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