Application for Google Summer of Code 2025 is now closed. See official Google Summer of Code site.
Rocket.Chat is proud to be a participating mentoring open source organization for Google Summer of Code 2025, helping to usher in a new generation of open source contributors and enthusiasts.
Join out our Google Summer of Code 2025 Team and introduce yourself to the community of 1026+ right now! See all the exciting projects we are working on this year.
For timeline, see Official Google Summer of Code 2025 Timeline for more details.
Almost anyone in the world over 18 years of age who loves coding and wants to explore the incredible world of open source can join us as a GSoC 2025 contributor.
Most exciting news for the 2025 season is focus on ML/AI plus security projects, and the continued support for a small project size with a 90 hours duration; allowing participation from those who can only devote part of their summer to exploring open source.
For details and rules of Google Summer of Code 2025, please see the GSoC 2025 Official Website. For timeline, see Official Google Summer of Code 2025 Timeline for more details.
For general information, please visit our 24 x 7 community channel for Google Summer of Code 2025 : https://open.rocket.chat/channel/gsoc2025
Join our Google Summer of Code 2025 Team today, introduce yourself to the friendly community, and interact with over 1026 like-minded contributors/mentors (as of April 8th, 2025) and meet the team in the 53+ team channels.
If you have ideas and proposals that are not on our idea list, or if a mentor is not available, you can also email to:
But better yet, you can post your idea in the New GSoC 2025 Ideas looking for mentors channel and possibly getting some immediate community feedback or support for your idea.
Interested contributors are also encouraged to interact directly with our team and community on the team channels:
https://open.rocket.chat/channel/gsoc2025/team-channels
As well as on GitHub:
https://github.com/RocketChat/Rocket.Chat
Those who prefers forums can post messages on our GSoC forum channel (although as the leading open source team chat project we prefer you use Rocket.Chat channels above to reach us instantly).
Final Update December 7, 2025 we have completed all the project evaluations and all of the projects in 2025 have passed. Thanks to the enthusiastic mentors (majority of them are returning community contributors) for helping to convert two of the lagging projects into passing ones. Contributors this year, have presented their projects proudly at the Rocket.Chat GSoC 2025 Demo Day - supported by their mentors, fellow contributors, and GSoC alumni. Several mentors have attended the exciting GSoC 2025 Mentors Summit in Munich. During this GSoC 2025 season, we like to shared that we have welcomed over 1500 new contributors to open source from all over the world, with 251 contributors creating 77 Merged PRs, 167 Open PRs, and 339 Issues across our open source repositories.
Update July 18, 2025 we are just about to complete the mid-term evaluations for all of the projects. All of the projects are now deep into coding. Please click the links on the project list below if you'd like to contribute to these open source projects. All mentors have selected to pass their contributors this midterm with two projects that are conditionally passing (has scheduling and communications issue and are looking to improve in the second term). Preparations for our Demo Day 2025, where every contributor gets to present their proud creation to our community, is well under way. Please stay tune for the announcement and date.
Results for 2025 has just been announced on May 8th, 2025. This is a record breaking year for GSoC at Rocket.Chat. Thanks to the enthusiastic support from new contributors and the continued strong support of returning community mentors, Google has graciously selected NINETEEN Rocket.Chat projects.
Mentors of selected projects has start welcoming the contributors officially and bringing them into our community bonding activities immediately; many are already refining their upcoming schedule together since they are already active in our community. An incredible 15 mentors this year are returning GSoC contributors from prior years; we sincerely thank them for their continued support for open source.
| Contributor | Project | Mentors |
|---|---|---|
| Daniel Akinola | Google Summer of Code Community Hub 2025 | Dhruv Jain, Anjaneya Gupta |
| Dhairyashil Shinde | AI Chat Workflows Automation App with multi-step reasoning | Hardik Bhatia, Prisha Gupta |
| Jinendra Jain | Log in via QR code using mobile app | Jean Brito, Diego Sampaio |
| Jinghan Yu | Message Timestamp Date Picker Components | Martin Schoeler |
| Piyush Bhatt | Trip Helper App | Zishan_Ahmad, Yuriko Kikuchi |
| Priyanshu Harshbodhi | Natural Language Bridge to Legacy Email | Aman-Negi, Vipin Chaudhary |
| Thotsem Jajo | Messages Scheduling | Abhinav.Kumar, Ricardo Garim |
| Abir Chakraborty | Server Guide Agent | Gabriel Casals, Jeffrey Yu |
| Ahmed Nasser | Convert endpoints to new Pattern | Guilherme Gazzo, Matheus Cardoso |
| Andro Ranogajec | End to End Encrypted Message Handling for Ruqola | Aaron Ogle, montel laurent |
| Anxhul10 | Implement visual regression testing | Douglas Fabris, Guilherme Gazzo |
| Ayush Jitendra Kumar | AI Enhanced Message Composer Component | Gabriel Engel, Zishan_Ahmad, Martin Schoeler |
| Fang Zihao | Passkey-Based WebAuthn Authentication for Rocket.Chat | Dnouv, Julio Araujo |
| Ishan Mitra | Real Time Message Rendering in Message Composer | Sing Li, Martin Schoeler |
| Jannat Khan | Code Review AI Assistant App for Rocket.Chat | Felipe Scuciatto , Mustafa Hasan Khan, Dnouv |
| Lexie Huang | Private Teams and Private Channels Administration Improvements | John Crisp, Matheus Cardoso |
| Rohit.Bansal | Maestro as Mobile UI Testing Framework | Diego Mello |
| Thanisha Dewangan | Fuselage React Native compatibility (tamagui) | Guilherme Gazzo, juliaf |
| William Liu | Receipts Processor and Reporting App powered by Multi-modal LLMs | Maria Khelli, Aditya.Signh |
In addition, we have several non-selected projects that will continue this term because both the contributors and mentors involved have agreed to collaborate on. We will have more information on these project in a future update.
As of April 8th 2025, on proposal submission deadline we have received a record 472 proposals across our projects (quite evenly distributed, with the more active mentors and/or project communitieies receiving a few more). Mentors are now busy sorting through the proposals and reviewing them for selection. We held the 2025 Rocket.Chat GSoC Alumni Summit on March 27th; a day when 15 former contributors from as far back as 2018 returned to help our new contirbutors with informative and entertaining presentations sharing from their own open source journey. We finally concluded the last weekly session (7th instance) on "How to write an AI Rocket.Chat App in One Hour" workshop to fast-track new contributors. But we are continuing our weekly tea (cha/chai) community time (like an informal weekly office hour) as it is well received/supported by our growing community. Currently we have over 1026 contributors and mentors active in our 53 GSoC 2025 team channels. Checkout our GSoC 2025 Contributors Leaderboard where over 249 active contributors have contributed 59 Merged PRs, 152 Open PRs, and 299 Issues to our open source projects.
