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<p>Data retriever provides a number of publicly available datasets. These datasets are not stored at a single location but are downloaded from various publically available data repositories. New datasets are being added to retriever regularly so it becomes crucial to check the <strong>availability</strong> and <strong>installation</strong> of all datasets. This is a tedious task so the process needs to be automated.</p> <p>The goal of this project is to create a status server and dashboard where maintainers and users can see the status of datasets i.e. whether the datasets are installing properly or not and the changes that have been made to the dataset.</p>

<p>DIRAC is a highly-scalable software used for accessing distributed resources from various distributed systems. DIRAC’s main contributor is LHCb and also its initiator. LHCb uses the different type of computing technologies in order to distribute and process the collected physics data and DIRAC is one the software which is used because of its scalability and the level of orchestration and monitoring it provides for the distributed resources which is the main requirement of the LHCb collaboration. Further, my task will be to upgrade DIRAC’s monitoring system further by assuring high-scalability as when the LHCb gets upgraded there can be an unpredictable type of data which needs to be molded easily by DIRAC for which we will use ElasticSearch which is one of the widely used NoSQL technology. With this, I will have to migrate the Service and Agent components and DIRAC's RequestManagementSystem and DataManagementSystem to support ES backend along with tests and documentation.</p>

This project addresses a key challenge in biomedical data management: enabling secure, standards-compliant access to clinical FHIR data while integrating with the Gen3 platform. The goal is to develop an open-source, containerized FHIR proxy that enforces Gen3 access control via Arborist and supports asynchronous API interactions for high-performance data access. The solution includes: 1) A proof-of-concept FastAPI proxy that routes FHIR requests to a HAPI FHIR JPA server, adding Gen3 security tags for each resource. 2) Scripted ingestion of synthetic NCPI FHIR resources to facilitate testing and reproducibility. 3) Comprehensive unit tests and example configurations for local and cloud deployment. 4) Full containerization with Docker and Docker Compose, with plans for Kubernetes deployment via Helm. By providing a secure, FAIR-aligned, and standards-based framework for FHIR data, this project empowers researchers and developers to access and manage biomedical data efficiently while maintaining strict compliance with security and privacy requirements.

This project implements a debuginfod-compatible server within Debusine to provide automated debug symbol resolution for Debian developers. Currently, debugging binaries built by Debusine requires manual installation of -dbgsym packages. This project aims to eliminate this manual labor by allowing tools like gdb to fetch symbols automatically via HTTP. Based on an analysis of the elf_classify logic in elfutils, I have designed and prototyped a pipeline that identifies and extracts .debug ELF files from -dbgsym.deb packages and parses their Build-IDs. The implementation integrates this extraction into the isolated sbuild worker task. These symbols are then indexed as debian:debug-symbol artifacts and served via a custom Django view that supports the buildid/<id>/debuginfo and executable endpoints, including HTTP Range requests for partial content. Key Deliverables: 1) An isolated extraction pipeline using pyelftools within the sbuild task. 2) A Django-based artifact storage and indexing model (debian:debug-symbol). 3) debuginfod-compliant HTTP endpoints (/debuginfo and /executable) with support for mandatory headers and 206 Partial Content. 4) Documentation and integration tests for gdb symbol resolution.

This project aims to develop an application that has the ability to delegate membership of a room based on the user’s interaction with other third party services. A user can either be invited, kicked or put in a “standby” state for a room based on their past interactions. There are five main components in this project: 1) A reusable frontend component that handles the login UI for both Matrix and other third-party services. 2) A server-side component and a bot that verifies a user’s authorization to a room and decides their membership. 3) An admin panel to configure the relationship between matrix rooms and groups in third party services. 4) An external invite URL that can be used to get an invite to a room. 5) Token authenticated account registration for existing authorized users. I will be using the matrix-nio library mainly for building the bot since it has most of the functions required for interacting with the server and building a feature-rich tool.

