OpenAI's $6.5 Billion Jony Ive Acquisition: The AI Hardware Revolution and the Windsurf Counter-Coup

"""
OpenAI Smartphone Hardware Architecture
Based on supply chain analysis and industry reports
"""

class OpenAISmartphoneSpec:
    """
    Specification class for OpenAI's AI Agent Phone
    Target: Compete directly with iPhone in post-smartphone era
    """
    
    # Processing Architecture
    PROCESSOR = "MediaTek Dimensity 9600 (custom)"
    FAB_NODE = "TSMC N2P (2nm-class)"
    MANUFACTURER = "Luxshare Precision Industry (exclusive)"
    
    # Dual AI Processor Configuration
    AI_PROCESSORS = {
        "primary": {
            "purpose": "Vision and Language Processing",
            "capability": "Multi-modal AI inference"
        },
        "secondary": {
            "purpose": "Real-time Context Processing", 
            "capability": "Agentic task orchestration"
        }
    }
    
    # Memory and Storage
    MEMORY_CONFIG = {
        "type": "LPDDR6",
        "capacity_range": "16GB - 24GB",
        "bandwidth": "High-bandwidth for AI workloads"
    }
    
    STORAGE_CONFIG = {
        "type": "UFS 4.0",
        "capacity_range": "256GB - 1TB"
    }
    
    # Vision System
    CAMERA_SPECS = {
        "manufacturer": "Sunny Optical",
        "isp": "Enhanced HDR Pipeline",
        "features": [
            "Real-world spatial sensing",
            "Enhanced depth mapping",
            "Low-light optimization"
        ]
    }
    
    # Security
    SECURITY_FEATURES = {
        "tee": "Trusted Execution Environment",
        "biometrics": "Advanced biometric isolation",
        "process_isolation": "Secure enclave for AI processing"
    }
    
    def get_target_shipments(self) -> dict:
        """Projected shipments based on development timeline"""
        return {
            "2027_h1": "Initial production run",
            "2027_total": "10-15 million units",
            "2028_total": "15-20 million units",
            "combined": "30 million units over two years"
        }


class DeviceComparison:
    """Compare OpenAI device with competitors"""
    
    DEVICES = {
        "openai_smartphone": {
            "launch": "2027 H1",
            "chip": "MediaTek Dimensity 9600",
            "manufacturers": ["Luxshare Precision"],
            "shipments_target": "30M (2027-2028)"
        },
        "apple_iphone_ai": {
            "launch": "WWDC 2026",
            "chip": "A20 Bionic",
            "manufacturers": ["Foxconn"],
            "focus": "On-device AI with Siri evolution"
        },
        "google_pixel_ai": {
            "launch": "Q4 2026",
            "chip": "Tensor G5",
            "manufacturers": ["Foxconn"],
            "focus": "Gemini Nano integration"
        }
    }


# Example usage
if __name__ == "__main__":
    phone = OpenAISmartphoneSpec()
    print(f"Processor: {phone.PROCESSOR}")
    print(f"Manufacturing Partner: {phone.MANUFACTURER}")
    print(f"Target Shipments: {phone.get_target_shipments()}")

"""
OpenAI Device AI Software Stack
Voice-first, context-aware AI architecture
"""

from dataclasses import dataclass
from typing import List, Optional, Dict
from enum import Enum


class AIComponent(Enum):
    """Core AI components in OpenAI device"""
    GROK_MODEL = "Grok3/GPT-5.5 base model"
    VOICE_AI = "GPT-Realtime-2 voice interface"
    VISION_SYSTEM = "Multi-modal vision processor"
    CONTEXT_ENGINE = "Agentic context orchestration"
    SECURITY_LAYER = "Privacy-preserving computation"


@dataclass
class VoiceAIConfig:
    """Configuration for voice AI system"""
    model: str = "GPT-Realtime-2"
    latency_target_ms: int = 50
    languages: int = 70  # Supported languages
    features: List[str] = None
    
    def __post_init__(self):
        self.features = [
            "Natural speech indistinguishable from human",
            "Real-time interruptions",
            "Simultaneous feedback during user speech",
            "Contextual understanding",
            "Multi-turn reasoning"
        ]


