This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend I built ResumeForge AI for a friend who was having difficulty deciding what project to build based on their skills, career goals, experience level, and available time.
ResumeForge AI is a personalized AI project advisor. A student enters their branch or domain, skills, experience level, career goal, and available time. The system then generates three project recommendations tailored to that profile.
Each recommendation includes:
I built and tested the application with a friend. Their feedback was:
"these are useful projects for me"
That feedback helped confirm that the project recommendations were useful for the problem I was trying to solve.
Demo video:
The application runs locally and generates project recommendations using a locally running open-weight AI model.
GitHub repository:
https://github.com/msairitvik/ResumeForge-AI ResumeForge AI is built using:
The architecture is:
React frontend
→ Spring Boot API
→ Ollama
→ Qwen3 0.6B
The student's profile is sent to the local Spring Boot backend, which builds a structured prompt and sends it to the locally running Qwen model through Ollama.
The model generates exactly three project recommendations using the student's branch, skills, experience level, career goal, and available time.
I also tested the application with the internet disconnected after the required software, dependencies, and model had already been installed. The application was still able to generate recommendations locally.
Using an open-weight model running locally made this project possible without depending on a proprietary AI API.
For ResumeForge AI, this is important because the information entered by a student can remain on their own computer instead of being sent to a third-party AI service.
It also gives the project more flexibility. The AI model can be changed or upgraded without redesigning the entire application.
Local inference also makes it possible to continue generating recommendations without an internet connection after the required model and dependencies have been installed.
Most importantly, open AI tools allowed me to experiment with the model, prompting, and application architecture while keeping the AI component under my control.
This project helped me understand how a locally running open-weight AI model can be integrated into a real application rather than being used only through a chatbot interface.
I learned how to connect a React frontend to a Spring Boot backend, send structured user information to an AI model, design prompts for consistent output, and turn the model's response into a usable application experience.
I also learned that making an AI application useful is not only about generating text. The input structure, prompt design, output format, and user experience all matter.
The project was built around a real problem faced by my friend: deciding what project would be realistic and useful for their skills, career direction, and available time.
Instead of giving a generic project list, ResumeForge AI tries to make the recommendations specific to the individual.
After trying the application, my friend said:
That was the feedback I wanted to validate the idea with.
Some improvements I would like to add in the future include:
ResumeForge AI is my attempt to build a practical AI tool around a real problem faced by a friend, while keeping the AI component local and based on open-weight technology.
The goal is simple: instead of asking a student to search through hundreds of generic project ideas, help them find a few realistic projects that actually match who they are and what they want to do.