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<p>Bassa is an automated download queue for enterprises use. If a software can download all your files in the background without keeping it open then it's all the best. Bassa can be used best if the Bassa will be a server where people can put the download in the queue and after some time your download is done. Bassa still doesn't provide an option to download the file from the server. This project will lead to making files downloadable from the Bassa server in a single click. In this project, I would like to</p> <ol> <li>Change the frontend with download feature</li> <li>Implement compression methods to reduce file size</li> <li>Develop and Implement unique URL algorithm and integrate it with front end</li> <li>Develop RESTful API for file downloading</li> <li>Write unit tests and component tests</li> <li>Make the build, document it and upload to Travis within GSoC time period. I have sound knowledge of Python, AngularJs Flask, RESTful APIs and databases which are the main technological requirement of the Bassa Project.</li> </ol>

<p>The project is about implementing the computing extensive FindSim program on a cloud server and building communication between the web server and the computation server. With a high-performance server specifically set up for computing simulation tasks, the system can process more data with less time and the web server can provide better web service.</p>

<p>CLIO -- a system for maintaining software component information.</p> <p>CLIO is also a backronym for the system of maintaining Components, Licenses, IP and Other information.</p>

This project aims to address the challenge of testing and ensuring consistency across @Composable components in Compose Multiplatform projects. The proposed solution is to develop a Compose Multiplatform component gallery generator that allows developers to design and verify comprehensive component systems within their applications. The deliverables for this project will include: 1. A Gradle plugin: The main purpose of this Gradle plugin is to generate a runnable app showcasing the Story components written by the developer, tailored for their target platform. This plugin will facilitate the automatic generation of user interfaces for testing components. 2. A Runtime Library: The Runtime Library enables developers to define the specifications of the @Composable components they want to test. This will include an annotation system to mark components as Story items, with the ability to specify component properties and target platforms. In practice, developers can integrate this tool into their project structure, which will encompass common and platform-specific modules such as commonMain, androidMain, and desktopMain etc... our Gradle tasks will offer a streamlined process to create an app per platform to display all Story components.

WasmEdge is a lightweight inference runtime for AI and LLM applications. The LlamaEdge project (https://github.com/LlamaEdge) has developed an OpenAI-compatible API server (https://github.com/LlamaEdge/LlamaEdge/tree/main/api-server) and a server-side RAG app (https://llamaedge.com/docs/user-guide/server-side-rag) based on WasmEdge. In this project, we aim to use the LlamaEdge components to build a new API server that incorporates real-time Internet search results into LLM answers. Expected Outcome: An OpenAI-compatible local LLM API server that uses Google Search for supplemental context

The Language Server Protocol (LSP) provides a way to write an IDE/editor extension that is compatible with any IDE/editor that supports the LSP. The project aims to address the limitations of the existing JSON Language server in Visual Studio Code (VSCode) by developing a comprehensive JSON Schema Language Server. Leveraging the Language Server Protocol (LSP), this project seeks to develop a Language Server Extension enhancing support for JSON Schema across a variety of platforms. The envisioned solution will fill the gaps in the current VSCode built-in server by introducing essential features such as schema linting, validation, JSON validation against a schema, and JSON linting based on schema. By packaging the language server as a .vsix file, it will be easily distributable and installable, facilitating widespread adoption among developers. Furthermore, the project includes the implementation of a robust automated testing system to ensure the reliability and quality of the language server, complemented by manual testing in both VSCode and Neovim environments to guarantee compatibility and usability.

QEMU is able to export its display over D-Bus. We can create a server that uses the provided API to export the display over the network. There is already an experimental VNC server, but RDP is more popular and would provide more features and better efficiency.

The main point is to create an abstract class in both Swift and Java, shared among all AI components, which will contain all the necessary functions for such components. This abstract class will then be inherited by the ImageClassifier and AudioClassifier classes, allowing them to perform correct classification, especially thanks to asynchronous operations, utilizing AsyncTask in Java and GCD in Swift, respectively.

