ThetaVerse: AI-Powered Interview Preparation Platform, Demystifying Technical Interviews

ThetaVerse is a full-stack project based on Spring Boot and React, integrating Groq LLM to provide three AI interviewer personas, Ghost performance benchmark comparisons, and personalized learning path planning. It also incorporates real interviewer booking functionality, offering an intelligent solution for technical interview preparation and addressing pain points like lack of targeted preparation and delayed feedback in traditional methods.

Technical interviews are a critical hurdle for programmers' career development. Traditional preparation methods (problem-solving practice, interview experiences, friend simulations) have issues like time-consuming processes, uncertainty, lack of targeting, or delayed feedback. As a complete interview preparation ecosystem, ThetaVerse integrates AI simulation and personalized planning functions, representing the trend of interview tools evolving toward intelligence and personalization.

Backend: Spring Boot 3.x (mature ecosystem, dependency injection, auto-configuration), Spring Security + JWT for stateless authentication, MySQL 8.0 + Hibernate/JPA for data relationship handling. Frontend: React19 + TypeScript (static type checking improves maintainability), Vite7 (fast cold start and hot reload), Tailwind CSS4 (utility-first responsive styling).

1. AI Interviewers: Three personas (Strict type simulating FAANG's high-pressure atmosphere, Gentle type balancing feedback, Friendly type encouraging beginners) to adapt to users' needs at different stages.
2. Ghost Performance Benchmark: Tracks ideal learning speed, visualizes user progress comparisons, and stimulates self-motivation.
3. Personalized Learning Path: Generates daily plans based on target position/company/time, breaking down tasks to reduce anxiety.
4. Real Interviewer Booking: Supports slot publishing and automatic Google Meet link generation, combining virtual and real elements to complement the limitations of AI simulations.

• Job Seekers: Covers the entire life cycle of interview preparation (ability assessment → targeted improvement → practical drills), improving preparation efficiency.
• Interviewers: Provides a channel for knowledge monetization, forming a positive cycle in the two-sided market.
• Educational Institutions: Open-source code can serve as a starting point for developing customized interview training platforms.

Technical Debt: Missing demo videos, incomplete details of the AI validation suite. Future Directions:

1. Multilingual support to expand the user base;
2. Integrate a code editor to support real-time code evaluation;
3. Develop an enterprise version with batch interview and assessment report functions;
4. Optimize the mobile native App experience.