@dataclass
class VisionSystemConfig:
    """Configuration for vision processing"""
    isp_pipeline: str = "Enhanced HDR"
    sensors: List[str] = None
    processing_capabilities: List[str] = None
    
    def __post_init__(self):
        self.sensors = [
            "Primary camera (high resolution)",
            "Depth sensor",
            "Ambient light sensor",
            "Spatial awareness array"
        ]
        self.processing_capabilities = [
            "Real-world object identification",
            "Environmental mapping",
            "Gesture recognition",
            "Gaze detection (for smart glasses integration)"
        ]


class ContextEngine:
    """
    Agentic AI system for continuous context awareness
    Captures user state across multiple dimensions
    """
    
    def __init__(self):
        self.context_dimensions = [
            "location", "activity", "communication",
            "calendar", "health", "preferences", "habits"
        ]
        self.state_buffer_size = 10000  # tokens
        
    def capture_user_state(self) -> Dict[str, any]:
        """
        Capture comprehensive user state for AI inference
        Returns real-time context for agentic operations
        """
        return {
            "location": "Real-time GPS + WiFi positioning",
            "activity": "Movement patterns + app usage",
            "communication": "Messages + calls + emails",
            "calendar": "Upcoming events + deadlines",
            "health": "Heart rate + activity + sleep (wearable)",
            "preferences": "Learned user preferences",
            "habits": "Routine patterns"
        }
    
    def generate_agentic_action(self, user_state: Dict) -> Dict:
        """
        Generate proactive AI actions based on context
        Moving from reactive to predictive computing
        """
        return {
            "suggestion": "Context-aware recommendation",
            "action": "Pre-emptive task completion",
            "notification": "Intelligent timing",
            "automation": "Routine task handling"
        }


class AISoftwareStack:
    """
    Complete AI software architecture for OpenAI devices
    Voice-first, context-aware, privacy-preserving
    """
    
    def __init__(self):
        self.voice_ai = VoiceAIConfig()
        self.vision_system = VisionSystemConfig()
        self.context_engine = ContextEngine()
        self.cloud_sync_enabled = True
        self.offline_mode = True
        
    def get_stack_components(self) -> List[AIComponent]:
        """Return ordered list of AI components"""
        return [
            AIComponent.GROK_MODEL,
            AIComponent.VOICE_AI,
            AIComponent.VISION_SYSTEM,
            AIComponent.CONTEXT_ENGINE,
            AIComponent.SECURITY_LAYER
        ]
    
    def get_response_time(self) -> Dict[str, int]:
        """Expected response times for different AI operations"""
        return {
            "voice_command": 50,      # ms
            "context_update": 100,    # ms
            "vision_processing": 200, # ms
            "cloud_sync": 500,        # ms
            "complex_reasoning": 1000 # ms
        }

"""
Windsurf-Gemini Integration Architecture
Demonstrates how Google's acquisition enhances coding capabilities
"""

from typing import List, Dict, Optional
from dataclasses import dataclass


@dataclass
class CodingAgentConfig:
    """Configuration for AI coding agent system"""
    
    # Core Model Configuration
    primary_model: str = "Gemini 3.5"
    fallback_model: str = "Gemini 3.0"
    
    # Context Windows
    context_window: int = 2000000  # 2M tokens
    
    # Code Intelligence Features
    capabilities: List[str] = None
    
    def __post_init__(self):
        self.capabilities = [
            "Autonomous code generation",
            "Multi-file refactoring",
            "Bug detection and correction",
            "Test generation",
            "Code review automation",
            "Repository-scale understanding",
            "CI/CD integration"
        ]


class CodebaseContextBuilder:
    """
    Builds comprehensive codebase context for AI inference
    Essential for repository-scale coding tasks
    """
    
    def __init__(self, repo_path: str):
        self.repo_path = repo_path
        self.parsed_files = {}
        self.dependency_graph = {}
        self.architecture_map = {}
        
    def build_context(self, 
                      max_files: int = 1000,
                      include_history: bool = True) -> str:
        """
        Build comprehensive codebase context string
        Optimized for Qwen3-Coder-Next-style repo-scale understanding
        """
        
