Best practices for organizing code repository for multiple machines? What about deployment?

This page summarizes the projects mentioned and recommended in the original post on /r/NixOS

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  • nixos-config

  • digga

    A flake utility library to craft shell-, home-, and hosts- environments.

  • I like the concept digga/devos uses (unfortunately their stuff kind of is an overengineered incomprehensible mess): They use: - modules: for modules like in nixpkgs (i.e. stuff that defines options and generates configuration based on that options; are included into every host) - profiles: concrete configuration, can be included to host definitions - suites: sets of profiles (so you can for example have a desktop suite with all your profiles with "desktop" configuration options and apply that to all your desktop computers)

  • WorkOS

    The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.

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  • deploy-rs

    A simple multi-profile Nix-flake deploy tool.

  • I've messed around with deploy.rs. Simple enough to know what's going on.

  • .dots

    just my .dotfiles

  • As for organization, I think it's a matter of finding what works for you. I try to keep system installations small as possible, abstract out the shared parts and include the programs I need through home manager. Anything I don't use all the time, I adhoc install through nix-shell -p. Try to read through as many configs as you have the patience. To add to your list here's mine: https://github.com/dmadisetti/.dots

  • NUR

    Nix User Repository: User contributed nix packages [maintainer=@Mic92]

  • As for the community repo, there's nur but only 120 people use it.. I think the reason nixpkgs is so big is that the community is pretty accepting of accepting loads of different packages.

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    Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

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NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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