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<p>Currently, the Rocket.Chat.Electron desktop application has a wide range of useful features but I chose the project “Download Manager on Desktop App” because it would take the application even further and greatly improve user experience. On the web, we are comfortable with using the download managers built into our browsers which makes a download manager inside the web app a bit redundant. But this feature becomes desirable when we are using the desktop app.This improves the experience of the user in many ways, a few of which are briefly mentioned below:</p> <ol> <li>Track downloads made through the app. </li> <li>Track download progress of files.</li> <li>Search and filter for downloaded files based on the server that they are downloaded from as well as other attributes.</li> <li>Pause, restart or cancel downloads.</li> <li>Re-download cancelled or broken downloads.</li> </ol>

Jitsi Meet’s current whiteboard—built on an outdated Excalidraw fork and Socket.IO v2.5—only supports basic “smart objects,” lacks file/image upload, and suffers from performance and maintenance challenges. This project will: Upgrade real‑time transport by moving to Socket.IO 4.x and optimizing WebSocket message flow (batching, compression) for lower latency. Migrate to the latest Excalidraw release, ensuring full compatibility with Jitsi Meet and access to new drawing features. Implement a pluggable storage backend (starting with MiniIO) so users can share files directly on the whiteboard. Deliverables: -A collaboration server upgraded to Socket.IO 4.x with measurable performance gains -An updated Jitsi whiteboard using the newest Excalidraw version -A modular storageBackend abstraction plus working MiniIO module

<p>Implement decentralised client-side recording mechanism in Jitsi Meet, where each device records and only records the audio of the participant on that device, directly using the stream from audio recording device. Afterwards, the recordings are merged offline to form a single track.</p> <p>Potential use cases include:</p> <ul> <li>Hosting a podcast by remote collaboration.</li> <li>Remotely interviewing people over the internet, with the intention of using the recording in a radio / TV program afterwards.</li> <li>Collecting stories and oral history. Something like StoryCorps DIY but without the need to meet physically.</li> <li>Virtual choir, where multiple people sing the same song together but remotely. Audio quality is crucial here.</li> </ul> <p>This feature will potentially extend Jitsi Meet's user base to a lot more of audio content creators.</p>

My proposal focuses on enhancing the Rocket.Chat platform with an AI-driven feature, "AI Query Assistant," aimed at solving the challenge of efficiently handling user queries. Leveraging a Retrieval-Augmented Generation (RAG) pipeline, the assistant will accurately interpret and respond to user requests using AI-based image recognition. This integration will significantly streamline the process of request handling, reducing response time, and improving overall user satisfaction. The project involves configuring the RAG pipeline with advanced AI components, ensuring adaptability to different service domains, such as healthcare and IT support. Key deliverables include a fully integrated AI assistant within Rocket.Chat, capable of intelligent interaction, alongside comprehensive documentation for ease of use and future scalability. The successful implementation of this project will result in a more intuitive, efficient, and user-friendly query resolution mechanism within the Rocket.Chat environment.

Analyzing massive codebases like Rocket.Chat via iterative LLM agents causes context bloat, rapidly exceeding free-tier limits. This project builds a graph-native code navigator exposed as a Gemini MCP server. An offline indexer parses the full Rocket.Chat source via ts-morph, extracts a typed dependency graph (11 edge kinds, including Rocket.Chat-specific patterns like callbacks.run/add and Meteor method dispatch that break standard import analysis), and compresses function bodies into signature-only skeletons for ~4× token reduction. At query time, three composable MCP tools—search, graph, implement—let an LLM navigate by following real edges rather than ranking documents. An evaluation framework with ground-truth test cases (collected by Rocket.Chat experts and Claude) and 5 measured metrics closes the loop.

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.

This project aims to design and implement a complete chat moderation and editing system for Jitsi Meet. Currently, Jitsi’s chat supports basic messaging but lacks important features such as message editing, deletion, moderation controls, and search, which are essential in modern communication platforms. The goal of this project is to introduce these missing capabilities using XMPP standards like XEP-0308 (message editing), XEP-0424 (message retraction), and XEP-0425 (moderation). The implementation will follow a structured approach, starting from the XMPP layer in lib-jitsi-meet and extending to the Redux and UI layers in jitsi-meet. Key features include message editing, message deletion, moderator-controlled actions, chat muting, user reporting, message search, and reply threading. Additionally, server-side enforcement using Prosody plugins will be explored to ensure reliable moderation. This project will improve user experience, provide better control in meetings, and bring Jitsi’s chat system closer to modern messaging standards while maintaining consistency with its existing architecture.

