Claude vs GPT: Building Superior AI Coding Assistants

// Context management strategy for large codebases
class ContextManager {
  private contextWindows: Map<string, CodeContext> = new Map();
  
  async processLargeCodebase(files: CodeFile[], model: 'claude' | 'gpt') {
    if (model === 'claude') {
      // Leverage large context window
      return await this.processWithFullContext(files);
    } else {
      // Implement chunking strategy for GPT
      return await this.processWithChunking(files);
    }
  }
  
  private async processWithFullContext(files: CodeFile[]) {
    const fullContext = files.map(f => f.content).join('\n');
    return await claudeAPI.analyze(fullContext);
  }
  
  private async processWithChunking(files: CodeFile[]) {
    const chunks = this.createSemanticChunks(files);
    const results = await Promise.all(
      chunks.map(chunk => gptAPI.analyze(chunk))
    );
    return this.mergeResults(results);
  }
}

import { Anthropic } from '@anthropic-ai/sdk';
class ClaudeCodeAssistant {
  private client: Anthropic;
  private conversationHistory: Array<Message> = [];
  
  constructor(apiKey: string) {
    this.client = new Anthropic({ apiKey });
  }
  
  async analyzeCode(code: string, requirements: string): Promise<Analysis> {
    const prompt = this.buildAnalysisPrompt(code, requirements);
    
    const response = await this.client.messages.create({
      model: 'claude-3-opus-20240229',
      max_tokens: 4000,
      messages: [
        ...this.conversationHistory,
        {
          role: 'user',
          content: prompt
        }
      ]
    });
    
    this.updateConversationHistory(prompt, response.content);
    return this.parseAnalysisResponse(response.content);
  }
  
  private buildAnalysisPrompt(code: string, requirements: string): string {
    return 

    Analyze this code for potential improvements and alignment with requirements:
    
    Requirements: ${requirements}
    
    Code:
    \\\
    ${code}
    \\\

    
    Please provide:
    1. Detailed analysis of current implementation
    2. Specific improvement recommendations
    3. Alternative architectural approaches
    4. Potential edge cases and error handling
    ;
  }
}

import OpenAI from 'openai';
class GPTCodeAssistant {
  private openai: OpenAI;
  private systemPrompt: string;
  
  constructor(apiKey: string) {
    this.openai = new OpenAI({ apiKey });
    this.systemPrompt = this.buildSystemPrompt();
  }
  
  async generateCode(specification: CodeSpec): Promise<GeneratedCode> {
    const response = await this.openai.chat.completions.create({
      model: 'gpt-4-turbo-preview',
      messages: [
        { role: 'system', content: this.systemPrompt },
        { role: 'user', content: this.buildUserPrompt(specification) }
      ],
      functions: this.getAvailableFunctions(),
      function_call: 'auto'
    });
    
    return this.processResponse(response);
  }
  
  private getAvailableFunctions() {
    return [
      {
        name: 'generateComponent',
        description: 'Generate a React component with TypeScript',
        parameters: {
          type: 'object',
          properties: {
            componentName: { type: 'string' },
            props: { type: 'object' },
            functionality: { type: 'string' }
          }
        }
      },
      {
        name: 'optimizeCode',
        description: 'Optimize existing code for performance',
        parameters: {
          type: 'object',
          properties: {
            codeToOptimize: { type: 'string' },
            optimizationGoals: { type: 'array', items: { type: 'string' } }
          }
        }
      }
    ];
  }
}

class HybridCodingAssistant {

  private claudeAssistant: ClaudeCodeAssistant;
  private gptAssistant: GPTCodeAssistant;
  
  constructor(claudeKey: string, gptKey: string) {
    this.claudeAssistant = new ClaudeCodeAssistant(claudeKey);
    this.gptAssistant = new GPTCodeAssistant(gptKey);
  }
  
  async planAndImplement(requirements: ProjectRequirements): Promise<Implementation> {
    // Use Claude for high-level architectural planning
    const architecture = await this.claudeAssistant.planArchitecture(requirements);
    
    // Use GPT for rapid component generation
    const components = await Promise.all(
      architecture.components.map(spec => 
        this.gptAssistant.generateComponent(spec)
      )
    );
    
