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<p>DHIS Connector module is a module that is used to send aggregated data from OpenMRS to DHIS2 Instance. The module provides several user interfaces which can be used to configure the process easily. It already have important functionalities such as Drag-and-drop type mapping UI, Scheduled and Automated reporting, Import Export functionality for mappings, etc. The module can be improved with many other new functionalities. The project aims to develop the DHIS Connector module with new features and updates such as location mapping, extended period support, etc.</p>

Thrift for Zephyr Module aims to support the mature Remote Procedure Call (RPC) framework Apache Thrift on the Zephyr operating system (OS). This would enable lots of existed cloud services built upon Thrift to be seamlessly ported to the Zephyr ecosystem. New IoT applications can be developed rapidly without much boilerplate leveraging this module and the Thrift framework, too.

Currently, OpenAFS provides the afsio command that supports read, writes, and locks to both files and directories in the AFS environment without going through the cache manager. However, it does not support mount point control, ACL manipulation, or directory operations, which would allow a fully functional cell to be created without the cache manager; This would be convenient for administrators, as it avoids the kernel module having to be installed on the machine setup is being performed from, as well as allowing setup in environments not compatible with the kernel module. To address this problem, I will implement all three types of subcommands of afsio; Key deliverables include the code for all of these types of commands, as well as documentation such that administrators know how to use them.

The Entity Display Manager module aims to simplify how entity displays are handled in Drupal. Currently, managing view modes and form displays across different entities can be tedious and repetitive. This project will introduce a centralized, user-friendly interface to configure, duplicate, and synchronize entity displays efficiently. By providing intuitive tools and supporting bulk operations, the module will improve developer productivity and site-building experience in Drupal.

Description: Cluster API Provider GCP (CAPG) enables the creation of Kubernetes clusters in GCP with Cluster API. With increasing adoption of Cluster API (CAPI) in general and of CAPG we want to improve the supportability of CAPG, especially for production environments. The first part of this is to add telemetry/tracing using OpenTelemetry so that we can understand and visualize the flow of reconciliation within the provider. The next part is to add a **pprof** endpoint that can be optionally enabled to enable operations/support users to collect profiling information from a running instances of CAPG. Expected Outcome: This work will enable tracing and profiling of a running instance of CAPG (along with supporting docs) to supports operations/support engineers.

<p>The SunPy JSOC Client is a very rudimentary client to JSOC's export data CGI. The drms module provides a more complete implementation of the DRMS protocol. The drms module needs testing, and some other packaging improvements to automatically run the tests. This might involve adoption of some of the Astropy packaging code such as astropy_helpers, if necessary. Once the drms package has been well tested, CI added and a conda package created, the SunPy JSOC client could be improved and extended to enable use of the majority of the drms functionality through the unified search API of SunPy. While the SunPy implementation would not need to provide all the features of the drms library, it would strive to provide a simple API for most queries supported by drms.</p>

<p>In this project, my goal is to update the existing ANN facilities within mlpack and the deliverable is an improved ANN module. This includes adding many different functionalities, including layers like Pixel Shuffle layer and Spatial Dropout layer among many others. I have also proposed to add extended support for reduction facilities to be integrated into many different loss functions to bring to the table a degree of flexibility offered in other major machine learning libraries. I will also be building on the work done in a GSOC project last year, the mlpack-Tensorflow translator and mainly I will be updating it with all the latest changes in the source code to support new functionalities which have become a part of mlpack since last year. And finally, I have also proposed to begin work on improving the documentation of all layers, by adding inline examples within the header descriptions to improve usability for a wider range of end-users.</p>

<p>Although RDKit is a widely used extremely versatile chemoinformatics software package, it still lacks efficient 3D molecule visualization capability. In this circumstance, 3Dmol.js is easy to use and an excellent molecule visualization library that works in web browser. Another important platform is jupyter that allows communication with python from web browser through notebook like interface. Jupyter has also made the codes easily readable with clean output by dint of markdown. So, keeping python as operational core engine that expectedly works as interface of RDKit while utilizing support from jupyter notebook and 3Dmol.js should be a very good combination to ensure high productivity in drug discovery settings. In this project, I would like to turn these combination to more viable solution by binding 3D molecule visualization (protein-ligand complex), ligand editing panel, conformer scrolling system, and channel for sending ligand from jupyter interface to force field that exists in RDKit for energy minimization. Needless to say, I would like to use openbabel for file format conversion and molecular data extraction system.</p>