        # File parsing and filtering
        source_files = self._filter_relevant_files(max_files)
        
        # Build dependency graph
        dependency_context = self._build_dependency_context()
        
        # Architectural understanding
        architecture_context = self._extract_architecture()
        
        # Git history analysis (optional)
        history_context = ""
        if include_history:
            history_context = self._analyze_git_history()
        
        return f"""
Repository Structure: {self._get_structure()}
Dependencies: {dependency_context}
Architecture: {architecture_context}
Recent Changes: {history_context}
Files: {source_files}
""".strip()
    
    def _filter_relevant_files(self, max_files: int) -> List[str]:
        """Filter and prioritize files for context"""
        return [
            "src/main.py",
            "src/models/*.py",
            "src/services/*.py",
            "tests/**/*.py",
            "config/*.yaml"
        ]
    
    def _build_dependency_context(self) -> str:
        """Build dependency relationship context"""
        return """
Module Dependencies:
- models/user.py -> services/auth.py
- models/order.py -> services/payment.py
- services/*.py -> utils/helpers.py
"""
    
    def _extract_architecture(self) -> str:
        """Extract architectural patterns and decisions"""
        return """
Architecture: Layered (MVC pattern)
Database: PostgreSQL + Redis
API: REST + GraphQL
Frontend: React + TypeScript
"""
    
    def _analyze_git_history(self) -> str:
        """Analyze recent git history for context"""
        return """
Last 10 commits:
- Fix: Authentication middleware (2 hours ago)
- Feature: User dashboard redesign (1 day ago)
- Refactor: Payment processing service (3 days ago)
"""


class GeminiCodingAgent:
    """
    AI Coding Agent powered by Gemini
    Integrated with Windsurf's IDE expertise
    """
    
    def __init__(self, config: CodingAgentConfig):
        self.config = config
        self.codebase_context = None
        self.active_models = ["gemini-3.5-pro", "gemini-3.5-flash"]
        
    def generate_code(self, 
                      prompt: str, 
                      language: str = "python",
                      context: Optional[str] = None) -> str:
        """
        Generate code with full codebase context
        Leverages Windsurf's context understanding
        """
        
        enhanced_prompt = f"""
Language: {language}
Context:
{context or self.codebase_context}

Task: {prompt}

Generate code that:
1. Follows existing patterns in the codebase
2. Includes appropriate error handling
3. Has comprehensive type hints
4. Contains inline documentation
"""
        
        # Would call Gemini API here
        return "# Generated code..."
    
    def refactor_code(self, 
                      file_path: str, 
                      objective: str) -> Dict[str, str]:
        """
        Autonomous code refactoring
        Multi-file awareness enabled
        """
        
        # Analyze current implementation
        current = self._read_file(file_path)
        
        # Generate refactoring plan
        plan = self._create_refactor_plan(current, objective)
        
        # Execute with multi-file awareness
        changes = self._execute_refactor(plan)
        
        return changes
    
    def run_code_review(self, diff: str) -> Dict[str, any]:
        """
        AI-powered code review
        Detects bugs, security issues, style violations
        """
        
        return {
            "bugs": [],
            "security_issues": [],
            "style_violations": [],
            "suggestions": [],
            "overall_quality": "high"
        }


class WindsurfIntegration:
    """
    Windsurf-specific features integrated with Gemini
    Maintains Codeium's IDE excellence
    """
    
    def __init__(self):
        self.ide_features = {
            "code_completion": True,
            "inline_errors": True,
            "refactoring_tools": True,
            "contextual_search": True
        }
        
    def get_suggestions(self, cursor_position: Dict) -> List[str]:
        """
        Real-time code suggestions
        Combines Gemini intelligence with IDE context
        """
        
        return [
            "function auto-complete",
            "import suggestions",
            "type inference",
            "docstring generation"
        ]

package openai_hardware

import (
	"fmt"
	"time"
)

// DeviceType represents different OpenAI hardware products
type DeviceType int

const (
	Smartphone DeviceType = iota
	SmartSpeaker
	SweetpeaEarbuds
	SmartGlasses
	SmartLamp
)

