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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/

CloudStack has recently made the first release of its own terraform provider, called CloudStack-Terraform Provider v0.4.0 and as of today it supports 25 resource types, and 1 Data Source type. I would like to extend the list of data sources supported by the CloudStack Terraform provider and implement data sources for the existing cloudstack resources. To solve this issue, I will implement data sources for all the resources already supported via the Terraform CloudStack provider and possibly extend that list of supported resources and data sources to other major CloudStack resources which are not available yet.

The aim is to build the textual portions of the FastAI.jl package, inspired by the fastai Python library, which will provide high-level components that can quickly and easily provide state-of-the-art results for all kinds of tasks, and provide low-level components that can be mixed and matched to build new approaches. This will include handling textual data, performing transformations on it, creating a model using best practices (inspired from fastai), being able to train the created model, and interpreting and visualizing the results of the model. All this will be done without compromising on ease of use, flexibility, or performance, due to the benefits Julia provides, along with the well-designed three-layered architecture.

<h3><strong>Improving Accessibility for the Visually Impaired</strong></h3> <p>My project revolves around making improvements to MuseScore’s design that acknowledge, dismantle, and prevent the barriers holding back a minority of Partially Sighted individuals from comfortably reading and writing music. In particular, I am going to be targeting the existing High Contrast mode within the application.</p> <p>The changes include, but are not limited to:</p> <p>• Score Page Colour Inversion</p> <p>• Re-colourization of the existing High Contrast mode</p> <p>• Different High Contrast modes (Light/Dark) with appropriate Colour Schemes</p>

<p>Scalable congestion controls such as DCTCP improve performance over Reno and Cubic, which perform badly in high-speed networks (because of their slow response with large congestion windows). Several additional modifications over DCTCP have been drafted into the protocol called TCP Prague, that aims to integrate scalable congestion control into the Internet while still allowing it to coexist with current Classic protocols. This project would complete the integration and testing of fallback detection, RTT independence and pacing into the TCP Prague model of ns-3. The project would also validate the aforementioned implemenation against Linux and document the changes made.</p>

<p>SymbiFlow does not currently support partial reconfiguration regions. Partial reconfiguration regions are crucial to ongoing FPGA research including reducing verilog to bitstream compilation times through separate compilation. Two major hurdles to the support of partial reconfiguration regions are SymbiFlow and VPR support for restricted placement/routing and the ability to generate and upload a partial bitstream. I will be taking on the first of these two hurdles.</p>

<p>The project aims to deliver a responsive web-based analytics dashboard, integrated to an in-house catalogue of circRNAs identified from multiple species with the functionalities like searching on the basis of genome coordinates and host gene names, a visual representation of inferred circRNA structure, comparing isoforms across different samples that would be linked to Ensembl genome browser.</p>

<p>I plan to add support for various 3D video formats to libVLC.</p>

<p>Improving the current md5 exporter for blender which is better suited to the needs of Terasology. The script will automate most of the tasks while providing an easy to use GUI for the blender environment. Features like automating triangulation of faces, separating faces, exporting multiple animations, creating a prefab file for specific AI animations and GUI development will be implemented during this project. Using the bpy library for blender script will be written in python.</p>

<p>The Wiki Ed Dashboard / Programs &amp; Events Dashboard is a Ruby on Rails + Javascript application that helps people organize groups of newcomers to contribute to Wikipedia. This project aims to use ORES (Objective Revision Evaluation Service) to provide specific useful editing suggestions to newcomers about how they can improve existing Wikipedia articles or article drafts they are working on. This can give an idea of what kind of improvements are needed for the article and gives them a jumpstart in their contributions</p>

<p>Additional modules and improvements to the libindic platform.</p> <ol> <li>New module: Sandhi Splitter in Malayalam.</li> <li>Integration of Sandhi Splitter to Spellchecker.</li> <li>REST API for libindic.</li> </ol>

<p>The aim of this project is to create a lot of packages, so that it will be way easier to maintain bears, and to update them with awesome features. This will also include easier debugging as we will only have to focus on one bear at a time.</p>

<p>The goal of my proposal is updating the bookmarks subsystem by refactoring the current code and implementing the new bookmarks menu design proposed by the designers.</p>

The Network Simulation Bridge (NSB) is a powerful co-simulation framework, but its current onboarding experience creates a barrier for new users due to fragmented documentation, absence of a clear “first success” path, and setup complexity. This project aims to design and develop a user-centric website that transforms onboarding into a structured and intuitive flow. The core focus is enabling users to run a working NSB example quickly through a quickstart-first approach, followed by progressive introduction of concepts, configuration, and simulator integration. The solution includes building a complete website with a guided Get Started system, reorganized documentation, and clearly defined learning paths, along with a progressive simulator integration strategy that begins with a simplified environment before moving to full simulator workflows. Key deliverables include a deployable NSB website with structured navigation, a guided onboarding flow centered on first success, unified documentation migrated from the repository, and tutorials with progressive learning pathways, validated through user testing and iteration. This project will reduce onboarding friction, improve usability, and make NSB more accessible to students, researchers, and developers.

Maths-Mantra is an inclusive Android math learning app designed for visually impaired and sighted learners, transforming the way mathematics is explored through touch, sound, and interaction. Building on a foundation of accessible learning, the app introduces Quick Play, Learning Mode, and additional game modes, offering varied, engaging experiences for learners of different skill levels. It integrates device sensors such as the gyroscope, accelerometer, and multi-touch inputs to make geometry, number lines, and spatial concepts interactive and tangible. With gesture-based navigation, audio-tactile feedback, adjustable difficulty levels, and localization support, Maths-Mantra creates a multi-sensory, barrier-free environment that empowers students to understand and enjoy mathematics in a playful, engaging, and fully accessible way.

