Opening Plenary (Others)

• Till Kamppeter (OpenPrinting)
• Aveek Basu

Room: Room 1: Plenary

My LFX Mentorship Journey: From Application to KubeEdge Contributor (Community)

• Abhishek Kumar (The Linux Foundation)

Room: Room 2: Breakout Getting selected for an LFX Mentorship feels like winning a lottery - but it's not about luck. It's about strategy, persistence, and understanding what makes a strong application stand out among thousands of applicants. In this talk, I'll take you through my complete LFX mentorship journey - from the moment I first heard about the program to receiving that life-changing acceptance email from KubeEdge. This isn't just another success story; it's a practical roadmap based on real experience. I'll share the exact steps I took to research and choose the right project, how I crafted my application to demonstrate genuine interest and capability, and the mistakes I made along the way that you can avoid. You'll learn about the application timeline, what mentors actually look for in candidates, and how to make your proposal stand out in a competitive pool. We'll dive into what the LFX mentorship experience is really like - the weekly check-ins, the learning curve, the challenges of contributing to a complex project like KubeEdge, and how it's transforming my understanding of open source development. I'll also discuss the technical skills you need, the soft skills that matter just as much, and how to prepare yourself to be a valuable mentee. KubeEdge, being at the forefront of edge computing and Kubernetes, presents unique challenges and opportunities. I'll share insights into the project's ecosystem, the community dynamics, and what it's like to work on cutting-edge technology that's shaping the future of distributed computing. This talk is perfect for students and early-career developers who are curious about LFX but don't know where to start, those who have applied before and been rejected, or anyone wanting to understand how to break into major open source projects through structured mentorship programs. By the end, you'll have a clear action plan for your own LFX application, realistic expectations about the program, and the confidence to take that first step toward becoming an open source contributor.

Future of Open Source AI: From Cloud to the Edge (Artificial Intelligence, Machine Learning, Data Science)

• Neeraj Poddar (NimbleEdge)
• Saiyam Pathak

Room: Room 1: Plenary Open source is powering the next wave of AI innovation, but the real challenge lies in making AI infrastructure accessible, secure, and scalable across environments—from cloud-native platforms to the edge devices. In this talk, we’ll begin with a high-level perspective on the role of open source in shaping AI adoption across industries. From there, we’ll dive into practical architectures that enable AI to run efficiently in the cloud leveraging Kubernetes, exploring how virtual clusters (vCluster) provide secure multi-tenancy for teams while ensuring resource efficiency. We’ll connect this with running AI on-device, highlighting how AI can extend beyond datacenters to devices at the edge, enabling real-world applications that demand low latency and privacy-preserving computation. We will focus on the next frontier of AI in making it personal, private and accessible. Attendees will walk away with a clearer understanding of the opportunities and challenges in running AI at scale using open source—whether in the cloud, or at the edge.

Accelerating Scilab Toolbox Creation Using AI (Artificial Intelligence, Machine Learning, Data Science)

• Nikitha Dhanabal
• Akash Sankaranarayanan

Room: Room 2: Breakout This work presents a practical and efficient approach to accelerating the development of the Scilab Control System Toolbox through AI-assisted workflows. The focus is on reimplementing key control system functions from GNU Octave within the Scilab environment, thereby enhancing native capabilities of Scilab. Traditionally, the development of a single function, including code implementation, test case creation, and documentation, could require up to two weeks. With the integration of AI-assisted tools, this process can be reduced to a matter of hours, enabling significantly faster development while maintaining reliability and functional accuracy. The methodology involves analyzing existing Octave functions, generating corresponding Scilab functions using AI, followed by expert review, validation against Octave outputs, and refinement where necessary. While AI expedites the development process, human oversight remains essential to guide the workflow and ensure correctness, as AI-generated code may contain errors or incomplete logic. To date, approximately 50 out of a targeted 150 functions have been successfully reimplemented using this approach. The work contributes to the open-source community by broadening access to advanced control system tools in Scilab and demonstrates the potential of combining AI capabilities with domain expertise to accelerate scientific software development. The same methodology can be extended to other toolboxes and domains within the open-source ecosystem. Keywords: Scilab, GNU Octave, Control Systems, AI-assisted Development, Open Source Software, Scientific Computing

OpenPrinting - Q&A (OpenPrinting)