// ManufacturingPartner represents a supply chain partner
type ManufacturingPartner struct {
	Name        string
	Role        string
	Capacity    int // units per month
	LeadTime    time.Duration
	QualityScore float64
}

// SupplyChain represents OpenAI's manufacturing network
type SupplyChain struct {
	Partners map[string]ManufacturingPartner
}

// NewSupplyChain initializes OpenAI's supply chain
func NewSupplyChain() *SupplyChain {
	return &SupplyChain{
		Partners: map[string]ManufacturingPartner{
			"foxconn": {
				Name:        "Foxconn (Hon Hai)",
				Role:        "Primary Manufacturer",
				Capacity:    15000000, // 15M units/month
				LeadTime:    45 * 24 * time.Hour,
				QualityScore: 0.98,
			},
			"luxshare": {
				Name:        "Luxshare Precision",
				Role:        "Smartphone Exclusive",
				Capacity:    8000000, // 8M units/month
				LeadTime:    60 * 24 * time.Hour,
				QualityScore: 0.95,
			},
			"tsmc": {
				Name:        "TSMC",
				Role:        "Chip Fabrication (N2P)",
				Capacity:    100000, // wafers/month
				LeadTime:    90 * 24 * time.Hour,
				QualityScore: 0.99,
			},
			"mediatek": {
				Name:        "MediaTek",
				Role:        "AI Processor Supply",
				Capacity:    5000000, // chips/month
				LeadTime:    75 * 24 * time.Hour,
				QualityScore: 0.96,
			},
			"sunny_optical": {
				Name:        "Sunny Optical",
				Role:        "Camera Modules",
				Capacity:    10000000, // modules/month
				LeadTime:    30 * 24 * time.Hour,
				QualityScore: 0.97,
			},
		},
	}
}

// DeviceSpec represents specifications for a device
type DeviceSpec struct {
	Type           DeviceType
	Name           string
	TargetPrice    float64
	TargetUnits    int
	LaunchDate     time.Time
	Manufacturers  []string
	Components     []string
}

// GetDeviceRoadmap returns the product roadmap
func GetDeviceRoadmap() []DeviceSpec {
	return []DeviceSpec{
		{
			Type:          SmartSpeaker,
			Name:          "AI Smart Speaker",
			TargetPrice:   250.00, // $200-$300 range
			TargetUnits:   10000000,
			LaunchDate:    time.Date(2027, 1, 1, 0, 0, 0, 0, time.UTC),
			Manufacturers: []string{"foxconn"},
			Components:    []string{"camera", "microphone_array", "speaker"},
		},
		{
			Type:          Smartphone,
			Name:          "AI Agent Phone",
			TargetPrice:   999.00,
			TargetUnits:   30000000,
			LaunchDate:    time.Date(2027, 6, 1, 0, 0, 0, 0, time.UTC),
			Manufacturers: []string{"luxshare"},
			Components:    []string{"dimensity_9600", "dual_ai_processor", "camera"},
		},
		{
			Type:          SweetpeaEarbuds,
			Name:          "Sweetpea AI Earbuds",
			TargetPrice:   299.00,
			TargetUnits:   40000000,
			LaunchDate:    time.Date(2027, 9, 1, 0, 0, 0, 0, time.UTC),
			Manufacturers: []string{"foxconn"},
			Components:    []string{"2nm_chip", "ai_processor", "sensors"},
		},
		{
			Type:          SmartGlasses,
			Name:          "AI Smart Glasses",
			TargetPrice:   599.00,
			TargetUnits:   15000000,
			LaunchDate:    time.Date(2028, 1, 1, 0, 0, 0, 0, time.UTC),
			Manufacturers: []string{"foxconn"},
			Components:    []string{"display", "camera", "ai_processor"},
		},
	}
}

// ProductionPlan calculates production targets and timelines
type ProductionPlan struct {
	Device          DeviceSpec
	MonthlyTargets  []int // units per month
	QualityTargets  []float64
	CostTargets     []float64
}

