🚀 SonicPrep AI: Real-Time Voice Interview Intelligence

Elevating Engineering Interviews with Gemini-Powered Voice Intelligence

📋 Table of Contents

✨ 1. Key Features
🎥 2. Live Demo
- ⚡ Zero-Latency Performance
🏗️ 3. Architecture & Flows
🛠️ 4. Tech Stack & Ecosystem
📊 5. Database & Schema
📦 6. Installation & Setup
- ⚙️ 6.1 Environment Config
- 🚀 6.2 Production Build
🤝 7. Contributing
- 🗺️ 7.1 Future Roadmap
👨‍💻 8. Developed By

1.✨ Key Features

SonicPrep isn't just a mock interview tool; it's a Real-Time Career Intelligence Agent designed to simulate the high-pressure environment of top-tier technical interviews.

🎙️ Real-Time Voice AI	🧠 Intelligent RAG	📊 Behavioral Scoring
Sub-500ms Latency: Built on Vapi WebRTC for natural, human-like dialogue without the "AI pause."	Domain-Specific: Uses Pinecone to retrieve actual interview questions for your specific tech stack (MERN, Python, etc.).	Post-Call Analytics: Uses Gemini to analyze your tone, confidence, and technical accuracy.

🚀 Advanced Functionalities

⚡ The "Sonic" Pipeline: A custom-orchestrated audio path that handles STT (Speech-to-Text), LLM reasoning, and TTS (Text-to-Speech) simultaneously for zero-lag interactions.
🛠️ Tech-Stack Adaptation: The AI dynamically adjusts its difficulty based on your selected stack—asking deep-dive questions on Next.js Server Actions or NoSQL Schema Design.
📂 Persistent Interview History: Automatically saves your transcripts and scores to Firebase, allowing you to track your growth over time.
🌐 Community Exploration: A public "Hall of Fame" where users can share their high-scoring interview sessions to help others learn.
🤖 Agentic Behavioral Feedback: Beyond just text, the agent provides a "Communication Confidence" score based on your response pacing and vocabulary.

🎨 Cinematic User Experience

Glassmorphic UI: A modern, high-contrast Dark Mode interface built with Tailwind v4.
Fluid Animations: Interactive 3D elements and transitions powered by GSAP and Framer Motion for a "Cinematic" feel.

2.🎥 Live Demo

Experience SonicPrep AI in action. Our ultra-low latency voice pipeline allows for natural, fluid conversation without the typical AI "lag."

🚀 Try the Live Demo Now

(Note: Requires Microphone Access and a stable internet connection for the best experience)

⚡ Zero-Latency Performance

Process	Status	Latency
STT (Speech-to-Text)	🟢 Optimized	~150ms
LLM Reasoning (Gemini 2.5 Pro)	🟢 Real-time	~350ms
TTS (Text-to-Speech)	🟢 Fluid	~100ms
Total Round-Trip (Sonic Path)	🚀 Elite	< 600ms

🏗️ 3. Architecture & Flows

SonicPrep AI is built on a Decoupled 3-Tier Architecture, separating the real-time voice stream from the persistent data and intelligence layers.

📐3.1 High-Level Project Architecture

graph TD
    %% Global Styling
    classDef clientNode fill:#111827,stroke:#22d3ee,stroke-width:2px,color:#fff;
    classDef edgeNode fill:#111827,stroke:#0ea5e9,stroke-width:2px,color:#fff;
    classDef brainNode fill:#111827,stroke:#A855F7,stroke-width:2px,color:#fff;

    %% Layer 1: Frontend (The Top Surface)
    subgraph Layer_1 [ ]
        direction LR
        U_CLIENT[💻 Dashboard UI]:::clientNode
        U_VOICE[🎤 Voice Widget]:::clientNode
    end
    
    %% Layer 2: Network (The Middle Bridge)
    subgraph Layer_2 [ ]
        direction LR
        E_ACTIONS[⚡ Server Actions]:::edgeNode
        E_VAPI[📶 Vapi Pipeline]:::edgeNode
    end

    %% Layer 3: Intelligence (The Foundation)
    subgraph Layer_3 [ ]
        direction LR
        C_GEMINI[🧠 Gemini Engine]:::brainNode
        C_PINE[🔍 Pinecone RAG]:::brainNode
        C_STORE[🗄️ Firestore]:::brainNode
    end

