


MediaSeg is a local macOS utility that splits large media files into upload-ready chunks while preserving quality. It was produced and directed with full AI assistance, and shipped in 2 days from idea to public release. Originally created to streamline long-recording upload prep, MediaSeg is useful for NotebookLM and other size-limited upload destinations.
MediaSeg is a local macOS utility that splits large media files into upload-ready chunks while preserving original quality. Built specifically for Apple Silicon Macs running macOS 15 Sequoia or later, it processes MP4 and WEBM files entirely on your machine—no cloud uploads, no file size limits imposed by third-party servers. The tool was produced and directed with full AI assistance, shipped in just 2 days from idea to public release, and defaults to a 200MB target chunk size that you can adjust per session.
MediaSeg runs entirely on your Mac using local ffmpeg and ffprobe. The PySide6 desktop interface supports drag-and-drop file input, output folder selection, a collapsible session log, and clear processing states so you always know what's happening.
You set the maximum chunk size (default 200MB), and MediaSeg attempts to keep each output file within 90–98% of that limit. The hard upper bound is your configured value, so you never accidentally exceed a platform's upload cap.
MP4 files are split directly with no quality loss. WEBM files are first converted to MP4 using macOS VideoToolbox (no libx264 dependency), then split—preserving as much original quality as possible. The conversion step may take longer and use more CPU, but the output remains local and lossless in terms of re-encoding.
Output files are named sequentially (e.g., TrainingVideo_001.mp4, TrainingVideo_002.mp4) and placed in a timestamped folder. A session log records every step, making it easy to debug or share with the developer via GitHub Issues.
"Strong direction and production can turn an idea into a practical product, fast."
MediaSeg was built from scratch in 48 hours with full AI assistance—not as a gimmick, but as a demonstration that focused execution matters more than tooling. The result is a genuinely useful utility that solves a real pain point: preparing long recordings for size-limited upload destinations without sacrificing quality or privacy. It's intentionally small, open-source, and designed to be easy to audit or extend.
You regularly work with large media files that need to fit under a specific size cap—especially for NotebookLM, podcast hosting, or video collaboration tools. You value local processing over cloud services, and you appreciate a tool that's transparent about what it does and how it works. If you're curious about what AI-assisted development can produce when guided by clear direction, MediaSeg is a practical example worth exploring.
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