// CalculateProductionPlan generates a detailed production plan
func CalculateProductionPlan(device DeviceSpec, months int) ProductionPlan {
	plan := ProductionPlan{
		Device:          device,
		MonthlyTargets:  make([]int, months),
		QualityTargets:  make([]float64, months),
		CostTargets:     make([]float64, months),
	}
	
	// Ramp up production over time
	for i := 0; i < months; i++ {
		rampPercentage := float64(i+1) / float64(months)
		plan.MonthlyTargets[i] = int(float64(device.TargetUnits) * rampPercentage / float64(months))
		plan.QualityTargets[i] = 0.95 + (0.04 * rampPercentage)
		plan.CostTargets[i] = device.TargetPrice * (1.5 - (0.5 * rampPercentage))
	}
	
	return plan
}

// Main function demonstrating supply chain analysis
func SupplyChainDemo() {
	sc := NewSupplyChain()
	roadmap := GetDeviceRoadmap()
	
	fmt.Println("=== OpenAI Hardware Supply Chain ===\n")
	
	for _, partner := range sc.Partners {
		fmt.Printf("Partner: %s\n", partner.Name)
		fmt.Printf("  Role: %s\n", partner.Role)
		fmt.Printf("  Capacity: %d units/month\n", partner.Capacity)
		fmt.Printf("  Lead Time: %v\n", partner.LeadTime)
		fmt.Printf("  Quality Score: %.2f%%\n\n", partner.QualityScore*100)
	}
	
	fmt.Println("=== Product Roadmap ===\n")
	
	for _, device := range roadmap {
		plan := CalculateProductionPlan(device, 12)
		fmt.Printf("Device: %s\n", device.Name)
		fmt.Printf("  Target Price: $%.2f\n", device.TargetPrice)
		fmt.Printf("  Target Units: %d\n", device.TargetUnits)
		fmt.Printf("  Launch: %s\n", device.LaunchDate.Format("January 2006"))
		fmt.Printf("  Manufacturers: %v\n\n", device.Manufacturers)
	}
}

"""
Voice AI System Implementation
Based on OpenAI's GPT-Realtime-2 and translation capabilities
"""

import asyncio
from typing import AsyncIterator, Callable, Optional
from dataclasses import dataclass
from enum import Enum


class VoiceMode(Enum):
    """Voice interaction modes"""
    FULL_DUPLEX = "full_duplex"      # Simultaneous input/output
    HALF_DUPLEX = "half_duplex"      # Turn-based
    INTERRUPTIBLE = "interruptible"   # Can be interrupted


@dataclass
class VoiceConfig:
    """Voice AI configuration"""
    model: str = "gpt-realtime-2"
    voice: str = "alloy"  # Voice style
    language: str = "auto"  # Auto-detect
    mode: VoiceMode = VoiceMode.FULL_DUPLEX
    latency_target_ms: int = 50
    
    # Translation settings
    translation_enabled: bool = True
    source_languages: list = None
    
    def __post_init__(self):
        if self.source_languages is None:
            self.source_languages = ["en", "zh", "es", "fr", "de", "ja", "ko"]
        # Plus 70 total supported languages


class RealtimeVoiceSession:
    """
    Full-duplex voice session with OpenAI's Realtime API
    Supports interruption, translation, and real-time transcription
    """
    
    def __init__(self, config: VoiceConfig):
        self.config = config
        self.audio_buffer = []
        self.transcript_buffer = []
        self.is_active = False
        
    async def connect(self, api_key: str):
        """Establish WebSocket connection to Realtime API"""
        # Would use OpenAI's Realtime API WebSocket
        self.is_active = True
        print(f"Connected to Realtime API with model: {self.config.model}")
        
    async def send_audio(self, audio_chunk: bytes) -> Optional[str]:
        """
        Send audio chunk and receive immediate transcription
        Target latency: 50ms
        """
        
        # Simulate low-latency transcription
        transcription = await self._transcribe(audio_chunk)
        
        # Update buffer
        self.transcript_buffer.append(transcription)
        
        return transcription
    
    async def _transcribe(self, audio: bytes) -> str:
        """Simulate transcription with Whisper-level accuracy"""
        # In production, this calls GPT-Realtime-Whisper
        await asyncio.sleep(0.01)  # Simulate processing
        return "Transcribed text..."
    
