This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend
What I Built
I built PanelPrep, a local AI interview-practice coach designed to help someone prepare for important interviews.
The problem wasn't finding another list of interview questions. The difficult part was actually practicing out loud.
PanelPrep turns the user's own study material into an interactive mock interview. Users can upload PDFs, DOCX files, text files, images, or scanned documents, and PanelPrep generates questions based on that material.
Users can answer by speaking or typing, receive follow-up questions, and review measurable feedback such as speaking speed, filler words, answer length, and important terms used.
It supports both English and Hindi, making interview practice more comfortable and accessible.
Demo
https://drive.google.com/file/d/148w7j5kkjxSWfAF4OC9RqslQQr40H3aJ/view?usp=sharing
Code
https://github.com/tanisha-naruka/Panelprep.git
How I Built It
The core of PanelPrep is Gemma, Google's open-weight model, running locally through Ollama.
The user provides their own preparation material, which is processed by the application and used to generate interview questions through the local Gemma model.
The application is built with JavaScript and Node.js. It also includes document extraction, OCR, voice interaction, interview metrics, profiles, history, backups, and progress tracking.
The AI generates the interview questions, while normal application code calculates measurable results such as speaking speed, filler words, answer length, and important terms used.
If the model produces invalid output or Ollama is unavailable, PanelPrep can fall back to a built-in question bank instead of simply breaking.
Why Does Open Innovation Matter?
Interview preparation can involve personal information, resumes, academic records, and private preparation material.
Using an open-weight model locally means the core AI experience can run directly on the user's computer instead of requiring their personal preparation material to be sent to a closed AI service.
This gives PanelPrep:
- Privacy for personal preparation material
- No per-session cloud AI cost
- The ability to change or compare local models
- More control over how AI output is handled
- An AI experience that can work locally without depending on a paid API
For this project, open innovation wasn't just a technical choice.
It made it possible to build a more private and controllable interview coach for a real person.
Prize Categories
Best Use of Gemma
PanelPrep uses Gemma 3 locally through Ollama as the core AI model for generating interview questions from the user's own material.This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend
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