Building CareerCraft Studio

// Simplified example of the agent routing system
const supervisorAgent = {
  route_to_agent: (userIntent) => {
    if (userIntent.includes("add work experience")) return "data_manager";
    if (userIntent.includes("generate resume")) return "resume_generator";
    if (userIntent.includes("analyze job posting")) return "job_posting_manager";
    // ... intelligent routing logic
  }
};

// Example of the intelligent parsing system
class ResumeParsingService {
  async parseResume(resumeText: string, userId: string) {
    // AI extraction of structured data
    const extractedData = await this.llm.extract(resumeText);
    
    // Batch database operations for performance
    await this.db.transaction(async (tx) => {
      await this.createWorkHistory(tx, extractedData.workHistory);
      await this.createSkills(tx, extractedData.skills);
      await this.createAchievements(tx, extractedData.achievements);
    });
    
    // Intelligent skill categorization
    await this.normalizeSkills(userId);
  }
}

enum SkillCategory {
  PROGRAMMING_LANGUAGE,
  MEDICAL_PROCEDURE,
  FINANCIAL_ANALYSIS,
  LEGAL_RESEARCH,
  MARKETING_STRATEGY,
  // ... 30+ categories across industries
}

// "React (hooks, context)" vs "React" - same skill, different detail levels

// End-to-end type safety from DB to UI
const trpcClient = api.chat.useSubscription({
  onData: (aiResponse) => {
    // TypeScript knows the exact shape of aiResponse
    // Autocomplete works perfectly here
    console.log(aiResponse.content, aiResponse.metadata);
  },
  onError: (error) => {
    // Proper error handling with typed errors
  }
});

• • Feature-based router organization (26KB+ files → focused modules)
• • Shared types and utilities across agents
• • Comprehensive tooling infrastructure
• • Clear separation between AI logic and business logic

1. Modern TypeScript tooling enables rapid AI application development
2. Agent-based architectures provide flexible and maintainable AI systems
3. End-to-end type safety is crucial for complex AI workflows
4. Comprehensive documentation amplifies both AI and human developer productivity

The future is AI-first applications built with traditional web development patterns. TypeScript and modern tooling make complex AI systems approachable, while focusing on real user problems ensures we're building something valuable.

The key is to start with the user problem, then let modern tools handle the complexity. CareerCraft Studio proves that individual developers can build sophisticated AI applications that compete with well-funded startups.