    async def interrupt(self):
        """Interrupt current response and clear buffers"""
        self.audio_buffer.clear()
        self.transcript_buffer.clear()
        print("Session interrupted")
        
    async def disconnect(self):
        """Close connection and cleanup"""
        self.is_active = False
        self.audio_buffer.clear()
        self.transcript_buffer.clear()
        print("Disconnected from Realtime API")


class TranslationService:
    """
    Real-time translation between 70 languages
    Based on GPT-Realtime-Translate capabilities
    """
    
    def __init__(self, pricing_per_minute: float = 0.034):
        self.pricing = pricing_per_minute
        self.supported_languages = [
            "en", "zh", "es", "fr", "de", "ja", "ko", "ar", "ru",
            "pt", "it", "hi", "th", "vi", "id", "ms", "tr", "pl",
            "nl", "sv", "da", "no", "fi", "cs", "el", "he", "hu"
            # ... 70 total languages
        ]
        
    async def translate_stream(
        self, 
        source_audio: AsyncIterator[bytes],
        source_lang: str,
        target_lang: str
    ) -> AsyncIterator[str]:
        """
        Stream translations in real-time
        Price: $0.034/minute
        """
        
        accumulated_text = ""
        translated_buffer = []
        
        async for chunk in source_audio:
            accumulated_text += await self._process_chunk(chunk)
            
            # Batch translations for efficiency
            if len(accumulated_text) > 100:
                translation = await self._translate_batch(
                    accumulated_text, source_lang, target_lang
                )
                translated_buffer.append(translation)
                accumulated_text = ""
                
            yield "\n".join(translated_buffer)
        
        # Flush remaining
        if accumulated_text:
            final_translation = await self._translate_batch(
                accumulated_text, source_lang, target_lang
            )
            yield final_translation
    
    async def _translate_batch(
        self, 
        text: str, 
        source: str, 
        target: str
    ) -> str:
        """Translate a batch of text"""
        # Would call translation API
        return f"[Translated from {source} to {target}]: {text}"


class VoiceAIDemo:
    """Demonstration of voice AI capabilities"""
    
    async def demo_full_duplex(self):
        """Demonstrate simultaneous conversation"""
        config = VoiceConfig(
            mode=VoiceMode.FULL_DUPLEX,
            latency_target_ms=50
        )
        
        session = RealtimeVoiceSession(config)
        await session.connect("demo-api-key")
        
        print("\n=== Full-Duplex Voice Demo ===")
        print("Say something... (simulated)")
        
        # Simulate conversation
        for i in range(5):
            audio = b"audio_chunk_" + str(i).encode()
            transcript = await session.send_audio(audio)
            print(f"You said: {transcript}")
            await asyncio.sleep(0.1)
            
        await session.disconnect()
        
    async def demo_translation(self):
        """Demonstrate real-time translation"""
        translator = TranslationService()
        
        print("\n=== Translation Demo ===")
        
        async def fake_audio_stream():
            for phrase in ["Hello", "How are you", "Thank you"]:
                yield phrase.encode()
                await asyncio.sleep(0.5)
        
        translation_stream = translator.translate_stream(
            fake_audio_stream(),
            source_lang="en",
            target_lang="zh"
        )
        
        async for translation in translation_stream:
            print(f"Translation: {translation}")


# Run demo
async def main():
    demo = VoiceAIDemo()
    await demo.demo_full_duplex()
    await demo.demo_translation()


if __name__ == "__main__":
    asyncio.run(main())

"""
Design Team Integration System
Tracks Jony Ive's team contributions to OpenAI hardware
"""

from dataclasses import dataclass, field
from typing import List, Dict, Optional
from datetime import datetime
from enum import Enum


class Role(Enum):
    """Team member roles"""
    PRODUCT_DESIGN = "Product Design Lead"
    INDUSTRIAL_DESIGN = "Industrial Design"
    UX_RESEARCH = "UX Research"
    HARDWARE_ENGINEERING = "Hardware Engineering"
    SYSTEMS_DESIGN = "Systems Design"


class FormerEmployer(Enum):
    """Previous employers"""
    APPLE = "Apple Inc."
    LOVE_FROM = "LoveFrom"
    OTHER = "Other"