    %% Vertical Stack Connectivity (The 3D Depth)
    U_CLIENT --- E_ACTIONS
    U_VOICE --- E_VAPI
    E_ACTIONS --- C_STORE
    E_VAPI --- C_GEMINI
    C_GEMINI --- C_PINE

    %% Logical Flow (The Sonic Path)
    U_VOICE ==>|WebRTC Audio| E_VAPI
    E_VAPI ==>|Context| C_GEMINI
    C_GEMINI ==>|Behavioral Analysis| C_STORE
    C_GEMINI ==>|TTS Feedback| E_VAPI
    E_VAPI ==>|Response| U_VOICE

    %% Layer Styling to Create "Depth"
    style Layer_1 fill:#09090b,stroke:#22d3ee,stroke-width:4px
    style Layer_2 fill:#0c1a25,stroke:#0ea5e9,stroke-width:4px
    style Layer_3 fill:#151329,stroke:#A855F7,stroke-width:4px

3.2🧠 App Logic & State Flow

The interview process is managed through a specialized state machine to ensure zero-latency feedback and accurate behavioral tracking.

graph TD
    %% Define Global Node Styling for stable render
    classDef clientNode fill:#111827,stroke:#22d3ee,stroke-width:2px,color:#fff;
    classDef voiceNode fill:#111827,stroke:#0ea5e9,stroke-width:2px,color:#fff;
    classDef brainNode fill:#111827,stroke:#A855F7,stroke-width:2px,color:#fff;
    classDef finalNode fill:#111827,stroke:#ffCA28,stroke-width:2px,color:#fff;

    %% Layer 1: User Interactions (The Top Plane)
    subgraph Layer_1 [👤 USER ACTIONS]
        direction LR
        A_LOGIN[Log In Firebase]:::clientNode
        A_DASH[Enter Dashboard]:::clientNode
        A_START[Click 'Start Interview']:::clientNode
    end
    
    %% Layer 2: Real-Time Interview Loop (The Middle Plane)
    subgraph Layer_2 [📶 REAL-TIME INTERVIEW LOOP]
        direction TB
        V_INIT[Initializing Vapi WebRTC]:::voiceNode
        V_LIST[LISTENING User Speech]:::voiceNode
        V_STT[Processing STT Context]:::voiceNode
        V_THINK[THINKING Gemini Inference]:::voiceNode
        V_SPEAK[SPEAKING Vapi TTS]:::voiceNode
    end

    %% Layer 3: Persistence & Intelligence (The Foundation)
    subgraph Layer_3 [🧠 Persistance & Intelligence]
        direction LR
        I_RAG[🔍 RAG Tech Stack Lookup]:::brainNode
        I_SAVE[🗄️ Save Transcript to Firestore]:::brainNode
        I_BEH[📊 Behavioral Analysis Scoring]:::brainNode
        I_SCORE[🏆 Final Score Calculation]:::brainNode
    end

    %% --- Connecting the Stack (Vertical Depth) ---

    %% Authentication to Vapi Initialization
    A_DASH ==>|Starts Session| V_INIT
    
    %% Vapi Loop Data Connections
    V_LIST ===> V_STT
    V_STT ===> V_THINK
    V_THINK <===>|RAG Technical Match| I_RAG
    V_THINK ===> V_SPEAK
    V_SPEAK ===> V_LIST

    %% Interview Loop to Analysis Foundation
    V_SPEAK -.->|Saves Transcript| I_SAVE
    I_SAVE -.->|Scores Communicaton/Tone| I_BEH
    I_BEH -.->|Aggregates| I_SCORE
    I_SCORE -.->|Returns to Dashboard| A_DASH

    %% Special Highlight on the 'End Interview' action
    U_END[U_END: Click 'End Interview']:::finalNode
    V_SPEAK ===> U_END
    U_END ===> I_SCORE

    %% Apply 3D Styling to Layers
    style Layer_1 fill:#09090b,stroke:#22d3ee,stroke-width:4px
    style Layer_2 fill:#0c1a25,stroke:#0ea5e9,stroke-width:4px
    style Layer_3 fill:#151329,stroke:#A855F7,stroke-width:4px

