Project Info

ClipGoal AI

Devpost

Inspiration

Ever since 2nd grade, I’ve been a die-hard soccer fan. The first time I tried to edit and share my own match highlights, I was defeated by the frame-by-frame hunting → timeline alignment → export grind⁠—my excitement fizzled out, and I lost a valuable chance to review my play. So I teamed up with equally sports-obsessed Hackathon mates, fused my CS background with our love for other sports, and built ClipGoal AI: a one-tap highlight generator that will soon evolve into stats + motion-correction for any ball sport (soccer, basketball, volleyball, …)―putting AI in service of passion.

Challenges we ran into

After nailing the welcome & home pages, the camera system stalled: reliably spotting a scoring zone across all sports proved tricky. Relying solely on generic algorithms was overkill, so we simplified by classifying sports into “Ground-Sport” vs “Goal-Oriented Sport” buckets, cutting both workflow and algorithmic complexity. Accomplishments we’re proud of Under brutal time pressure, we shipped a functional MVP that already meets the highlight-editing and training-video needs of everyday athletes.

What we learned

The new React Native + Expo build pipeline & live debugging (npx expo start) How to write custom Vision-Camera Frame Processor plugins First exposure to event-driven design and bringing Expo Camera into production

What's next

Full motion-correction mode – AI-powered coaching for amateurs Richer data dashboards – player stats & in-game performance visualisation More sports support – extend to additional ball sports with custom event rules

Analysis

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Technology

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  • FastAPIIn code
  • HTMLIn code
  • PythonIn code
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  • ReactIn code
  • TypeScriptIn code
  • SupabaseClaimed

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AI coding agents

  • Claude CodeConfig · Commits

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Codebase size

Source size

215 KB

Source files

33

Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.

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