@dataclass
class DesignTeamMember:
    """Represents a design team member"""
    name: str
    role: Role
    former_employer: FormerEmployer
    expertise: List[str]
    years_experience: int
    joined_date: datetime
    contributions: List[str] = field(default_factory=list)
    
    def add_contribution(self, contribution: str):
        """Record a design contribution"""
        self.contributions.append(contribution)


@dataclass
class DesignPrinciple:
    """Design principle from Jony Ive's philosophy"""
    name: str
    description: str
    implementation_status: str
    
    def get_guidelines(self) -> str:
        """Get implementation guidelines"""
        return f"{self.name}: {self.description}"


class LoveFromDesignSystem:
    """
    Design system integrating Jony Ive's philosophy
    Human-centric, technology-disappearing design
    """
    
    def __init__(self):
        self.principles = [
            DesignPrinciple(
                name="Technology Disappears",
                description="Technology should become invisible, enhancing human experience without demanding attention",
                implementation_status="core"
            ),
            DesignPrinciple(
                name="Human-Centric Form",
                description="Form follows human needs and behaviors, not manufacturing convenience",
                implementation_status="core"
            ),
            DesignPrinciple(
                name="Material Honesty",
                description="Use materials for what they are, not as imitations of other materials",
                implementation_status="established"
            ),
            DesignPrinciple(
                name="Quiet Confidence",
                description="Products should project confidence through restraint, not visual noise",
                implementation_status="established"
            ),
            DesignPrinciple(
                name="Contextual Integration",
                description="Devices should harmonize with their environment and use contexts",
                implementation_status="emerging"
            )
        ]
        
        self.team = self._initialize_team()
        
    def _initialize_team(self) -> List[DesignTeamMember]:
        """Initialize the design team roster"""
        return [
            DesignTeamMember(
                name="Tang Tan",
                role=Role.PRODUCT_DESIGN,
                former_employer=FormerEmployer.APPLE,
                expertise=["iPhone development", "product strategy", "supply chain"],
                years_experience=15,
                joined_date=datetime(2025, 6, 1),
                contributions=[
                    "Smartphone industrial design",
                    "Component integration strategy"
                ]
            ),
            DesignTeamMember(
                name="Evans Hankey",
                role=Role.INDUSTRIAL_DESIGN,
                former_employer=FormerEmployer.APPLE,
                expertise=["iPad design", "material selection", "user experience"],
                years_experience=12,
                joined_date=datetime(2025, 6, 1),
                contributions=[
                    "Form factor specification",
                    "Material palette development"
                ]
            ),
            DesignTeamMember(
                name="Scott Cannon",
                role=Role.HARDWARE_ENGINEERING,
                former_employer=FormerEmployer.APPLE,
                expertise=["hardware architecture", "thermal design", "durability"],
                years_experience=18,
                joined_date=datetime(2025, 7, 1)
            ),
            DesignTeamMember(
                name="Jony Ive",
                role=Role.SYSTEMS_DESIGN,
                former_employer=FormerEmployer.LOVE_FROM,
                expertise=[
                    "holistic design",
                    "Apple ecosystem",
                    "minimalist aesthetics",
                    "human-computer interaction"
                ],
                years_experience=30,
                joined_date=datetime(2025, 5, 1),
                contributions=[
                    "Overall product vision",
                    "Design philosophy integration",
                    "Strategic direction"
                ]
            )
        ]
    
    def get_apple_talent_count(self) -> int:
        """Count former Apple employees"""
        return sum(
            1 for member in self.team 
            if member.former_employer == FormerEmployer.APPLE
        )
    
    def generate_design_spec(
        self, 
        product_type: str,
        user_requirements: Dict
    ) -> Dict:
        """Generate design specification following LoveFrom principles"""
        
        return {
            "product_type": product_type,
            "design_principles": [p.name for p in self.principles if p.implementation_status == "core"],
            "form_factor": self._determine_form_factor(product_type),
            "materials": self._select_materials(product_type),
            "user_experience": self._define_ux_goals(user_requirements),
            "team_leads": [
                {"name": m.name, "role": m.role.value}
                for m in self.team[:2]
            ]
        }
    
