PrepAI: An Offline AI Interview Coach Powered by Open-Source AI
This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle What I Built Preparing for technical interviews can be difficult, especially for students who don't have access to mentors or enough opportunities to practice. Many AI interview preparation tools depend on cloud
Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle Submission 馃捀
This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle
Preparing for technical interviews can be difficult, especially for students who don't have access to mentors or enough opportunities to practice.
Many AI interview preparation tools depend on cloud-based APIs, which can introduce recurring costs and require users to send their answers to external services.
I built PrepAI, an AI Interview Coach that helps students practice interviews and improve their answers using a locally running AI model.
PrepAI simulates an interview by asking questions, evaluating responses, and providing actionable feedback.
https://github.com/danish16102005-hub/Ai-Interview-Coach
I built PrepAI using React for the interface and Google's Agent Development Kit to manage the interview conversation.
Technology Stack Technology Purpose React Build the interview interface Vite Frontend development and build tooling Python Implement the AI agent Google ADK Manage the agent's instructions, tools, and conversation Ollama Run the AI model locally Qwen 2.5 3B Power the interview agent LiteLLM Connect ADK to the local model
Open innovation matters because useful AI tools should not always require expensive infrastructure or proprietary APIs.
For PrepAI, open-source and open-weight technologies made it possible to combine an agent framework with a locally running language model.
When inference runs locally, interview answers can remain on the user's computer, provided the application does not send them elsewhere.
Users do not need to pay a cloud provider for each model request when using local inference. They still need suitable hardware and must account for electricity and setup costs.
Developers can experiment with different compatible models and improve the agent's instructions and evaluation tools without redesigning the entire application.
Using an open agent framework and a local model makes it easier to understand how the system works instead of treating AI as a black box.
For me, the biggest benefit is being able to build an AI-powered application while learning how agent frameworks, tools, and local inference work together.