latency: < 100ms
  • Video processing latency: < 50ms per frame
  • Voice command response: < 500ms
  • Cloud sync latency: < 5 seconds for photos

Power Constraints:

  • Battery life: 4+ hours of active use
  • Standby time: 24+ hours
  • Low-power mode for background operations
  • Adaptive power management

Storage:

  • Local storage: 32GB-128GB
  • Cloud storage: Unlimited (with subscription)
  • Efficient storage compression

Connectivity:

  • WiFi: 802.11ac/ax
  • Bluetooth: 5.0+
  • Cellular: LTE/5G (optional)
  • Offline mode: Core functionality without connectivity

Reliability:

  • No data loss
  • Graceful degradation
  • Automatic recovery
  • Data backup and restore

Clarifying Questions

Device Capabilities:

  • Q: What are the device specifications?
  • A: ARM processor, limited RAM (2-4GB), embedded GPU, cameras, microphones, speakers

Use Cases:

  • Q: What are the primary use cases?
  • A: Social media content creation, hands-free photography, live streaming, AI-powered features

Connectivity:

  • Q: What connectivity options?
  • A: WiFi, Bluetooth, optional cellular

Power:

  • Q: What’s the battery capacity?
  • A: Small battery, optimized for all-day use with power management

Processing:

  • Q: What processing happens on-device vs. cloud?
  • A: Real-time processing on-device, heavy ML inference in cloud, hybrid approach

Capacity Estimation

Storage Estimates

Local Storage:

  • Photo: ~5MB (12MP JPEG)
  • Video: ~100MB per minute (1080p)
  • 32GB device: ~6,400 photos or 320 minutes of video
  • With compression: 2x storage capacity

Cloud Storage:

  • 1M users × 100 photos/day = 100M photos/day
  • 100M × 5MB = 500TB/day
  • With compression: ~200TB/day
  • Annual: ~73PB

Throughput Estimates

Media Upload:

  • 1M active users × 10 photos/day = 10M photos/day
  • Average: ~115 photos/second
  • Peak: ~1,000 photos/second

Video Streaming:

  • 100K concurrent streams
  • Average bitrate: 2Mbps
  • Total bandwidth: 200Gbps

Real-Time Processing:

  • 1M active users
  • Processing: 1 photo per second per user (peak)
  • Total: 1M processing requests/second

Network Bandwidth

Per Device:

  • Photo upload: 5MB × 10 photos/day = 50MB/day
  • Video upload: 100MB × 1 video/day = 100MB/day
  • Streaming: 2Mbps during streaming
  • Total: ~150MB/day average

Total System:

  • Photo uploads: 500TB/day
  • Video uploads: 1PB/day
  • Streaming: 200Gbps
  • Total: ~1.5PB/day

Core Entities

Device

  • Attributes: device_id, user_id, device_type, firmware_version, battery_level, connectivity_status, last_sync_at
  • Relationships: Belongs to user, captures media, syncs to cloud

Media

  • Attributes: media_id, device_id, user_id, media_type, file_path, cloud_url, size, created_at, metadata
  • Relationships: Belongs to device and user, has processing jobs

Media Processing Job

  • Attributes: job_id, media_id, processing_type, status, started_at, completed_at, result_url
  • Relationships: Belongs to media

User

  • Attributes: user_id, username, email, subscription_tier, storage_quota, created_at
  • Relationships: Owns devices, has media, has settings

API

Device API

Upload Photo

POST /api/v1/device/photos
Content-Type: multipart/form-data
Authorization: Bearer {device_token}

{
  "photo": <binary>,
  "metadata": {
    "timestamp": "2025-11-04T10:00:00Z",
    "location": {...},
    "filters": [...]
  }
}

Response: 202 Accepted
{
  "photo_id": "uuid",
  "status": "queued",
  "local_path": "/storage/photos/uuid.jpg"
}

Upload Video

POST /api/v1/device/videos
Content-Type: multipart/form-data
Authorization: Bearer {device_token}

{
  "video": <binary>,
  "metadata": {...}
}

Response: 202 Accepted
{
  "video_id": "uuid",
  "status": "uploading"
}

Cloud API

Get User Photos

GET /api/v1/users/{user_id}/photos?start_time=2025-11-01&limit=50

Response: 200 OK
{
  "photos": [
    {
      "photo_id": "uuid",
      "url": "https://cdn.example.com/photo.jpg",
      "thumbnail_url": "https://cdn.example.com/thumb.jpg",
      "metadata": {...},
      "created_at": "2025-11-04T10:00:00Z"
    }
  ],
  "total": 1000,
  "next_cursor": "..."
}

