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Hello VideoLan, I want to improve Lua support for VLC and make it a more first-class citizen. Lua is beautiful language that's simple and easy to embed and deserves more love, so the layman has more tools to tinker with! I plan on doing this by moving extensions and extensions manager out of gui and into the libvcore. Create a better api for stream parsing to make it more user friendly for developers by creating abstractions that will streamline and reduce read() operations. And then create a testing framework with testing suites and error handling.

This project aims to modernize the VideoLAN crash reporting system (CrashDragon) to improve how VLC crashes are collected, analyzed, and managed. The current system, while functional, uses outdated technologies and lacks modern interactivity. I'll tackle this in two phases: First, enhancing the Go backend API with improved error handling, better crash grouping algorithms, and optimized database queries. Second, completely rewriting the frontend using Vue.js to replace server-rendered templates with a modern single-page application.

<p><strong>To rework the VLC interface heavily and make it beautiful and useful again.</strong></p> <p>I'll be working on redesigning the new interface(VLC 4.0) to make it feature compliant with the old interface.</p> <p>This includes making Qt models to transfer data from/to the different VLC components. The data in the models will be accessible via a UI which will be created using qml.</p> <p>More specifically i'll be working on:</p> <ul> <li>The video view tab which has:<ul> <li>Videos</li> <li>Shows</li> <li>Camera Roll</li> </ul> </li> <li>Internet tab that hooks up with:<ul> <li>Radios </li> <li>Podcasts</li> </ul> </li> </ul>

This proposal aims to update the User Interface and add new features to catch up with the Android version of VLC, enhancing and making the user experience safer and easier. The main feature of this project will be adding to VLC iOS app the Parental Control feature already available on the VLC Android app. This will enable iOS users to restrict access to the app settings and to sensitive actions, such as deleting media files or modifying playlists, unless the user successfully authenticates via a PIN code or biometrics. This is particularly useful in environments where children or unauthorized users might otherwise access media libraries unsupervised. This feature will ensure that VLC for iOS offers the same level of protection and control alongside consistent design guidelines and accessibility currently available in VLC for Android. As part of this effort, some components of the settings interface and media actions will also be reviewed and updated to align with modern iOS UI patterns. Implementing these improvements will strengthen the app’s usability in shared environments and contribute to a safer, more polished experience for all users.

External subtitles are one of the basic and most used features of VLC. VLC already has functionality to allow auto downloading of subtitles. But sometimes these subtitles go out of sync mainly due to frame rate mismatches between the video and subtitle file, different video versions (e.g., Director's Cut), or technical issues like faulty encoding or laggy streaming. This causes subtitles to be consistently early/late or to gradually drift further off-sync over time. Currently vlc doesn’t have any inbuilt auto sync feature. Due to this users have to manually sync (by setting offset and frame drift) or rely on the external program to sync the subtitle. This project aims to solve this issue by using light VAD (Voice Activity Detection) and the lightest whisper model to match subtitles. The main goal would be to make it as efficient as possible so it can even run on mobile devices. Deliverables: 1. Implement a dummy solution using python. 2. Implement native vlc module to match subtitles. 3. Optimize and test the module on various platforms. 4. Update documentation to reflect project contributions.

The CrashDragon project aims to enhance its functionality and user experience through a three-fold approach for Google Summer of Code 2024. The project will begin with a thorough review of the existing codebase to identify areas for improvement. This will involve analyzing the current architecture, identifying bottlenecks, and proposing solutions for optimization. Additionally, I will work on enhancing the API in Go to ensure smoother integration with the frontend and better performance overall. One of the primary objectives is to optimize the backend infrastructure to enhance the overall performance of the service. This will involve optimizing SQL queries, refactoring code for better efficiency, and implementing best practices to improve scalability and reliability. Furthermore, I will focus on incorporating unit tests to ensure the robustness and maintainability of the backend codebase. A new frontend will be developed using Vue.js to provide an intuitive and responsive interface. This will involve designing and implementing user interfaces for various functionalities and integrating frontend components with the backend API. By accomplishing these tasks, the CrashDragon project aims to deliver a more efficient, scalable, and user-friendly platform for managing crash reports.

<p>Currently development of rav1e and other AV1 encoders use open source AV1 analysis tools to extract metadata which is produced at decode time. This data is useful for reporting statistics and ultimately to fine-tune parameters. However, there is an inadequate amount of advanced analysis which would give more in-depth statistics. Access to this information would allow a deeper understanding of how specific changes impact factors such as bitrate, objective quality.</p> <p>AV1 instrument tools are found in the reference decoder (libaom) which is a large codebase making it hard to extend. This project aims to create lightweight AV1 analysis tools in the dav1d codebase. It will achieve this by adding a decoder metadata extraction tool the dav1d example decoder with structured output so that rapid testing can be performed in an automated fashion. Once the relevant metadata is extracted, the tool will produce visualisations and report on other metrics that can be used for tuning encoder parameters.</p> <p>The desire is to create a light-weight tool that gives detailed information about encoded streams for use in encoder development, conformance and regression testing and third-party analysis.</p>

<p>This project involves fixing the last remaining kinks in the EvalAI UI. The goal of this project would be to improve the new UI as we replace the existing UI with the new UI. We will be improving on the new UI and incorporating the feedback we will receive from the challenge hosts and participants for the AI challenges organized this year.</p>

<p>Revamp the interface design according to the community standards and craft a better user experience for the Fedora Community application.</p>

