Community Update  ·  June 05, 2024

Best Student Open-Source Contributions, 2023-2024 School Year

Tyler Menezes, Executive Director and Board Member

Each year, thousands of students contribute to open source software as part of CodeDay Labs, our program which provides independent experience contributing to real-world, large-scale software projects, so they can gain problem-solving skills. At the end of their experience, they’re also With this school year wrapping up, we’re excited to recognize several stand-out contributions.

After reviewing hundreds of contributions for both technical merit and write-up quality, our judges selected the following three contributions as the best of 2023-2024:

Contributing To Microsoft’s Semantic Kernel Project

Alisha Maddy (Portland Community College/CTI)
Sebastian Santoyo (CSUMB/CTI)

Sneha De (UCSC/CTI)
With mentorship from Shiv Bijlani

Microsoft’s Semantic Kernel is a software development kit that enables C# and Python developers to integrate AI large language models (LLM) such as Chat-GPT into their existing apps. Semantic Kernel makes use of AI services like OpenAI, Azure OpenAI, and Hugging Face through the service’s API’s. This makes AI technology easy for developers to utilize by allowing for orchestration of AI plugins.

Our mentor encouraged us to challenge ourselves and work on issues individually. He suggested we each come up with our own idea for a connector to add to the project. This guidance and encouragement was really valuable because I may not have thought to do something so ambitious on a project I was just introduced to! And I also did not know what a connector even was! This is where the adventure really begins.

(Read Alisha’s post here:

What our judges had to say:

This is a great contribution for a few reasons. First, the intern worked on integrating Google PaLM, a large language model, into the Semantic Kernel project. This expands the capabilities of Semantic Kernel and makes it more powerful for users. Second, the intern had to overcome challenges such as understanding the codebase and simulating API calls. This demonstrates the intern’s problem-solving skills and ability to work independently. Finally, the intern’s work on querying structured data sources with natural language shows that they are up-to-date on the latest advancements in natural language processing. Overall, this internship contribution is impressive and shows that the intern has a strong understanding of large language models and software engineering.

fix(arcgis): DeckGL rendering integration

Jared Scarr (North Seattle College)
With mentorship from Omar Shehata is a library for visualizing large geospatial datasets and maps with WebGL. A typical user of this framework is an individual or team, probably in a developer or data science type role. See their showcase for a great selection of example reports and applications.

The issue I worked on was to address the broken ArcGis integration which meant that developers could not render DeckGL data on top of the ArcGis map and, while the documentation looked correct, the library was not. To start I had to isolate where in the code the problem existed. There were two places that were relevant for addressing this issue: 1) the example application provided for guidance and 2) the arcgis module. My strategy at the beginning was to identify error messages in the console and break them up into sub-problems then search for examples that existed already in the code base that were implemented in a similar manner and use them as a guide. It turned out that none of the errors were immediately helpful. A certain amount of the code was closed-box that I did not have visibility into. My strategy immediately failed. How was I to debug a system that I couldn’t step through?

(View the contribution here:

What our judges had to say:

We selected this contribution to for resolution of a broken ArcGis integration. Jared demonstrated exemplary problem-solving skills by initially isolating the issue within the code and tackling it through a methodical approach of breaking down error messages and seeking similar implementations within the codebase. Despite facing the challenge of closed-box code that obstructed direct debugging, the contributor’s perseverance and innovative thinking led to a successful resolution, significantly enhancing the functionality and reliability of for its users. This contribution not only fixed a critical issue but also showcased the contributor’s ability to navigate complex problems, making it a standout effort in the open source community.

Adding Support for Wasabi and Digital Ocean in Firefiles

Brian Palomar-Salazar (CSUMB/CTI)
Nathan Pham (Evergreen Valley College/CTI)
With mentorship from Sabrina Reyes

Firefiles is an open-source application that serves as an alternative to storage platforms like Dropbox and Google Drive. This application allows users to seamlessly manage their files across multiple storage providers while allowing for more privacy compared to its competitors.

The issue being addressed here for Firefiles is to integrate and implement the Wasabi storage platform into the existing codebase. By adding support for Wasabi as an additional cloud storage provider, Firefiles’ capabilities will be extended and further amplify its goal of having a versatile selection of storage providers.

(Read Brian’s post here:

What our judges had to say:

This contribution not only extended Firefiles’ support to include a new cloud storage provider but also demonstrated meticulous attention to detail in modifying the existing codebase, ensuring seamless communication among various storage providers. The team’s thorough approach, involving the creation of new credential management files, adapting existing hooks, and adjusting provider-specific functions, exemplified outstanding technical skill and collaboration. Moreover, their adept handling of challenges, such as CORS configuration and local hosting, underscored their problem-solving abilities and commitment to delivering a robust, user-friendly solution.

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