JobiFC: A Finishing Coach Built for My Friend

Rudraksh Karmakar — Sun, 04 Oct 2026 18:20:04 +0000

This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend.

What I Built

I built JobiFC for my friend, Jobi Anand. He told me finishing is the area he especially wants to improve, so I made it the app’s default coaching focus. After trying the finishing drill, Jobi said it fits him.

JobiFC lets him record match stats, review a small performance snapshot, and ask for a practical training suggestion. It is a practice aid, not a player rating or a replacement for his coach.

Demo

Watch the JobiFC walkthrough

The walkthrough shows JobiFC’s finishing focus, a match-stat entry, and a finishing drill from the coach.

Code

JobiFC source on GitHub

How I Built It

The interface uses HTML, CSS, and JavaScript, with a Python FastAPI backend. The backend validates match entries, imports CSV files, calculates the visible stats, and stores match history in a local JSON file.

For coaching, JobiFC sends the question and recent match data to Gemma 3 4B (gemma3:4b) running locally through Ollama. The app checks whether Gemma’s answer follows Jobi’s chosen focus. If it switches to another skill area, JobiFC shows a clearly labeled finishing drill instead.

Why Does Open Innovation Matter?

Running Gemma locally let me build around Jobi’s specific goal without sending his match history to a hosted chatbot API. I can inspect and change the model call, choose where inference happens, and keep the data on the same computer during this demo. Open model tooling made that control practical for a small, personal project.

Prize Categories

Best Use of Gemma — JobiFC uses Gemma 3 4B through Ollama for its coaching flow.