    // Use Claude for final integration review
    const review = await this.claudeAssistant.reviewIntegration(
      components, 
      architecture
    );
    
    return {
      architecture,
      components,
      review,
      integrationGuidance: review.recommendations
    };
  }
}

class OptimizedAssistant {

  private cache: Map<string, CachedResponse> = new Map();
  private responseQueue: PriorityQueue<Request> = new PriorityQueue();
  
  async getCodeSuggestion(context: CodeContext): Promise<Suggestion> {
    const cacheKey = this.generateCacheKey(context);
    
    // Check cache first
    if (this.cache.has(cacheKey)) {
      const cached = this.cache.get(cacheKey)!;
      if (!this.isCacheExpired(cached)) {
        return cached.suggestion;
      }
    }
    
    // Prioritize requests based on user activity
    const priority = this.calculatePriority(context);
    const request: Request = { context, priority, timestamp: Date.now() };
    
    return new Promise((resolve) => {
      this.responseQueue.enqueue(request, (result) => {
        this.cache.set(cacheKey, {
          suggestion: result,
          timestamp: Date.now()
        });
        resolve(result);
      });
    });
  }
  
  private calculatePriority(context: CodeContext): number {
    // Higher priority for active editing, lower for background analysis
    return context.isActivelyEditing ? 10 : 
           context.hasErrors ? 8 : 
           context.requestType === 'completion' ? 6 : 3;
  }
}

class CostOptimizedManager {

  private tokenBudget: TokenBudget;
  private intelligentCache: IntelligentCache;
  
  async processRequest(request: AssistantRequest): Promise<Response> {
    // Estimate token cost before processing
    const estimatedCost = this.estimateTokenUsage(request);
    
    if (!this.tokenBudget.canAfford(estimatedCost)) {
      // Fallback to cached or simplified response
      return this.getFallbackResponse(request);
    }
    
    // Choose model based on complexity and budget
    const model = this.selectOptimalModel(request, estimatedCost);
    
    const response = await this.processWithModel(model, request);
    
    // Update budget tracking
    this.tokenBudget.deduct(response.actualTokensUsed);
    
    return response;
  }
  
  private selectOptimalModel(request: AssistantRequest, cost: number): ModelConfig {
    if (request.complexity === 'high' && cost < this.tokenBudget.remainingBudget * 0.1) {
      return { provider: 'claude', model: 'opus' };
    } else if (request.requiresSpecialization) {
      return { provider: 'gpt', model: 'gpt-4-turbo' };
    } else {
      return { provider: 'gpt', model: 'gpt-3.5-turbo' };
    }
  }
}

class SecureCodeAssistant {

  private sanitizer: CodeSanitizer;
  private validator: CodeValidator;
  
  async generateSecureCode(prompt: string, context: SecurityContext): Promise<SecureCode> {
    // Sanitize input prompt
    const sanitizedPrompt = this.sanitizer.sanitizePrompt(prompt);
    
    // Add security constraints to the prompt
    const securePrompt = this.addSecurityConstraints(sanitizedPrompt, context);
    
    const generatedCode = await this.generateCode(securePrompt);
    
    // Validate generated code for security issues
    const validation = await this.validator.validateCode(generatedCode);
    
    if (validation.hasSecurityIssues) {
      return this.remediateSecurityIssues(generatedCode, validation.issues);
    }
    
    return {
      code: generatedCode,
      securityScore: validation.score,
      recommendations: validation.recommendations
    };
  }
  
  private addSecurityConstraints(prompt: string, context: SecurityContext): string {
    const constraints = [
      'Ensure all user inputs are properly validated and sanitized',
      'Use parameterized queries for database operations',
      'Implement proper authentication and authorization checks',
      'Follow OWASP security guidelines'
    ];
    
    return ${prompt}
Security Requirements:

${constraints.map(c => - ${c}).join('\n')}
Security Context: ${JSON.stringify(context)};
  }
}

class MultiModelOrchestrator {

  private models: Map<string, AIModel> = new Map();
  private routingEngine: RoutingEngine;
  
  constructor() {
    this.models.set('claude-reasoning', new ClaudeModel('opus'));
    this.models.set('gpt-generation', new GPTModel('gpt-4-turbo'));
    this.models.set('codex-completion', new GPTModel('gpt-3.5-turbo'));
    
    this.routingEngine = new RoutingEngine(this.buildRoutingRules());
  }
  
  async processComplexRequest(request: ComplexCodeRequest): Promise<SynthesizedResponse> {
    // Break down complex request into subtasks
    const subtasks = await this.decomposeRequest(request);
    