• Akarshan Kapoor (Indian Institute of Technology, Mandi, India)
• Till Kamppeter (OpenPrinting)
• Aveek Basu
• Alexander Pevzner
• Mohammed Imaduddin

Room: Room 1: Plenary Now, after Till has given you an overview about OpenPrinting, you probably have many questions, for example: - How to get started as contributor to OpenPrinting? - What can I do if I am not a coder? How do I get printing into my apps? - How do I design a driver for a non-IPP legacy/specialty printer? - How do I report issues? - ... Till, Aveek, Alexander, Akarshan, Mohammed (and perhaps other people from OpenPrinting) will answer your questions. [More about OpenPrinting ...][1] [1]: https://openprinting.github.io/OpenPrinting-News-25-years-of-working-full-time-for-printing-with-free-open-source-software/

Data Structures and Algorithms Visualizer (algorithima), with quiz and practice questions (Development/Coding)

• Manvith Kumar (Indian Institute Of Information Technology Kottayam)
• Prajwal Kumar Karnad (Indian Institute Of Information Technology Kottayam)
• Shaun Sebastian (IIITK)

Room: Room 1: Plenary ## 🧠 What is DSA Visualizer? **DSA Visualizer** is an open-source, interactive web platform that brings Data Structures and Algorithms (DSA) to life through real-time visualizations, code execution tracking, and memory simulation. Built at **IIIT Kottayam** by the student-led **Openverse** club, it aims to make algorithm learning intuitive, engaging, and fully accessible to all learners. --- ## 🎯 What Makes It Special? This is more than a visualizer. It is a **comprehensive DSA learning environment**, featuring: - **Step-by-step animated visualizations** for: - Linked Lists, Stacks, Queues - Trees, Sorting, Pathfinding (Dijkstra, A*) - **Hash Tables**, **Recursion**, **Graphs**, **Search**, and **Greedy Algorithms** - **Interactive quizzes** across all major topics to test conceptual understanding - **Code execution tracking** synced with C language logic - **Memory visualization** with address-level pointer simulation - **Practice zone** with: - **IIITK-specific PYQs and DSA121 practice sets** - **Topic-wise curated LeetCode problems**, neatly sorted for guided progression - **Live search functionality** across visualizers and problem banks - **Responsive UI** for desktop, tablet, and mobile use --- ## 💡 Who is it for? - **Students** seeking clarity in abstract DSA concepts - **Educators** wanting interactive tools for classroom demonstrations - **Self-learners** preparing for technical interviews - **Open-source contributors** passionate about accessible education --- ## ⚙️ Technical Stack | Category | Tools & Libraries | |----------------|----------------------------------| | Frontend | React 19, Vite 6 | | Animations | Framer Motion | | Code Display | Monaco Editor, React Syntax Highlighter | | Design System | Custom dark-mode UI | | Backend (Future) | flask, local storage | | Hosting | IIITK self Hosted (soon) | Code logic is primarily implemented in **C**, in sync with IIITK’s **DSA121 course**. --- ## 🧪 Additional Highlights - Quizzes dynamically scored and stored - Roadmap-based design for expanding to advanced topics - Contribution-friendly architecture for easy new algorithm integration - Sorted practice bank and visual-first approach make it ideal for flipped classrooms --- ## 🔓 Open Philosophy This project is built with the belief that **CS education should be visual, interactive, and open to all**. DSA Visualizer is being developed as a free educational resource — made at IIITK, by IIITK, for everyone. We invite educators, developers, and students worldwide to contribute, adapt, and expand the platform. --- **Live Demo:** [https://visualalgo-ov.web.app](https://visualalgo-ov.web.app) **Github Repo**: https://Openverse-iiitk/DSA-Website **Made with 💚 by Openverse, IIIT Kottayam**

Data Center Virtualization 2025 (Others)

• Saquib Akhtar (Nokia)

Room: Room 1: Plenary Virtualization at the Core: Trends, Hypervisors, and the Shift to Cloud-Native Platforms Abstract Virtualization continues to be the bedrock of modern IT infrastructure, enabling flexible, efficient, and scalable environments. This paper outlines key trends in virtualization, explains the role of hypervisors, and provides a concise understanding of how platforms like OpenStack and OpenShift build upon these technologies. It aims to present a 360-degree view of how virtualization continues to evolve and support emerging computing paradigms.