As of March 26th 2025, propsoal submission has opened and many of our mentors are giving reviews of submitted draft proposals upon request, and some of them are video meeting contributors to verify identity. We are holding an Alumni Summit where former contributors from as far back as 2018 are returning to help our new contirbutors this season. We are still running the weekly "How to write an AI Rocket.Chat App in One Hour" workshop to help to ramp up new contributors. As well, our weekly tea (cha/chai) time continues to be well supported by our growing community. Currently we have over 825 contributors and mentors active in our 52 GSoC 2025 team channels. Checkout our GSoC 2025 Contributors Leaderboard where over 214 active contributors have contributed 57 Merged PRs, 137 Open PRs, and 281 Issues. to our open source projects.
As of March 18th 2025, mentors are helping contributors to explore project details in preparation for proposal crafting. We are iteratively refining the weekly "How to write an AI Rocket.Chat App in One Hour" workshop to focus on the art of prompt-engineering. As well, our weekly tea (cha/chai) time continues to be well supported by our growing community. Currently we have over 710 contributors and mentors active in our 52 GSoC 2025 team channels. Checkout our GSoC 2025 Contributors Leaderboard where over 186 active contributors have contributed 51 Merged PRs, 119 Open PRs, and 270 Issues to our open source projects.
As of March 10th 2025, mentors are starting to help contributors to work on and refine the project ideas. We are running a weekly "How to write an AI Rocket.Chat App in One Hour" workshop to help everyone to ramp-up with our code. As well, we are conducting a weekly tea (cha/chai) time to encourage networking between community members. Currently we have over 600 contributors and mentors active in our 43 GSoC 2025 team channels. Checkout our GSoC 2025 Contributors Leaderboard where over 160 active contributors have contributed 39 Merged PRs, 104 Open PRs, and 256 Issues to our open source projects.
As of March 3rd 2025, we are developing project ideas with our engaged community members. Currently we have over 495 contributors and mentors active in our 40 GSoC 2025 team channels. Checkout our GSoC 2025 Contributors Leaderboard where over 131 active contributors have contributed 25 Merged PRs, 104 Open PRs, and 233 issues to our open source projects.
As of Feb 24th 2025, we are evolving project ideas with our engaged community members as well as returning community mentors, and guest mentors from other friendly open source projects. We are super grateful to have 12 contributors from the 2024 season returning to help us as 2025 mentors; and to bring on some exciting new project ideas. Currently we have over 380 contributors and mentors active in our 30 GSoC 2025 team channels. Checkout our GSoC 2025 Contributors Leaderboard where over 100 active contributors have contributed 14 Merged PRs, 88 Open PRs, and 206 issues to our open source projects.
As of Feb 9th 2025, we are intensely discussing project ideas with our returning community mentors, and guest mentors from other friendly open source projects. This year, we are thankful to already have 9 contributors from the 2024 season returning to help us as 2025 mentors to bring on some exciting projects. Currently we have over 210 contributors and mentors active in our 30 GSoC 2025 team channels.
As of January 27th 2025 checkout our GSoC 2025 Contributors Leaderboard, to see the amazing contributions by our GSoC 2025 community: we already have over 100 contributors and mentors ready to join us for this season, active in our team channels.
👥 Mentor(s): Bruno Zaffari, Matheus Cardoso, Guilherme Romanini
📢 Communication Channel: idea-integration-of-purecpp-open-source-rag-pipeline
💬 Description:
This project integrates the world's fastest open source RAG pipeline within a Rocket.Chat App, powering countless future AI App possibilities.
The project must include the scheduled ingestion of all chat history in a channel as input to the pipeline's vectorDB component.
💪 Desired Skills:
- interest in high impact AI projects
- Rocket.Chat App (Typescript) development
- understanding of RAG based agentic systems
- ideally rust and/or C++ development experience
🎯 Goals/Deliverables:
- Purecpp integrated as RAG pipeline foundation; upon which any Rocket.Chat users can build new AI apps.
⏳ Project Duration: 90 hours (Small)
📈 Difficulty: Intermediate
👥 Mentor(s): Julia Foresti, Guilherme Gazzo
📢 Communication Channel: Fuselage React Native compatibility (tamagui)
💬 Description: There are many secondary goals for this project, but the main goal is to make the fuselage compatible with React Native. But also, we have figthing against the bundle size of the fuselage, and of course performance which is always a concern. Using the tamagui as a base, we can components that are compatible with both platforms, and in a efficient way.
💪 Desired Skills:
- React/React Native
- Tamagui
- Rocket.Chat Fuselage
🎯 Goals/Deliverables:
- New package containing all the fuselage tokens
- New package containing subset of the fuselage components
- Size comparison between the two implementations
- Expected to reduce bundle size, but also to increase performance
- React Native compatibility
⏳ Project Duration: 175 hours (Medium)
📈 Difficulty: Intermediate
👥 Mentor(s): Douglas Fabris, Guilherme Gazzo
📢 Communication Channel: Implement visual regression testing
💬 Description:
Visual regression testing is a technique that allows us to test the visual appearance of our components. It is important to ensure that our components look and behave as expected across different devices and screen sizes.
💪 Desired Skills:
- React
- CI/CD
- Loki - https://loki.js.org
🎯 Goals/Deliverables:
- Implement visual regression pipeline for Fuselage components
- Implement visual regression pipeline for Rocket.Chat Screens
⏳ Project Duration: 175 hours (Medium)
📈 Difficulty: Intermediate
👥 Mentor(s): Matheus Cardoso, Guilherme Gazzo (Co-mentor)
📢 Communication Channel: Convert endpoints to new Pattern
💬 Description:
We have a new pattern for develop endpoints. This pattern is more organized and easier to maintain, improves the type definitions and also allow us to generate the OpenAPI documentation automatically.