<p>Fedora has an android app which lets a user browse Fedora Magazine, Fedora Ask, Fedocal etc within it. This app is build using the Ionic Framework, Angular and Cordova. Essentially it is a cross-platform hybrid app.</p> <p>In the current form, most of the functions rely on an in-app browser to render content. This project aims to improve the existing Fedora App for Android for speed, utility, and responsiveness, introduce a deeper native integration and make the app more personal for the user.</p>

<p>EvalAI is a platform to host and participate in AI challenges around the globe. To host a challenge, challenge creation is one of the core features which is utilized by challenge hosts to create AI challenges. The idea is to use private GitHub repositories to host the challenge files instead of the zip file. The next step is to build and integrate a continuous deployment pipeline with GitHub so that for every new commit in the challenge repository, the changes are automatically reflected on the UI. We will also build support for tests so that new commits are fully tested before they are pushed to the live challenge hosted on EvalAI. The goal is to enhance the challenge creation experience for challenge hosts and set up a challenge on EvalAI by the challenge hosts involving minimal human effort from the EvalAI team.</p>

<p>This project mainly focuses adding some missing features and improving the frontend of the EvalAI.</p>

<p>The aim of this project is to rewrite Cloud CV Web App into a modern web app with a REST API backend with continuous integration and deployment.</p>

<p>Currently, the submission worker that evaluates the challenge requires manual scaling. For auto-scaling, I'll be migrating it to AWS Fargate from EC2.</p> <ul> <li>The goal of this project is to write a robust test suite for submission worker, port it to AWS Fargate to setup auto-scaling and logging. </li> <li>The tasks will also include giving control to challenge hosts over the submission worker from the UI in terms of starting, stopping and restarting it, and automate the container jobs based on some conditions. </li> <li>Among other deliverables is to setup a pipeline for creating challenges with custom requirements, Challenge config verification etc.</li> </ul>

This project is focused on significantly improving EvalAI’s usability by enhancing exsiting comprehensive test suite alongside a series of user experience enhancements. By increasing our test coverage, automating critical workflows, and refining the platform’s interface and documentation, this initiative aims to create a more robust, user-friendly, and resilient environment for both challenge hosts and participants. The enhanced test suite will ensure that all core functionalities, from challenge creation to submission processing are verified, reducing bugs and increasing system reliability. In parallel, targeted user experience improvements will simplify navigation, enhance error reporting, and streamline user interactions, leading to a more intuitive and supportive EvalAI ecosystem.

<p>Happiness Packets encourages and makes it easier for people to send positive feedback to their peers (anonymously if they like). This project extends Happiness Packets by giving a Fedora Badge to anyone who sends a Happiness Packet to another contributor. To do this we need to add support for Fedora’s authentication system and a fedmsg hook.</p> <p><a href="https://goo.gl/62GwE1" target="_blank">Proposal</a></p>

<p>Complete support for <a href="https://github.com/openshift/origin" target="_blank">OpenShift</a> and <a href="https://github.com/kubernetes/kubernetes" target="_blank">Kubernetes</a> as a backend in <a href="https://github.com/fedora-modularity/conu" target="_blank">conu</a></p>

<p>This is a GUI client for the fedora QA team for rating updates.</p>

This project aims to enhance the user experience for both challenge hosts and participants by developing an intelligent, RAG (Retrieval Augmented Generation) based chatbot. The chatbot will efficiently address queries related to challenge hosting, guidelines, troubleshooting, and FAQs. By integrating state-of-the-art NLP techniques with robust retrieval mechanisms, the solution will ensure prompt, accurate, and context-aware responses that reduce support overhead and streamline communication. Using the RAG approach, the chatbot will retrieve relevant information from challenge documentation and combine it with generative models to create coherent and helpful answers. This will empower hosts to manage challenges more effectively and assist participants in resolving queries, ultimately contributing to a smoother and more interactive challenge experience. Deliverables include : RAG Framework Implementation Chat UI Comprehensive Documentation Testing, Feedback, and Continuous Improvement

<p>I am trying to propose <strong>features</strong> and <strong>improvement</strong> in Pagure, which is a code review management system.</p>

<p>As the number of challenges on EvalAI are increasing, we want to focus on improving the performance of our services. As a first step, we will focus on monitoring and measuring all the key metrics of our services. Insights from these will allow us to efficiently utilize our infrastructure, improve uptime and reduce costs. The project will concentrate on setting up metric reporting and alerts infrastructure, writing REST API’s, plotting relevant graphs and building analytics dashboards to help EvalAI admins maintain and monitor the services.</p>

<p>The rise of reinforcement learning based problems or any problem which requires that an agent must interact with an environment introduces additional challenges for benchmarking. In contrast to the supervised learning setting where performance is measured by evaluating on a static test set, it is less straightforward to measure generalisation performance of these agents in the context of the interactions with the environment. Evaluating these agents involves running the associated code on a collection of unseen environments that constitutes a hidden test set for such a scenario. The goal of this project is to set up a robust pipeline for uploading prediction code in the form of Docker containers that will be evaluated on remote machines and the results will be displayed on the leaderboard.</p>

<p>This project will focus on improving the existing UI of EvalAI to improve the experience of both challenge organizers and participants. Beyond this, the project intends to improve the discoverability of all the features that are supported on EvalAI. The goal of the project is to ease the pipeline for challenge creation, enhancing the user experience of the platform, adding plots for displaying the progress of state-of-the-art algorithms, for displaying the progress of participant team in a challenge over the years and several other features.</p>