This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend I built PortfolioPilot AI, an AI-powered portfolio generator designed for a friend who wanted a professional developer portfolio without spending hours writing content, designing sections, and putting everything together manually.
The idea is simple: my friend describes themselves in natural language, and PortfolioPilot AI turns that description into a structured, recruiter-ready portfolio.
It can generate sections such as:
It also provides a live preview, editable sections, multiple templates, portfolio scoring, and AI-powered improvement suggestions.
The project is currently designed to run locally with Ollama for AI inference.
GitHub repository:
https://github.com/abhishek25001011-boop/PortfolioPilot-AI The repository contains the complete frontend and FastAPI backend needed to run the project locally.
GitHub:
https://github.com/abhishek25001011-boop/PortfolioPilot-AI The main branch contains the stable version of the project.
PortfolioPilot AI is a full-stack application built with:
The core flow is:
User Prompt → FastAPI → Ollama → Qwen Coder → Structured JSON → Portfolio Preview
Instead of using a closed AI API, I used local inference through Ollama. The model generates structur bhaied portfolio information which the frontend then renders into the portfolio UI.
Open innovation made this project possible to run with AI inference locally.
Using an open-weight model through Ollama means the core AI generation does not have to depend on a paid closed API. A user can run the model on their own machine and keep their portfolio information local. It also makes the AI layer more flexible: the model can be changed or configured without redesigning the entire application.
For a personal tool like this, that gives users more control over their data, model choice, and running costs. Not included.
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