🔄 Data Flow Diagram (DFD)

graph TD
    %% Define Global ClassDef Styling for stable render
    classDef clientNode fill:#111827,stroke:#22d3ee,stroke-width:2px,color:#fff;
    classDef edgeNode fill:#111827,stroke:#0ea5e9,stroke-width:2px,color:#fff;
    classDef brainNode fill:#111827,stroke:#A855F7,stroke-width:2px,color:#fff;
    classDef voicePath stroke:#A855F7,stroke-width:4px,color:#fff;

    %% Layer 1: Client Interactions (The Top Plane)
    subgraph Layer_1 [👤 CLIENT INTERFACES]
        direction LR
        U_DASH[💻 Dashboard UI]:::clientNode
        U_VOICE[🎤 Voice Widget]:::clientNode
    end
    
    %% Layer 2: Network & Pipeline (The Middle Plane)
    subgraph Layer_2 [📶 NETWORK & PIPELINE]
        direction LR
        E_ACTIONS[⚡ Server Actions]:::edgeNode
        E_VAPI[📶 Vapi WebRTC]:::edgeNode
    end

    %% Layer 3: Intelligence & Persistence (The Foundation)
    subgraph Layer_3 [🧠 Persistance & Intelligence]
        direction LR
        C_GEMINI[🧠 Gemini Engine]:::brainNode
        C_PINE[🔍 Pinecone RAG]:::brainNode
        C_STORE[🗄️ Firestore]:::brainNode
    end

    %% --- Connecting the Stack (Vertical Depth) ---

    %% Authentication & Standard Data Flow
    U_DASH ===>|1. Sign-In / Query Data| E_ACTIONS
    E_ACTIONS -.->|2. Validates/Save| C_STORE

    %% Real-Time Voice Flow (The "Sonic" Path)
    U_VOICE ===>|3. WebRTC Audio Stream| E_VAPI
    E_VAPI ===>|4. STT Context| C_GEMINI
    C_GEMINI <==>|5. RAG Lookups| C_PINE
    C_GEMINI ==>|6. Analysis & Transcripts| C_STORE
    C_GEMINI ==>|7. Voice Response TTS| E_VAPI
    E_VAPI ===>|8. Play Real-Time Audio| U_VOICE

    %% Apply 3D Styling to Layers
    style Layer_1 fill:#09090b,stroke:#22d3ee,stroke-width:4px
    style Layer_2 fill:#0c1a25,stroke:#0ea5e9,stroke-width:4px
    style Layer_3 fill:#151329,stroke:#A855F7,stroke-width:4px

🔄 Data Interaction Model

graph TD
    %% Define Global ClassDef Styling for stable render
    classDef uiNode fill:#111827,stroke:#22d3ee,stroke-width:2px,color:#fff;
    classDef middlewareNode fill:#111827,stroke:#0ea5e9,stroke-width:2px,color:#fff;
    classDef coreNode fill:#111827,stroke:#A855F7,stroke-width:2px,color:#fff;

    %% Layer 1: Presentation & State (The Top Plane)
    subgraph Layer_1 [🌐 PRESENTATION & CLIENT STATE]
        direction LR
        U_NAV[Next.js App Router]:::uiNode
        U_HOOKS[React Hooks / Context]:::uiNode
    end
    
    %% Layer 2: API & Logic Middleware (The Middle Plane)
    subgraph Layer_2 [📡 API & INTERACTION MIDDLEWARE]
        direction LR
        M_SA[Next.js Server Actions]:::middlewareNode
        M_VAPI[Vapi SDK / WebRTC]:::middlewareNode
    end

    %% Layer 3: Intelligence & Persistence (The Foundation)
    subgraph Layer_3 [🧠 DATA INTELLIGENCE & STORAGE]
        direction LR
        C_GEM[Gemini LLM Engine]:::coreNode
        C_FIRE[Firebase Firestore]:::coreNode
        C_AUTH[Firebase Auth]:::coreNode
    end

    %% --- Vertical Interaction Flows ---

    %% User Auth Flow
    U_NAV ==>|JWT Request| M_SA
    M_SA ==>|Verify Token| C_AUTH
    C_AUTH -.->|Session Granted| U_NAV

    %% Voice Interaction Flow
    U_HOOKS ==>|Audio Stream| M_VAPI
    M_VAPI ==>|Context Ingestion| C_GEM
    C_GEM <==>|Real-time Analysis| C_FIRE

