This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle
What I Built
I built SkillMatch AI, a local AI-powered job-readiness assistant that helps students and job seekers understand how well their resumes match a job description.
It identifies matching skills, highlights skill gaps, suggests resume improvements, recommends important keywords, and creates a personalized 7-day preparation plan with interview questions.
The idea came from a common problem: many students apply for jobs without knowing which skills they are missing or how to prepare effectively. SkillMatch AI helps them focus on what matters instead of guessing.
Students, job seekers, and placement-training institutes could benefit from this tool.
The application is designed to run locally using Ollama, so users can explore its core AI features without relying on a paid AI API.
Code
GitHub Repository: https://github.com/Rushindhra/SkillMatch-AI
The project includes a React frontend, a Node.js and Express backend, and integration with Ollama for local AI inference.
How I Built It
I built SkillMatch AI using React, Vite, Tailwind CSS, Node.js, and Express. For the AI component, I used Ollama with an open-weight model such as Qwen, which can be configured based on the available hardware.
The application extracts text from a resume, compares it with the job description, identifies matching and missing skills, and generates personalized recommendations.
I also designed the analysis to combine deterministic skill matching with AI-generated suggestions, making the results more transparent and useful.
My goal was to build something practical that students could actually use, rather than just another AI chatbot.
Why Does Open Innovation Matter?
For me, open innovation means having more control over how AI works and where our data goes.
Resumes contain personal information, so I wanted SkillMatch AI to support local processing instead of sending documents to an external AI service. Using Ollama makes it possible to run inference locally, experiment with different models, and customize the analysis without depending on a paid AI API.
It also makes the project more accessible to students and small organizations that may not have the budget for recurring API costs.
Open-source AI gave me the flexibility to experiment, learn, and build a useful application while keeping privacy and affordability in mind.
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