R-AI-D is a Distrobox pet container for artificially intelligent sound analysis and synthesis. R-AI-D features:
- the Ubuntu 24.04 LTS “Noble Numbat” operating system,
- the R programming language (R Core Team 2022),
- bspm: Bridge to System Package Manager (Ucar 2026),
- the Quarto scientific and technical publishing system,
- R packages for package development (H. Wickham and Bryan 2023),
- R packages for interfacing with AI tools:
- ellmer (Hadley Wickham et al. 2025),
- mcptools (Couch, Chang, and Gao 2026),
- ollamar (Lin and Safi 2025),
- ragnar (Kalinowski and Falbel 2026),
- shinychat (Cheng et al. 2025),
- vitals (Couch 2025),
- R packages for sound analysis and synthesis (Sueur 2018),
- the Ollama framework for managing local models, and
- a modern command line including Ollama-supported coding assistants.
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, so the release includes scripts that 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. The Ollama-launchable coding assistants OpenCode, Codex, and Claude CLI are also included.
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 the R AI packages cover a lot of the bases already, such as interfacing with Ollama, retrieval augmented generation (RAG), and model context protocol (MCP).
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, January. https://doi.org/10.21105/joss.07211.
R Core Team. 2022. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.
Sueur, J. 2018. Sound Analysis and Synthesis with r. Use r! Springer International Publishing.
Ucar, Iñaki. 2026. Bspm: Bridge to System Package Manager. https://cran4linux.github.io/bspm/.
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.