Change Details

== Profile Information == Name: Ayush Khati GitHub: https://github.com/AyushkhatiDev Portfolio: https://portfoliokhati.netlify.app/ Location: India I am a backend-focused software developer with experience in building scalable systems, APIs, and data processing pipelines. I have worked on production-grade systems involving performance optimization, caching, and distributed architectures. My interests lie in building reliable and data-driven tools that support real-world applications. As part of this application, I completed both Outreachy microtasks (T418285 and T418286) and improved them based on mentor feedback. --- == Synopsis == The Lusophone Technological Wishlist is a community-driven initiative aimed at identifying improvements that enhance the experience of contributors across Wikimedia projects. For this internship, I propose to work on Wishlist #8: adding Wikidata support to the Wikiscore tool. This feature will allow Wikiscore to track and evaluate Wikidata contributions, enabling edit-a-thons and contests to include Wikidata edits. It expands the scope of the tool and supports a broader contributor base. --- == Selected Wishlist == Wishlist #8: Wikidata support for Wikiscore Currently, Wikiscore primarily focuses on Wikipedia edits. However, Wikidata contributions are increasingly important and should be included in scoring systems. The goal is to: - Fetch Wikidata edits for users - Process and validate contribution data - Integrate it into the scoring system - Ensure accurate and efficient computation --- == Technical Approach == The implementation will follow MediaWiki development practices and integrate cleanly with the existing Wikiscore architecture, ensuring maintainability and compatibility with Wikimedia tooling. 1. Data Retrieval - Use Wikidata APIs (e.g., recent changes or user contribution endpoints) to fetch user edits - Handle pagination, rate limits, and API constraints - Ensure reliability with proper error handling 2. Data Processing - Parse and normalize contribution data - Filter relevant edit types - Handle edge cases such as duplicate entries, reverted edits, minor edits, and incomplete metadata 3. Scoring Logic - Define scoring rules for Wikidata edits - Ensure compatibility with existing Wikiscore logic - Maintain consistency across different contribution types 4. Integration - Integrate Wikidata data into the existing Wikiscore pipeline - Ensure modular, testable, and maintainable code - Use caching (e.g., Redis) to optimize repeated queries 5. Performance Considerations - Optimize API calls using batching strategies - Implement caching to reduce redundant computations - Consider API rate limiting and throttling constraints - Ensure scalability for large datasets and high user activity --- == Timeline == Week 1–2 (May 18 – May 31): Set up the development environment, analyze the Wikiscore architecture, and explore Wikidata APIs. Finalize the implementation approach with mentors. Week 3–4 (June 1 – June 14): Implement the data fetching layer using Wikidata APIs, including pagination, error handling, and rate limiting. Week 5–6 (June 15 – June 28): Design and implement scoring logic for Wikidata edits, ensuring compatibility with existing systems. Week 7–8 (June 29 – July 12): Integrate Wikidata contributions into the Wikiscore pipeline and optimize performance using caching and batching. Week 9–10 (July 13 – July 26): Test with real-world datasets, validate scoring accuracy, and handle edge cases. Week 11–12 (July 27 – August 9): Write documentation, perform code cleanup, and refine implementation based on mentor feedback. Week 13 (August 10 – August 17): Finalize implementation, conduct testing, and prepare for submission and handover. == Impact == This feature will enable organizers to include Wikidata contributions in edit-a-thons and contests, improving participation and recognition. It will also make Wikiscore more versatile and inclusive, supporting a broader range of Wikimedia contributions and encouraging engagement with structured data. --- == Why Me == My experience in backend development, API design, and data processing aligns well with this project. I have worked on systems involving: - REST APIs and large-scale data processing - Performance optimization using caching and indexing - Asynchronous workflows and distributed systems I am comfortable working with complex data pipelines and ensuring reliability and scalability. I have also worked on research-driven projects, including Physics-Informed Neural Networks (PINNs) and an AI-powered data extraction system. These projects involved building scalable pipelines, working with structured data, and designing efficient processing systems. Research Work: - PINNs Research Paper: https://github.com/AyushkhatiDev/Physics-Informed-Neural-Networks-for-Solving-Partial-Differential-Equations/blob/main/PINN_PDE_Solver_Research_Paper.pdf - Data Extraction Research Paper: https://github.com/AyushkhatiDev/data-extractor/blob/main/paper/DataExtractor%20copy.pdf I am comfortable iterating based on feedback and delivering incremental improvements, which is essential for contributing effectively to open-source projects like Wikimedia. --- == Post-Internship Contribution == I plan to continue contributing to Wikimedia by: - Maintaining and improving Wikidata integration - Contributing to related tools and features - Supporting new contributors in onboarding Thank you for your consideration.