Jitsi Meet confines all conference content to a single browser window, leaving second monitors completely unused. Presenters cannot see the audience while screen sharing, moderators cannot watch the gallery while focusing on the active speaker, and educators cannot monitor student reactions while presenting. This project uses the Window Management API and React createPortal to open a coordinated secondary browser window on a different physical display while sharing the same Redux store and WebRTC connection. Each window can display an independent layout such as active speaker view or gallery view with no duplicate participants and zero extra bandwidth. The implementation includes smart screen detection via getScreenDetails, per tile video track management with stale frame prevention, a four tier active speaker priority chain using real time audio levels, responsive gallery grid with ResizeObserver, two tier style inheritance, and full window lifecycle management including conference leave cleanup. A working proof of concept with active speaker video, gallery view, layout switching toolbar, and speaking indicator borders is already integrated into the Jitsi codebase.

Rocket.Chat currently uses multiple virtualization patterns across list-heavy UI surfaces, especially in core room and contextual views. This makes scrolling behavior, pagination, testing, and future maintenance less consistent, and increases the effort required to review or extend list implementations. My project will establish a shared virtualization baseline for Rocket.Chat using a reusable wrapper integrated with the existing OverlayScrollbars-based scrollbar system, then apply that baseline to a focused set of core user-facing surfaces such as Threads, Discussions, and Message Search. The implementation will build on existing Rocket.Chat frontend patterns, including TanStack Query-based pagination hooks and shared query/cache infrastructure, so the migration can be incremental rather than disruptive. The main deliverables are: a reviewed shared virtualization abstraction suitable for merge, a reusable migration pattern for flat list surfaces, migrations of selected core components, focused test coverage for the wrapper and migrated screens, and contributor documentation covering wrapper usage, pagination integration, scrollbar wiring, and testing. If time allows, I will also migrate an additional core surface such as Room Files or prepare the path for future grouped-list migrations.

<p>A crucial component of the user experience with bots in Rocket chat android app is rich messages. Currently, there is only minimal support for rich messages in the Android app with the only support for Horizontal Buttons, Multiple Buttons, Image Buttons with URL and Buttons with URL.</p> <h4>I propose to enhance the user experience with bots by implementing other rich messages types, interactive rich messages, welcome screen for bots into the existing Rocket chat Android Client.</h4> <p>The goal is to improve user experience with bots by:</p> <ul> <li>Adding Webview support to the existing Rich message types.</li> <li>Adding support to the deferred rich message types like temporary text buttons, and other types which requires editing of the message.</li> <li>Implementing other Rich messages types like section with menus, overflows, buttons, Date pickers, Articles.</li> <li>Adding interactivity to the rich messages to increase usability with bots.</li> <li>Implementing a welcome screen for bots similar to facebook.</li> <li>Writing unit tests and espresso tests for UI level testing</li> <li>Implementing a bot with all the new features.</li> <li>Solving random crashes and left issues and improving documentation.</li> </ul>

<h3>Debug and Profile Bears</h3> <p>The aim of this project is to create a better quality Bears by creating a tool which will have the ability to jump directly into Bear code and move through it using a debugger interface such as pdb and make debugging of Bears as easy as writing the Bear.</p> <p>A Debugger should also have the ability to investigate the settings passed to the Bear and inspect the results created by the Bear.</p> <p>Implementation of a Profiling Interface which will help Bear writer to create a Better Performant Bear.</p>

<p>The BeagleBone AI is equipped with a high amount of processing power due to the Dual Core ARM Cortex-A15 chip as a main computing unit and its accompanying co-processors. This makes the AI a perfect fit for highly demanding applications regarding CPU consumption, like media applications which have extremely strong realtime constraints. Professional audio/video studios have to guarantee for small latencies when transmitting media signals between different devices. Different media channels in a transmitted stream need to be carefully synchronized to guarantee for e.g. lip synchronicity. To use the AI in such a scenario, the AVB protocol stack should be implemented in the Beagleboard Linux Kernel, which allows for synchronization of media streams within a local area network. Furthermore the CTAG Face audio drivers should be ported from BeagleBoard-X15 and HDMI audio out capabilities should be deployed, to allow the in- and output of audio data.</p>

CodeLabz is a platform where the users can engage with online tutorials and the organizations can create tutorials for the users. The platform is developed using ReactJS front-end library and the back end is developed using the Google Cloud Firestore and Google Firebase Real-Time database. This project aims to bring changes to the UI to make the website seamless and provide more functionalities to users.