    // Route each subtask to optimal model
    const subtaskResults = await Promise.all(
      subtasks.map(async (subtask) => {
        const optimalModel = this.routingEngine.selectModel(subtask);
        return {
          subtask,
          result: await optimalModel.process(subtask),
          confidence: optimalModel.getConfidenceScore(subtask)
        };
      })
    );
    
    // Synthesize results using the reasoning model
    const synthesizedResponse = await this.models.get('claude-reasoning')!.synthesize(
      subtaskResults,
      request.originalContext
    );
    
    return synthesizedResponse;
  }
  
  private buildRoutingRules(): RoutingRule[] {
    return [
      {
        condition: (task) => task.type === 'architectural-design',
        model: 'claude-reasoning',
        reason: 'Complex reasoning and planning required'
      },
      {
        condition: (task) => task.type === 'code-generation' && task.complexity === 'low',
        model: 'codex-completion',
        reason: 'Fast generation for simple code'
      },
      {
        condition: (task) => task.type === 'debugging',
        model: 'gpt-generation',
        reason: 'Strong pattern recognition for bug identification'
      }
    ];
  }
}

class CollaborativeAssistant {

  private collaborationState: CollaborationState;
  private conflictResolver: ConflictResolver;
  
  async handleCollaborativeEdit(
    edit: CollaborativeEdit, 
    sessionId: string
  ): Promise<AssistantResponse> {
    // Update collaboration state
    await this.collaborationState.applyEdit(edit, sessionId);
    
    // Check for conflicts with other users or AI suggestions
    const conflicts = await this.detectConflicts(edit, sessionId);
    
    if (conflicts.length > 0) {
      const resolution = await this.conflictResolver.resolve(conflicts);
      return {
        type: 'conflict-resolution',
        suggestions: resolution.suggestions,
        mergedCode: resolution.mergedCode
      };
    }
    
    // Generate contextual suggestions based on current state
    const context = await this.collaborationState.getContext(sessionId);
    const suggestions = await this.generateContextualSuggestions(context);
    
    // Broadcast suggestions to relevant collaborators
    await this.broadcastSuggestions(suggestions, sessionId);
    
    return {
      type: 'collaborative-suggestion',
      suggestions,
      collaborationContext: context
    };
  }
}

class PropTechCodeAssistant extends MultiModelOrchestrator {

  private propTechKnowledge: DomainKnowledgeBase;
  
  constructor() {
    super();
    this.propTechKnowledge = new DomainKnowledgeBase({
      domain: 'proptech',
      specializations: [
        'mls-integration',
        'property-valuation',
        'real-estate-apis',
        'mapping-services',
        'property-management'
      ]
    });
  }
  
  async generatePropTechSolution(requirement: PropTechRequirement): Promise<Solution> {
    // Enrich requirement with domain knowledge
    const enrichedRequirement = await this.propTechKnowledge.enrich(requirement);
    
    // Apply PropTech-specific patterns and best practices
    const domainPrompt = this.buildDomainSpecificPrompt(enrichedRequirement);
    
    // Use specialized routing for PropTech use cases
    const solution = await this.processComplexRequest({
      ...enrichedRequirement,
      domainPrompt,
      specializations: this.propTechKnowledge.getRelevantPatterns(requirement)
    });
    
    // Validate against PropTech compliance requirements
    const compliance = await this.validateCompliance(solution);
    
    return {
      ...solution,
      compliance,
      domainSpecificGuidance: this.propTechKnowledge.getImplementationGuidance(solution)
    };
  }
}

interface FutureProofArchitecture {
  // Modular model integration
  modelRegistry: ModelRegistry;
  
  // Extensible capability framework
  capabilityEngine: CapabilityEngine;
  
  // Adaptive learning pipeline
  learningPipeline: LearningPipeline;
  
  // Performance monitoring and optimization
  performanceOptimizer: PerformanceOptimizer;
}
class AdaptiveAssistant implements FutureProofArchitecture {
  async adaptToNewModel(modelConfig: ModelConfiguration): Promise<void> {
    // Register new model capabilities
    await this.modelRegistry.register(modelConfig);
    
    // Update routing rules based on new capabilities
    this.capabilityEngine.updateRouting(modelConfig.capabilities);
    
    // Begin learning pipeline for optimization
    this.learningPipeline.initializeForModel(modelConfig.id);
    
    // Monitor performance impact
    this.performanceOptimizer.beginMonitoring(modelConfig.id);
  }
}