<p>Overall, the project is divided into two parts:</p> <ul> <li>Refactoring and creating new custom components that implement FHIR resources using the <code>lit</code> library.</li> <li>Developing a demo EHR application that implements the most common workflows using the created web components.</li> </ul> <p>An EHR is a digital real-time, patient-centered record that makes information available instantly and securely to authorized users. This involves a lot of data and information transfer requiring a standard data model for improving interoperability which is given by FHIR (Fast Healthcare Interoperability Resources) Specification.</p> <p>In the previous versions of GSoC this project was primarily based on creating web components for the FHIR resources with a goal to simplify the making of an EHR. For this year primary focus will be on refactoring and improving the components by implementing a demo EHR system utilising the FHIR components that will be created to be used together along with the components that are already present.</p>

<p>Kubernetes offers two ways to extend the core API, by using the CustomResourceDefinitons or by setting up an aggregated API server. This ensures users don’t need to modify the core API in order to add the features needed for their workflow, which later ensures the more stable and secure core API.</p> <p>One missing part is how to efficiently store data used by aggregated API servers. This project implements a Storage API, with a main goal to share the cluster’s main etcd server with the Aggregated API Servers, allowing it to use cluster’s main etcd just like it would use it’s own etcd server.</p>

<p>Feature completion for the Client/Server API, Sync Server, Room Server, and Federation component, plus various fixes and improvements to Dendrite and its related projects.</p>

<p>Among the projects followed by the <strong>Developers Italia</strong> community, there are few Kits to help web development phase, that are based on a shared design system. Currently, the foundation for many kits has been laid. Some of these are in the early stages while others are stable. The designed Kit needs to cater to a broad spectrum of users: from the expert designers to first-time visitors. The goal of this project will be to complete the UI Kit for React which is based on <strong>Bootstrap Italia</strong> and <strong>React Storybook</strong>.</p>

<p>Xi-Editor currently has a support of plugins but the Plugin API is quite small. The current plugin API has support for implementing things like indentation and syntax check. A lot more useful features can be realized by adding a plugin that supports the Language Server Protocol. It would enable advanced features in the editor like:</p> <ul> <li>Intelligent Autocompletion</li> <li>Hover definitions</li> <li>Go-to reference and definition of a symbol.</li> <li>Highlight references of a symbol</li> </ul> <p>This project aims at adding Language Server Protocol client plugin to the Xi Editor</p>

This project improves access to the CDLI database by enabling natural language queries instead of complex search forms and syntax. It allows researchers and students to explore the database more easily without needing detailed knowledge of its structure. The system consists of an MCP server that translates plain English queries into structured CDLI API calls, along with an AI-powered research workspace embedded on the CDLI website. Users can search, retrieve, and explore data through a simple chat interface, and generate structured research outputs. Key deliverables include a production-ready MCP server, an interactive research interface, a guided research paper generation workflow, and comprehensive documentation with testing support.

<p>This project aims to improve the functionalities of the Apache OODT OPSUI React.js User Interface and implement a REST API for the resource manager component of Apache OODT. The current OPSUI React.js dashboard which has been developed in the Apache Wicket framework will be replaced with the React.js UI on the completion of this project.</p>

<p>I've been using different technologies over the past few years such as PHP (Laravel), Nodejs, VueJS, React, and React Native. Also, I've been using GraphQL for over a year and I made a couple of full-stack/backend applications such as: -- a basic twitter clone using React Native ( using react-apollo), and nodejs (using Appollo. server).</p> <p>-- I made microservice e-commerce using NestJS alongside GraphQL. <a href="https://github.com/secmohammed/microservices-e-commerce-nest-api" target="_blank">Link</a></p> <p>-- slack clone using React ( with Typescript), and NestJS <a href="https://github.com/secmohammed/slack-clone" target="_blank">Link</a></p> <p>-- meetups clone API using (Golang and gqlgen) <a href="https://github.com/secmohammed/golang-graphql-meetups" target="_blank">Link</a></p> <p>-- ideas API clone using NestJS and GraphQL <a href="https://github.com/secmohammed/ideas-api" target="_blank">Link</a></p> <p>I've been working with GraphiQL to test my endpoint especially when not having the frontend built yet. and I'm keen on participating in this project to help with what I could to improve it.</p>

Currently, MIT App Inventor users lack a straightforward way to incorporate Retrieval Augmented Generation (RAG) for context-specific tasks requiring large or multiple documents. Teachable-LLM addresses this gap by integrating an on-device RAG pipeline, allowing users to build domain-specific chatbots without relying on external cloud services. A dedicated web app will serve as both a data management tool—enabling document import/export to a custom RAG database—and a playground for testing. Meanwhile, the ChatBot component will be extended to support RAG using the imported database. Key deliverables include the updated ChatBot component, a React-based web interface, thorough documentation, and pilot testing. This approach emphasizes accessibility, privacy, and simplicity, bringing advanced language model capabilities into the App Inventor ecosystem.