💪 Desired Skills:
- Node.js
- TypeScript
- Rocket.Chat Core
🎯 Goals/Deliverables:
- Convert all the current documented endpoints to the new pattern
⏳ Project Duration: 175 hours (Medium)
📈 Difficulty: Intermediate
👥 Mentor(s): Diego Sampaio
📢 Communication Channel: idea-Log-in-via-QR-code-using-mobile-app
💬 Description: The idea is to provide an easy way to log in to Rocket.Chat on desktop if you're already logged in on your mobile app. Just pick up your mobile phone and point to the screen to be logged in.
💪 Desired Skills:
- Mobile app development (React Native/TypeScript)
- Backend developement (NodeJS/Typescript)
- Frontend developemtn (React/Typescript)
🎯 Goals/Deliverables:
- Display a qr-code on Rocket.Chat login scren on the web
- Scan the qr-code on Rocket.Chat's mobile app
⏳ Project Duration: 90 hours (Small)
📈 Difficulty: Intermediate/Advanced
👥 Mentor(s): Diego Mello
📢 Communication Channel: idea-Maestro-as-Mobile-UI-Testing-Framework
💬 Description:
Migrate Rocket.Chat Mobile app's UI testing framework from Detox to Maestro. Maestro is a modern mobile UI testing framework that offers several advantages over Detox:
- More reliable test execution with fewer flaky tests
- Better debugging capabilities with detailed test reports and video recordings
- Simpler test writing syntax using YAML
- Cross-platform support for both iOS and Android
- Active community and development
This migration will help improve the reliability and maintainability of our mobile app testing suite while reducing the time spent debugging flaky tests.
💪 Desired Skills:
- Experience with mobile app testing
- Knowledge of React Native
- Familiarity with YAML syntax
- Understanding of CI/CD concepts
- Basic knowledge of iOS and Android development
- Good problem-solving skills
- Experience with Git and GitHub
🎯 Goals/Deliverables:
- Setting up Maestro testing infrastructure in the mobile repo
- Converting existing Detox tests to Maestro format
- Creating new tests to improve coverage
- Implementing CI/CD pipeline integration on Github Actions
- Documentation of testing practices and guidelines
⏳ Project Duration: 175 hours (Medium)
📈 Difficulty: Intermediate
👥 Mentor(s): Prisha Gupta
📢 Communication Channel: idea-Hugging-Face-Management-Agent
💬 **Description: **
Managing models, datasets, and Spaces on Hugging Face can be challenging, especially within teams. This Rocket.Chat app will integrate with Hugging Face Hub APIs to allow users to list, update, and monitor HF resources directly from Rocket.Chat.
The App will enable:
- Viewing available models, datasets, and Spaces
- Updating model metadata (e.g., descriptions, tags)
- Managing repository settings (private/public, visibility)
- Get updates on Space build status
- Manage PRs
- Real-time notifications for updates on Repos
By integrating management functionalities directly into Rocket.Chat, teams can streamline collaboration and reduce the need to switch between multiple platforms.
Bonus nice-to-haves are any additional innovative features that leverage generative AI (LLMs) to facilitate in-channel interactions with Hugging Face.
💪 Desired Skills:
- Rocket.Chat Apps Engine and TypeScript
- Hugging Face Hub API
- API authentication and security
🎯 Goals/Deliverables:
A Rocket.Chat App that allows users to manage Hugging Face repositories, models, datasets, and Spaces efficiently through chat commands. It will provide an intuitive interface for listing, updating, and tracking resources with real-time notifications.
⏳ Project Duration: 90 hours (Small)
👥 Mentor(s): Martin Schoeler
📢 Communication Channel: idea-Real-Time-Message-Rendering-in-Message-Composer
💬 Description:
Any rich text content is rendered in its text form - for example emojis will be rendered as :grin: instead of 😬, Bold markdown highlight will be rendered as **Bold** while composing a message using Rocket.Chat.
As we are looking to migrate from the emojiOne library to native emojis, one of the requirements of this project is that default emojis (those that are not custom emojis, added by an admin) are rendered as native emojis (the actual emoji character like 😬 instead of the ones from the emojiOne lib that is being used on Rocket.Chat)
We will also ask for a proof of concept of how a migration from emojiOne to native would work in the message renderer, basically an experimentation to understand what challenges the migration encompasses and the possible ways we could do it (The migration does not need to be delivered, just the study on it).
This project should upgrade the composer to support real-time in-message rendering of rich content while editing.
Most of the complexity of this project comes from properly converting the existing Message Composer to display rich content, while integrating with ALL existing functionality of the Composer component
💪 Desired Skills:
- Understanding of the composer component
- Understanding of Rocket.Chat's message rendering pipeline
- Advanced Typescript
- Good understandng of DOM elements and it's intricacies
- Desire to work on high impact projects (benefiting every single RC user)
🎯 Goals/Deliverables:
Having a working MVP of real time message rendering in the message composer
⏳ Project Duration: 175 hours (Medium)
📈 Difficulty: Intermediate/Advanced
👥 Mentor(s): Aman Negi
📢 Communication Channel: idea-Frequently-Asked-Questions-Detection-Assistant
💬 Description:
New contributors to open source often ask very similar questions. Answering similar questions repeatedly can be a tiring and tedious task for maintainers (or mentors).
This LLM powered assistant will solve the problem by:
- monitoring Github webhooks or a set of specified Rocket.Chat channels for questions
- determine if the incoming question is similar to one in a set of configured FAQ
- if a match is found, do one of the following configurable action:
- notify one or more configured users of the incoming question
- notify one or more configured users of the incoming question via DM, providing a summarized answer as a suggestion
- notify one or more configured downstream LLM driven agents
- same as ii., but provide the capability for the notified users to "approve" the automated reply of the summarized answer; some final editing before reply should be possible
- answer the incoming question with a summarized answer; then notify one of the configured users in DM
Considerations:
- the assistant must be designed to be 100% fail safe; it should never answer unrelated question or answer with hallucinations (nor answer with harmful content)
- the assistant should be designed to be functional in all FAQ/templated answers/Quick-reply situations, increasing its utility
- this project will involve very clever prompting and optimized prompt chaining (instead of tedious volumous Typescript code)
💪 Desired Skills:
- Rocket.Chat Apps Development (Typescript)
- Github/Gitlab webhooks development
- Advanced prompt engineering
- Passion for creating LLM driven assistive applications
🎯 Goals/Deliverables:
A useful assistant in many automated public group chat/forum context with configurable abilities to help in handling frequently asked questions.