== Profile Information == Name: Ayush Khati GitHub: https://github.com/AyushkhatiDev Portfolio: https://portfoliokhati.netlify.app/ Location: India I am a backend-focused software developer with experience in building scalable systems, APIs, and data processing pipelines. I have worked on production-grade systems involving performance optimization, caching, and distributed architectures. My interests lie in building reliable and data-driven tools that support real-world applications. As part of this application, I completed both Outreachy microtasks (T418285 and T418286) and improved them based on mentor feedback. --- == Synopsis == The Lusophone Technological Wishlist is a community-driven initiative aimed at identifying improvements that enhance the experience of contributors across Wikimedia projects. For this internship, I propose to work on Wishlist #8: adding Wikidata support to the Wikiscore tool. This feature will allow Wikiscore to track and evaluate Wikidata contributions, enabling edit-a-thons and contests to include Wikidata edits. It expands the scope of the tool and supports a broader contributor base. --- == Selected Wishlist == Wishlist #8: Wikidata support for Wikiscore Currently, Wikiscore primarily focuses on Wikipedia edits. However, Wikidata contributions are increasingly important and should be included in scoring systems. The goal is to: - Fetch Wikidata edits for users - Process and validate contribution data - Integrate it into the scoring system - Ensure accurate and efficient computation --- == Technical Approach == The implementation will follow MediaWiki development practices and integrate cleanly with the existing Wikiscore architecture, ensuring maintainability and compatibility with Wikimedia tooling. 1. Data Retrieval - Use Wikidata APIs (e.g., recent changes or user contribution endpoints) to fetch user edits - Handle pagination, rate limits, and API constraints - Ensure reliability with proper error handling 2. Data Processing - Parse and normalize contribution data - Filter relevant edit types - Handle edge cases such as duplicate entries, reverted edits, minor edits, and incomplete metadata 3. Scoring Logic - Define scoring rules for Wikidata edits - Ensure compatibility with existing Wikiscore logic - Maintain consistency across different contribution types 4. Integration - Integrate Wikidata data into the existing Wikiscore pipeline - Ensure modular, testable, and maintainable code - Use caching (e.g., Redis) to optimize repeated queries 5. Performance Considerations - Optimize API calls using batching strategies - Implement caching to reduce redundant computations - Consider API rate limiting and throttling constraints - Ensure scalability for large datasets and high user activity --- == Timeline == Week 1 (May 18 – May 24): Set up development environment, run Wikiscore locally, and review project architecture. Explore Wikidata APIs and identify relevant endpoints for user contributions. Week 2 (May 25 – May 31): Analyze data flow in Wikiscore, define integration points for Wikidata, and finalize implementation plan with mentors. Week 3 (June 1 – June 7): Implement initial data fetching layer using Wikidata APIs, including basic request handling and response parsing. Week 4 (June 8 – June 14): Extend data retrieval to support pagination, error handling, and rate limiting. Validate data consistency. Week 5 (June 15 – June 21): Design scoring logic for Wikidata edits and implement core scoring functions. Week 6 (June 22 – June 28): Integrate scoring logic with fetched data and ensure compatibility with existing Wikiscore system. Week 7 (June 29 – July 5): Integrate Wikidata contribution pipeline into Wikiscore backend and ensure correct data flow. Week 8 (July 6 – July 12): Optimize performance using caching (e.g., Redis) and batching API requests. Week 9 (July 13 – July 19): Test system with real-world datasets and validate scoring accuracy. Week 10 (July 20 – July 26): Handle edge cases such as reverted edits, duplicate entries, and incomplete metadata. Week 11 (July 27 – August 2): Write technical documentation and document design decisions. Week 12 (August 3 – August 9): Refactor code, improve test coverage, and incorporate mentor feedback. Week 13 (August 10 – August 17): Finalize implementation, perform end-to-end testing, and prepare for submission and handover. == Impact == This feature will enable organizers to include Wikidata contributions in edit-a-thons and contests, improving participation and recognition. It will also make Wikiscore more versatile and inclusive, supporting a broader range of Wikimedia contributions and encouraging engagement with structured data. --- == Why Me == My experience in backend development, API design, and data processing aligns well with this project. I have worked on systems involving: - REST APIs and large-scale data processing - Performance optimization using caching and indexing - Asynchronous workflows and distributed systems I am comfortable working with complex data pipelines and ensuring reliability and scalability. I have also worked on research-driven projects, including Physics-Informed Neural Networks (PINNs) and an AI-powered data extraction system. These projects involved building scalable pipelines, working with structured data, and designing efficient processing systems. Research Work: - PINNs Research Paper: https://github.com/AyushkhatiDev/Physics-Informed-Neural-Networks-for-Solving-Partial-Differential-Equations/blob/main/PINN_PDE_Solver_Research_Paper.pdf - Data Extraction Research Paper: https://github.com/AyushkhatiDev/data-extractor/blob/main/paper/DataExtractor%20copy.pdf I am comfortable iterating based on feedback and delivering incremental improvements, which is essential for contributing effectively to open-source projects like Wikimedia. --- == Post-Internship Contribution == I plan to continue contributing to Wikimedia by: - Maintaining and improving Wikidata integration - Contributing to related tools and features - Supporting new contributors in onboarding Thank you for your consideration.

== Profile Information == Name: Ayush Khati GitHub: https://github.com/AyushkhatiDev Portfolio: https://portfoliokhati.netlify.app/ Location: India I am a backend-focused software developer with experience in building scalable systems, APIs, and data processing pipelines. I have worked on production-grade systems involving performance optimization, caching, and distributed architectures. My interests lie in building reliable and data-driven tools that support real-world applications. As part of this application, I completed both Outreachy microtasks (T418285 and T418286) and improved them based on mentor feedback. --- == Synopsis == The Lusophone Technological Wishlist is a community-driven initiative aimed at identifying improvements that enhance the experience of contributors across Wikimedia projects. For this internship, I propose to work on Wishlist #8: adding Wikidata support to the Wikiscore tool. This feature will allow Wikiscore to track and evaluate Wikidata contributions, enabling edit-a-thons and contests to include Wikidata edits. It expands the scope of the tool and supports a broader contributor base. --- == Selected Wishlist == Wishlist #8: Wikidata support for Wikiscore Currently, Wikiscore primarily focuses on Wikipedia edits. However, Wikidata contributions are increasingly important and should be included in scoring systems. The goal is to: - Fetch Wikidata edits for users - Process and validate contribution data - Integrate it into the scoring system - Ensure accurate and efficient computation --- == Technical Approach == The implementation will follow MediaWiki development practices and integrate cleanly with the existing Wikiscore architecture, ensuring maintainability and compatibility with Wikimedia tooling. 1. Data Retrieval - Use Wikidata APIs (e.g., recent changes or user contribution endpoints) to fetch user edits - Handle pagination, rate limits, and API constraints - Ensure reliability with proper error handling 2. Data Processing - Parse and normalize contribution data - Filter relevant edit types - Handle edge cases such as duplicate entries, reverted edits, minor edits, and incomplete metadata 3. Scoring Logic - Define scoring rules for Wikidata edits - Ensure compatibility with existing Wikiscore logic - Maintain consistency across different contribution types 4. Integration - Integrate Wikidata data into the existing Wikiscore pipeline - Ensure modular, testable, and