InterviewSim – Open Source AI Technical Interview Simulator

🚀 Overview

InterviewSim is a fully open-source AI-powered technical interview simulator designed to replicate real-world coding interviews.

Unlike traditional coding platforms that simply check for correct outputs, InterviewSim:

• Acts as a live interviewer

• Asks clarification questions

• Evaluates reasoning step-by-step

• Scores performance across multiple dimensions

• Suggests improvement paths

• Recommends related problems

• Suggests curated learning resources

• Optionally pushes results to GitHub

The goal is to simulate structured interview preparation rather than simple problem solving.

🎯 Problem Statement

Many students preparing for technical interviews face challenges:

• No realistic interview simulation

• Lack of structured feedback

• No communication evaluation

• No guided improvement roadmap

• No workflow integration for tracking preparation

Existing platforms focus on solving problems but do not simulate the interviewer mindset.

InterviewSim addresses this gap.

🧠 Key Features

1️⃣ Interviewer Mode

• User pastes a full problem statement.

• The system switches into interviewer mode.

• AI asks:

  • Clarification questions

  • Edge case discussions

  • Approach validation

  • Complexity analysis

  • Optimization follow-ups

It behaves like a real human interviewer.

2️⃣ Step-by-Step Evaluation

Each session is evaluated across:

• Problem Understanding

• Logical Approach

• Edge Case Handling

• Time & Space Complexity

• Communication Clarity

Final score: 0–100%

Structured feedback is generated.

3️⃣ Mistake Analysis & Feedback

The system:

• Highlights weak reasoning

• Identifies missed optimizations

• Explains better approaches

• Provides improvement suggestions

4️⃣ Related Problem Recommendation

Based on detected pattern (e.g., Sliding Window, Two Pointers, DP):

• Suggests easier practice problem

• Suggests similar-level problem

• Suggests harder follow-up

This builds progressive learning.

5️⃣ Learning Recommendation Engine

If performance is low:

• Curated YouTube learning resources are suggested

• Concept summary notes are generated

• Pattern explanation is provided

This converts failure into structured improvement.

6️⃣ GitHub Integration (Optional)

After completing a session:

Users can choose to:

• Push the solution and feedback to their GitHub repository

• Or skip

If enabled, it stores:

• Problem

• Final solution

• Score

• Feedback

• Notes

7️⃣ Notion Save-Later Feature (Optional)

If a user stops midway:

• Session is saved

• Partial solution is stored

• Can resume later

🏗 Architecture

Frontend:

• React

Backend:

• Spring Boot

• REST APIs

• Service-layer architecture

• JPA + PostgreSQL

AI Layer:

• Ollama (local LLM runtime)

• Open-source LLM models

Integrations:

• GitHub REST API

• Notion API

Fully self-hostable and open-source.

🧩 Open Source Compliance

• Uses only open-source technologies

• No proprietary AI APIs

• Self-hostable architecture

• MIT License

• Public GitHub repository

• Clear contribution guidelines

📅 Development Timeline

Week 1:

• Architecture setup

• Spring Boot base project

• Basic interview API

Week 2:

• LLM integration

• Interview flow logic

Week 3:

• Scoring engine

• Recommendation engine

Week 4:

• UI polishing

• Testing

• Documentation

• Demo recording

All development progress tracked via consistent GitHub commits.

🌍 Real-World Impact

InterviewSim helps:

• Students preparing for placements

• Self-learners without mentorship

• Colleges running mock interviews

• Developers tracking structured growth

It transforms interview preparation into a measurable, guided, and repeatable process.

🔮 Future Scope

• Voice-based interview mode

• Real-time code editor

• Peer interview simulation

• Performance analytics dashboard

• College placement integration

📌 Vision

InterviewSim aims to democratize technical interview preparation through open-source AI-driven structured simulation.