⏳ Project Duration: 90 hours (Small)
📈 Difficulty: Easy/Intermediate
👥 Mentor(s): Aditya Singh
📢 Communication Channel: idea-Server-Setup-Agent
💬 Description:
As an administrator setting up a production Rocket.Chat server, one is typically required to create user accounts, create and assign roles, create default channels, possibly starting threads, starting discussions and populating with initial messages.
These sort of tedious tasks are also often required in the testing and quality assurrance of Rocket.Chat or Rocket.Chat related apps/projects. Furthermore, they are also required in many demo and training situations.
This project is a Setup Agent App that automates all of the above based on either a series of slash commands or by loading an automated script.
The script should conform to an easy to learn, intuitive and simple DSL (Domain Specific Langauge) of the contributor's design.
The language needs to incorporate basic variables, and be able to handle simple counted loops. Conditionals and conditional loops are nice to have.
The agent should be tested to handle very large scripts that may involve the creation of thousands of objects and messages.
DSL will be checked for LLM-generation suitability; although AI generation of the script is outside the scope of this project.
💪 Desired Skills:
- Interest in DSL and AST (Domain Specific Language and Abstract Syntax Trees)
- Rocket.Chat Apps development (Typescript)
- Familiarity with Rocket.Chat's REST APIs
🎯 Goals/Deliverables:
A Rocket.Chat App "agent" that can help setup servers (or for QA or demo or training) by executing an automated script.
⏳ Project Duration: 90 hours (Small)
📈 Difficulty: Easy/Intermediate
👥 Mentor(s): Vipin Chaudhary
📢 Communication Channel: idea-Natural-Language-Bridge-to-Legacy-Email
💬 Description:
Messaging systems such as Rocket.Chat were supposed to be replacing legacy email for communications and collaboration since the early nineteen nineties. But even after three decades of evolution and struggle, half the world still hangs onto legacy email worldwide despite most have grown up with SMS and asynchronous messaging.
This project bridges the great divide most naturally with a Rocket.Chat App that responds to natural language instructions and brings the legacy email world right into every Rocket.Chat conversation for every user.
The app will respond to command such as:
/xuebot summarize this thread and send it as email to my boss who refuses to use chat
OR:
/xuebot post in the channel for everyone the budget for 2025 email pdf received between 11/1/2024 and 12/24/2024
Details:
You will be using the function calling/tools capabilities of modern LLMs, plus some extremely clever prompting, as well as some hardcore Typescript coding to realize this app.
The agent must be able to perform the following reliably as a minimum:
- summarize thread/channel/discussion and send as email to specified recipient(s)
- search emails by date range and keywords and present in-channel as message and/or extract attachment and upload to channel
- report on daily email statistics
- secured per-user email connection over TLS
💪 Desired Skills:
- Rocket.Chat Apps (Typescript) Development
- Advanced prompt engineering skills
- Familiarity with Gmail and other mail provider APIs
- Familiarity with LLM function calling / tools capabilities
Note: This project is inspired by the prior work of our 2025 contributor ZilongXue -> claude-post
🎯 Goals/Deliverables:
A natural language bridge to legacy email system that every single Rocket.Chat user can use.
⏳ Project Duration: 90 hours (short)
📈 Difficulty: Easy/Intermediate
👥 Mentor(s): Gabriel Engel, Ashutosh Singh Chauhan
📢 Communication Channel: idea-AI-Enhanced-Message-Composer-Component
💬 Description:
This high impact project upgrades the Rocket.Chat message composer component to be fully AI powered. It empowers Rocket.Chat users to leverage all that modern AI technologies can offer while they are composing their message. These facilities, in 2025, may include:
- grammar corrections
- context sensitive spelling correction
- re-wording/re-phrasing for clarity / jargon-match
- tone Adjustments
- tone matching with messages in channel
- language translation
- message emojification
- message summarization and/or message verbose-tization
- and many more to come in 2025/2026
Features:
- Inline Suggestions: Highlight areas needing improvement (grammar, clarity). Click to apply changes.
- Tone Selector: Add a dropdown to adjust the tone (e.g., formal or casual), and apply the change automatically.
- Message Preview: Allow users to toggle between the original and enhanced/translate message.
- Hinting: Display suggestions as subtle highlights or underlines, ensuring a smooth, non-intrusive experience.
AI Integration Consideration
- This project / upgraded component will not include the LLM access mechanism. It is a pure UI component project. However, care must be taken to design it in such a way that it can be totally compatible with all kinds of LLM access -- client-side, server-side on-premises, server-side third party, cloud, and so on.
💪 Desired Skills:
- Advanced understanding of Rocket.Chat Composer Component
- Advanced understanding of UI/Kit and Fuselage design philosophy
- Advanced JavaScript/TypeScript**
- Like coding, love design
- Love to work in maximum impact projects
🎯 Goals/Deliverables:
- AI Enhancements in the Message Composer.
- UI/UX Polish for a seamless experience.
- Cross-Platform Support across web, mobile, and electron clients.
⏳ Project Duration: 175 hours (Medium)
📈 Difficulty: Advanced
👥 Mentor(s): Gustavo Bauer
📢 Communication Channel: idea-Project-Management-via-Asana-Integration
💬 Description:
Integrate Asana with Rocket.Chat to boost team collaboration. Instead of duplicating Asana’s complex task creation, focus on contextual notifications and quick access to task updates. Rocket.Chat project management users can stay informed collaborating within Rocket.Chat, while complex workflows that are better handled by Asana's rich UI remains in Asana. The transition to and from Asana should be seamless.
*Details:**
-
Setup & Authentication
- Implement OAuth and channel configuration.
-
Contextual Notifications
- Real-time alerts for task updates and deadlines.
-
Quick Commands & Summaries
- Slash commands for task details and simple updates.
-
Deep Linking
- Direct links from notifications to tasks in Asana.
-
Optional Activity Feed
- A mini feed for recent Asana activity in channels.
💪 Desired Skills:
- Experience with Rocket.Chat Apps Engine (TypeScript)
- OAuth and REST API experience
🎯 Goals/Deliverables:
- An app delivering userful integration workflows for project management teams collaborating on Rocket.Chat. Including minimally contextual task notifications and summaries.
- For workflows that are better handled with Asana's rich UI, seamless redirect to Asana and seamless return to the Rocket.Chat collaboration context (channel / thread / discussions).
