Explore Projects

Music Blocks has a feature to detect the color of pixels generated from drawing within the program, but it cannot detect the color of pixels from images that are either uploaded or from a webcam. By adding a feature to detect color from both uploaded images and a live webcam stream, users would be able to implement Lego music notation for the blind and similarly interactive programs. The goal of the project is to develop extended functionality to our existing tools of turtle/mouse glyph movement and limited color detection to sense color from uploaded images, as well as the real-time feed from a webcam. Upon successful implementation, the turtle/mouse glyph will be able to detect the color of pixels underneath it, regardless of whether those pixels were drawn by the turtle/mouse itself, part of an uploaded image stamped to the canvas, or part of a live webcam video feed into Music Blocks. One test of success is to run our Lego music notation for the blind project with a live feed. The result should be able to playback and record the abstract brick notation based on its contrasting colors.

The project idea aims to reduce power consumption on Flotta-device agents on small factor edge devices at several levels. OS toolings would be implemented to get data about the energy consumption at the CPU level of the device agent using a Custom Kepler monitoring system and a power meter. After the confirmation of the data, these parameters would be passed to the control plane via metrics using Prometheus as Internal Prometheus TSDB is already used by the flotta-device agent, Required research on variation in energy consumption and performance with the number of workloads and resources allocated to them would be done and the obtained findings is to be summarised in a blogpost. The research part would be helpful for the development of better ideas for workload allocation and containers for Flotta, and IoT based container workload projects in general. Two energy profiles would be developed namely Flotta-PowerSaving Mode and Flotta-UltraPowerSaving Mode which would aim to turn - off specific kernel modules and operations which are not feasible and are unnecessary for running container workloads at low energy, The data from the research would be integrated with these for determining the ranges in which it would operate.

<p>JSON Schema is a vocabulary to specify the structure of a JSON document. JSON Schema is widely used and well supported and sees a growing use for many GA4GH standard representations and schema collection is growing and currently, there is no mechanism to manage these JSON schemas. As a solution, introducing JSON schema service which has the ability to manage growing JSON schemas and maintains the consistency among GA4GH JSON schemas by introducing a standard template as a meta schema. The proposed system consists of 3 main components such as Schema Service, Schema Repository and Schema UI. Schema service is designed as a REST full web service and mainly it manages GA4GH JSON Sachems. Schema Repository is the persistence layer for Schema Service and JSON schemas can be stored within the schema repository for retrieval. Schema Repository is implemented using a document-oriented database model. Schema UI is a single page web application for the application layer of the system and provide a user interface for JSON Schema management.</p>

<p>Learning Bricks is a client side JavaScript framework for visual programming tools. Unlike traditional text based programming, visual programming provides a smoother learning curve for children and beginners as different components in the code could be visualised as UI elements and easily interacted with. In contrast to popular existing libraries and frameworks for visual programming, Learning Bricks is less opinionated, easily customisable, lightweight and performant. Learning Bricks was original developed for Music Blocks v4 but could me easily customised to cater to the needs of any project. Music Blocks is a Visual Programming Language and collection of manipulative tools for exploring musical and mathematical concepts in an integrative and fun way. This project is an effort towards the complete overhaul of MusicBlocks for performance improvements and new features and leverages modern JavaScript technologies and a better application architecture to ensure a smooth and reliable user experience.</p> <p>Among other contributions over the years, a MVP for an on-demand Kubernetes deployment system was developed and hundreds of bug fixes and enhancements were made to MusicBlocks v3.</p>

<p>Knative is a Kubernetes-based platform to deploy and manage serverless workloads. Eventing is a key feature for Serverless Computing. They deliver their information to a specified target to make a whole system interactive. The problem is that every kind of event cannot be treated as the same one in the Knative ecosystem because the shape of each event depends on its sources. Knative Eventing is designed to be consistent with the CloudEvents definition to keep interoperability. To make this happen, the Knative Eventing community already has several kinds of event type definitions such as GitHub, AWS S3, GCP Pub/Sub, etc.<br> This project aims to add two event types:</p> <ul> <li>an event source that is able to generate CloudEvents when a new container image is uploaded to Docker Hub.</li> <li>another event source that is able to generate CloudEvents on success/failure of Task/Pipeline and every step of a Pipeline created by Tekton CI/CD.</li> </ul> <p>Both components would help application developers focus on developing applications itself and make Serverless Computing more diverse.</p>