    %% Dash Data Flow
    U_NAV -.->|Fetch Interview History| M_SA
    M_SA -.->|CRUD Operations| C_FIRE

    %% Apply 3D Styling to Layers
    style Layer_1 fill:#09090b,stroke:#22d3ee,stroke-width:4px
    style Layer_2 fill:#0c1a25,stroke:#0ea5e9,stroke-width:4px
    style Layer_3 fill:#151329,stroke:#A855F7,stroke-width:4px

4.🛠️ Tech Stack & Ecosystem

graph TD
    %% Define Global ClassDef Styling
    classDef frontend fill:#111827,stroke:#22d3ee,stroke-width:2px,color:#fff;
    classDef backend fill:#111827,stroke:#0ea5e9,stroke-width:2px,color:#fff;
    classDef aiCore fill:#111827,stroke:#A855F7,stroke-width:2px,color:#fff;
    classDef devops fill:#111827,stroke:#34d399,stroke-width:2px,color:#fff;

    %% Layer 1: Frontend Experience
    subgraph Layer_1 [🎨 FRONTEND & UX ECOSYSTEM]
        direction LR
        FE_NEXT[Next.js 15]:::frontend
        FE_TW[Tailwind v4]:::frontend
        FE_GSAP[GSAP/Framer]:::frontend
        FE_SHAD[Shadcn UI]:::frontend
    end
    
    %% Layer 2: Logic & Integration
    subgraph Layer_2 [⚙️ LOGIC & API MIDDLEWARE]
        direction LR
        BE_SA[Server Actions]:::backend
        BE_VAPI[Vapi SDK]:::backend
        BE_FIRE[Firebase SDK]:::backend
    end

    %% Layer 3: AI & Intelligence
    subgraph Layer_3 [🧠 AI & DATA CORE]
        direction LR
        AI_GEM[Gemini 1.5 Pro]:::aiCore
        AI_RAG[Pinecone RAG]:::aiCore
        AI_TRANS[LangChain]:::aiCore
    end

    %% Layer 4: Infrastructure
    subgraph Layer_4 [🚀 DEPLOYMENT & INFRA]
        direction LR
        DO_VER[Vercel Edge]:::devops
        DO_FIRE[Firestore/Auth]:::devops
        DO_TURBO[Turbopack]:::devops
    end

    %% --- Vertical Integration ---
    Layer_1 === Layer_2
    Layer_2 === Layer_3
    Layer_3 === Layer_4

    %% Apply 3D Styling to Layers
    style Layer_1 fill:#09090b,stroke:#22d3ee,stroke-width:4px
    style Layer_2 fill:#0c1a25,stroke:#0ea5e9,stroke-width:4px
    style Layer_3 fill:#151329,stroke:#A855F7,stroke-width:4px
    style Layer_4 fill:#064e3b,stroke:#34d399,stroke-width:4px

🚀 Ecosystem Breakdown

Layer	Primary Technologies	Engineering & Business Value
🎨 Presentation		Utilizes Turbopack for 70% faster HMR and Tailwind v4 for hardware-accelerated CSS, ensuring a fluid 60fps "Sonic" UI experience.
⚙️ Middleware		Orchestrates ultra-low latency (sub-500ms) WebRTC voice-to-voice streams while keeping sensitive API logic securely off the client-side.
🧠 Intelligence		Executes real-time technical & behavioral analysis using Retrieval-Augmented Generation to pull domain-specific questions.
📦 Infrastructure		A serverless backbone providing global distribution, secure JWT-based authentication, and real-time Firestore persistence.

🛠️ Technical Highlights

Sub-500ms Response Time: Optimized WebRTC pipeline ensures no awkward pauses during AI mock interviews.
Domain-Specific RAG: Not just a chatbot—the system retrieves actual interview questions for MERN, Python, or Java based on the user's selected tech stack.
Behavioral Intelligence: Analyzes more than just "what" you said—it evaluates "how" you said it, providing a Behavioral Score for confidence and tone.

📊 5. Database & Schema

SonicPrep AI utilizes Firebase Firestore with a flat, high-performance NoSQL structure. This ensures sub-100ms data retrieval for real-time interview dashboards.

5.1 Collections Schema

The database is organized into four core collections, optimized for cross-referenced queries.

