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<p>This project aims to create a new kind of channel where instead of sending messages, users would be able to create task cards with several custom fields.</p> <p>Those tasks would then work as a thread header, allowing comments in the format of a thread.</p> <p>The tasks will be stored in a new collection in MongoDB and MiniMogo</p> <p>The client would then render this task message with a different renderer, displaying the assigned user and the current task status</p>
Kyverno lacks the ability to operate on some important subresources like /scale and /status in areas such as validation and mutation. Upstream Issue (URL): - https://github.com/kyverno/kyverno/issues/3118 - https://github.com/kyverno/kyverno/issues/2843 - https://github.com/kyverno/kyverno/issues/4313
As Generative AI technology is improving and being implemented in various applications, I am interested in using it in Rocket.Chat. While we already have a Giphy App that can fetch GIFs from Giphy, I propose to create an RC app that can generate GIFs based on user prompts. The RC app will allow users to enter their prompts, wait for the generation process, and then use the generated GIF seamlessly anywhere. We will be using an open-source & self-deployed model to generate the GIF. To ensure the app can serve multiple users in parallel, we will use the concept of databases as Task Queues, to store the status of multiple GIF generations in parallel. Excited to see what people generate using this feature.✨🚀
This proposal outlines the development of an AI-powered Image Analysis App for Rocket.Chat. The project aims to introduce a feature that allows users to simply upload images, while the app automatically analyzes them using a multi-modal Large Language Model (LLM). The app will process images to identify locations, landmarks, points of interest, or event venues and retrieve up-to-date event and happening information from the internet. By integrating advanced image reasoning with real-time data retrieval, this solution enhances situational awareness and provides users with valuable insights. This proposal seeks to streamline information discovery, improve engagement, boost productivity, and elevate the overall user experience within the Rocket.chat ecosystem.
This project aims to automate the creation of ZIM files for Wikipedia selections on WP1 by allowing users to schedule updates at regular intervals (e.g., 1, 3, 6, or 12 months). Users provide an email address to receive notifications once their updated ZIM file is ready, and the system will automatically remove unclaimed files.
<p>Implements a Share extension to iOS and Android OS to ReactNative client Rocket.Chat.</p>
<p>Enabling Rocket.Chat server administrator to use a NextCloud/ownCloud instance for storage of upload. Integrate NextCloud/ownCloud to Rocket.Chat and within Rocket.Chat client user interface to allow access to the contents of a user’s NextCloud/ownCloud instance.</p>
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.
Text embellishment plays a vital role in enhancing communication, fostering engagement, and making written content more impactful. This project aims to create a Rocket.Chat App that enables users to emojify or embellish their text messages using LLMs (large language models) and prompt engineering. The AI-generated text will be presented through an interactive message interface, with action buttons such as send, redo, edit, and copy, visible only to the user. Depending on the action button clicked, the user can send the message to the channel, regenerate the AI-generated message, edit the message, or copy it, respectively. This app adds a new dimension of creativity to enhancing communication within Rocket.Chat.
The Code Review Assistant app for Rocket.Chat addresses the common challenge of delayed code reviews, where pull requests often remain unattended for extended periods. This app intelligently analyzes the files modified in a pull request, examines the commit history of those files, leverages the CODEOWNERS file, and reviews past activity to identify the most relevant reviewers and does preliminary LLM review. By notifying only the most appropriate contributors, the app streamlines the review process. Integrated directly within Rocket.Chat, it provides user-friendly reminders and updates, LLM does the sanity review, enabling developers to stay informed and collaborate more effectively on code reviews.
Action sheets are a very common UX pattern which are used to interact with quick actions and can be found in almost every mobile app . There are already multiple places where Bottom Action Sheets are used in Rocket.Chat app like the message actions menu. While there are also several other components like Server dropdown, Reaction Picker, Reactions modal etc. which have to be migrated to Bottom Action Sheets to improve the user experience. The main goal of the project is to redesign and add more features to the existing Emoji Picker, Reactions Modal and the other components, improving the overall user experience and then render them as Bottom Action Sheet.
<p>To create an Angular 4 based Progressive Web App (PWA) lite client that would be able to used as an alternative of native clients like Rocket.Chat.Android or Rocket.Chat.iOS client.</p>
<p>The project aims to program a demuxer in the libavformat and a parser and decoder in the libavcodec library. As DICOM files contain lots of metadata about the medical procedure done, the demuxer should be able to correctly identify and separate the data in the required number of streams. The DICOM decoder should decode the compressed pixel data and can extract a frame ( or multiple frames, depending on the stream ) from it.</p>
This project is a significant upgrade to Rocket.Chat message composer elements to provide users, AI assistance in writing messages using Context-sensitive spelling corrections, inline grammar correction recommendations, and clarity-enhancing rewording, Inline hinting are among the major features. The project also provides language translation, summarization, emoji, and tone control features. This project majorly improves the speed and clarity of message writing for users.
