This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass
What I Built
Preparing for interviews is notoriously draining. Developers and professionals spend hundreds of hours glued to screens grinding through static question banks and repetitive mock sessions.
AI Interview Simulator was built to solve this: a high-efficiency pair interviewer powered by open-source AI that delivers hyper-focused practice. Upload your resume or choose your field, run a realistic 5-minute drill, get instant answer coaching and model solutions, and **close your laptop to go touch grass.
Key Highlights:
- 📄 Resume Personalization: Upload your PDF/TXT resume to get questions that target your actual projects and claimed tech stack.
- 🌱 Broad Career Disciplines: Not just coding — includes Environmental Sciences, Healthcare Administration, Clinical Research, Field Operations, and Business Management.
- 💡 "How to Improve Your Answer" Coaching: Pinpoints missing elements (STAR technique, edge cases, metrics, trade-offs).
- ✨ Ideal / Model Answers: An interactive reveal toggle showing the exact industry-standard response expected by hiring managers.
- 📈 Comprehensive Report: Instant circular scoring, hiring decision recommendations, top strengths, and growth areas.
Demo
- 🌐 Live Web Application: https://ai-interview-simulator-nu-weld.vercel.app/
- ⚡ Live API Service: https://ai-interview-simulator-9v89.onrender.com/
Code
The entire codebase is open-source:
🤖 AI Interview Simulator
Practice job interviews with an AI powered by open-source LLMs via Groq. Get real questions, instant feedback, and a full performance report — free.
Built for Hacktoberfest 2026 Open-Source AI Challenge.
🌐 Live Demo (Frontend): https://ai-interview-simulator-nu-weld.vercel.app/
⚡ Live API (Backend): https://ai-interview-simulator-9v89.onrender.com/
✨ Features
- 📄 Resume-Driven Customization — Upload your resume (PDF/TXT) or paste your experience; AI inspects your actual tech stack, past companies, and project achievements to generate tailored, deep-dive interview questions
- 🎯 Multi-Industry Career Fields — Pre-configured for Tech, AI & Data, Product & Management, Design, Marketing & Sales, Finance & Accounting, and Healthcare & Sciences. Also supports custom write-in roles for any profession.
- 📊 3 Experience Levels — Junior, Mid, Senior
- 💬 3 Interview Types — Technical, Behavioral, Mixed
- 💡 "How to Improve Your Answer" Coaching — Actionable advice on how you can elevate your specific answer (e.g., structuring with STAR…
- GitHub Repository: https://github.com/sage106/ai-interview-simulator
How I Built It
- Frontend: Built with React 18, Vite, Tailwind CSS, and Lucide Icons, deployed on Vercel.
- Backend: Node.js & Express with
multerandpdf-parsefor in-memory resume extraction, deployed on Render. - Database: MongoDB Atlas storing session logs, candidate scores, and coaching breakdowns.
- AI Engine: Open-source large language model (qwen/qwen3.8-27b) via high-speed Groq API for zero-latency question generation and structured JSON evaluations.
Why Does Open Innovation Matter?
Open-source AI and open-weight models democratize career preparation:
- Privacy & Data Sovereignty: Candidates upload sensitive career histories and resumes. Open-source models allow self-hosting and zero data retention, ensuring personal data isn't locked inside proprietary corporate models.
- Accessible Career Coaching for Everyone: Professional mock interviews often cost upwards of $100/hr. Using open-source models with sub-second inference makes high-caliber career coaching accessible to any candidate worldwide for free.
3. Modularity & Flexibility: We can swap, fine-tune, or calibrate evaluation prompts across diverse domain vocabularies without vendor lock-in.
Prize Categories
- Best Use of Render: The backend API service is deployed and running live on Render Web Services.
- Best Use of MongoDB Atlas: All session transcripts, resume context, and historical evaluations are stored in MongoDB Atlas.
Built with ❤️ for #hf26challenge!
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