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# Old approach: Re-entering context every time
def ask_ai_old_way():
    prompt = """
    I'm a Python developer who primarily uses FastAPI,
    prefers type hints, and emphasizes error handling.
    Please improve the following API endpoint:
    [code]
    """
    return get_ai_response(prompt)

# New approach: AI remembers developer context
def ask_ai_with_memory():
    # AI already knows developer's style and preferences
    prompt = "Please improve the following API endpoint: [code]"
    return get_ai_response_with_memory(prompt)

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# Automatic UI generation from screenshots
def generate_ui_from_screenshot(image_path: str) -> str:
    """
    Analyze design screenshots to automatically generate React component code
    """
    prompt = f"""
    Please analyze the attached design screenshot and generate:
    1. React component using Tailwind CSS
    2. Responsive design implementation
    3. Accessibility considerations
    4. TypeScript type definitions
    
    Screenshot: {image_path}
    """
    
    return multimodal_ai.generate(
        text=prompt,
        image=image_path,
        output_format="react_component"
    )

# Automatic code explanation generation via voice
def generate_code_explanation(code: str) -> str:
    """
    Analyze code to generate voice explanation script
    """
    return multimodal_ai.analyze(
        code=code,
        output_format="audio_script",
        style="educational"
    )

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class ProjectAnalyzer:
    """
    Tool for analyzing entire large-scale projects with AI
    """
    
    def __init__(self, project_path: str):
        self.project_path = project_path
        self.context_manager = ExtendedContextManager()
    
    async def analyze_entire_project(self) -> ProjectAnalysis:
        """
        Analyze entire project at once
        """
        # 1. Load entire codebase
        codebase = await self.load_codebase()
        
        # 2. Provide full context to AI
        analysis = await self.ai_analyze_full_context(codebase)
        
        return ProjectAnalysis(
            architecture_issues=analysis.architecture,
            security_vulnerabilities=analysis.security,
            performance_bottlenecks=analysis.performance,
            refactoring_opportunities=analysis.refactoring,
            dependency_optimization=analysis.dependencies
        )
    
    async def ai_analyze_full_context(self, codebase: CodeBase) -> dict:
        """
        Analyze entire project with 200K token context
        """
        prompt = f"""
        Here is the complete project codebase:
        
        Project Structure: {codebase.structure}
        All Source Files: {codebase.source_files}
        Configuration Files: {codebase.config_files}
        Documentation: {codebase.documentation}
        
        Please analyze from a holistic perspective:
        1. Architecture improvements
        2. Security vulnerabilities
        3. Performance optimization opportunities
        4. Code quality improvements
        5. Dependency optimization
        
        Provide specific code modification suggestions for each improvement.
        """
        
        return await extended_context_ai.analyze(prompt)

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class AdvancedPromptEngine:
    """
    Advanced prompt engineering for next-generation AI models
    """
    
    def __init__(self):
        self.memory_context = {}
        self.project_context = {}
        self.style_preferences = {}
    
    def create_contextual_prompt(self, task: str, code: str = None) -> str:
        """
        Generate optimized prompts using memory and context
        """
        base_context = self.build_base_context()
        task_context = self.build_task_context(task)
        
        if code:
            code_context = self.analyze_code_context(code)
            return f"""
            {base_context}
            
            Current Task: {task}
            {task_context}
            
            Code to Analyze:
            {code}
            {code_context}
            
            Please process the request based on the above context.
            """
        
        return f"""
        {base_context}
        
        Task Request: {task}
        {task_context}
        
        Please respond considering my development style and project context.
        """
    
    def build_base_context(self) -> str:
        """
        Build base context for AI to remember
        """
        return f"""
        Developer Profile:
        - Primary Language: {self.style_preferences.get('language', 'TypeScript/Python')}
        - Architecture: {self.style_preferences.get('architecture', 'Clean Architecture')}
        - Testing: {self.style_preferences.get('testing', 'TDD Preferred')}
        - Coding Style: {self.style_preferences.get('style', 'Functional Programming Preferred')}
        
        Current Project:
        - Type: {self.project_context.get('type', '')}
        - Stack: {self.project_context.get('stack', '')}
        - Constraints: {self.project_context.get('constraints', '')}
        """

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class AIModelOptimizer:
    """
    Optimal AI model selection and utilization by task type
    """
    