I will be developing DashBot - the AI assistant for API Dash which supercharges developer productivity by helping developers automate tedious tasks, follow best practices, interact & obtain contextual suggestions, all via natural-language input. DashBot will be designed in a modular and extensible manner and provide the following list of features: 1. Explain responses & identify any discrepancy 2. Debug requests based on Status codes & Error messages 3. Generate API documentation 4. Understand API and generate tests 5. Generate plots & visualizations for API responses along with ability to customize 6. Generate API integration frontend code for frontend frameworks like React, Flutter, etc. For each of the tasks benchmark evaluations will also be done so that it is easier for end users to choose the right backend LLM. Add support for various API Authentication Methods such as, Basic authentication, API Auth, API key, Bearer token, JWT Bearer, Digest Auth, OAuth 1.0, OAuth 2.0.

<p>Music Blocks is a free and open-source, browser-based software, designed basically for students to learn music in fun and interactive way. To get started with “music blocks”, newbies need to have a fundamental understanding of the software, what are the things can be done and importantly how things can be done. To address this requirement, “music block” has a user guide. But the existing user guide lacks most of the important information about the software components, how each building blocks in the software can be related to music and especially some examples of how to create music with the software. The objective of this project is to create a user manual which would be a simple and comprehensive guide while providing samples and related explanations. Deliverables of the project will be as follows:</p> <ul> <li>Improve the Music blocks user guide content to be easily approachable to newbies. </li> <li>Implement a documentation website for Music Blocks user guide. </li> <li>Prepare a downloadable version of the Music Blocks user manual in Portable Document Format(PDF).</li> <li>Make the website expandable for online contributions to documentation from the website itself.</li> </ul>

<p>Firefox Developer Tools offers good tooling for monitoring HTTP traffic between the current page and the server. The existing Network panel allows intercepting and inspecting all data transferred over the wire including headers, GET/POST data, responses, detailed timings, etc. Unfortunately, Firefox Developer Tools doesn’t offer a way to inspect WebSocket (WS) traffic in Firefox Quantum.</p> <p>This project aims at providing support for WebSocket monitoring and inspection in Firefox Developer Tools. The feature should be built on top of the existing Network panel user interface (UI) and be responsible for visualizing data (i.e. WS frames) sent through a WebSocket connection. Users should be able to perform common tasks such as pausing/resuming monitoring, clearing frames, searching/filtering, looking at summary data etc. Light and Dark Themes should be supported.</p> <p>Furthermore, support for popular protocols such as Socket.IO, SockJS, plain JSON, and MQTT will be implemented. There is also ongoing work for supporting binary message viewer and showing WebSocket messages in HAR exports.</p>

Variant filtration is key to rare disease discovery. For a rare disease discovery app to be successful, it must be able to cleanly represent the relevant variants in a patient’s genome. Phenopackets present a method of filtering genes based on a patient’s phenotype. Phenotype data can narrow down the set of at risk genes clinicians need to parse during rare disease discovery using tools like exomiser. This project will use phenopackets to dynamically filter variants for the purpose of rare disease discovery. This project has two deliverables, each with their own set of requirements: A method to filter genes using phenopacket data. Must be optimized for speed, given this is a dynamic application. However, speed will be limited by the FHIR Genomics Operations reference implementation response time. A clean, easily understandable presentation of variants useful for manual review. Two views should be provided: one for the most at risk variants, and another for a more detailed view of each variant in at risk genes. The former should have a graphical component. The latter does not need to have as clean of a presentation. Users should be able to identify the most at risk variants within 5-10 seconds of the page loading.