Collection	Data Role	Primary Keys
`users`	Identity & Progress	`uid`, `email`, `avgScore`
`interviews`	Session Metadata	`interviewId`, `userId`, `role`
`transcripts`	Real-time Dialogue	`interviewId`, `content`, `timestamp`
`feedback`	AI Analysis	`interviewId`, `technicalScore`, `behavioralScore`

5.2 Document Blueprints (JSON)

Interviews Collection

Stores the configuration and metadata for every mock session.

{
  "id": "int_88291k",
  "userId": "user_slny26",
  "role": "Frontend Developer",
  "techStack": ["Next.js 15", "Tailwind v4", "GSAP"],
  "status": "completed",
  "isPublic": true,
  "createdAt": "2026-04-18T15:00:40Z"
}

Feedback Collection

Stores the deep-dive analysis generated by the Gemini 1.5 Pro engine.

{
  "interviewId": "int_88291k",
  "scores": {
    "technical": 8.5,
    "communication": 9.0,
    "confidence": 7.5
  },
  "aiAnalysis": {
    "strengths": ["Strong understanding of RAG", "Clear articulation"],
    "improvements": ["Work on React Server Component lifecycle explanation"],
    "behavioralFeedback": "Tone was professional but slightly fast-paced."
  }
}

🔒 5.3 Security Rules

service cloud.firestore {
  match /databases/{database}/documents {
    
    /**
     * @collection interviews
     * Logic: Users can CRUD their own interviews. 
     * Community can read only if 'isPublic' is true.
     */
    match /interviews/{interviewId} {
      // Allow full access to the owner of the document
      allow read, write: if request.auth != null && 
                         request.auth.uid == resource.data.userId;
      
      // Allow the community to explore high-scoring public sessions
      allow read: if resource.data.isPublic == true;
    }

    /**
     * @collection users
     * Logic: Strict Privacy. Only the authenticated user 
     * can view or modify their profile metadata.
     */
    match /users/{userId} {
      allow read, write: if request.auth != null && 
                         request.auth.uid == userId;
    }

    /**
     * @collection feedback
     * Logic: Feedback is sensitive AI analysis. 
     * Only the owner can view it.
     */
    match /feedback/{feedbackId} {
      allow read: if request.auth != null && 
                  request.auth.uid == resource.data.userId;
      allow create: if request.auth != null;
    }
  }
}

6.📦 Installation & Setup

Follow these steps to get a local development instance of SonicPrep AI running on your machine.

1. Prerequisites

Node.js: v18.17.0 or higher
npm or bun (recommended for Next.js 15)
A Firebase project (for Auth & Database)
A Vapi.ai account (for Voice WebRTC)
A Google AI Studio account (for Gemini API)

2. Clone & Install

git clone [https://github.com/salonyranjan/sonic-prep.git](https://github.com/salonyranjan/sonic-prep.git)
cd sonic-prep
npm install

6.1 Environment Configuration

3.Create a .env.local file in the root directory and populate it with your credentials:

# --- AI & VOICE CONFIGURATION ---
NEXT_PUBLIC_VAPI_PUBLIC_KEY=your_vapi_public_key
VAPI_API_KEY=your_vapi_private_key
GEMINI_API_KEY=your_google_gemini_api_key

# --- FIREBASE CONFIGURATION ---
NEXT_PUBLIC_FIREBASE_API_KEY=your_api_key
NEXT_PUBLIC_FIREBASE_AUTH_DOMAIN=your-project.firebaseapp.com
NEXT_PUBLIC_FIREBASE_PROJECT_ID=your-project-id
NEXT_PUBLIC_FIREBASE_STORAGE_BUCKET=your-project.appspot.com
NEXT_PUBLIC_FIREBASE_MESSAGING_SENDER_ID=your_sender_id
NEXT_PUBLIC_FIREBASE_APP_ID=your_app_id

# --- NEXT.JS CONFIG ---
NEXT_PUBLIC_BASE_URL=http://localhost:3000

🗄️ 4. Database Setup

SonicPrep AI uses Firebase Firestore for its real-time NoSQL persistence and Firebase Auth for secure session management.

1. Enable Firebase Services

Go to the Firebase Console.
Authentication: Enable the Email/Password and Google providers in the Sign-in method tab.
Firestore Database: Create a database in Production Mode (or Test Mode for the demo) and choose a location nearest to you (e.g., asia-south1 for India).