Start Streaming

POST /api/v1/streaming/start
Authorization: Bearer {token}
Content-Type: application/json

{
  "user_id": "user-123",
  "destination": "instagram",
  "quality": "1080p"
}

Response: 200 OK
{
  "stream_id": "uuid",
  "rtmp_url": "rtmp://stream.example.com/live/...",
  "stream_key": "...",
  "status": "active"
}

Data Flow

Photo Capture and Upload Flow

  1. User captures photo → Device Camera System
  2. Camera System → Local Storage (save photo)
  3. Camera System → Media Processing Service (on-device processing)
  4. Media Processing Service → Local Storage (save processed photo)
  5. Sync Service → Cloud API (upload photo)
  6. Cloud API → Object Storage (store photo)
  7. Cloud API → Media Database (store metadata)
  8. Cloud API → CDN (cache photo)
  9. Response returned to device

Video Streaming Flow

  1. User starts streaming → Device
  2. Device → Video Encoder (encode video)
  3. Video Encoder → Streaming Service (send video stream)
  4. Streaming Service → CDN (distribute stream)
  5. Streaming Service → Social Media Platform (forward stream)
  6. Viewers receive stream from CDN

Voice Command Flow

  1. User speaks command → Device Microphone
  2. Microphone → Voice Processing Service (on-device)
  3. Voice Processing Service → Cloud NLP Service (if needed)
  4. NLP Service → Command Processor
  5. Command Processor → Device (execute command)
  6. Response returned to user

Database Design

Schema Design

Devices Table:

CREATE TABLE devices (
    device_id VARCHAR(36) PRIMARY KEY,
    user_id VARCHAR(36) NOT NULL,
    device_type VARCHAR(50) NOT NULL,
    firmware_version VARCHAR(50),
    battery_level INT,
    connectivity_status ENUM('wifi', 'bluetooth', 'cellular', 'offline'),
    last_sync_at TIMESTAMP,
    created_at TIMESTAMP,
    INDEX idx_user_id (user_id),
    INDEX idx_last_sync (last_sync_at)
);

Media Table:

CREATE TABLE media (
    media_id VARCHAR(36) PRIMARY KEY,
    device_id VARCHAR(36) NOT NULL,
    user_id VARCHAR(36) NOT NULL,
    media_type ENUM('photo', 'video') NOT NULL,
    local_path VARCHAR(512),
    cloud_url VARCHAR(512),
    size BIGINT,
    metadata JSON,
    created_at TIMESTAMP,
    synced_at TIMESTAMP,
    INDEX idx_user_id (user_id),
    INDEX idx_device_id (device_id),
    INDEX idx_created_at (created_at),
    FOREIGN KEY (device_id) REFERENCES devices(device_id)
);

Processing Jobs Table:

CREATE TABLE processing_jobs (
    job_id VARCHAR(36) PRIMARY KEY,
    media_id VARCHAR(36) NOT NULL,
    processing_type VARCHAR(50) NOT NULL,
    status ENUM('pending', 'processing', 'completed', 'failed') DEFAULT 'pending',
    started_at TIMESTAMP,
    completed_at TIMESTAMP,
    result_url VARCHAR(512),
    FOREIGN KEY (media_id) REFERENCES media(media_id),
    INDEX idx_status (status),
    INDEX idx_media_id (media_id)
);

Database Sharding Strategy

Shard by User ID:

  • User data, devices, and media on same shard
  • Enables efficient user queries
  • Use consistent hashing for distribution