The "Musical Ideation through Generative AI" project aims to integrate a Language Learning Model (LLM) with Sugar Labs' MusicBlocks platform, enabling users to compose and enhance musical compositions. It will start with implementing an efficient ABC parser for MusicBlocks, ensuring robust extraction of musical information from ABC notation. Next, the project will tune and benchmark the LLM to generate high-quality musical compositions in ABC format. Once tuned, the LLM will be deployed to a server environment, and API endpoints will be set up to enable interaction with MusicBlocks. Users will initiate compositions in MusicBlocks, which will be sent to the LLM for enhancement. The LLM will generate multiple compositions based on the user's input, which will be converted back to MusicBlocks format for further enhancement. Throughout the project, integration testing and flow testing will ensure seamless interaction between MusicBlocks and the LLM API. Comprehensive documentation will provide insights into the implementation process, results of benchmarking and testing, and recommendations for future improvements. This integration of AI-generated compositions into MusicBlocks aims to inspire creativity, deepen musical understanding, and enhance the learning experience for users

The GA4GH Service Info API is a required component across standards such as TES, WES, and TRS, providing essential metadata about a service. Currently, each implementation must manually develop and maintain its own /service-info endpoint, leading to duplication, inconsistent metadata, and difficulty in supporting new capabilities such as security and extensibility features. This project proposes a reusable GA4GH ServiceInfo Sidecar a lightweight proxy service that runs alongside existing GA4GH services and handles /service-info requests centrally, while forwarding all other requests transparently to the backend. The sidecar will generate standard-compliant responses using a combination of static configuration (config.yaml) and dynamic metadata collected through a plugin system, enabling seamless support for service-specific and custom extensions without modifying backend code. The project will deliver a production-ready implementation with schema validation, extensibility aligned with GA4GH specifications, and deployment support via Docker and Kubernetes. Additionally, the sidecar will support Trusted Execution Environments (TEE) by advertising attestation capabilities via the ServiceInfo API and delivering attestation reports through a dedicated endpoint, enabling verifiable trust without implementing low-level attestation logic.

Thousands of bioinformatics workflows registered on public platforms like Dockstore and WorkflowHub cannot run on modern cloud platforms. Not because the science is wrong, but because the engineering hasn't caught up. Missing containers, hardcoded paths, HPC-specific executors, and unstructured inputs and outputs make these workflows incompatible with GA4GH's WES standard and cloud-based research platforms. Workflow Clinic fixes this automatically. It is a Python-based CLI tool with two core components. The Workflow Critic scans a workflow repository, runs a deterministic rule engine followed by an optional AI agent team, and produces a structured diagnosis.json report identifying every cloudification gap with severity levels and remediation suggestions. The Workflow Doctor reads this report, applies fixes directly to the workflow files using a 3-layer strategy (AST-based, regex-based, and AI-assisted as a last resort), and opens Pull Requests to propose the changes. GitHub interaction is purely optional examine and fix work fully offline without any credentials. When a GitHub PAT is provided, the tool can additionally publish findings as GitHub issues and open Pull Requests. Without a PAT, the same outputs are generated locally as issue.md and fix.md files.

<p>Sugarizer Deployment Tool is a tool for Sugarizer. He is for deploying Sugarizer on multiple device a keep it updated. Make the installation and updated easy for the user.</p> <p>Sugarizer is a way to use Sugar on any device using web technologies. Sugarizer can be deployed on many devices including Android phones and tablets. The goal of this specific project is to create the "Sugarizer Deployment and Management Tool for Android".</p> <p><strong>Expected results</strong></p> <p>Sugarizer Server Deployment and Management Tool is a GUI Tool working on GNU/Linux, Mac and Windows to deploy and configure Android devices using ADB (Android Debug Bridge).</p> <ul> <li>Inventory : Collect and store devices data like serial number, os version, device name, installed applications, current Android launcher and Sugarizer availability.</li> <li>Application Manager : Install or remove applications using APK files.</li> <li>File Manager : Add / Remove files</li> <li>Input Manager : Click / Touch coordinates on the screen, launch applications to perform automations.</li> <li>Scenario Writer : Allow non developpers to create sequences to perform all the steps previously defined.</li> </ul>