    TASK_MODEL_MAPPING = {
        "code_generation": {
            "primary": "gpt-4o",
            "fallback": "claude-3.5-sonnet",
            "context_size": "medium",
            "memory_required": True
        },
        "code_review": {
            "primary": "claude-3.5-sonnet",
            "fallback": "gpt-4o",
            "context_size": "large",
            "memory_required": False
        },
        "documentation": {
            "primary": "gemini-ultra",
            "fallback": "claude-3.5-sonnet",
            "context_size": "extra_large",
            "multimodal": True
        },
        "refactoring": {
            "primary": "gpt-4o",
            "fallback": "claude-3.5-sonnet",
            "context_size": "full_project",
            "memory_required": True
        }
    }
    
    async def optimize_task_execution(self, task_type: str, input_data: dict) -> dict:
        """
        Execute with optimal AI model for task type
        """
        config = self.TASK_MODEL_MAPPING.get(task_type)
        if not config:
            raise ValueError(f"Unknown task type: {task_type}")
        
        try:
            result = await self.execute_with_primary_model(config, input_data)
            return result
        except Exception as e:
            # Use fallback model if primary fails
            return await self.execute_with_fallback_model(config, input_data)
    
    async def execute_with_primary_model(self, config: dict, input_data: dict) -> dict:
        """
        Execute task with primary AI model
        """
        model = self.get_model(config["primary"])
        
        if config.get("memory_required"):
            model.enable_memory()
        
        if config.get("multimodal"):
            model.enable_multimodal()
        
        return await model.process(
            input_data,
            context_size=config["context_size"]
        )

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# Expected H2 2025: Autonomous AI Development Agents
class AutonomousDevAgent:
    """
    AI agent that performs development tasks completely autonomously
    """
    
    def __init__(self, project_context: ProjectContext):
        self.memory = PersistentMemory()
        self.tools = DevelopmentTools()
        self.project = project_context
    
    async def autonomous_development(self, requirements: str) -> DeployedApplication:
        """
        Complete automation from requirements to deployment
        """
        # 1. Analyze requirements and decide tech stack
        architecture = await self.analyze_requirements(requirements)
        
        # 2. Create project structure
        project_structure = await self.create_project_structure(architecture)
        
        # 3. Generate complete codebase
        codebase = await self.generate_full_codebase(project_structure)
        
        # 4. Write and run automated tests
        test_results = await self.create_and_run_tests(codebase)
        
        # 5. Performance optimization
        optimized_code = await self.optimize_performance(codebase, test_results)
        
        # 6. Automated deployment
        deployment = await self.deploy_application(optimized_code)
        
        # 7. Setup monitoring
        monitoring = await self.setup_monitoring(deployment)
        
        return DeployedApplication(
            code=optimized_code,
            deployment=deployment,
            monitoring=monitoring,
            documentation=await self.generate_documentation(optimized_code)
        )

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class DeveloperPreparationPlan:
    """
    Developer preparation plan for the AI era
    """
    
    PREPARATION_ROADMAP = {
        "immediate": {
            "ai_tools_mastery": [
                "Advanced GPT-4o utilization",
                "Claude 3.5 context optimization",
                "Gemini Ultra multimodal usage"
            ],
            "prompt_engineering": [
                "Context design",
                "Memory utilization patterns",
                "Multimodal prompting"
            ]
        },
        "short_term": {
            "ai_workflow_integration": [
                "AI-powered CI/CD pipelines",
                "Automated code review systems",
                "AI assistant integrated development environments"
            ],
            "domain_expertise": [
                "Business logic understanding",
                "System architecture design",
                "User experience optimization"
            ]
        },
        "long_term": {
            "ai_specialization": [
                "AI model customization",
                "AI agent design",
                "AI ethics and governance"
            ],
            "leadership_skills": [
                "AI strategy development",
                "Team AI adoption management",
                "AI-driven innovation leadership"
            ]
        }
    }
    
    def create_personalized_plan(self, current_skills: dict, career_goals: dict) -> dict:
        """
        Generate personalized preparation plan
        """
        return {
            "assessment": self.assess_current_level(current_skills),
            "learning_path": self.design_learning_path(current_skills, career_goals),
            "milestones": self.define_milestones(career_goals),
            "resources": self.recommend_resources(current_skills)
        }