Mixxx currently allows assigning a single free-text genre to each track, which often leads to inconsistencies such as duplicate or fragmented categories (e.g., "Hip-Hop", "Hip Hop", "Hiphop"). This project proposes a complete rework of genre metadata handling, introducing three key features: local genre autocompletion with fuzzy matching, support for assigning multiple genres to a single track, and optional online genre suggestions using MusicBrainz and Discogs APIs. As a long-term extension, the project also explores a customizable genre mapping layer, enabling users to define how external genres are grouped or renamed according to personal preferences. Deliverables include: a redesigned genre database schema, enhanced UI for editing and filtering genres, API integration for genre lookups, and internal tools for mapping and translation of genre labels.

EchoGem introduces a novel batching engine designed to answer multiple questions about the same source parallelly to reduce response times heavily. It does so by building a modular smart batching engine for Gemini that teaches the model to think in context-aware batches. What sets EchoGem apart is its focus on modularity and testing—each component is designed to be independently swappable and improvable allowing for open testing and matching different strategies for different parts of the engine. Instead of a naive sliding window or top-k search approach, EchoGem uses semantic clustering of both chunks and questions to form intelligent batch groups. It is also committed to measurable, reproducible gains. Every design decision—whether it's a new chunking strategy or a different context ranking model—can be rigorously backtested using a structured evaluation suite.

I propose to integrate native text tokenization support into OpenCV’s DNN module so that Large Language Models (LLMs) can be run end-to-end directly in OpenCV. Currently, to feed text into models like GPT-2 or GPT-3, users must rely on external libraries (e.g. tiktoken) for tokenization. My project will create a built-in tokenizer utility—implemented in C++ with Python bindings—that can convert raw text into token IDs and decode model outputs back into text. The core deliverable is a new cv::dnn::Tokenizer class supporting Byte Pair Encoding (BPE), along with the ability to load typical vocabulary/merges files and handle special tokens. Once implemented, this will allow a seamless pipeline for text-based or multimodal models within OpenCV: raw text -> tokens -> DNN inference -> tokens -> decoded text. I will also write comprehensive documentation and test this functionality against known tokenizer outputs (e.g., Hugging Face), ensuring correctness and good performance. By adding native tokenization, OpenCV’s DNN module becomes more versatile for cutting-edge AI tasks involving both vision and language. This reduces external dependencies, makes it easier to deploy LLMs in pure C++ or Python environments, and helps unify image and text processing in a single library—further strengthening OpenCV as a comprehensive open-source toolkit.

The Borg Collective offers essential Python-based backup tools like Borg, Borgmatic, and Vorta, which help users with secure, efficient, and reliable data backups. Vorta provides a user-friendly desktop GUI for Borg, while Borgmatic serves as a command-line interface for managing backups. This proposal outlines four projects designed to improve the functionality, security, and usability of both Vorta and Borgmatic. For the first project aims to add a new feature to the Vorta GUI that lets users change their repository passphrase. Changing a Borg repository passphrase is a feature that’s often requested but hasn’t been fully implemented yet within Vorta. This would make security management more accessible and eliminate the need for users to use Borg directly for such tasks. The second project aims to develop a custom file selector with PyQt6 FileDialog, which will handle multiple file and directory selections in one window. At the moment, Vorta has separate dialogs for selecting files and directories. This allows users to select both files and directories in a single window. The third project aims to introduce a much-needed improvement by adding a file selector dialog that allows users to pick files and directories to exclude directly through the UI. Right now, users who want to exclude certain files or directories from their backup have to manually type in paths or use patterns. This will allow users to eliminate the need for manual match expressions and reducing errors. These enhancements will make Vorta and user-friendly and feature-complete, ultimately improving the quality of backup process for users across different platforms.

The goal of this project is to enhance the Katib Experiment APIs to support various parameter distributions such as uniform, log-uniform, and qlog-uniform. This improvement aims to align Katib more closely with other hyperparameter tuning frameworks like Hyperopt, which offer a broader range of parameter distributions. Currently, Katib is limited to supporting only uniform distribution for integer, float, and categorical hyperparameters. By introducing additional distributions, Katib will become more flexible and powerful in conducting hyperparameter optimization tasks.

The goal of this project is to develop a deep learning model that can accurately recognize various categories of objects in digital photos, and generate corresponding keywords that can be automatically assigned and stored in digiKam's database for each photo. This will improve the efficiency of organizing and searching for photos based on their content. In addition, the facial recognition engine will be improved to increase its accuracy and reduce its inference time. The results of this work will be merged into the master branch, allowing for more reliable and effective facial recognition capabilities in the digiKam application

Java Pathfinder (JPF) is a Java virtual machine that can run compiled Java programs as well as model checking it. It is also an extensible software analysis framework for Java bytecode. As it has been created for about two decades, supporting new Java language features is of vital importance for its wide adoption. JPF community has taken steps to add Java 11 support, but there are still some unsolved issues. In this project, I plan to add better Java 11 support for JPF. This includes more comprehensive Java bytecode support, especially for invokedynamic, more JDK API support, and some bug fixes. It will fix all 13 failing tests JPF faces now on Java 11 and enable more programs to run on JPF.

Overview: In the current scenario, the Knative Eventing Kafka Broker's data-plane communication with Apache Kafka for consuming and producing records is done via the Vert.x-Kafka-client library which is basically a wrapper for communications with Apache Kafka inside the Vert.x threading model. Objictive: This project idea aims to implement the Knative Kafka Broker data-plane communication with the native Apache-kafka-client library working on Java 19 and evaluate OpenJDK 19's Project Loom and leverage its virtual threads for efficient and concurrent communication with the Apache Kafka cluster.