    def _determine_form_factor(self, product_type: str) -> str:
        """Determine optimal form factor"""
        form_map = {
            "smartphone": "Edge-to-edge display, minimal bezels, pocket-friendly",
            "smart_speaker": "Organic curves, ambient presence, non-intrusive",
            "earbuds": "Metal pebble, detachable capsules, behind-ear fit",
            "glasses": "Lightweight frame, balanced weight distribution"
        }
        return form_map.get(product_type, "Minimalist enclosure")
    
    def _select_materials(self, product_type: str) -> List[str]:
        """Select materials following material honesty principle"""
        return [
            "Anodized aluminum (primary body)",
            "Recycled stainless steel (accents)",
            "Borosilicate glass (display protection)",
            "Bio-based polymers (internal components)"
        ]
    
    def _define_ux_goals(self, requirements: Dict) -> Dict:
        """Define UX goals based on requirements"""
        return {
            "primary_interaction": requirements.get("interaction", "voice"),
            "attention_demand": "minimal",
            "learning_curve": "intuitive within 5 minutes",
            "accessibility": "WCAG 2.1 AA compliant"
        }


class DesignReviewSystem:
    """System for design review and iteration"""
    
    def __init__(self, design_system: LoveFromDesignSystem):
        self.design_system = design_system
        
    def review_design(
        self, 
        design: Dict, 
        review_level: str = "full"
    ) -> Dict:
        """Review design against LoveFrom principles"""
        
        issues = []
        suggestions = []
        
        for principle in self.design_system.principles:
            compliance = self._check_principle_compliance(
                design, principle
            )
            
            if not compliance["compliant"]:
                issues.append({
                    "principle": principle.name,
                    "issue": compliance["issue"],
                    "suggestion": principle.get_guidelines()
                })
                
        return {
            "approved": len(issues) == 0 if review_level == "full" else True,
            "issues": issues,
            "review_level": review_level,
            "timestamp": datetime.now().isoformat()
        }
    
    def _check_principle_compliance(
        self, 
        design: Dict, 
        principle: DesignPrinciple
    ) -> Dict:
        """Check compliance with a specific principle"""
        
        # Simplified compliance checking
        checks = {
            "Technology Disappears": lambda d: d.get("visual_complexity", 1) < 3,
            "Human-Centric Form": lambda d: d.get("ergonomic_score", 5) >= 4,
            "Material Honesty": lambda d: d.get("material_imitation", False) == False,
            "Quiet Confidence": lambda d: d.get("visual_noise", 1) < 3
        }
        
        check_fn = checks.get(principle.name, lambda d: True)
        
        return {
            "compliant": check_fn(design),
            "issue": "Visual complexity too high" if not check_fn(design) else None
        }


# Demonstration
def design_system_demo():
    """Demonstrate design system capabilities"""
    
    ds = LoveFromDesignSystem()
    review = DesignReviewSystem(ds)
    
    print("=== LoveFrom Design System Demo ===\n")
    
    print("Design Principles:")
    for p in ds.principles:
        print(f"  - {p.name}: {p.description[:50]}...")
    
    print(f"\nTeam Size: {len(ds.team)} members")
    print(f"Apple Talent: {ds.get_apple_talent_count()} ex-Apple employees")
    
    print("\nTeam Members:")
    for member in ds.team:
        print(f"  - {member.name} ({member.role.value})")
        print(f"    Former: {member.former_employer.value}")
        print(f"    Expertise: {', '.join(member.expertise[:2])}")
    
    print("\nSmartphone Design Spec:")
    spec = ds.generate_design_spec(
        "smartphone",
        {"interaction": "voice", "mobility": "high"}
    )
    for key, value in spec.items():
        print(f"  {key}: {value}")
    
    print("\nDesign Review:")
    review_result = review.review_design({
        "visual_complexity": 2,
        "ergonomic_score": 4.5,
        "material_imitation": False,
        "visual_noise": 2
    })
    print(f"  Approved: {review_result['approved']}")


if __name__ == "__main__":
    design_system_demo()