Developer automates game footage analysis for $9 using rented GPU and open-source AI

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A developer building an automated YouTube Shorts pipeline used four AI models — CLIP, BLIP, OWLv2, and YOLO — to extract and crop 20,675 game objects from 904 frames of gameplay footage. The process ran overnight on a rented RTX PRO 4500 GPU via RunPod, costing just $9 including failed attempts and iteration. The resulting image dataset will be used to train a reusable object-detection model, dubbed SpriteHarvester, for roughly $10 more. The model is intended to replace the manual step of scouting timestamps in raw footage, automatically identifying key gameplay moments like boss appearances and UI events. The project highlights how rented cloud compute is lowering the barrier for independent developers to build sophisticated AI-driven content tools.

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