<p>'LOKLAK SERVER' is a distributed message search server to anonymously collect, share, dump and index social media search results.</p> <h3>CURRENTLY</h3> <p>SearchServlet in Loklak Server only able to scrape the tweets from Twitter according to query input. To use other scrapers, one needs to use it separate endpoints.</p> <h3>PROPOSAL</h3> <ul> <li>The idea is to integrate scrapers to SearchServlet to make scrapers capable to work concurrently through one endpoint.</li> <li>Make the system Easy-To-Integrate with any scrapers to be added in future.</li> </ul>

FreeBSD previously lacked process-level power profiling, relying only on ACPI whole-system statistics. To address this, I built a FreeBSD-native power profiling tool inspired by Linux’s PowerTOP, keeping the design lightweight and integrated with FreeBSD’s user-space libraries. The core of the project was implementing librapl, a library to read Intel/AMD RAPL counters via /dev/cpuctl, and combining it with process data from libkvm. This allowed attributing energy usage from domains such as Package, Cores, and DRAM to individual processes. Special handling for multi-threaded processes ensured accurate per-thread runtime tracking, validated through workloads like OpenSSL (CPU stress) and Firefox (multi-threaded). The prototype currently outputs real-time per-process power usage, providing actionable insights into system energy behavior. Next steps include daemonizing the tool for continuous monitoring, extending support to ARM64 (via AMUs or SCMI) and Intel TPMI, and adding user-friendly visualization. This work establishes the first foundation for granular power profiling on FreeBSD and opens the path for advanced power management features.

<p>At the present stage, Apache OODT provides a web app to monitor the status of each component and ingested files, metadata and workflows. This main dashboard is known as OPS UI which is based on Apache wicket java web framework. Though it provides basic monitoring functionalities like retrieval of product status, metadata,workflow and platform health information etc. still it lacks of few important features like querying over products, product removal and workflow termination etc. Further the existing UI is not so user friendly and prints stack traces when backend errors occurred. Moreover users have to deploy the complete OPS UI, even whether they are embedding components individually in their applications. The intention of this proposed project idea is to address all those loop holes by implementing a new component based React UI with enhanced REST APIs. Moreover the implementation of the idea discussed here, is decided to be released with Apache OODT 2.0.</p>

<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>

The Kubeflow SDK gives AI practitioners a clean Python interface to submit, monitor, and manage distributed training jobs on Kubernetes via TrainerClient. However, LLM-based developer tools currently have no standardized way to access this runtime context — they cannot see a TrainJob's status, stream its logs, or reason about why a job failed. This project extends the existing MCP server MVP (tracked in kubeflow/community#936) for the Kubeflow Trainer SDK. The goal is to deliver a production-ready MCP server that exposes the full TrainJob lifecycle through well-typed, LLM-accessible tools — enabling AI assistants to actively help developers debug, monitor, and operate their Kubeflow training workloads in real time.

<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.

DICOMHawk is an open-source honeypot that emulates a vulnerable DICOM medical imaging server to attract and log unauthorized access attempts. The current v3.0 rewrite is functional at the core protocol level but presents a single fixed identity, lacks a web interface, has incomplete DIMSE handler coverage, and performs no analysis on attacker-submitted payloads. This project transforms DICOMHawk into a profile-driven deception platform. A profile system allows the honeypot to impersonate different medical imaging systems through YAML-backed configuration, controlling the DICOM identity, web-facing surface, and honeytrap behavior per deployment. An attacker-facing web extension built with Flask exposes realistic viewer interfaces and honeytrap endpoints, while browser and environment fingerprinting collects telemetry about attacker infrastructure. On the protocol side, the project completes missing DIMSE handlers (associate, release, abort) and adds DICOMweb (QIDO-RS, WADO-RS, STOW-RS) endpoints. A structured logging pipeline with session correlation ties DICOM, web, and fingerprint events together. A static-analysis sandbox quarantines and analyzes uploaded payloads without execution, shipping with starter YARA rules. Finally, Docker deployment is hardened for safer operation. Deliverables: Profile-based architecture with two starter profiles, attacker-facing web extension with per-profile templates, operator Web API, browser fingerprinting with correlation database, completed DIMSE and DICOMweb handlers, unified JSON event logging with session correlation, static-analysis sandbox with YARA support, and hardened Docker deployment.

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>