<p>Primarily, my main focus of summers would be whole settings subsystem. I will work on making it more robust and improving the quality of the code.</p>

<p>Adds a new List component to React Admin which implements infinite scroll to the Navidrome Data Views</p>

Integrate low-overhead wall-time profiling for FastAPI and AsyncIO task loops on Python 3.12+.

<p><a href="https://wiki.debian.org/FreedomBox/Plinth" target="_blank">Plinth</a> is a web interface to administer the functions of the FreedomBox which is a Debian based project, and the main goal of this idea is to make it available for Fedora.</p> <p>To solve this issue, I made up a scenario in this proposal, it can be divided roughly into two parts. The first one is modifying the source code module by module to convert it to RPM-based, including replacing the apt command code with the dnf command code or fit both of them, changing the Deb-based packages into RPM-based packages which play the same roles and testing after each module finished. The second part is a little scattered, including making a RPM package for Plinth and setting up a repo for it in <a href="https://copr.fedorainfracloud.org/" target="_blank">Copr</a>, testing inside a Virtual Machine, and modifying some configuration files and the documents for users to fit the changes of Plinth during this project, at the same time, a new wiki page for Plinth in Fedora is needed.</p>

The power of LLMs has been witnessed to be helpful to generate several short code pieces to fulfil the user’s demand, and be injected into channel chats or GitHub repositories. In this project, we propose to develop a Rocket.Chat app that enables the AI programmer functionalities where users can utilize the tool to create a short piece of code in C/C++, Java, Javascript, Typescript or Python based on specification supplied by the user. This implementation includes modern open source LLMs (Mistral, CodeLlama, WizardCode, Llama2, Phi, and so on) for a simplified configuration. This functionality also allows users to ask for a new variation of the code or augment/fine-tune the system for a more precise code generation. To achieve this, we propose to design a more intuitive and interactive UX to simplify users’ interaction. Finally, we will explore to bridge the generated codes with sharing APIs, allowing them to be shared to external applications and added to GitHub as a pull request. The AI Programmer app will be published on Rocket.Chat Marketplace together with specified documentation.

<p>The goal for this project is to integrate chatbot platforms like Dialogflow and RASA with Rocket.Chat.</p> <p>These integrations are built to work with the Livechat module of Rocket.Chat. Livechat is a modern helpdesk application that helps connect a live-agent with a visitor so that the live-agent and answer visitor's queries.</p> <p>With these new Integrations of Dialogflow and Rasa, Rocket.Chat will now allow site visitors to interact with a Bot instead of a live-agent. These Bots would be developed and maintained on Dialogflow and RASA.</p> <p>This is a very common approach which most of the businesses use nowadays, where a visitor would first interact with a Bot. These bots can handle simple interactions like answering FAQ's for a business, to complex interactions like ordering an item. If these bots are not able to answer visitor questions, then the visitor would be transferred automatically to an online live-agent who can address their query.</p>

<p>An app developed on Rocket.Chat App engine to integrate Google Calendar with Rocket.Chat. This can be used to create and manage personal calendar events. It can also be used to create public events which can be shared with multiple users and takes cares of reminders and notifications.</p>

This project aims to deliver a Rocket.Chat App that uses generative AI to summarize any conversations across threads, channels, and DMs. For example, a user can say, "summarize my unread messages yesterday in the #engineering channel". The users can ask AI any questions regarding the messages, and subscribe to personal digests that summarize selected channels on a recurring schedule. The AI summarizer will boost the productivity of Rocket.Chat users with faster information digest, and improve community engagement by keeping everyone on the same page.

<p>According to an estimation, over 15% or one billion people of the total world population experience significant disabilities. These disabilities can be major or minor but the barriers in social and economic sectors for such people are the same. Even in the field of technology, people with disabilities are usually ignored. They find it tough to use websites and mobile applications. Considering such people a part of society and to make the live chat widget accessible to such people, I will implement a user-accessible mode. This accessible mode will help them to use the exciting features of the live chat widget.</p>

<p>The v2+ of the android app of Rocket Chat is already under progress and seems to work well so far. For the summer of code'18, I intend to extend Rocket Chat to be used in ubiquitous platforms like android <strong>wear</strong>. I'll be building the wear app from scratch this summer. Apart from this, I also intend to work on more advanced features for the android app like integrating <strong>google smart lock</strong> , adding support for <strong>private chat groups</strong> , and will keep working on issues reported during the GSoC period to improve the user experience for the mobile app.</p>