Basketball Highlight Editor
Find every made basket in your game footage, locally
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Drop in a full pickup game video and BHE finds the likely made baskets with a local YOLO model. A frame-by-frame rim check filters obvious misses: on three hand-reviewed games it missed no makes and cut the review list by 44%. Review candidates, tag players, and export one reel or a personal reel for each player. No uploads, no subscription, no account. Free and open source for Mac, Windows and Android.
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PH · launched Oct 27, 2026 · 1 upvote (top 67%)
- Media & entertainment
- Workflow automation
- Prosumer
- AI feature
- Commercial product
- Open Source
- Sports
- Artificial Intelligence
- GitHub
Similar launches
See all 80 similar launches →Could you build this?
Partial The upload, review, tagging, and export workflow is vibe-codeable, but a reliable made-basket detector depends on a custom-trained YOLO model that needs labeled basketball footage.
What it would actually take: The core is a video pipeline (FFmpeg frame extraction, a YOLO detector, and a rim-proximity check) with a review UI and reel export. The hard part is accurate detection, which requires a labeled dataset of game footage and some model training or tuning, plus ML familiarity to set thresholds and evaluate misses.
Competitors
Other products that read as similar to this one : 80 launches clear the similarity bar, closest 8 shown.
Attention rank: #33 of 81 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).
Launched 144 days after the earliest competitor.
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Other launches for this product
- No other launches for this product.