The Music Blocks v4 project is a complete overhaul of the original Music Blocks project, which was derived from the Turtle Blocks project. The existing implementation, which started in 2015 using native Vanilla JavaScript, lacks modern front-end tooling and ecosystem standards, leading to difficulties in maintenance and subpar performance. The v4 project uses improved application architecture, modern tools (such as Vite, ESLint, Prettier, Docker, Jest, Cypress, etc.), better languages (TypeScript and Sass), and libraries (React for UI rendering). This will significantly enhance the overall developer and user experience, maintainability, and performance. The proposal focuses on implementing one of the main features of Music Blocks, the "Project Builder", which is currently missing in the new version (v4). The Project Builder is a graphical blocks manager module used to create Music Blocks programs, including interactive blocks like Start, Rhythm, Note, Pitch, Instrument, and many more. These blocks can be dragged, dropped, and clicked to perform actions or open context menus. By implementing the Project Builder feature end-to-end, users will be able to create different musical patterns seamlessly. This will bring the v4 project on par with the capabilities of the old version (v1) and provide a more accessible, efficient, and user-friendly application for users to create music through an interactive web application.

Music Blocks students have no ownership of the projects they create. When a project is saved it lands in a MySQL database they can never see, with no version history and no guarantee it will exist next year. This project completes the migration from that aging PHP and MySQL stack to a modern system backed by GitHub and SQLite, making the transition completely invisible to students while giving every project a permanent, version-controlled home. The work happens in four phases. First, setting up the migration environment with a read-only database connection, GitHub App credentials, and a SQLite database that will power the new backend. Second, running a streaming migration pipeline that processes all 10,000+ existing Planet projects one at a time, pushing each one to GitHub as a repository with full metadata and thumbnail, while skipping duplicates using SHA-256 content hashing. Third, building the seven API endpoints the Planet frontend needs to browse, search, like, and load projects, all backed by SQLite for sub-millisecond response times. Fourth, connecting the frontend to the new backend through a single drop-in adapter file, overhauling the UI with proper modals, a clean commit history timeline, and an interactive Git onboarding walkthrough that teaches students how to save versions, go back in time, and fork projects using Music Blocks language with no Git jargon.

The Music Blocks v4 project is a complete overhaul of the original Music Blocks project, which was derived from the Turtle Blocks project. The existing implementation, which started in 2015 using native Vanilla JavaScript, lacks modern front-end tooling and ecosystem standards, leading to difficulties in maintenance and subpar performance. The v4 project uses improved application architecture, modern tools (such as Vite, ESLint, Prettier, Docker, Jest, Cypress, etc.), better languages (TypeScript and Sass), and libraries (React for UI rendering). This will significantly enhance the overall developer and user experience, maintainability, and performance. The proposal focuses on implementing one of the main features of Music Blocks, the "Masonry Framework" (previously code-builder/project-builder), which is currently missing in the new version (v4). The is a graphical blocks manager module used to create Music Blocks programs, including interactive blocks like Start, Rhythm, Note, Pitch, Instrument, and many more. These blocks can be dragged, dropped, and clicked to perform actions or open context menus. By implementing the Masonry feature end-to-end, users will be able to create different musical patterns seamlessly. This will bring the v4 project on par with the capabilities of the old version (v3) and provide a more accessible, efficient, and user-friendly application for users to create music through an interactive web application.

The Sugarizer Human Activity Pack project aims to enhance the educational tools provided by Sugar Labs by developing two engaging and interactive activities. The first, the 3D Human Body Activity, will expand on the existing anatomical visualization tool by integrating multiple 3D models, including skeletal, human body and organ system. It will allow users to toggle between different layers, interact with body structures dynamically, and engage in real-time collaborative learning. Additional features such as interactive quizzes, UI/UX refinements, and localization support will ensure accessibility and usability across different regions and devices. The second component, the Stickman Animation Activity, will introduce an interactive keyframe animation tool where users can manipulate a stick figure by adjusting its nodes to create animations. This tool will support essential animation features such as playback, speed control, and exporting animations as videos. It will also include a collection of pre-designed templates for common movements like running, jumping, and dancing. A standout feature of this activity is the ability to import a photo of a human body and generate a corresponding stick figure in the same pose using AI-assisted pose detection. By the end of the project, Sugar Labs will gain two fully functional, optimized, and localized activities that provide immersive learning experiences in human anatomy and animation principles. The enhancements to the 3D Human Body Activity will enable students to explore anatomy in a more interactive and engaging way, moving beyond static textbook images to dynamic 3D models. The Stickman Animation Activity will encourage creativity and spatial understanding by allowing students to experiment with motion and animation concepts. Together, these activities will significantly contribute to making education more engaging, interactive, and widely accessible.

