
During February 2026, NanmiCoder enhanced the MediaCrawler repository by implementing configurable storage paths for data and media, enabling flexible deployment across platforms with a default data folder fallback. Leveraging Python and asynchronous programming, NanmiCoder introduced command-line IP proxy configuration and a proxy pool to improve data collection reliability in restricted network environments. An initial file-based logging system with configurable paths and log levels was developed to support better diagnostics, though it was later rolled back to maintain stability. Additionally, error handling for XiaoHongShuClient sub-comments was improved, reducing process interruptions and contributing to more robust backend operations overall.

February 2026 — NanmiCoder/MediaCrawler: Delivered configurable storage paths for data/media across platforms with a default data folder, added CLI IP proxy configuration and proxy pool to enhance data collection reliability in restricted networks, initiated file-based logging with configurable paths and levels for better observability (feature later rolled back to prior behavior), and improved error handling for XiaoHongShuClient sub-comments to reduce interruptions. A rollback of the log-to-file feature was performed to restore prior rollout stability. These changes collectively improve deployment flexibility, reliability of data collection, and operational diagnostics while maintaining system stability.
February 2026 — NanmiCoder/MediaCrawler: Delivered configurable storage paths for data/media across platforms with a default data folder, added CLI IP proxy configuration and proxy pool to enhance data collection reliability in restricted networks, initiated file-based logging with configurable paths and levels for better observability (feature later rolled back to prior behavior), and improved error handling for XiaoHongShuClient sub-comments to reduce interruptions. A rollback of the log-to-file feature was performed to restore prior rollout stability. These changes collectively improve deployment flexibility, reliability of data collection, and operational diagnostics while maintaining system stability.
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