High-Level Design

System Components

┌─────────────────────────────────────────────────────────────┐
│                    Meta Glass Device                         │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐      │
│  │  Camera      │  │  Audio       │  │  Sensors    │      │
│  │  System      │  │  System      │  │  (GPS, IMU) │      │
│  └──────────────┘  └──────────────┘  └──────────────┘      │
│         │                 │                 │               │
│         ▼                 ▼                 ▼               │
│  ┌─────────────────────────────────────────────────────┐   │
│  │           Edge Processing Layer                      │   │
│  │  ┌──────────────┐  ┌──────────────┐  ┌────────────┐ │   │
│  │  │  Media       │  │  AI/ML      │  │  Real-Time │ │   │
│  │  │  Processor   │  │  Engine     │  │  Encoder   │ │   │
│  │  └──────────────┘  └──────────────┘  └────────────┘ │   │
│  └─────────────────────────────────────────────────────┘   │
│         │                                                   │
│         ▼                                                   │
│  ┌─────────────────────────────────────────────────────┐   │
│  │           Device Management Layer                      │   │
│  │  ┌──────────────┐  ┌──────────────┐  ┌────────────┐ │   │
│  │  │  Storage     │  │  Power       │  │  Network   │ │   │
│  │  │  Manager     │  │  Manager     │  │  Manager   │ │   │
│  │  └──────────────┘  └──────────────┘  └────────────┘ │   │
│  └─────────────────────────────────────────────────────┘   │
└──────────────────────┬──────────────────────────────────────┘
                       │
                       │ WiFi/Bluetooth/Cellular
                       ▼
┌─────────────────────────────────────────────────────────────┐
│                    Cloud Infrastructure                       │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐      │
│  │  Media       │  │  AI/ML       │  │  Sync        │      │
│  │  Storage     │  │  Service     │  │  Service     │      │
│  └──────────────┘  └──────────────┘  └──────────────┘      │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐      │
│  │  Streaming   │  │  Analytics   │  │  User        │      │
│  │  Service     │  │  Service     │  │  Service     │      │
│  └──────────────┘  └──────────────┘  └──────────────┘      │
└──────────────────────┬──────────────────────────────────────┘
                       │
                       ▼
┌─────────────────────────────────────────────────────────────┐
│                    External Services                          │
│  (Facebook, Instagram, WhatsApp, Third-party APIs)            │
└─────────────────────────────────────────────────────────────┘

Core Components

Device Side:

  1. Camera System: Photo/video capture, multiple cameras
  2. Audio System: Microphone, speaker, audio processing
  3. Edge Processing: On-device ML inference, real-time processing
  4. Storage Manager: Local storage, compression, cache management
  5. Power Manager: Battery optimization, power modes
  6. Network Manager: Connectivity management, sync coordination
  7. Device Controller: User interface, voice commands

Cloud Side:

  1. Media Storage Service: Photo/video storage (S3)
  2. AI/ML Service: Heavy ML inference, model training
  3. Sync Service: Device-cloud synchronization
  4. Streaming Service: Live video streaming infrastructure
  5. Analytics Service: Usage analytics, insights
  6. User Service: User management, preferences, settings

Deep Dive

Component Design

Detailed Design

Device Architecture

Operating System:

  • Custom lightweight OS (based on Android/AOSP)
  • Real-time processing capabilities
  • Power-efficient kernel

Application Layer:

class MetaGlassDevice:
    def __init__(self):
        self.camera_system = CameraSystem()
        self.audio_system = AudioSystem()
        self.edge_processor = EdgeProcessor()
        self.storage_manager = StorageManager()
        self.power_manager = PowerManager()
        self.network_manager = NetworkManager()
        self.sync_service = SyncService()
    
    def capture_photo(self):
        # Capture photo
        photo = self.camera_system.capture()
        
        # Process on device
        processed_photo = self.edge_processor.process_photo(photo)
        
        # Store locally
        local_path = self.storage_manager.store(processed_photo)
        
        # Queue for cloud sync
        self.sync_service.queue_for_upload(local_path)
        
        return local_path

Edge Processing Service

On-Device ML Models:

  • Lightweight object detection
  • Face detection
  • Scene classification
  • Voice command recognition

Implementation:

class EdgeProcessor:
    def __init__(self):
        self.ml_engine = MLInferenceEngine()
        self.load_models()
    
    def load_models(self):
        # Load optimized models for edge device
        self.object_detector = self.ml_engine.load_model('object_detection_v1.tflite')
        self.face_detector = self.ml_engine.load_model('face_detection_v1.tflite')
        self.voice_recognizer = self.ml_engine.load_model('voice_commands_v1.tflite')
    
    def process_photo(self, photo):
        # Detect objects
        objects = self.object_detector.detect(photo)
        
        # Detect faces
        faces = self.face_detector.detect(photo)
        
        # Apply filters/effects
        processed = self.apply_filters(photo, objects, faces)
        
        # Add metadata
        metadata = {
            'objects': objects,
            'faces': faces,
            'timestamp': datetime.now(),
            'location': self.get_location()
        }
        
        return processed, metadata
    
    def process_video_frame(self, frame):
        # Real-time frame processing
        # Optimized for low latency
        processed_frame = self.object_detector.detect(frame)
        return processed_frame
    
    def process_voice_command(self, audio):
        # Voice command recognition
        command = self.voice_recognizer.recognize(audio)
        return command