The project's objective is to create a Rocket.Chat app that can create multiple bots with the ability to give all of the essential Botpress features inside of all key chat spaces (Direct, Channel, and Omnichannel).
RocketChat UIKit Playground is a proposed Component playground that will allow us to build, test, and browse our UIKit components in isolation. When executed correctly, they make prototyping new experiences both easy and fast, thanks to the consistency they create. The playground should also allow developers to generate configuration by dragging and dropping components from the library. Deployed Here --> https://intvivek.github.io/RCplayground/
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. Miro is #1 cloud based online whiteboard for visual collaboration. Through my project, I aim to take a step in this direction by implementing Miro right in our chatbox. Through the ability of managing user’s boards and making rooms with members as assignees of a board and/or as members of a team, it would ameliorate user experience, as it would allow them to quickly view/update/share their boards with other channel members without even leaving the channel let alone the browser tab. The provides 10x more value than what our competitor's Miro app offer for their platform. We will give users ability to preview boards and embed boards in channels using Miro's live embed and give the feature of dynamic modal rendering specific to board items.
<p>The project Rocket.Chat.ReactNative is already about 2 years old and there are lots of features. So the chances of introducing new bugs by changing the existing code or adding new features are really high. And before each build the application needs a thorough testing to make sure nothing is broken.</p> <p>Currently project has some end-to-end tests for IOS platform but unfortunately no tests for Android. And also the Continuous Integration job on Circle CI with IOS tests is failing, the build stays always ‘red’.</p>
<p>For every deployment of Rocket.chat, there is a team or community that generates a lot of content. Rocket.chat itself is the repository for much of that content but the rest typically resides in other applications such as github or jira or google docs, many of which are third party services and so the content is not owned by the community that hosts the Rocket.chat server. In one particular scenario, a user may want to write an article and “publish” it to groups or contacts within Rocket.chat. Today that would have to be done typically using a third party service. What if instead article creation, searching, and storage were integrated with Rocket.chat such that each user could maintain his or her own library of articles and contacts of that user could subscribe to see new articles posted by that user and to discuss those articles using something like Rocket.chat Threads.</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>
Open Rocket.Chat communities face a growing problem where new accounts join, flood multiple public channels with spam, and disappear before any moderator can respond. Current moderation is entirely reactive and manual, which is simply too slow for coordinated spam campaigns. This project builds a proactive, human-validated anti-spam system that monitors new users during their first 6 to 10 weeks and responds through a graduated restriction system called Moderation Levels, where every action is proportional, reversible, and requires admin approval before anything permanent happens. The system is built as a Rocket.Chat App on the Apps Engine with a seven gate detection pipeline running entirely in memory for speed. Gates catch exact duplicates via hash matching, polymorphic spam via cosine similarity, cross channel floods, rate flooding, rapid room spread, and phishing link campaigns. A four tier Moderation Level system escalates naturally with repeat behavior, from a friendly DM warning through posting cool downs to a permanent block that only lifts when an admin vouches for the user. The admin control layer includes a live dashboard, a full slash command suite, and AI powered on demand summaries so admins have everything they need to make confident moderation decisions without opening a single log file. Deliverables include a fully functional installable Rocket.Chat App covering the complete detection pipeline, a configurable Moderation Level system, a behavioral scoring engine, enriched admin notifications with message history, AI powered user analysis commands, a paginated admin dashboard built for scale, daily scheduled reporting, and complete documentation covering architecture, configuration, and known limitations.
<p>Proposal for integrating <strong>Out of Office</strong> Settings into the Rocket.Chat codebase.</p>
Rocket.Chat's current message parser uses PeggyJS, which adds bundle size overhead and performance bottlenecks. This project replaces it with a fully hand-written TypeScript recursive-descent parser that produces an identical AST, passes all existing tests, and ships with zero external dependencies. Deliverables include a complete lexer and parser implementation, 100% test suite parity, property-based fuzz testing with fast-check, and a benchmark suite demonstrating measurable performance improvements and reduced bundle size.
Description- This Project is related to a Code tour using Visual Studio Code Tour Extension and the main focus of this project is to make the development and understanding of the codebase Easier for the new contributors who are new to Open-Source and create future Gsoccers. Since Rocket.chat's codebase is huge, many new developers face problem understanding code and they find it overwhelming, It would be great to motivate them to work towards Open-Source community