The project focuses on upgrading the Eventyay platform components to VueJS 3 within the unified Eventyay codebase. While minor adjustments are required for the ticketing components, the primary focus lies on modernising the talk and video modules, which currently rely heavily on VueJS 2, as well as on updating project-specific plugins. The objective is to replace all existing VueJS 2 code with VueJS 3 across the entire platform while preserving existing functionality and API compatibility. As the Eventyay platform is being consolidated into a single repository, the project also includes implementing additional API features where necessary to support the unified infrastructure. The upgrade improves the maintainability and performance of the platform, aligns it with current best practices, and ensures long-term sustainability of the frontend codebase. Throughout the project, Vue dependencies are upgraded, and components are systematically refactored to address breaking changes introduced in Vue 3. Extensive compatibility testing is carried out to ensure stability across all migrated features. Updated components are made fully functional under Vue 3, with a gradual transition from the Options API to the Composition API where appropriate, leveraging the benefits of improved structure, reusability, and maintainability. In parallel, the continuous integration pipeline is refined to support automated testing and deployment, including the seamless rollout of updated documentation that reflects the evolving architecture and feature set of the unified platform.

<p>Open Event is an application that allows users to find and book tickets for events. This proposal is about integrating major missing components of Open Event (adding missing API components and payment gateways for events) as well as improving existing components by adding features, polishing the UX/UI and improving the codebase.</p>

<p>So far Thanos has successfully used pieces of old Prometheus UI with some improvements. However, since Prometheus now moved all UI pages to React, it opens new possibilities for Thanos as well. The task is to move all UI to React and enrich them with Thanos features. As mentioned in the proposed idea, I also plan to add UI to every component of Thanos ensuring consistency and improve the experience of using Thanos even more.</p>

The Language Server Protocol (LSP) is a communication protocol between editors and analysis engines. Many major editors support this protocol, making Language Servers—development support tools that communicate with editors via LSP—highly significant in modern development environments. In this proposal, I aim to implement and enhance some new features for a new Language Server called JETLS, which is deeply integrated with the latest Julia compiler infrastructure. I will strive to deliver low-latency, precisely source-mapped feedback to users, enhancing the overall development experience by leveraging JET.jl, Revise.jl, JuliaSyntax.jl, and JuliaLowering.jl. JETLS will provide powerful editor support for programmers working on large-scale Julia codebases, helping them discover potential bugs and write more efficient code.

<p>LibreHealth EHR is an open source, clinically focused EHR which was designed for easy use and customizable for variety of settings. LibreHealth is a community that works to develop and implement Health Information Technology application to deliver better health outcomes using these technology platforms one among them being the LibreHealth HER. This is an open source, clinically focused EHR which was designed for easy use and customizable for variety of settings. The back end of this EHR is forked from OpenEMR data model. To move away from OpenEMR data storage, efficient reading and writing clinical data and improving interoperability, we need to move to the standard of FHIR (Fast Healthcare Interoperability Resources) Specification. This project aims to build components for LibreHealth EHR based on FHIR with Polymer components. This will enable the EHR developers to upgrade to a standard data model. The resources from modules in level3 and level4 of FHIR with maturity level above 3 will be developed using Polymer components. These resources were specifically selected as they contribute to the patient centric feature of the EHR.</p>

<p>My proposal is based on getting full support for JavaScript within the RoboComp framework. For this, the current state of generation of written components in the JavaScript language must be improved. Last year during the 2017 GSoC the functionality of the RoboComp component generator was extended to provide component supports based on NodeJS. A first approximation was achieved but still lacks robustness, reliability and should be extended to include more features. In addition, recently Zeroc ICE has added support for JavaScript interfaces, therefore the component generator must be extended to support this feature, both as a server and as a client. The component model of RoboComp must be able to generate components in this language with the same functionality as its counterparts in C ++ and Python, thus achieving complete support for JavaScript within RoboComp.</p>