An Offline AI Assistant for eSim: Easier, Accessible, Open-Source Circuit Design and Debugging (Artificial Intelligence, Machine Learning, Data Science)

• Varad Patil (FOSSEE Project, Indian Institute of Technology, Bombay)
• Shanthi Priya (FOSSEE Project, Indian Institute of Technology, Bombay)
• Kannan M. Moudgalya (Indian Institute of Technology, Bombay)
• Sumanto Kar (FOSSEE Project, Indian Institute of Technology, Bombay)
• Rudra Mani Upadhyay (Dronacharya College of Engineering, Gurgaon)
• Myo Thinzar Kyaw (Myanmar Institute of Information Technology)
• Aditya Bhattacharya (Rajiv Gandhi Institute of Petroleum Technology)

Room: Room 2: Breakout We present an offline AI assistant that uses open-source tools to help with the design and debugging of electronic circuits. The system works with **[eSim][1]**, an Electronic Design Automation (EDA) software that was made as part of the [FOSSEE][2] project at IIT Bombay by integrating various Open Source tools like KiCad, Ngspice, GHDL, Verilator and many more. This program encourages students and teachers in resource-limited settings to use free and open-source software to get around problems they face. It is aimed at people in areas with poor internet access and schools like tier-3 colleges that can't afford expensive proprietary EDA tools. This makes it possible for everyone to have access to high-quality circuit design workflows. Traditional EDA workflows, on the other hand, often include complicated toolchains, hard-to-understand simulation errors, and a lot of manual work to fix them. These problems are even worse for new designers and students who don't have access to commercial support. Our assistant solves these problems by giving automated, easy-to-understand help throughout the whole design process. The assistant uses a modular pipeline that works well on low-end hardware and doesn't need an internet connection to work. An automated watcher service keeps an eye on file changes in popular tools like KiCad and Ngspice. When it sees a change, it parses netlists and error logs to keep design states and diagnostics up to date. OpenCV is used for visual support, which lets users monitor schematics based on images, recognize components, and check layouts to find and fix design mistakes early on. The user interface is meant to work well in both terminal-based and graphical environments. It is designed to work with the most common Linux distributions used in schools. The assistant uses a retrieval-augmented generation (RAG) framework to give high-quality, context-aware answers when people talk to it in natural language. A vector-search retrieval system keeps track of local documentation and domain-specific knowledge bases. This lets the query generation module put together structured prompts. The Qwen 1.8B language model processes these, which are hosted locally by the Ollama framework. The fact that it works completely offline protects users' privacy and lets them use chain-of-thought reasoning to get detailed, step-by-step explanations for fixing designs, figuring out simulation errors, analyzing logic, and evaluating circuits. A session-based memory module keeps conversations going, so users can build complex designs step by step with the same help each time. The assistant makes advanced circuit design workflows easier to access, faster, and easier to debug by combining multimodal analysis, semantic retrieval, and fully offline language-model inference into one workflow. This helps make modern circuit design and prototyping more accessible to everyone by lowering design mistakes, speeding up development cycles, and helping students and teachers learn new skills when they can't afford expensive tools or have reliable internet. Keywords: Electronic Design Automation, offline AI, circuit debugging, eSim, Qwen LLM, OpenCV, KiCad, Ngspice, RAG, Circuit Analysis, RTL code analysis, Linux UI, session-based memory, chain-of-thought reasoning. [1]: https://esim.fossee.in/home [2]: https://fossee.in/

Venturial and PyVNT: Simplifying CFD Case Creation in OpenFOAM (Development/Coding)

• Diptangshu Dey (FOSSEE, IIT Bombay)