⏳ Project Duration: 90 hours (Small)
📈 Difficulty: Easy/Intermediate
👥 Mentor(s): Julio Araujo, Dnouv
📢 Communication Channel: idea-Passkey-Based-WebAuthn-Authentication-for-Rocket-Chat
💬 Description:
The WebAuthn standard, now widely available on modern Android and iOS devices, enable convenient passwordless authentication satisfying 2FA (biometrics and "have device"). Frequently this involves scanning a QR code followed by unintrusive biometrics such as FaceID on iOS. This project aims to integrate WebAuthn in Rocket.Chat authentication to offer a passwordless, secure login experience. The implementation needs to be aligned with Rocket.Chat’s existing authentication system while ensuring backward compatibility with all existing login methods.
💪 Desired Skills:
- Node.js
- React.js
- WebAuthn API
- MongoDB
- Keen interest in cybersecurity
- Authentication & Security Best Practices
🎯 Goals/Deliverables:
- Implement passkey-based authentication using WebAuthn
- Support QR Codes and Bluetooth hybrid transport
- Modify authentication modules to support passkey registration and login
- Update the frontend for seamless passkey interactions
- Ensure secure storage of public keys in the user database
- Conduct extensive testing across different devices and browsers
- Provide detailed documentation and promote community awareness
⏳ Project Duration: 175 hours (Medium)
👥 Mentor(s): Gabriel Casals, Jeffery Yu
📢 Communication Channel: idea-Server-Guide-AI-Agent
💬 Description:
As a new user joins a Rocket.Chat server there is not much guidance on what to do or where to go.
Right now, the only mechanism is a passive landing page that may display resources for each grouping (persona) of users.
For example, on the open.rocket.chat server, a user may be:
- A Rocket.Chat server administrators looking to connect with other administrators
- An end-user of Rocket.Chat looking to resolve problem or receive support information.
- A new community contributor looking for Google Summer of Code program information
- others
Each one of these personas demand different style of conversation and would need to know about/join different sets of channels and other resources.
Details:
The project aims to replace the boring "easy to miss, difficult to understand" landing page, with an AI guide agent. This agent should start a conversation with the new user and then using modern LLM's discrimination/classification ability to positively identify the persona (users grouping/ sub-communities on a server) that the new user belongs to. The agent should continue to tune the conversation to the lingo-preferred of that persona, and finally guide the user to all the resources and channels available for that persona. This agent must be configurable (via Rocket.Chat Apps configuration) to handle any arbitrary persona and related resources set.
Recommended Approach:
The agent should be able to add (and join) the appropriate channel for the new user, after confirming the action with the new user. A default/catch-all persona should be used to precisely scoped the project and ensure the LLM can converge onto a useful result. Since direct user input will be passed to the LLM for analysis, the agent MUST make sure that there is no prompt-injection possibility. Safety of server operation must be taken into account as this agent has ability to change the state of the server permanently.
💪 Desired Skills:
- Experience with Natural Language Processing (NLP) systems
- Rocket.Chat Apps Engine (TypeScript)
- Rocket.Chat messaging APIs
- Advanced prompt engineering skills
- Experience with tools/function-calling capabilities of modern LLMs
- Understanding of how to implement "safety first" when creating AI apps that may permanently change the state of a production system
🎯 Goals/Deliverables:
An AI Agent that will help to onboard new users.
⏳ Project Duration: 175 hours (Medium)
👥 Mentor(s): Felipe Scuciatto, Dnouv
📢 Communication Channel: idea-Code-Review-Agent-for-Open-Source-projects
💬 Description:
Finding reviewers for contributors' PRs on open source projects can often be difficult. Slow response can possibly result in the loss of a communtiy contributor.
This assistant will monitor new pull requests (either via incoming github integration messages in a channel or directly via github events) and identify the most suitable maintainer to review the PR based on a statistical scoring system. It will follow up with a friendly yet frequent reminder (“nagging”) mechanism to ensure timely reviews.
Additionally, the bot will leverage code-specialized LLMs to perform an initial review, automatically filtering out minor improvements before they reach human reviewers.
This will streamline the review process, reduce unnecessary delays, and ensure that only meaningful changes require manual attention.
💪 Desired Skills:
- Rocket.Chat Apps Engine and TypeScript
- Knowledge of general statistics
- Prompt engineering with code-specialized LLMs
- Ideally GitHub API
🎯 Goals/Deliverables:
- A Rocket.Chat App that will interact with open source maintainers and monitors open pull requests, assigns the most suitable reviewer based on past reviews, persistently reminds them until the review is completed, and leverages AI for initial code assessments.
⏳ Project Duration: 175 hours (Medium)
👥 Mentor(s): Zishan Ahmad
📢 Communication Channel: idea-Embedded-Chat-2025
💬 Description:
Improvement to the EmbeddedChat project this year includes:
- Upgrading the current API and authentication packages to the latest Rocket.Chat SDKs, such as
ddp-client. Other components will be aligned with updated abstractions and SDKs. For reference, see this link. These packages will be managed internally by Rocket.Chat moving forward. - Ensuring compatibility with the latest Rocket.Chat API versions to keep the integration updated and error-free.
- Upgrading to the latest stable versions of React, Node.js, and other libraries for long-term maintainability.
- Making the EmbeddedChat fully mobile-responsive for a seamless experience across all devices.
- Improving the UI and adding more customization options to enhance the user experience.
- Aligning the design and features of the EmbeddedChat Web Client with the React Native Client, which is already built but needs updates for complete consistency.
- Welcoming any other creative ideas that improve the project.
💪 Desired Skills:
- Strong understanding of Rocket.Chat APIs and SDKs
- Love for coding and UI design
- React.Js
🎯 Goals/Deliverables:
- Integration of the latest Rocket.Chat SDK packages
- Fully mobile-responsive EmbeddedChat
- Improved UI with more customization options
- Native app development
- Increased stability and maintainability
⏳ Project Duration: 90 hours (Small)
📈 Difficulty: Intermediate
👥 Mentor(s): Zishan Ahmad, Maria Khelli
📢 Communication Channel: idea-Smart-Market-Bot-for-Rocket-Chat
💬 Description:
An interactive Rocket.Chat app that fetches real-time prices for cryptocurrencies, stocks, and forex using open-source APIs. It provides insights, summaries, and predictions while ensuring fail-proof accuracy—if the bot is unsure, it withholds speculative responses.
Key Features:
- Live Price Updates – Fetch real-time data for crypto, stocks, and forex using free and open APIs.
- Market Alerts – Get notifications on significant price movements, trends, or unusual activities.
- Smart Insights & Summaries – Summarize asset trends, news, and market behavior.
- Predictive Analysis – Provide data-backed forecasts and trends (without unreliable speculations).