Cloud Sync Service

Sync Architecture:

  • Background sync when connected
  • Queue-based upload
  • Incremental sync
  • Conflict resolution

Implementation:

class SyncService:
    def __init__(self):
        self.upload_queue = UploadQueue()
        self.sync_manager = SyncManager()
        self.network_manager = NetworkManager()
    
    def queue_for_upload(self, local_path):
        # Queue for upload
        self.upload_queue.add({
            'local_path': local_path,
            'type': 'photo',
            'priority': 'normal',
            'retry_count': 0
        })
        
        # Trigger sync if connected
        if self.network_manager.is_connected():
            self.start_sync()
    
    def start_sync(self):
        # Process upload queue
        while not self.upload_queue.empty():
            item = self.upload_queue.get()
            
            try:
                # Upload to cloud
                self.upload_to_cloud(item)
                
                # Mark as synced
                self.mark_as_synced(item)
            except Exception as e:
                # Retry logic
                self.handle_upload_error(item, e)
    
    def upload_to_cloud(self, item):
        # Read file
        file_data = self.read_file(item['local_path'])
        
        # Upload to S3
        s3_key = self.generate_s3_key(item)
        self.s3_client.upload_file(file_data, s3_key)
        
        # Update metadata
        self.update_metadata(s3_key, item)

Media Storage Service

Storage Architecture:

  • S3 for object storage
  • CDN for delivery
  • Compression and optimization
  • Tiered storage (hot/cold)

Implementation:

class MediaStorageService:
    def __init__(self):
        self.s3_client = S3Client()
        self.cdn = CDNService()
        self.compressor = MediaCompressor()
    
    def store_photo(self, photo_data, user_id, metadata):
        # Compress photo
        compressed = self.compressor.compress_photo(photo_data)
        
        # Generate storage key
        storage_key = f"users/{user_id}/photos/{uuid.uuid4()}.jpg"
        
        # Upload to S3
        self.s3_client.upload(
            bucket='meta-glass-photos',
            key=storage_key,
            data=compressed,
            metadata=metadata
        )
        
        # Invalidate CDN cache
        self.cdn.invalidate(storage_key)
        
        return storage_key
    
    def store_video(self, video_data, user_id, metadata):
        # Video processing pipeline
        # 1. Upload raw video
        raw_key = self.upload_raw_video(video_data, user_id)
        
        # 2. Trigger transcoding
        self.trigger_transcoding(raw_key, user_id)
        
        # 3. Store transcoded versions
        transcoded_keys = self.store_transcoded_videos(raw_key, user_id)
        
        return transcoded_keys

AI/ML Service

Cloud ML Processing:

  • Heavy ML inference
  • Model training
  • Custom model serving
  • Real-time inference

Implementation:

class MLService:
    def __init__(self):
        self.inference_engine = MLInferenceEngine()
        self.model_registry = ModelRegistry()
    
    def process_photo_heavy(self, photo_data):
        # Heavy ML processing in cloud
        # Object recognition with high accuracy
        objects = self.inference_engine.detect_objects(photo_data, model='high_accuracy')
        
        # Scene understanding
        scene = self.inference_engine.classify_scene(photo_data)
        
        # Image enhancement suggestions
        enhancements = self.inference_engine.suggest_enhancements(photo_data)
        
        return {
            'objects': objects,
            'scene': scene,
            'enhancements': enhancements
        }
    
    def process_voice_transcription(self, audio_data):
        # Speech-to-text
        transcription = self.inference_engine.transcribe(audio_data)
        
        # Language detection
        language = self.inference_engine.detect_language(audio_data)
        
        # Translation (if needed)
        if language != 'en':
            translation = self.inference_engine.translate(transcription, target='en')
        else:
            translation = transcription
        
        return {
            'transcription': transcription,
            'language': language,
            'translation': translation
        }

Streaming Service

Live Streaming Architecture:

  • Real-time video encoding
  • Adaptive bitrate streaming
  • Multi-platform distribution
  • Connection management

Implementation:

class StreamingService:
    def __init__(self):
        self.encoder = VideoEncoder()
        self.stream_manager = StreamManager()
        self.cdn = CDNService()
    
    def start_stream(self, user_id, destination):
        # Create stream
        stream_id = self.stream_manager.create_stream(user_id, destination)
        
