jak0
My experience so far has been amazing. I'm managing the configuration of different machines with 'Business profiles' that have claude code settings directly managed by me. I set up the repos i want them to have in their machine, clone them with activation scripts, settings, a 'company level' claude.md declared in home manager. I'm seriously considering to use NixOS as the only OS of my company. Of course just to manage your expectations and give you context. I've been building my startup and we are so far me an my 'Business' cofounder and i don't know yet how to scale it but i definitely want to give it a try. I was shocked that he's using NixOS as daily driver coming from Windows and basically barely knowing what linux is...
This setup allow to share CC best practices and settings at speed of light. Skills and plugins are centralized and automatically installed at every build so my colleague basically doesn't even need to know what is a skill.
I'm also managing declatively hardware settings of all the different machines, from an old HP to my brand new Framework 16..
If you want more details on my config is public on github here https://github.com/jacopone/nixos-config would love to have feedback !
I will definitely look into guix thanks!
I'm not a developer. My background is data science and finance. Six months ago I couldn't tell you what a Nix derivation was.
I started because I wanted Claude Code to manage my system, not just my code. Tried it on Ubuntu first and it was a disaster — Claude would edit .bashrc and break my shell, install packages that conflicted with each other, no way to undo any of it cleanly. A friend mentioned NixOS, I installed it on my Framework laptop, and within a week I realized this is what AI-assisted system management should look like. Claude edits a .nix file, I rebuild, if it breaks I roll back in one command. No more "what did the AI just do to my system?"
It snowballed from there. ~470 commits later I'm running 7 machines off this config — my Framework 16 dev workstation, a ThinkPad, and a bunch of business laptops for people who've never opened a terminal. Two profiles: a "tech" profile with 350+ packages and a full AI toolchain (Claude Code, Cursor, local speech-to-text), and a "business" profile with ~40 curated packages for office use. Adding a new machine is three lines in flake.nix.
Some things I built along the way:
A script that spins up Claude Code in a sandboxed git worktree with bubblewrap + seccomp so it can work autonomously in the background. It runs in a tmux session and loops with fresh context up to 5 times. I use it for overnight refactoring.
Custom NixOS installer ISOs — I ship a USB stick to someone, they plug it in, and they get a working system with Claude Code pre-configured as their "sysadmin." They ask Claude to install software, Claude edits the config, they rebuild. I manage their machines remotely via git push.
CI/CD with BATS tests, ShellCheck, security scanning. A two-branch model where personal (my dev branch) auto-syncs to master via CI with path sanitization so nothing personal leaks to the public repo.
The core insight: NixOS is the only OS I've used where AI can't permanently break anything. Declarative config means Claude always knows the exact system state. Atomic upgrades mean every rebuild either succeeds completely or doesn't happen. If something goes wrong, I pick the previous generation from the boot menu. I've bricked my system maybe 15 times and recovered in under a minute every time.
What still sucks: the Nix learning curve is real even with AI. Claude writes non-idiomatic Nix all the time and I can't always tell. Flake lock updates break things in ways that take hours to debug. Error messages are famously terrible. And NixOS is not for everyone — it's a tradeoff between upfront complexity and long-term reliability.
Is anyone else doing something like this? Not just using AI to write code, but to manage and evolve their actual operating system?
It's Data Mining from a statistician point of view. You can download the entire book for free on the website and the graphs and computations are done with R.
This book is also one of Hal Varian's favourites: http://www.dataspora.com/blog/sexy-data-geeks/