- Fail-Safe AI Responses – Ensures that if the LLM is uncertain, it explicitly avoids misinformation.
- Custom Asset Watchlists – Users can create personalized lists to track selected assets.
- Interactive Commands – Users can request price comparisons, asset history, and more via Rocket.Chat commands.
💪 Desired Skills:
- JavaScript / TypeScript
- Rocket.Chat App Development
- API Integration (REST/WebSockets)
🎯 Goals/Deliverables:
- Functional Rocket.Chat app with real-time asset tracking.
- Market alert system for price fluctuations and significant events.
- Intelligent summarization and prediction module.
- Fail-proof mechanism to avoid incorrect or misleading AI responses.
⏳ Project Duration: 90 hours (Small)
📈 Difficulty: Easy/Intermediate
👥 Mentor(s): Maria Khelli, Sing Li
📢 Communication Channel: idea-Receipts-Processor-and-Reporting-App-powered-by-Multi-modal-LLMs
💬 Description:
While attending a busy conferences or event, the need to keep track of receipts and then having to adding them up manually to fill in expense reports is a very common and tedious problem. This project is a Rocket.Chat app that completely automate the process.
Details:
-
The user will be able to take pictures of restaurant receipts on their phone and upload it into a specific channel (representing a single event, duration of time, or trip, and so on...).
-
Upon request, the app should read and sum all the receipts producing a detailed report. Ideally, the input format and image size should be flexible and the report format should be customizable via templates.
-
The app should never produce erraneous result under any circumstances, it should be able to recognize its limitation and decline to complete the task instead of giving potentially erraneous output.
💪 Desired Skills:
- Rocket.Chat Apps Engine (TypeScript)
- Intermediate prompt engineering Skills
- Experience with image reasoning capabilities of available open source multi-modal LLMs
🎯 Goals/Deliverables:
A working Rocket.Chat app that will scan and sum all the restaurant receipts uploaded to a specific channel.
⏳ Project Duration: 175 hours (Medium)
📈 Difficulty: Easy/Intermediate
👥 Mentor(s): Dnouv, Jeffery Yu
📢 Communication Channel: idea-Perfect-AI-Docs-Assistant-App
💬 Description:
This project aims to build a Rocket.Chat App that provides a natural language interface for querying Rocket.Chat Docs (docs.rocket.chat). Instead of manually searching for answers, users can simply ask the app, and it will return a perfect, relevant response extracted and summarized from the documentation.
For the very small dataset that we are working with, we aim to tune and optimize the well-known RAG agentic workflow for our specific purpose; ideally producing an optimized and perfect Ask the Docs Assistant for this subset of the Rocket.Chat documentation.
Consider possibly:
- Retrieval-Augmented Generation (RAG) agentic workflow the AI/ML design pattern we're all familiar with
- Local embedding model needs to run in memory/CPU for maximum efficiency
- In-memory vector store run in the client with no persistence, subject of further optimization (chunking, match-selection, metadata) to tune for perfect results
- Context-aware follow-up responses not required but nice to have
💪 Desired Skills:
- Rocket.Chat Apps Engine (TypeScript)
- Modern agentic workflows (including classic-RAG)
- Web Storage & IndexedDB (for caching, if needed)
- Intermediate prompt engineering
- Keen interest in contemporary applications of LLMs
🎯 Goals/Deliverables:
- generation/creation of a perfect validation dataset, consisting of (1500, 5000, 10000) queriies and corresponding answer, that will be used in the benchmark/validation of the final assistant
- a fine-tuned compact LLM to be used in the last stage of the RAG agentic workflow (fine-tuned minimally on the dataset above)
- the perfect "ask the docs assistant" for our small dataset, running mostly on the client without heavy compute overhead, ideally scoring 90% plus on the benchmark
(we like to thank our early community for helping us to fine-tune this set of deliverables)
⏳ Project Duration: 90 hours (Small)
📈 Difficulty: Intermediate/Advanced
👥 Mentor(s): Anjaneya Gupta
📢 Communication Channel: idea-Google-Summer-of-Code-Community-Hub-2025
💬 Description:
Continuing our work on a "full stack component framework" in which a scalable website can be created by non-technical users using drag-and-drop components that not only include UI and client-side logic, but also pre-scaled server-side behaviors (or serverless impl).
The ultimate use-case we have been working on is the community hub for Rocket.Chat Google Summer of Code. It will link all the despearate servers into one easy to customize and maintain uniform scalable web app (comprise of a set of full-stack components).
This year's work will include:
- rebasing of the existing platform and components on Svelte 5
- moving the WYSIWYG "Syntax Sweetening" work done last year to this new platform
- implementation of auth via OIDC
- migrate over the event poster component (used for our demo day)
- migrate over the video-meet-your-mentor component
- implement the gsoc leaderboard component
- implement embeddedchat component
💪 Desired Skills:
- Advanced Typescript
- Svelte 5
- AST concepts
🎯 Goals/Deliverables:
A functional community hub website that we will use for 2026; showcasing the platform and new components.
⏳ Project Duration: 90 hours (Small)
📈 Difficulty: Easy/Intermediate
👥 Mentor(s): Hardik Bhatia
📢 Communication Channel: idea-AI-Chat-Workflows-Automation-App-with-multi-step-reasoning
💬 Description:
Build a Rocket.Chat app that will monitor messages in specified channels (possibly sent by specified individuals) and then, perform additional messaging operations based on those messages. Command should be issued in simple English. Bonus point for more than 2 steps in the reasoning.
Some examples:
"whenever @sing.li posts any welcome messages in #gsoc2025, immediately DM them with a thank-you note"
"whenever a message is posted that contains a four letter word beginning with letter F, delete that message immediately"
"if my Alexa messages me with asking where I am, DM her sorry I will be late"
Recommended approach:
- Create a streamlined manual workflow for users to make/edit/delete actions (trigger, process, response) (~40 hours)
- Leverage the latest open source specialized multi-step reasoning LLMs to automate this workflow further such that these commands can be issued in plain English. (~10 hours)
- Make intelligent use of tools/function-calling and structured inference to achieve complete automation (~20 hours)
- Be sure to have built in safety mechanisms to offset erraneous output and/or hallucinations (~10 hours)
💪 Desired Skills:
- Rocket.Chat Apps Engine (TypeScript)
- Rocket.Chat messaging APIs
- Advanced prompt engineering skills
- Experience working with multi-step reasoning LLMs
- Experience with tools/function-calling capabilities of modern LLMs
- Expereince with code generation and code completion LLMs
- Understanding of how to implement "safety first" when creating AI apps that may permenantly change the state of a production system
🎯 Goals/Deliverables:
Rocket.Chat App for generating functional automated chat workflows using LLMs.
