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: Set up the development environment, analyze the Wikiscore architecture, and explore Wikidata APIs. Finalize implementation approach with mentors. Week 3–4: Implement the data fetching layer using Wikidata APIs, including pagination, error handling, and rate limiting. Week 5–6: Design and implement scoring logic for Wikidata edits, ensuring compatibility with existing systems. Week 7–8: Integrate Wikidata contributions into the Wikiscore pipeline and optimize performance using caching and batching. Week 9–10: Test with real-world datasets, validate scoring accuracy, and handle edge cases. Week 11–12: Write documentation, perform code cleanup, and refine implementation based on mentor feedback. Week 13: 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 1–2 (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 1–2 (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.