Scala Center

Mentors: Student: Atharva-Kanherkar

This project aims to create a dedicated web-based user interface for the workflows4s library to address the current lack of visual debugging tools. By building a full-stack solution with a ScalaJS frontend, the UI will enhance workflow development by allowing developers to visually inspect detailed workflow state at any point in history, clearly see the execution paths taken within a workflow diagram, and easily navigate the workflow's history using a timeline. Ultimately, this visual feedback will make understanding, developing, and debugging complex workflows significantly more accessible and intuitive compared to relying solely on logs.

Mentors: Student: Liam Grossman

This project focuses on building a seamless API integration between MS Teams and the ChatOps4s prototype through a clean, efficient interface. The key goals are to ensure high performance, minimize dependencies, and maintain feature parity with other integrations, such as Discord. The MS Teams API interface will be well-documented, rigorously tested, and designed for interoperability, allowing developers to switch to another platform, like Discord, with minimal code modifications.

Mentors: Student: Rudra Beniwal

Cyfra is a GPU runtime and DSL that simplifies GPU programming in Scala by abstracting Vulkan. This project will build a real-time rendering pipeline with outputs to a window, data stream, and file. Deliverables include high-level GPU APIs, GLFW integration, efficient swap chain/synchronization, and clear documentation with examples—making high-performance graphics more accessible to Scala developers.

Mentors: Student: GOPI TRINADH MADDIKUNTA

This project aims to bring the power of Retrieval-Augmented Generation (RAG) into the Scala ecosystem by building a robust Scala interface to interact with RAG pipelines. I will design a Scala-compatible wrapper to connect with Python-based components like FAISS and Hugging Face Transformers via gRPC or HTTP APIs. The project will also include developing idiomatic Scala modules for preprocessing, querying, and evaluating textual data, making it easier for Scala developers to integrate RAG into NLP or search applications. By providing seamless interoperability and modular architecture, this will empower the Scala community to build intelligent, language-aware systems more efficiently.

Mentors: Student: Elvan Konukseven

LLM4S is building a Scala-native AI toolkit that leverages functional programming principles and strict compile-time checks to ensure the reliability and maintainability of LLM-powered applications. This project aims to extend the initial agentic loop and implement essential tools, empowering Scala developers to build robust, reliable, and explainable AI solutions. Key objectives include implementing a robust agentic loop, integrating MCP for dynamic tool interaction, developing core tools for file systems, ensuring context management, testing frameworks, and comprehensive documentation to support reliable LLM-powered applications.