This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass Preparing for technical and behavioral interviews often means being chained to a desk for 8+ hours a day—staring at problem sets, doomscrolling forum threads, and burning out in isolation.
VirtualMentor turns interview preparation inside out. Instead of another static coding test on a bright screen, it acts as an audio-first conversational sparring partner designed specifically for hands-free, outdoor walks. By untethering job seekers from their desks, VirtualMentor lets engineers and students practice system design explanations, behavioral storytelling (STAR method), and rapid technical questions while literally touching grass.
Who it's for:
The project is fully open-source. Check out the repository for the full-stack codebase, API routes, and prompt pipelines: *(https://github.com/AnuragYadav9219/pitchPilot)
VirtualMentor is built around an open-source, local/self-hosted AI pipeline prioritizing low latency, natural conversational cadence, and privacy:
Accessibility for Every Candidate: Closed commercial APIs charge heavy per-token and per-minute audio costs that quickly price out students and unemployed job seekers. Open weights and local inference make high-repetition verbal drills practically free to run.
Privacy on Sensitive Career Data: Candidates discuss personal stories, career gaps, failures, and previous proprietary projects. Open-source deployment guarantees user audio and transcripts never feed proprietary training loops.
Modularity & Offline Independence: Open weights allow local quantization, enabling developers to run localized interview engines on-device without tethering to constant cloud API uptime.