        # Get streaming URL
        stream_url = self.cdn.get_streaming_url(stream_id)
        
        return {
            'stream_id': stream_id,
            'stream_url': stream_url,
            'rtmp_url': self.get_rtmp_url(stream_id)
        }
    
    def process_stream_frame(self, stream_id, frame_data):
        # Encode frame
        encoded_frame = self.encoder.encode_frame(frame_data)
        
        # Stream to CDN
        self.cdn.stream_frame(stream_id, encoded_frame)
        
        # Update stream metadata
        self.stream_manager.update_stream(stream_id, {
            'frame_count': self.increment_frame_count(stream_id),
            'bitrate': self.calculate_bitrate(encoded_frame)
        })

Power Management Service

Power Optimization:

  • Adaptive processing
  • Power modes
  • Background task scheduling
  • Battery-aware operations

Implementation:

class PowerManager:
    def __init__(self):
        self.battery_monitor = BatteryMonitor()
        self.power_modes = {
            'high_performance': PowerMode(performance=1.0, battery_life=0.5),
            'balanced': PowerMode(performance=0.7, battery_life=0.8),
            'power_save': PowerMode(performance=0.4, battery_life=1.0)
        }
        self.current_mode = 'balanced'
    
    def optimize_for_battery(self):
        battery_level = self.battery_monitor.get_level()
        
        if battery_level < 20:
            self.set_power_mode('power_save')
        elif battery_level < 50:
            self.set_power_mode('balanced')
        else:
            self.set_power_mode('high_performance')
    
    def schedule_background_task(self, task, priority):
        # Schedule task based on battery level
        battery_level = self.battery_monitor.get_level()
        
        if battery_level < 30 and priority == 'low':
            # Defer low-priority tasks
            self.defer_task(task)
        else:
            # Execute task
            self.execute_task(task)
    
    def optimize_processing(self, processing_task):
        # Adjust processing based on power mode
        mode = self.power_modes[self.current_mode]
        
        if mode.performance < 0.7:
            # Reduce processing quality
            processing_task.reduce_quality()
        
        if mode.performance < 0.5:
            # Skip non-essential processing
            processing_task.skip_non_essential()

Technology Choices

Device Side

Operating System:

  • Android/AOSP: Custom lightweight version
  • Real-Time Kernel: For low-latency processing

ML Framework:

  • TensorFlow Lite: Optimized for edge devices
  • ONNX Runtime: Cross-platform ML inference
  • Core ML: Apple devices (if applicable)

Media Processing:

  • FFmpeg: Video/audio processing
  • OpenCV: Image processing
  • Hardware encoders: GPU-accelerated encoding

Cloud Side

Storage:

  • S3: Object storage for media
  • CDN (CloudFront): Media delivery
  • Glacier: Long-term archival

ML/AI:

  • TensorFlow Serving: Model serving
  • PyTorch: Model training
  • AWS SageMaker: ML pipeline
  • Custom ML infrastructure: For specialized models

Streaming:

  • Kinesis Video Streams: Video streaming
  • MediaLive: Live video processing
  • CloudFront: CDN for streaming

Database:

  • PostgreSQL: User data, metadata
  • Redis: Cache, real-time data
  • DynamoDB: High-throughput metadata

Key Design Considerations

Battery Life Optimization

Strategies:

  1. Adaptive Processing: Reduce processing based on battery level
  2. Power Modes: High performance, balanced, power save
  3. Background Task Scheduling: Defer non-essential tasks
  4. Hardware Acceleration: Use GPU instead of CPU when possible
  5. Connection Management: Reduce network usage when battery low

Offline Functionality

Offline Capabilities:

  1. Local Storage: Store media locally
  2. Queue-Based Sync: Queue uploads for when connected
  3. Cached Models: Keep ML models on device
  4. Offline Mode: Core functionality without connectivity

Real-Time Processing

Optimization:

  1. Frame Skipping: Process every Nth frame for video
  2. Resolution Scaling: Lower resolution for real-time processing
  3. Model Optimization: Quantized models for faster inference
  4. Hardware Acceleration: GPU/NPU for ML inference

Privacy and Security

Security Measures:

  1. End-to-End Encryption: Encrypt media in transit and at rest
  2. Local Processing: Process sensitive data on-device when possible
  3. User Consent: Explicit consent for data sharing
  4. Data Anonymization: Anonymize data for analytics
  5. Access Control: Role-based access to user data

Failure Scenarios

Device Disconnection

Scenario: Device loses connectivity

Handling:

  1. Queue all operations locally
  2. Retry when connectivity restored
  3. Graceful degradation (offline mode)
  4. Sync when reconnected

Cloud Service Failure

Scenario: Cloud service unavailable

Handling:

  1. Continue local operations
  2. Queue uploads for later
  3. Serve from cache
  4. Fallback to alternative regions

Battery Depletion

Scenario: Battery runs low

Handling:

  1. Enter power save mode
  2. Reduce processing quality
  3. Defer non-essential tasks
  4. Prioritize critical operations

Storage Full

Scenario: Device storage full

Handling:

  1. Auto-delete oldest cached media
  2. Compress existing media
  3. Prompt user to sync to cloud
  4. Clear temporary files

What Interviewers Look For

Edge Computing Skills

  1. On-Device Processing
    • Real-time ML inference
    • Power-efficient processing
    • Hardware acceleration
    • Red Flags: Cloud-only, high power, no acceleration
  2. Power Management
    • Battery optimization
    • Thermal management
    • Adaptive quality
    • Red Flags: Poor battery life, thermal issues, no optimization
  3. Offline Functionality
    • Core features offline
    • Local storage
    • Sync when online
    • Red Flags: No offline, network required, poor UX

AR/Smart Glass Skills

  1. Media Capture
    • High-quality capture
    • Real-time preview
    • Low-latency processing
    • Red Flags: Poor quality, high latency, slow capture
  2. AI/ML Integration
    • Voice commands
    • Object recognition
    • Live translation
    • Red Flags: No AI/ML, slow inference, poor accuracy
  3. Display & UI
    • AR overlays
    • Minimal UI
    • Privacy indicators
    • Red Flags: Poor UI, no privacy, intrusive

Distributed Systems Skills

  1. Cloud Integration
    • Heavy processing in cloud
    • Storage and sync
    • Red Flags: No cloud, poor sync, inefficient
  2. Hybrid Processing
    • Edge + cloud
    • Adaptive routing
    • Red Flags: Single approach, no adaptation, poor balance
  3. Scalability Design
    • Millions of devices
    • Horizontal scaling
    • Red Flags: No scale consideration, bottlenecks, poor scaling

Problem-Solving Approach

  1. Constraint Handling
    • Battery life
    • Connectivity
    • Storage
    • Red Flags: Ignoring constraints, poor handling
  2. Edge Cases
    • Network failures
    • Storage full
    • Battery low
    • Red Flags: Ignoring edge cases, no handling
  3. Trade-off Analysis
    • Power vs performance
    • Privacy vs features
    • Red Flags: No trade-offs, dogmatic choices

System Design Skills

  1. Component Design
    • Capture service
    • Processing service
    • Sync service
    • Red Flags: Monolithic, unclear boundaries
  2. Privacy & Security
    • Data protection
    • Privacy by design
    • Secure communication
    • Red Flags: No privacy, insecure, data leaks
  3. Real-Time Processing
    • Low-latency ML
    • Streaming support
    • Red Flags: High latency, no streaming, slow processing

Communication Skills

  1. Edge Computing Explanation
    • Can explain on-device processing
    • Understands power constraints
    • Red Flags: No understanding, vague explanations
  2. Architecture Justification
    • Explains design decisions
    • Discusses alternatives
    • Red Flags: No justification, no alternatives

Meta-Specific Focus

  1. Edge Computing Expertise
    • On-device processing knowledge
    • Power optimization
    • Key: Show edge computing expertise
  2. Privacy-First Design
    • Privacy by design
    • User control
    • Key: Demonstrate privacy focus

Conclusion

Designing a Meta Glass system requires:

  1. Edge Computing: On-device processing for real-time features
  2. Cloud Integration: Heavy processing and storage in cloud
  3. Power Management: Optimize for battery life
  4. Offline Support: Work without connectivity
  5. Real-Time Processing: Low-latency ML inference
  6. Privacy: Protect user data and privacy
  7. Scalability: Handle millions of devices

Key Design Principles:

  • Edge-First: Process on device when possible
  • Power-Aware: All operations consider battery impact
  • Offline-Capable: Core functionality works offline
  • Privacy by Design: Default to privacy-preserving approaches
  • Hybrid Processing: Combine edge and cloud processing
  • Adaptive Quality: Adjust quality based on constraints

This system design demonstrates understanding of edge computing, IoT systems, power optimization, real-time processing, and cloud integration—all critical for building production-grade smart glasses systems.