This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend
What I Built
I built InterviewBuddy, an AI-powered interview practice partner for friend who feel nervous or unprepared for job interviews.
Instead of being a general-purpose chatbot, InterviewBuddy simulates a real interview. The user provides the job title, job description, background, interview type, and nervousness level. The AI then asks interview questions one at a time and uses the conversation context to make the interview progressively more realistic.
After the interview, InterviewBuddy analyzes the complete conversation and provides a structured review covering communication, confidence, technical knowledge, relevance, clarity, problem solving, strengths, weak answers, corrections, and practical tips for the next attempt.
Live Demo
https://interviewbuddy-psi.vercel.app/
GitHub
https://github.com/laytontozvireva-star/Interviewly
Why did you build it?
The idea came from a simple problem.
Someone can have the technical skills and still struggle during an interview because they are nervous, don't know what to expect, or have not had enough opportunities to practice.
Practicing with another person is not always possible.
I wanted to build something that gives a person a safe place to practice repeatedly.
You can make mistakes without embarrassment.
You can try again.
You can see where your answers need improvement.
And eventually, you can walk into a real interview with more confidence.
That is why I did not want to build another general AI chatbot.
I wanted to build an AI practice partner for a specific human problem.
Why open innovation matters
For this project, I wanted the AI to be based on an open-weight model rather than making the application dependent on a closed AI chatbot.
InterviewBuddy uses OpenAI GPT-OSS 20B through Hugging Face Inference Providers.
This showed me an important part of open innovation: developers can take openly available models and build focused solutions around them.
The model itself is not the complete product.
The value comes from how it is used.
In InterviewBuddy, the model becomes an interviewer during the interview and a coach after the interview.
Open models also give developers more freedom to experiment, learn, and create specialized applications.
How does it work?
The application follows this flow:
User → React application → Vercel API → Hugging Face → GPT-OSS 20B → Interview response
During the interview, the application sends the relevant interview context to the AI, including previous questions and answers.
The AI is instructed to behave like a realistic interviewer and return one question at a time.
When the interview is complete, the full interview is sent to the AI coach.
The coach then produces a structured review containing scores, strengths, weaknesses, corrections, and suggestions for improvement.
The application also includes browser-based voice interaction to make the practice experience feel more like an actual interview.
What makes it different?
The main difference is the focus.
InterviewBuddy is not designed to answer random questions.
It is designed around one experience:
helping someone become better at interviews.
The AI has two distinct roles:
During the interview
It behaves like an interviewer.
It asks questions and does not immediately reveal the answer or give feedback.
After the interview
It becomes a coach.
It reviews the complete interview and explains what the candidate can improve.
This separation makes the experience closer to actual interview practice.
Technology
- React
- JavaScript
- Tailwind CSS
- Node.js
- Vercel Serverless Functions
- Hugging Face Inference Providers
- OpenAI GPT-OSS 20B
- Browser Speech APIs
- Git
- GitHub
What I learned
The biggest lesson was that building an AI application is not simply about connecting an API.
A large part of the work was designing the AI's behavior.
I had to make the model understand when it should act as an interviewer and when it should act as a coach.
I also learned about:
- Prompt design
- AI inference
- Maintaining conversation context
- Serverless API routes
- Error handling
- Voice interaction
- Deploying AI applications
- Working with open-weight models
I also learned that a strong AI project should begin with a real problem rather than simply starting with a model and asking what can be built with it.
What's next?
I would like to continue improving InterviewBuddy with:
- Interview history
- Progress tracking
- More personalized coaching
- More job-specific interview modes
- Better voice interaction
- Additional open models
- More detailed improvement tracking between attempts
The long-term goal is to make InterviewBuddy something a candidate can return to throughout their job search rather than something they use only once.
Final thought
I started with a simple question:
Can AI help someone become more confident before facing a real interviewer?
InterviewBuddy is my attempt to answer that question.
I didn't build another AI chatbot.
I built an interview practice partner.
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