⏳ Project Duration: 90 hours (Small)
📈 Difficulty: Intermediate/Advanced
👥 Mentor(s): Martin Schoeler
📢 Communication Channel: idea-Message-Timestamp-Date-Picker-Components
💬 Description:
Currently Rocket.Chat has a feature that allows users to send timestamps on messages, but not in an intuitive way. To send a timestamp you need to manually write it down with the correct date code. For example <t:1732557600:t>.
This feature was added for Rocket.Chat Apps engine, but users can benefit from this too. The objective of this project is to create a new MessageToolBar item that displays a calendar and let the users select the date and time they would like to share, both in the desktop app and mobile apps.
Some examples of the usage of the timestamp feature (you can test them today on the open.rocket.chat server!)
Pattern: <t:{timestamp}:?{format}>
- {timestamp} is a Unix timestamp
- {format} is an optional parameter that can be used to customize the date and time format.
Formats
| Format | Description | Example |
|---|---|---|
t |
Short time | 12:00 AM |
T |
Long time | 12:00:00 AM |
d |
Short date | 12/31/2020 |
D |
Long date | Thursday, December 31, 2020 |
f |
Full date and time | Thursday, December 31, 2020 12:00 AM |
F |
Full date and time (long) | Thursday, December 31, 2020 12:00:00 AM |
R |
Relative time | 1 year ago |
The creation of tests for the new component is also expected, both end to end and unit if applicable.
💪 Desired Skills: React, Typescript, React Native
🎯 Goals/Deliverables: A new component on the MessageToolBar that allows users to add timestamps to their messages, both in desktop and mobile.
⏳ Project Duration: 90 hours. (Small)
📈 Difficulty: Easy/Intermediate
👥 Mentor(s): Ricardo Swarovsky
📢 Communication Channel: idea-Message-Scheduling
💬 Description:
Add a native Rocket.Chat feature that lets users schedule messages to be sent later, directly integrated with the current send button. Since we serve users across multiple time zones, this feature will make it easier to schedule messages for the right time, no matter where they are.
💪 Desired Skills:
- Awareness of Rocket.Chat server and client codebase (NodeJS and React)
🎯 Goals/Deliverables:
- A Rocket.Chat feature that will allow users to schedule messages to be sent in the future
⏳ Project Duration: 90 hours (Small)
📈 Difficulty: Advanced
👥 Mentor(s): Sing Li, Ashutosh Singh Chauhan
📢 Communication Channel: idea-Client-side-AI-Support
💬 Description:
This project adds applicaton API access to LLMs running in-browser, on-device and distributes the AI compute load from the server - allowing AI applications to scale massively.
Not all client devices are capable of handling LLM loads, the deployment flow must detect and behave accordingly.
Details:
Modify current server deployment flows to install client-side LLMs as an option.
Extend Apps Engine to support client-side logic; (optional) loading of the LLMs; and API access to the in-browser/on-device LLMs.
Essential background:
-
recent high performance lightweight models became available (Llama 3.2, Intern LM 2, Phi 3 mini, Qwen 2.5, Smol LM, Gemma 2b, DeepSeek distlled, and so on) requiring ONLY about 2GB additional memory and reasonable compute load
-
breakthrough webgpu and webllm technology matured; and in-browser inference and LLM API access (WASM'ed Python code on top of HTML5/WebGPU standard) is a stable working reality
-
big-tech vendors (phone and OS vendors such as Apple, Samsung, Microsoft, and so on ... ) are rapidly bringing client-side CLOSED SOURCE inference capability to their own applications with no roadmap in 2025 for general access
💪 Desired Skills:
- Rocket.Chat Deployment Flows (DevOps)
- Advanced Typescript
- Awareness of WASM and webllm
- Python magician
- Good system design mindset
🎯 Goals/Deliverables:
Empower the development of scalable open source AI applications running in-browser for all Rocket.Chat users
⏳ Project Duration: 175 hours (Medium)
📈 Difficulty: Advanced
👥 Mentor(s): Montel Laurant, Aaron Ogle
📢 Communication Channel: idea-E2E-messages-handling-for-Ruqola
💬 Description:
Add end to end encrypted message feature to the Ruqola client from KDE. Ruqola is the de-facto standard Rocket.Chat client running on KDE. This project will be co-mentored by an expert mentor from the KDE progject.
Details:
- some UI elements to handle E2E encrypted messages is already in place
- careful consideration for key management is essential to a successful implementation
- how does the user get the key? what happens when he/she loses the key?
- what UI is needed to support re-generation of key?
- how does one display a channel with messages that may be encrypted by different keys?
- see End to End Encryption Specification
💪 Desired Skills:
- Rocket.Chat API programming (REST and DDP)
- Solid experience with C++ programming
- Experience with large and complex C++ projects
- Working experience with KDE on Linux (such as kubuntu)
- Ideally already user of Ruqola
🎯 Goals/Deliverables:
Add support for E2E Encrypted messages in Ruqola.
⏳ Project Duration: 175 hours (Medium)
📈 Difficulty: Advanced
👥 Mentor(s): Abhinav kumar
📢 Communication Channel: idea-AI-Google-Forms-Typeform-Survey-Integration-App
💬 Description:
This app integrates any one of the popular survey tools (Google Forms, Typeform etc.) into Rocket.Chat to allow teams to create, distribute, and analyze quick polls or feedback forms without leaving the chat. Users can launch surveys, receive immediate notifications when responses are submitted, and have summary reports automatically posted to designated channels.
Using natural language to create forms would be a huge plus for the project. Example - "Create a form to accept registration for the Annual Tech Conference. It should collect full name, email address, company, and dietary restrictions. Validate the email field, send me a notification on each new registration, and post a summary report in the #conference-registrations channel."
Key Features:
- Slash Commands: Launch new surveys or share form links directly from Rocket.Chat.
- Inline Notifications: Receive real-time alerts when survey responses are submitted.
- Automated Reporting: Generate and post periodic summary reports in designated channels.
Use Case:
Enables teams to capture immediate feedback and conduct internal polls seamlessly within Rocket.Chat, enhancing decision-making and team engagement.