2. Firestore Security Rules

To allow users to read and write their own interview data while keeping community interviews public, apply the following rules in the Rules tab:

service cloud.firestore {
  match /databases/{database}/documents {
    // Allow users to manage their own interview records
    match /interviews/{interviewId} {
      allow read, write: if request.auth != null && request.auth.uid == resource.data.userId;
      // Allow community exploration (Read-only for others)
      allow read: if resource.data.isPublic == true;
    }
  }
}

🚀 5. Run Development Server

Once your environment variables and database are configured, you are ready to launch the SonicPrep engine.

Start the Development Environment

We use Turbopack (included in Next.js 15) for lightning-fast Hot Module Replacement (HMR) and optimized build times.

# Using npm
npm run dev

# Or using Bun (Recommended for ultra-fast performance)
bun dev

Access the Application Open your browser and navigate to:

http://localhost:3000

6.2📦 Production Build

For the final deployment, SonicPrep AI leverages the Next.js Turbopack build engine and SWC (Speedy Web Compiler) to ensure the smallest possible bundle size and ultra-fast load times.

1. Optimize and Build

This command executes a production-ready build, performing dead-code elimination, image optimization, and static page generation.

# Using npm
npm run build
npm run start

# Or using Bun (for 2x faster build speed)
bun run build

7.🤝 Contributing

SonicPrep is an open-source project, and contributions are what make the developer community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

🛠️ How to Contribute

Fork the Project
Create your Feature Branch (git checkout -b feature/AmazingFeature)
Commit your Changes (git commit -m 'Add some AmazingFeature')
Push to the Branch (git push origin feature/AmazingFeature)
Open a Pull Request

📜 Contribution Guidelines

Code Quality: Ensure your code follows the existing project structure and uses TypeScript for type safety.
UI Consistency: Any new UI components should strictly follow the Sonic Dark Mode (Glassmorphism) guidelines using Tailwind v4.
Testing: If you add a new AI agent or utility, please include a basic test case.

7.1🗺️ Future Roadmap

Help us build the next generation of career intelligence. Current priorities include:

Video Analysis: Integrating body language and eye-tracking metrics.
Live Coding Sandbox: A shared IDE for real-time technical assessments.
Company-Specific Agents: Specialized RAG pipelines for companies like TCS, Infosys, and Google.
Mobile App: A React Native version for on-the-go mock interviews.

8.👨‍💻 Developed By

Salony Ranjan

"Building the future of AI-driven career intelligence, one voice at a time."

Patna | Kolkata | Remote

Name		Name	Last commit message	Last commit date
Latest commit History 17 Commits
app		app
components		components
constants		constants
firebase		firebase
lib		lib
public		public
types		types
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
components.json		components.json
eslint.config.mjs		eslint.config.mjs
next.config.ts		next.config.ts
package-lock.json		package-lock.json
package.json		package.json
postcss.config.mjs		postcss.config.mjs
tsconfig.json		tsconfig.json

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🚀 SonicPrep AI: Real-Time Voice Interview Intelligence

Elevating Engineering Interviews with Gemini-Powered Voice Intelligence

📋 Table of Contents

1.✨ Key Features

🚀 Advanced Functionalities

🎨 Cinematic User Experience

2.🎥 Live Demo

🚀 Try the Live Demo Now

⚡ Zero-Latency Performance

🏗️ 3. Architecture & Flows

📐3.1 High-Level Project Architecture

3.2🧠 App Logic & State Flow

🔄 Data Flow Diagram (DFD)

🔄 Data Interaction Model

4.🛠️ Tech Stack & Ecosystem

🚀 Ecosystem Breakdown

🛠️ Technical Highlights

📊 5. Database & Schema

5.1 Collections Schema

5.2 Document Blueprints (JSON)

Interviews Collection

Feedback Collection

🔒 5.3 Security Rules

6.📦 Installation & Setup

1. Prerequisites

2. Clone & Install

6.1 Environment Configuration

3.Create a .env.local file in the root directory and populate it with your credentials:

🗄️ 4. Database Setup

1. Enable Firebase Services

2. Firestore Security Rules

🚀 5. Run Development Server

Start the Development Environment

6.2📦 Production Build

1. Optimize and Build

7.🤝 Contributing

🛠️ How to Contribute

📜 Contribution Guidelines

7.1🗺️ Future Roadmap

8.👨‍💻 Developed By

Salony Ranjan

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