Application Metrics caught my broken size estimator

A developer of Cliparr, a self-hosted browser-based video clipping tool, used Sentry Application Metrics to evaluate and improve output file-size estimates without collecting users’ media or modifying self-hosted installations. By extracting the conversion engine into the public, static Cliparr Convert website and anonymously recording numeric metrics such as estimated-versus-actual size ratios, output formats, conversion quality, and completion status, the developer found that an AI-generated estimator based on resolution could underestimate files by as much as 83%. Replacing it with the standard bitrate-times-duration calculation brought estimates to within roughly 5% for transcoded videos, while the metrics also revealed that copy-mode exports were overestimated by about 16% and GIF estimates were consistently high by around 9%. The collected data showed MP4 was the most common output format and that users often converted files for compatibility rather than compression, while conversion counters showed no failures in the initial dataset. The project also instruments a progressive web app installation funnel to measure availability, prompts, acceptance, and completed installations across desktop, Android, and iOS.

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