
Valery Pavlovskaya developed and enhanced backend systems for the profcomff/rating-api and profcomff/rental-api repositories, focusing on robust API features and maintainability. She implemented a scalable monthly comment rate limiting system, introducing per-user and per-lecturer controls to reduce spam and improve user feedback. Her work included optimizing database queries, refining error handling, and automating code review workflows using Python, FastAPI, and GitHub Actions. Valery also expanded session management and GET endpoint reliability in rental-api, while increasing test coverage and unifying authorization logic. Her contributions emphasized code quality, performance, and clear communication of API constraints, demonstrating strong backend engineering depth.

April 2025 performance summary focusing on API reliability, test coverage, and maintainability across rental-api and rating-api, with measurable business value in reliability, security, and developer velocity.
April 2025 performance summary focusing on API reliability, test coverage, and maintainability across rental-api and rating-api, with measurable business value in reliability, security, and developer velocity.
March 2025 monthly summary focusing on delivered features, fixes, and impact across two services. Key improvements stabilized and expanded the rental-api and rating-api capabilities, with a strong emphasis on robust GET endpoints, dependable session management, and automated code quality processes that drive business value and maintainability.
March 2025 monthly summary focusing on delivered features, fixes, and impact across two services. Key improvements stabilized and expanded the rental-api and rating-api capabilities, with a strong emphasis on robust GET endpoints, dependable session management, and automated code quality processes that drive business value and maintainability.
December 2024: Implemented a scalable Monthly Comment Rate Limiting System in profcomff/rating-api, introducing per-user monthly counts, per-lecturer limits, and frequency controls; added create_ts/update_ts metadata, initialized monthly windows, and refined cutoff calculations to provide precise feedback on posting eligibility. Updated the comment creation endpoint and error handling with new exceptions (TooManyCommentRequests, TooManyCommentsToLecturer) and settings, enabling clearer user communication and reducing spam. Refactoring and performance improvements included efficient counting queries and robust window management. Reworked error model for rate-limit signaling and improved reliability of monthly rollovers.
December 2024: Implemented a scalable Monthly Comment Rate Limiting System in profcomff/rating-api, introducing per-user monthly counts, per-lecturer limits, and frequency controls; added create_ts/update_ts metadata, initialized monthly windows, and refined cutoff calculations to provide precise feedback on posting eligibility. Updated the comment creation endpoint and error handling with new exceptions (TooManyCommentRequests, TooManyCommentsToLecturer) and settings, enabling clearer user communication and reducing spam. Refactoring and performance improvements included efficient counting queries and robust window management. Reworked error model for rate-limit signaling and improved reliability of monthly rollovers.
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