Flotta Operator is a Kubernetes operator used to manage the workloads of the IoT edge devices via Kubernetes API. Flotta workloads running on low-resource devices like RaspberryPI, Nvidia Jetson Boards, etc. These devices can be located anywhere, under so many circumstances which introduces many problems that affect the performance of the network. The current situation is there is no example project for using Flotta which is making a barrier to adopting the Flotta Project. The project goals are twofold: Edge Example Application 1. Implementation of workload application that would run on a bunch of Flotta devices collecting video streams/pictures from webcam and/or getting other data from sensors. 2. Implementation of WebApp that runs in the Flotta cluster that presents the results of aggregated data from devices in a nice UI to help users get some insights from data. Sense the Internet Investigating the Internet network topology and measuring the performance of the network using devices managed by Flotta. This will be done by performing large-scale network topology discovery efficiently ( both at the IP level and at the router level), sensing the quality of the network using active probing tools like traceroute, ping ..etc, and presenting this data in clear graphs with filters on the WebApp that running in the cluster to help project users making better decisions. Deliverables Implement the workload app that is running on Flotta devices, with capabilities of 1. Reading & collecting from webcams and sensors. 2. Performing a large-scale network topology discovery. 3. Sharing collected data to the Flotta cluster. Implement a Web App in the cluster, with capabilities of 1. Present aggregated data from sensors and webcams in a nice format. 2. Present devices networks topology in graphs. 3. Present filters and Frontend functionalities to help users read collected data. Write Documentation for the Edge Example App.

R's standard file reading functions must ingest data into memory before any filtering can occur, creating a hard memory ceiling for large datasets. AWK evaluates records one line at a time, filtering data before it ever enters R's memory space. The awkreader package bridges these two worlds, letting R users express filter conditions in familiar R syntax while AWK handles the heavy lifting at the shell level. The existing codebase is ~80% complete for Mac/Unix. This project completes and delivers the full production-ready CRAN package by: (1) completing and verifying the full translation engine: mapping R filter expressions including comparisons, logical operators, %in%, grepl(), and is.na() to correct AWK syntax across all platforms. (2) auditing and restoring Mac/Unix correctness. (3) achieving full Windows/PC compatibility via robust shell quoting and Rtools detection. (4) formalizing the package with roxygen2 docs, a cross-platform CI test harness with regression locking, and CRAN submission, enabling R users to filter datasets on standard hardware without memory constraints.

<p>Building a project which uses ScanCode as a library in a web and REST API application that allows you to scan code on demand by entering a URL and then store the scan results. Travis or Github integration to scan on commit with webhooks. Including the feature to scan based on a received tweet of similar IRC or IM integration.</p>

<p>The SIMPLE Grid project is an extension of the SIMPLE Framework that combines popular configuration management technologies such as Puppet/Ansible and container orchestration technologies such as Docker Swarm/Kubernetes to allow deployment of complex computing clusters using a single site level configuration file. The proposed project aims to improve functionality, correctness, and efficiency of different stages of SIMPLE Grid Framework.</p>

<p>The aim of this project is to re-implement Warp with Fibers to demonstrate the effectiveness of Fibers. To complete that, the following goals need to be reached:</p> <ol> <li>Implement non-blocking I/O support for Fibers.</li> <li>Recognize the parts of code related with I/O and threads, and rewrite them with Fibers</li> <li>Write a benchmark for the performance of Warp.</li> <li>Perform a detailed analysis and tune performance.</li> </ol>

<p>During the GSoC I'm going to complete the following tasks:</p> <ol> <li>Enhance the suggestions sorting algorithm using the ML-way inspired by <a href="https://open.afterthedeadline.com/about/technology-overview" target="_blank">after the deadline</a>'s (section "The Spelling Corrector")</li> <li>Migrate the server-side of the LanguageTool to the modern lightweight framework</li> <li>Migrate the LanguageTool from Maven to Gradle</li> </ol>