Request for Community Hardware Grant for Open-Source ARIA

Varshith Reddy, an independent student and open-source developer, has requested a Hugging Face Community Hardware Grant to host ARIA (AI-Powered Repository Intelligence Agent), an MIT-licensed developer platform for analyzing and modifying unfamiliar code repositories, on Hugging Face Spaces. The project, which includes AST analysis, semantic retrieval, MCP support, and VS Code integration, requires CPU-based compute with sufficient memory for repository-wide analysis, and the hosted instance would be constrained to serve as a controlled community demonstration.

Hi Hugging Face team, I’m building ARIA AI-Powered Repository Intelligence Agent , an open-source developer platform designed to help engineers understand, investigate, and safely modify unfamiliar software repositories. Project GitHub: GitHub - VarshithReddy2006/ARIA: AI-powered repository intelligence platform with semantic code analysis, knowledge graphs, Model Context Protocol MCP support, FastAPI, Astro, and a VS Code extension. · GitHub https://github.com/VarshithReddy2006/ARIA License: MIT ARIA combines static code intelligence, semantic retrieval, execution analysis, and AI-assisted investigation into a single developer workspace. It currently provides: - Repository-wide AST analysis using Tree-sitter - Symbol indexing and code intelligence - Dependency and call graphs - Architecture intelligence - API surface and contract analysis - Semantic code retrieval and RAG - AI-assisted repository investigation - VS Code integration - MCP integration - Docker-based self-hosting Why compute support is needed ARIA’s analysis pipeline is significantly more computationally demanding than a typical web application. For each repository, the system may perform: Git acquisition → parsing → symbol extraction → graph construction → embedding generation → vector indexing → repository intelligence generation The hosted service therefore needs enough CPU and memory to perform repository-wide analysis reliably while supporting concurrent developer requests. The project is already containerized and designed to run as a production FastAPI service, so I am not requesting infrastructure for an experimental prototype. I am looking for compute support to provide a public, limited demonstration environment for an already-developed open-source project . Why Hugging Face I would like to host the public ARIA demonstration on Hugging Face Spaces because Hugging Face provides an excellent environment for making open-source AI developer tools accessible to the community. At the moment, my account shows Docker Spaces as requiring a paid plan. As an independent student/open-source developer, I am trying to maintain ARIA without personally taking on recurring infrastructure costs. I would therefore like to ask: Would ARIA be eligible for a Hugging Face Community Hardware Grant or another form of open-source compute sponsorship that would allow me to run the existing Dockerized application on suitable CPU hardware? I do not require a GPU specifically. CPU-based compute with sufficient memory for repository analysis would be enough. Current project maturity ARIA already includes: - Production-oriented Docker deployment - Concurrency-safe repository acquisition - Branch-isolated analysis targets - Bounded background analysis workers - Retrieval performance optimizations - Qdrant vector database integration - Gemini → DeepSeek provider failover - MCP API boundary - VS Code integration - Automated backend and frontend test suites - Self-hosted deployment support The hosted instance would be intentionally constrained with limits on: - Repository size - Number of files analyzed - Concurrent analyses - Analyses per user - Chat usage - LLM token consumption - Maximum analysis duration This would allow the hosted environment to remain a controlled community demonstration rather than an unrestricted public service. Goal My goal is to make ARIA useful to the open-source developer community while keeping the project completely self-hostable. A community-supported Hugging Face deployment would allow developers to try ARIA immediately without installing the full analysis stack themselves, while the MIT-licensed repository would remain available for anyone who wants to run it independently. Thank you for considering the project and for supporting open-source developers. I would be grateful for any guidance on the appropriate Community Hardware Grant or sponsorship process. Best regards, Varshith Reddy