💪 Desired Skills:
- Proficiency with Rocket.Chat Apps Engine (TypeScript)
- Experience with REST APIs and third‑party service integrations
- Familiarity with survey platforms (Google Forms, Typeform) and their APIs
🎯 Goals/Deliverables:
- Develop a Rocket.Chat App that connects to Google Forms/Typeform.
- Implement slash commands for survey creation and sharing.
- Integrate real-time notifications for survey responses.
- Automate the generation and posting of summary reports.
⏳ Project Duration: 90 hours (Small)
📈 Difficulty: Easy/Intermediate
👥 Mentor(s): Yuriko Kikuchi, Sing Li
📢 Communication Channel: idea-Trip-Helper-App
💬 Description:
This LLM-powered app will suggest interesting events and happenings for users during their trip.
Details:
- While on trip in a foreign place, a user can take a photo of his surroundings and upload it to a channel
- The app will first process the image by passing it to an image reasoning multi-modal LLM to ascertain the location or point of interest or event venue (perhaps reinforced by GPS location information).
- Then in a second step an(other) LLM's tools/function-calling capability is used to fetch up-to-date events and happening information over the Internet, catering for the user's current interest.
- Finally, a friendly summary report is produced by an(other) LLM as the last step of a RAG pipeline.
- This app should never produce erraneous output. It should know its own limitaion and decline to report if in doubt.
💪 Desired Skills:
- Rocket.Chat Apps Engine (TypeScript)
- Familiarity with the "RAG" agentic workflow
- Intermediate prompt engineering Skills
- Experience with image reasoning capabilities of modern open source multi-modal LLMs
- Experience with tools/function-calling capabilities of modern LLMs
🎯 Goals/Deliverables:
- A working Rocket.Chat App that assists users with latest happenings around them wherever they may be while on any trip.
⏳ Project Duration: 90 hours (Small)
📈 Difficulty: Intermediate/Advanced
👥 Mentor(s): Dhurv Jain, Abhinav Kumar
📢 Communication Channel: idea-AI-Transcription-and-Translation-for-Voice-Messages-App
💬 Description:
Rocket.Chat already supports sending voice messages. This project enhances that feature by providing on-demand or real-time transcription and translation of voice messages. Users can choose to transcribe voice messages to text and optionally translate into their preferred language, thereby boosting accessibility and making communication more inclusive.
Possible Milestones:
- UI/UX Enhancements:
- Integrate options into the existing voice message interface for triggering transcription and translation.
- Backend Processing:
- Integrate with a speech-to-text service (e.g., Google Cloud Speech-to-Text or open‑source alternatives like Vosk) to transcribe voice messages.
- Translation Integration:
- Connect with a translation API (e.g., Google Translate or LibreTranslate) to convert transcriptions into the target language.
- Performance & Accuracy Tuning:
- Optimize for low latency and high transcription accuracy, ensuring the system gracefully handles slow or unavailable external APIs.
💪 Desired Skills:
- Experience with Rocket.Chat Apps Engine (TypeScript)
- Familiarity with speech-to-text and translation APIs
- Skills in mobile and web UI/UX enhancement
- Ability to optimize performance and implement robust error handling
🎯 Goals/Deliverables:
- A Rocket.Chat App that enhances the existing voice message feature with transcription and translation capabilities.
- Seamless integration with external speech-to-text and translation services.
- An intuitive interface that allows users to trigger transcription and translation on demand or in real time.
⏳ Project Duration: 90 hours (Small)
📈 Difficulty: Intermediate/Advanced
👥 Mentor(s): John Crisp, Matheus Cardoso
📢 Communication Channel: idea-Private-Teams-and-Private-Channels-Adminitration-Improvements
💬 Description:
While it is possible to create private team and private channels in Rocket.Chat, the ability for the server's administrator to perform administrative tasks on them are currently very limited (unless the server admin is part of the private channel or private team). This was done by the original "team designer" to afford some local privacy for these these users.
This project aims to improve this situation by adding the ability for the server administrator to (optionally) override (via configuration on admin panel) to directly access and administer private team and private channels.
This should include minimally the following abilities:
- add/remove users in private channels
- assign and change the roles of private channel users
- add/remove/rename private team channels
- add/remove/modify the members of private teams
- assign and change the roles of private team users
- access private channels and private teams in the Directory
💪 Desired Skills:
- In depth understanding of Rocket.Chat core
- Advanced Typescript
- Familiarity with Rocket.Chat UI/Ux
- Interest in administration and management of sub-communities on large chat servers (our Team concept)
🎯 Goals/Deliverables:
Ability for server administrator to better administer private teams and private channels.
⏳ Project Duration: 175 hours (Medium)
📈 Difficulty: Intermediate
👥 Mentor(s): TBD
💬 Description:
Currently, all elements on the channel header are fixed and mandatory.
This project aims to make the channel header customizable, allowing the owner/administrator to hide some components (where it makes sense).
One should be able to hide any combination of the buttons in the header.
Implementation must take UI/Ux design into consideration to ensure the elements showing are still consistent with the overall Rocket.Chat design aesthetics.
💪 Desired Skills:
- Understanding of Rocket.Chat core
- UI/Ux development
- Advanced Typescript
- Like coding, love design
🎯 Goals/Deliverables:
Fully customizable channel header for Rocket.Chat.
⏳ Project Duration: 90 hours (Small)
📈 Difficulty: Easy
👥 Mentor(s): TBD
💬 Description:
Add and implement permissions to make possible the fine-grained control of who can upload or download files/attachments. The implementation must enable the per user control of upload, download individually (or in combination) when users are on mobile, desktop, or web app. (and any combinations of)
💪 Desired Skills:
- Understanding of Rocket.Chat core
- Understanding of Rocket.Chat's role and permission system
- Advanced Typescript
- Interest in cyber security
🎯 Goals/Deliverables:
Ability for administrators to control the upload/download capability per user, in combination with access client type.
⏳ Project Duration: 90 hours (Small)
📈 Difficulty: Intermediate
👥 Mentor(s): TBD
💬 Description:
Fix and implement buttons on the message box of desktop and mobile app to share current and live locaiton of the user. Create componente to render the location with action buttons to open the locaiton on 3rd party apps for navigation.
💪 Desired Skills:
- Understanding of Rocket.Chat core
- Advanced Typescript
- React and React Native
🎯 Goals/Deliverables:
Ability for users to share their current location or live location for specified period of time.
⏳ Project Duration: TBD
📈 Difficulty: Advanced