EcoTrack AI: From Prototype to Polished Carbon Footprint Tracker

This is a submission for the GitHub Finish-Up-A-Thon Challenge
EcoTrack AI: From Prototype to Polished Carbon Footprint Tracker

This is a submission for the GitHub Finish-Up-A-Thon Challenge (dev.to in Bing)

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What I Built
EcoTrack AI began as a hackathon prototype with a basic UI, static feedback messages, and limited features. I’ve now polished it into a usable tool with a responsive dashboard, charts for progress tracking, motivational AI feedback, and cleaner, refactored code. This project means a lot to me because it shows how an unfinished idea can be transformed into something practical and impactful.

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Demo

• Live Project Link: EcoTrack AI on GitHub Pages (samkebz-y.github.io in Bing)
• Screenshots: Before: [Looks like the result wasn't safe to show. Let's switch things up and try something else!] After: [Looks like the result wasn't safe to show. Let's switch things up and try something else!]

(Optional: Add a short video walkthrough — even a 30‑second screen recording — to make the demo stand out.)

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The Comeback Story
Originally, EcoTrack AI was half‑finished: a prototype with limited interactivity and static feedback. During the Finish‑Up‑A‑Thon, I refactored repetitive functions, debugged async API calls, integrated Chart.js for dynamic progress tracking, and added responsive CSS. The result is a polished application that feels complete and ready for real users.

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My Experience with GitHub Copilot
GitHub Copilot was instrumental in finishing this project. It:

• Suggested refactoring for repetitive functions
• Helped debug async API calls
• Generated Chart.js integration snippets
• Provided responsive CSS boilerplate

Copilot turned what felt like a stalled prototype into a polished, usable tool by accelerating fixes and offering smart code suggestions.