maintainable code - Use caching (e.g., Redis) to optimize repeated queries 5. Performance Considerations - Optimize API calls using batching strategies - Implement caching to reduce redundant computations - Consider API rate limiting and throttling constraints - Ensure scalability for large datasets and high user activity --- == Timeline == Week 1–2 (May 18 – May 31):24): Set up the development environment, analyze therun Wikiscore architecturelocally, and explore Wikidata APIsreview project architecture. Finalize the implementation approach with mentorsExplore Wikidata APIs and identify relevant endpoints for user contributions. Week 3–4 (June 1 – June 14):2 (May 25 – May 31): Implement thAnalyze data fetching layer usinglow in Wikidata APIs, including paginationscore, error handlingdefine integration points for Wikidata, and rate limitingfinalize implementation plan with mentors. Week 5–63 (June 15 – June 28):7): Design and implement scorImplement initial data fetching logic forayer using Wikidata editsAPIs, ensurincluding compatibility with existing systemsbasic request handling and response parsing. Week 7–84 (June 298 – July 12):ne 14): Integrate WikiExtend data contributions into the Wikiscore pipeline and optimize performance using caching and batchingretrieval to support pagination, error handling, and rate limiting. Validate data consistency. Week 9–105 (July 13ne 15 – July 26):ne 21): Test with real-world Design scoring logic for Wikidatasets, validate scoring accuracy, and handle edge cases edits and implement core scoring functions. Week 11–126 (July 27 – August 9):ne 22 – June 28): Write documentationIntegrate scoring logic with fetched data and ensure compatibility with existing Wikiscore system. Week 7 (June 29 – July 5): Integrate Wikidata contribution pipeline into Wikiscore backend and ensure correct data flow. Week 8 (July 6 – July 12): Optimize performance using caching (e.g., Redis) and batching API requests. Week 9 (July 13 – July 19): Test system with real-world datasets and validate scoring accuracy. Week 10 (July 20 – July 26): Handle edge cases such as reverted edits, duplicate entries, and incomplete metadata. Week 11 (July 27 – August 2): Write technical documentation and document design decisions. Week 12 (August 3 – August 9): Refactor code, perform code cleanupimprove test coverage, and refine implementation based onincorporate mentor feedback. Week 13 (August 10 – August 17): Finalize implementation, conductperform end-to-end testing, and prepare for submission and handover. == Impact == This feature will enable organizers to include Wikidata contributions in edit-a-thons and contests, improving participation and recognition. It will also make Wikiscore more versatile and inclusive, supporting a broader range of Wikimedia contributions and encouraging engagement with structured data. --- == Why Me == My experience in backend development, API design, and data processing aligns well with this project. I have worked on systems involving: - REST APIs and large-scale data processing - Performance optimization using caching and indexing - Asynchronous workflows and distributed systems I am comfortable working with complex data pipelines and ensuring reliability and scalability. I have also worked on research-driven projects, including Physics-Informed Neural Networks (PINNs) and an AI-powered data extraction system. These projects involved building scalable pipelines, working with structured data, and designing efficient processing systems. Research Work: - PINNs Research Paper: https://github.com/AyushkhatiDev/Physics-Informed-Neural-Networks-for-Solving-Partial-Differential-Equations/blob/main/PINN_PDE_Solver_Research_Paper.pdf - Data Extraction Research Paper: https://github.com/AyushkhatiDev/data-extractor/blob/main/paper/DataExtractor%20copy.pdf I am comfortable iterating based on feedback and delivering incremental improvements, which is essential for contributing effectively to open-source projects like Wikimedia. --- == Post-Internship Contribution == I plan to continue contributing to Wikimedia by: - Maintaining and improving Wikidata integration - Contributing to related tools and features - Supporting new contributors in onboarding Thank you for your consideration.