[ CONTRIBUTION REPORT - Shehrbano Ali ]

1. Profile

2. MY Project Contributions Details (Microtasks)

3. Mentors

4. My Introduction

I’m Shehrbano Ali, a 20 years old ML Model Developer. I am excited to apply for the Addressing the lusophone technological wishlist proposals project for this Outreachy cohort.

1. My Technical Journey: Python & ML I am a self taught developer I have build some Python based solo projects that are also linked with my Machine Learning skills, they are:

  1. Medical Report Anayzer
  2. Plant Disease Detector

These projects require a deep understanding of data structures, API integrations, and robust backend logic. I have spent the last year hardening my Python skills to handle complex system architectures, which aligns perfectly with the Python level required for this project.

2. Why I Chose This Project I am drawn to this project because one of it’s wish is focused on Python too. I believe my core skills allow me to handle complex backend logic with ease. Whereas, I also have knowledge of Javascript to handle frontend requirements too. With this mix of frontend awareness and deep backend Python/ML experience, I believe I am a strong fit for this lusophone technological project.

3. My Goal for the Internship My primary goals are to use my Python skills to:

  1. Automate contest scoring to make point counting fast and accurate
  2. Connect to Wikidata to automatically track what users are contributing
  3. Build requested features that help the Lusophone community grow
  4. Write clean code that is organized and easy for others to maintain

5. About the Project

The Lusophone Technological Wishlist is a community driven initiative to bridge technical gaps for Portuguese-speaking Wikimedia contributors. The project aims to solve long standing issues like duplicate references, broken links, and data quality on Wikidata.

My vision for this internship is to transform these “wishlist items” into stable, scalable tools. By focusing on Proposal #3 Reference Guarding and Proposal #8 Wikiscoring, I aim to provide the community with automated diagnostics that improve the reliability of the world’s largest open knowledge base.

6. Post-Contribution Phase Tasks

After submitting my final application on April 15, 2026 4pm UTC, I chose to continue my momentum by building live prototypes for the wishlist proposals.

>Task 1: WikiScore-Lusofonia Wish #8:

>Contribution Overview:

1. I designed a full system from scratch to solve Lusophone Wishlist #8.
2. This tool helps the community count and score Wikidata edits.
3. I created a Dual-Path system that shows the difference between Global edits and Portuguese edits.
4. I used a special search rule (regex) to make sure it counts both European and Brazilian Portuguese.
5. To keep people excited, I added a 6-level badge system for healthy competition.
6. These badges are only earned from Portuguese scores to keep everyone focused on the main goal.

>Technical Stack:

>Key Technical Innovations:

1. I used a concept similar to the Retrieval part of AI systems. My tool isn’t a full RAG system,
but it uses the same idea; finding and fetching raw data from a huge database (Wikidata) very fast for many users at once.
2. I used parallel processing to scan 20 users at the same time.
This keeps the system fast and responsive, so organizers don’t have to wait a long time for the results.
3. I used BigIntegerField in Django models to ensure the
system handles billions of Wikidata edits without crashing.
4. I also built a Strict-Mode (Anti-Cheat) indicator and
a Live QID Tracker so organizers can ensure all points are earned fairly.
5. My engine uses a recursive loop to keep flipping the page
until it finds every single edit a user has made, even if they have thousands.
6. I have optimized the scoring logic so it can be called directly by the existing CounterHandler.get_points() method.
(This ensures that the new Wikidata points are injected into the leaderboard
calculation without requiring any structural changes to the existing frontend)
7. Since many people use phones, I made a Swipeable Table with a neon-green pulsing hint to show users how to find their scores easily.
8. I developed a mathematical model to ensure that high-value contributions (like adding References or Images) are rewarded more than simple metadata updates.

This formula prioritizes the quality of edits over just the quantity:

\(Total Score = \sum (Labels \times w_L) + (Descriptions \times w_D) + (Facts \times w_F) + (References \times w_R) + (Images \times w_I)\)

7. Next Steps (Internship Phase)

If selected, my focus will shift from prototyping to production ready tool development:

8. Final Summary

This project represents the perfect intersection of my skills in backend automation and my passion for open source knowledge. Throughout the contribution period, I have demonstrated that I can not only follow instructions but also identify latent technical problems and propose creative solutions.

I am committed to dedicating 40 hours a week to ensure that the Lusophone community receives high-quality, resilient tools that last far beyond the internship period. I look forward to contributing my technical energy to the Wikimedia movement.

9. LINK

🌐 Click here to view My Report

Applicant, Outreachy 2026 Cohort Project
Shehrbano Ali