Room: Room 2: Breakout Venturial is an open source Python toolkit designed to expedite case generation in OpenFOAM, a C++ software for continuum mechanics. The toolkit aims to ease the learning curve of OpenFOAM for new users by offering a flexible workflow with multiple entry points for the user. The talk will demonstrate Venturial's main features through a sample workflow for generating a mesh for an OpenFOAM case. It will also highlight techniques for modifying the case and conclude with further development aspects to address the needs of intermediate/advanced users. ## Background Over the last few decades, improved hardware and algorithms have expanded general computing capabilities, fueling scientific tools, particularly in computational fluid dynamics (CFD), and making them accessible to a broader audience. One such tool, OpenFOAM, an open-source C++ library prominently used in CFD, is well known for its reusable code design. However, it poses a steep learning curve without a procedural workflow for defining cases. Due to this, new learners often feel discouraged to use this open source software and move to other proprietary software. ## Our Work OpenFOAM, by itself, is a CLI-based tool that needs a Linux-based Operating System to be used. New learners and people who deal with CFD often get discouraged by these prerequisites for using OpenFOAM. In response to this, Venturial[1], an endeavour by IIT Bombay’s CFD-FOSSEE team[2], envisions to benefit the scientific community by offering a complete CFD workflow with OpenFOAM within python. It provides a user-friendly Graphical User Interface to ease the process of case building for new learners and also provides a Python API to create and modify OpenFOAM cases using a Python script. PyVNT(Python Venturial Node Trees) is a Python API that can be used to read and write OpenFOAM case files and create an abstraction of OpenFOAM case data within Python in a Node Tree-based data structure. Venturial also includes domain-specific presets, proactive rule validation, FOAM-like meshing shortcuts, and personalisation utilities, ensuring a flexible yet structured CFD workflow. Venturial is inspired by Reynolds-blender[4], a reference implementation of Reynolds[5]. It commenced development in 2021 through FOSSEE’s internship program. Since then, Venturial has been published in SciPy 2024[6], SciPy India 2021 [7], the 18th OpenFOAM workshop [8], and the 2023 IEEE T4E Conference [9]. ## Results Our presentation will illustrate Venturial’s meshing workflow using a sample CFD case, emphasising its ease of use for beginners starting with OpenFOAM. We will also showcase PyVNT’s compatibility with OpenFOAM for intermediate and advanced CFD applications. The presentation will conclude with a discussion of Venturial’s future direction, highlighting its student-led development and forthcoming features. ## Conclusions This talk is intended for users and instructors within the CFD scientific community seeking a unified tool for learning or teaching OpenFOAM or developing a customised user interface to interact with various OpenFOAM features. It will provide valuable insights for those aiming to streamline CFD workflows while leveraging a structured and adaptable computational framework. ## References [1] Adak, Rajdeep, Diptangshu Dey, n.d. FOSSEE/Venturial - A GUI for OpenFOAM. Github: https://github.com/FOSSEE/venturial [2] “Home,” CFD, https://cfd.fossee.in/ [3] Dey, Diptangshu, Rajdeep Adak, n.d. FOSSEE/PyVNT: Python Venturial Node Trees. Github: https://github.com/FOSSEE/pyvnt [4] Surti, Deepak, Prabhu Ramachandran, and Shivasubramanian Gopalakrishanan. n.d. “dmsurti/reynolds-blender: GitHub. https://github.com/dmsurti/reynolds-blender. [5] Dmsurti (no date) Dmsurti/Reynolds: The preprocessing and solver python toolbox for openfoam., GitHub. Available at: https://github.com/dmsurti/reynolds [6] Adak, R., Murallidharan, J. S., & Ramachandran, P. (2024). Venturial: Generating CFD Workflows in Python. Python in Science Conference, 2024 (SciPy), Tacoma, Washington. SciPy. https://doi.org/10.25080/tpwg2365 [7] R. Adak and K. K. Thakur, “Venturial – A Python-based OpenFOAM GUI for CFD Simulations on Blender,” International conference on Python for education and scientific computing, https://scipy.in/2021. pdf available at: https://static.fossee.in/fossee/internship-reports/Python-Blender/Rajdeep_Adak.pdf [8] Adak, R., Srree Murallidharan, J. and Ramachandran, P. (2023) 18th OpenFOAM Workshop. Available at: https://oxford-abstracts.s3.amazonaws.com/83ca7ab4-c356-4411-be07-070eaeffd43a.pdf [9] Ramachandran, P., Murallidharan, J. S., & Adak, R. (2023, November 25). Developing Software to Enhance Learnability and Usability of OpenFOAM. Proceedings of International Conference on Technology 4 Education (T4E) 2023 (IEEE T4E), Indian Institute of Technology (IIT) - Bombay. https://doi.org/10.5281/zenodo.14162151

Closing Plenary (Others)

• Aveek Basu
• Till Kamppeter (OpenPrinting)

Room: Room 1: Plenary