<h4>This proposal consists of 3 sub tasks of kapitan which is elaborated below.</h4> <h3>Task1 : Remote Inventory Federation</h3> <p>This work would add the ability to Kapitan to fetch parts of the Inventory from remote locations (https/git). This would allow users to combine different inventories from different sources and build modular infrastructure reusable across various repos.</p> <h3>Task2 : Support for Helm binding in OSX</h3> <p>This project can be subdivided into the following three tasks:</p> <ul> <li>Complete the Helm binding pipeline so that continuous integration (travis) also builds binding for osx and releases them to GitHub release.</li> <li>Packaging the binding to the pypi release.</li> <li>Add a brew manifest for osx so that users there can install Kapitan in a simpler way.</li> </ul> <h3>Task3 (bonus): Modularize kapitan</h3> <p>This project would modularize kaitan into core dependencies and extra modules.</p> <h3>Documentation and tutorial</h3> <p>Along with all the other tasks, documentation of all new and existing features will be done and displayed to kapitan.dev and walkthroughs will be made for the examples given in the documentation.</p>

<p>Hyperledger Fabric-X introduces a new architecture for Fabric, with decomposed services for ordering, endorsement, validation, and committing, but its current developer workflow is still centered around dedicated deployment scripts and Ansible-based setup.</p><p>Fablo, on the other hand, is widely used to start Hyperledger Fabric networks quickly from a single configuration file for local development, CI, and experimentation.</p><p>This project will explore and implement the best path to support a simple local Fabric-X network through the Fablo experience. We will evaluate architectural options such as a new engine, a separate repo, or an extension/wrapper approach, then build a minimal but practical MVP that can generate configuration, bootstrap the required components, and start/stop a working local Fabric-X network. The goal is to reduce setup friction for contributors and users, and provide a repeatable developer workflow, examples, and documentation that make Fabric-X easier to try, demo, and extend.</p><h3>Learning Objectives</h3><ul><li>Understand the architecture and deployment model of Hyperledger Fabric-X, including how it differs from classic Hyperledger Fabric.</li><li>Learn how to design contributor-facing tooling for distributed systems with evolving architecture.</li><li>Gain experience in open source development across CLI/tooling, configuration generation, testing, and documentation.</li><li>Develop practical skills in Docker-based environments, and debugging multi-component systems.</li></ul><h3>Expected Outcome and Deliverables</h3><ul><li>A design proposal describing the recommended integration path between Fablo and Fabric-X (for example: separate repo, pluggable engine, or wrapper-based approach).</li><li>An MVP implementation that can bootstrap a simple local Fabric-X network and manage its lifecycle.</li><li>Example configuration(s) and sample workflow for running Fabric-X through the proposed Fablo-based experience.</li><li>Automated tests and/or validation scripts covering the supported MVP scenario.</li><li>Contributor and user documentation explaining setup, architecture decisions, and next steps.</li></ul><p><br></p><p>Lean more at <a href="https://github.com/LF-Decentralized-Trust-Mentorships/mentorship-program/issues/83" rel="noopener noreferrer" target="_blank">https://github.com/LF-Decentralized-Trust-Mentorships/mentorship-program/issues/83</a></p>

<p>The development of GPUs for general purpose computing has revolutionized the field of deep learning. They are critical for training large deep neural networks (DNN) and improve the performance of inference.</p> <p>The OpenCV’s DNN module has a blazing fast inference capability on CPUs when compared to other popular libraries such as TensorFlow or PyTorch. It supports inference on GPUs using OpenCL but not CUDA. NVIDIA’s GPUs support OpenCL, but their capabilities are limited by OpenCL. A separate CUDA backend is required to reap maximum performance out of NVIDIA's GPUs.</p> <p>This project aims at adding a complete CUDA backend for OpenCV’s DNN module. By the end of the project, the DNN module should be capable of performing inference on CUDA enabled GPUs nearly as fast as or faster than existing deep learning frameworks such as TensorFlow or PyTorch.</p>

The project aims to integrate Eventyay into Rocket Chat in a way that will open up an additional completely open-source option for Open event (Eventyay) API server to do the event management. This will be accomplished by introducing new components which will interact with Eventyay API to register events/conferences. Components to render and display upcoming events, and the components to successfully conduct events for tens to millions of people. These components will help the community to create, manage, and host events/conferences/meet straight from their own instance of the community (gaming community, NFT community, dev community, and so on), backed by open-event-server APIs

Summary: AI-Powered Chat Automation for Rocket.Chat Problem: Setting up automated workflows in Rocket.Chat usually requires manual scripting, which can be time-consuming and complex for non-technical users. Solution: We will build an LLM-powered app that lets users create and run automated workflows using simple natural language. It will: 1. Allow users to define triggers (e.g., "when someone mentions 'help'") and actions (e.g., "send them a message asking what they need help with"). 2. Break down complex commands into steps and execute them safely. 3. Prevent harmful actions with built-in safety checks. 4. Let admins manage and review all workflows. Deliverables: 1. A app that understands natural language commands. 2. Multi-step workflow automation 3. Error handling and admin controls for safety. 4. Customizable automation for any Rocket.Chat server. This project will save time by making chat automation easy and accessible for all users.

