R-AI-D (R AI Distrobox) - Rtificially Intelligent Sound Analysis and Synthesis

Introduction

R-AI-D is a Distrobox pet container for artificially intelligent sound analysis and synthesis. R-AI-D is based on the Rocker “Noble Numbat” r2u container image. R-AI-D features:

• the Ubuntu 24.04 LTS “Noble Numbat” operating system,
• the R programming language (R Core Team 2022),
• r2u: CRAN as Ubuntu Binaries
• bspm: Bridge to System Package Manager (Ucar 2026),
• the Quarto scientific and technical publishing system,
• R packages for package development (Wickham and Bryan 2023),
• R packages for interfacing with AI tools (Verde Arregoitia 2026):
  • ellmer (Wickham et al. 2025),
  • mcptools (Couch et al. 2026),
  • ollamar (Lin and Safi 2025),
  • ragnar (Kalinowski and Falbel 2026),
  • shinychat (Cheng et al. 2025),
  • vitals (Couch 2025),
• the Faust functional programming language for sound synthesis and audio processing,
• the Ollama framework for managing local models, and
• a modern command line, including the Ollama-launchable OpenCode open source AI coding agent.

R-AI-D was developed on Bluefin DX, but should run on any Linux host system supporting Distrobox. I test on a Raspberry Pi 5, and the release hosting setup scripts should work on any recent Debian or Ubuntu host. On x86_64 systems, an NVIDIA GPU will be detected and used automatically.

Like its Bluefix DX inspiration, the R-AI-D command line features Homebrew and the Starship cross-shell prompt generator. The Cascaydia Cove nerd font is included and other nerd fonts are available.

Licensing, contributing, roadmap, etc.

R-AI-D uses the Creative Commons CC0 license, which is a bunch of words that say mostly “R-AI-D is public domain.” R-AI-D is just some bash scripts that download some open source software and build it into a container. Why would I copyright that?

Contributing? Forks, bug reports and feature requests are welcome. Pull requests, on the other hand, probably not. I don’t have the time to review other peoples’ code beyond fixing typos.

The roadmap is mostly determined by what I need and when. The only feature I want to add is a “native” port to Ubuntu running in Windows Subsystem for Linux. Hosting setup scripts for Fedora, CentOS Stream, Arch Linux and openSUSE Tumbleweed are easy to do but I have no need for them. If you do, open an issue and I’ll put them in.

Later in my exploration of AI I will probably need some AI engineering tools like JupyterLab, PyTorch, and CUDA, but the image is pretty big already, so I won’t add them until I need them. And R AI packages cover a lot of the bases already, such as interfacing with Ollama and other local LLM tools, retrieval augmented generation (RAG), and model context protocol (MCP).

Building the Image and Container

See container-creation/README-Building-the-Image-and-Container.md

Raspberry Pi 5 Hosting

See hosting-setup/README-Setting-up-container-hosting.md

References

Cheng, Joe, Carson Sievert, Garrick Aden-Buie, and Barret Schloerke. 2025. Shinychat: Chat UI Component for ’Shiny’. https://posit-dev.github.io/shinychat/r/.

Couch, Simon. 2025. Vitals: Large Language Model Evaluation. https://github.com/tidyverse/vitals.

Couch, Simon, Winston Chang, and Charlie Gao. 2026. Mcptools: Model Context Protocol Servers and Clients. https://github.com/posit-dev/mcptools.

Kalinowski, Tomasz, and Daniel Falbel. 2026. Ragnar: Retrieval-Augmented Generation (RAG) Workflows. https://ragnar.tidyverse.org/.

Lin, Hause, and Tawab Safi. 2025. “Ollamar: An r Package for Running Large Language Models.” Journal of Open Source Software, ahead of print, January. https://doi.org/10.21105/joss.07211.

R Core Team. 2022. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. https://www.R-project.org/.

Ucar, Iñaki. 2026. Bspm: Bridge to System Package Manager. https://cran4linux.github.io/bspm/.

Verde Arregoitia, Luis D. 2026. Large Language Model Tools for r. https://doi.org/10.5281/zenodo.19260391.

Wickham, Hadley, Joe Cheng, Aaron Jacobs, Garrick Aden-Buie, and Barret Schloerke. 2025. Ellmer: Chat with Large Language Models. https://ellmer.tidyverse.org.

Wickham, H., and J. Bryan. 2023. R Packages: Organize, Test, Document, and Share Your Code. O’Reilly Media. https://books.google.com/books?id=kTHFEAAAQBAJ.