
Worked on the Olshansk/rss-feeds repository to enhance the reliability and accuracy of the Meta AI blog feed by addressing a persistent metadata extraction issue. Focused on correcting hero post mis-dating and category assignment, the solution involved making the extraction logic class-agnostic to withstand CSS changes and prevent empty category fields. Utilized Python and BeautifulSoup for robust web scraping and data extraction, confining changes to the hero block to minimize regression risk. Verification was performed through direct HTML parsing and updated test plans, resulting in improved feed freshness, reduced support overhead, and more maintainable code for ongoing blog feed management.
Consolidated monthly delivery for 2026-04 focused on reliability and accuracy of the Meta AI blog feed. Delivered a robust hero-post metadata correction in Olshansk/rss-feeds to ensure the latest post is shown with correct date and category, reducing subscriber confusion and improving engagement. Fixed mis-dating bugs caused by CSS-rotated selectors and empty category fields by making extraction class-agnostic and more resilient. The change confines to the hero block to avoid regressions elsewhere, with verification via direct HTML parsing and updated test plan. This improves feed freshness, reduces support/QA overhead, and demonstrates solid Python-based scraping robustness and maintainable code.
Consolidated monthly delivery for 2026-04 focused on reliability and accuracy of the Meta AI blog feed. Delivered a robust hero-post metadata correction in Olshansk/rss-feeds to ensure the latest post is shown with correct date and category, reducing subscriber confusion and improving engagement. Fixed mis-dating bugs caused by CSS-rotated selectors and empty category fields by making extraction class-agnostic and more resilient. The change confines to the hero block to avoid regressions elsewhere, with verification via direct HTML parsing and updated test plan. This improves feed freshness, reduces support/QA overhead, and demonstrates solid Python-based scraping robustness and maintainable code.

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