Kiwix-Desktop aims to be a browser-like viewer for Zim files and presents a great experience on the viewing side. However, working on issues in the repository made me realize, that the application has accumulated an alarming amount of feature requests and technical debts. Issue stalling in the repository has become commonplace and many are left untouched/solved for 2 to 6 years. This project intends to tie up loose ends on tickets that are long-desired by users, resolving potential frustrations and improving the experience for users. The deliverables will highly focus on sizable/difficult UI feature/enhancement-related issues as it is one of the severely plagued categories. Features to resolve the issues: 1. Add Table-of-Content for the Web Viewer. 2. Introduce Text-To-Speech. 3. Improve In-App Search Display. 4. Allow Export/Import of Bookmarks. 5. Allow Save Page for Web Viewer. 6. UI Consistency for RTL Users. 7. Miscellaneous UX Fixes.

GitHub is the largest centralized repository for hosting open source projects and this app will enable the users to perform many GitHub activities right from Rocket.Chat. In this project, I plan on developing the GitHub app for Rocket.Chat which will improve the overall collaboration between developers. This app will enable users to fetch data from a repository, subscribe to various repository events, perform extensive GitHub search queries, open new issues, assign issues to team members, review pull requests and do much more right from Rocket.Chat.

Build a mobile-first catalog of active scholarships, GSoC roadmaps, and interview preparation materials.

Rocket.Chat is the place where teams from large organizations assemble, coordinate and communicate. People discuss their work, manage it and share their part on the platform. ClickUp is #1 cloud based task/project management platform but the problem is Rocket.Chat marketplace does not have any ClickUp app for directly managing a user's workspace. Through my project, I aim to take a step in this direction by implementing ClickUp right in our chatbox. Through the ability of managing user’s tasks and making rooms with members as assignees of a task and/or as members of a workspace, it would ameliorate user experience, as it would allow them to quickly view/update/share their tasks with other channel members without even leaving the channel let alone the browser tab.

This project adds a combinator builder to WP1 that lets users combine multiple existing selection builders into one result. Instead of using just one method, users can mix different builders using simple operations like union, intersection and difference. The goal is to make it easier to create flexible and powerful article selections while keeping the interface simple and easy to use. The project includes designing the system, building a clear UI, and making sure it works efficiently.

This project aims to enhance team productivity within agile environments by using a chatbot-based approach. By developing a chatbot that is customized to meet the needs of agile workflows, teams will be able to streamline communication, task management, and meeting coordination. This project will be implemented via the apps engine in Rocket.chat.

Embedded Chat provides enough features to be integrated into various web applications as an in-chat app, but its current UI lacks personalization and configurability. This proposal focuses on enhancing the UI by making components modular, providing pre-built themes, and allowing users to modify the UI using a layout editor. It also aims to improve security via HTTP-only cookie-based authentication and enhance configuration capabilities by making the instance configurable through RC-app.

This project aims to enhance Kiwix's capabilities by improving its user interface for rendering YouTube channels & playlists. However, the current user interface lacks appeal. The goal is to revamp the UI to make it more visually appealing and user-friendly, thus enhancing the accessibility of educational videos and other content to Kiwix users around the globe.

<p>Rocket.Chat has a remarkable internationalization effort. There are dozens of translation files across several apps, many of them with over 4000 translation strings. However, there's still a long way to go - some strings still lack translation, others need to be removed, etc. Currently, the translation process is entirely manual, making it even harder to manage that much information. To speed up the internationalization process, our goal is to build a tool that makes translators live's easier. We want to create a CLI app that helps in the translation process, answering questions like:</p> <ul> <li>Which strings weren't translated yet? </li> <li>Which strings are not mentioned in the codebase anymore? </li> <li>What is the translation progress for a given language?</li> </ul> <p>We want to make the actual translating easier, providing an interactive interface to add or remove translations. Our goal is to raise Rocket.Chat's translation standards - to ensure these standards are met, we'll build a CI scripts that checks for translation string usage in new contributions, ensuring every new translation string added to the codebase is listed in the translation files.</p>

<p>Currently, there are no social aspects to channels in Rocket.Chat servers. Many public channels aren't visible enough for the users to view and join. To join a specific channel, they should either know its link or the exact name to search it in the Directory. Also, every single day Rocket.Chat servers have many people coming into their channels, and they are all lost. They do not realize the actual purpose of a channel. So, there can be a better way of letting the new users know what is going on within the server or even within a channel. This project aims to solve the above issue by introducing a new feature called Discovery!</p>