A developer with no image processing or video experience describes building a seasons time-lapse project using an LLM coding assistant. The project is a Python pipeline covering five steps: inventory (EXIF extraction), GPS-based filtering, image alignment, frame ordering by day-of-year, and video rendering. The author explains why Python was chosen over JVM (C++ backends for OpenCV and neural inference make interpreter overhead irrelevant), and introduces practical techniques like a --sample flag for faster iteration. Alignment is flagged as the hardest step and will be covered in a follow-up post.
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