IntervU Slayer is an AI-powered interview preparation platform designed to help developers prepare strategically instead of relying on random mock interviews.
Unlike traditional interview generators, IntervU Slayer combines structured interview roadmaps, targeted practice sessions, skill analytics, and readiness tracking to create a complete interview preparation experience.
🌐 Live Demo: https://intervuslayer.vercel.app/
Generate personalized interview preparation roadmaps based on:
- Target role
- Experience level
- Technology stack
- Job descriptions
Each roadmap is divided into modules that guide users through a structured learning and practice journey.
Generate realistic interview sessions tailored to:
- Technical interviews
- Behavioral interviews
- Mixed interviews
Practice with questions relevant to your goals and experience level.
Paste a real job description and IntervU Slayer automatically extracts:
- Role title
- Seniority level
- Relevant technologies
- Focus areas
- Suggested interview setup
Users can review and edit the extracted information before generating interviews or roadmaps.
Track performance across multiple interview dimensions, including:
- Communication
- Technical depth
- Problem solving
- Framework knowledge
- Behavioral skills
- System design
Identify strengths and uncover areas that need improvement.
Roadmaps unlock progressively as users complete interview rounds.
This encourages deliberate practice and helps candidates build confidence one skill area at a time.
After each interview session, users receive reports containing:
- Overall performance scores
- Category breakdowns
- Strengths
- Areas for improvement
- Actionable recommendations
IntervU Slayer is engineered to handle real-time voice streaming and automated performance evaluation through a decoupled event flow.
graph TD
A[User Voice Input] -->|WebRTC Streaming| B[Vapi AI Voice Gateway]
B -->|Text Stream| C[Groq LLM Engine]
C -->|Audio Synthesis| B -->|Voice Output| D[User Speaker]
E[Interview Ends] -->|Session Webhook| F[Next.js Server Actions]
F -->|Transcript Data| G[Groq Grading Pipeline]
G -->|JSON Schema Verification| H[Firebase Firestore]
H -->|Dynamic UI State| I[User Skill Graph & Roadmaps]
To simulate a real, high-pressure interview, the platform bypasses traditional text-input delays. By pairing Vapi AI (handling audio streaming via WebRTC) with Groq (handling ultra-fast LLM inference), the application maintains conversational pacing with minimal latency.
When a session concludes, a webhook triggers a secure Next.js Server Action. The raw text transcript is fed into an evaluation pipeline that enforces strict JSON formatting, instantly breaking the performance down into structured category metrics (Communication, Technical Depth, etc.).
All historical scores and generated roadmaps are stored in Firebase Firestore. The frontend tracks this user state to calculate progressive roadmap milestones, dynamically unlocking new interview modules as the developer's readiness scores improve.
- Next.js (App Router)
- React 19
- TypeScript
- Tailwind CSS
- Framer motion
- React Hook Form
- Zod
- Next.js Server Actions
- Firebase Authentication
- Firebase Firestore
- Groq API
- Vapi AI
Most AI interview tools focus only on generating questions. IntervU Slayer focuses on building interview readiness through structured improvement.
The goal isn't simply to complete mock interviews—it's to help users understand:
- What to practice
- Why they should practice it
- Whether they're actually improving
git clone https://github.com/tasmia-rafiq/intervuslayer
cd intervuslayer
npm install
Create a .env.local file and add the required credentials:
FIREBASE_PROJECT_ID=
FIREBASE_PRIVATE_KEY=
FIREBASE_CLIENT_EMAIL=
NEXT_PUBLIC_VAPI_WEB_TOKEN=
NEXT_PUBLIC_VAPI_WORKFLOW_ID=
GROQ_API_KEY=
npm run dev
Open:
http://localhost:3000
- AI Interview Coach
- Resume Analysis
- Company-specific interview preparation
- Personalized study recommendations
- Advanced progress analytics
Tasmia Rafiq Software Engineer — Full Stack Developer