
Worked on the TheAnonymous-stack/numi-scraper repository to deliver automated web scraping and data extraction pipelines focused on educational content. Developed robust workflows in Python and JavaScript using Playwright and BeautifulSoup to collect, process, and format large question datasets, including visual assets and answer extraction for multiple question types. Implemented JSON serialization and file handling utilities to streamline data management and artifact generation, supporting both QA and production data pipelines. Enhanced content extraction accuracy and automated testing, reducing manual review cycles. Expanded datasets for Grades 4 and 5, integrated CI/CD auto-approve workflows, and improved asset management to accelerate release velocity and data quality.
Month: 2025-07 — TheAnonymous stack/numi-scraper delivered data expansion, robustness improvements, and release automation that directly enhances learner value and release velocity. Major work focused on expanding and enriching graded question datasets, improving content extraction and visuals handling, and validating automated PR workflows.
Month: 2025-07 — TheAnonymous stack/numi-scraper delivered data expansion, robustness improvements, and release automation that directly enhances learner value and release velocity. Major work focused on expanding and enriching graded question datasets, improving content extraction and visuals handling, and validating automated PR workflows.
2025-06 Monthly Summary for TheAnonymous-stack/numi-scraper focusing on delivering automated scraping capabilities, data extraction, and artifact generation to accelerate QA/data pipeline quality. Highlights feature delivery, robust data extraction, and groundwork for scalable scrapers, aligning with business value of faster data collection, higher fidelity outputs, and reduced manual validation.
2025-06 Monthly Summary for TheAnonymous-stack/numi-scraper focusing on delivering automated scraping capabilities, data extraction, and artifact generation to accelerate QA/data pipeline quality. Highlights feature delivery, robust data extraction, and groundwork for scalable scrapers, aligning with business value of faster data collection, higher fidelity outputs, and reduced manual validation.

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