argo-navis VS orchest

argonavis.io | Lead Software Engineer | NYC | Full-time or Part-time

Do you like space exploration, modular SSTO spaceplanes and player-driven economies?

Argo Navis is an early stage, deep tech game project based in NYC.

We're looking for a part-time to full-time lead software engineer that can potentially continue as co-founder. Compensation is heavy on equity but we have funding for market rate cash pay. The role can be partially remote.

We're working on a new multiplayer space-based sandbox game. Our current focus is physics simulation, procedural generation and resource-based economics. Two major influences are Kerbal Space Program and Eve Online.

The game engine, server and client code are all in an embarrassingly early stage. The plan is to flesh these out with prototype implementations of spatial reference frames, Orbital / Newtonian mechanics and collision detection / resolution. All game states and state transitions will be wrapped in a cryptographic layer to support verification of the current game world, its history and replays of partial world states. Deterministic randomness (not a contradiction; see BLS crypto) will drive the procedural world generation. Players interact with the server exclusively through secure cryptographic signatures - enabling the game and all its interaction to form a secure protocol. But don't worry, we're not building a blockchain nor shilling any tokens :)

The project is radically open: all server and client code is open source and all digital / sound assets are open and free to use. Drafts of first principles, source code:

https://github.com/star-formation/argo-navis/blob/master/doc...

You can check out our OSS https://github.com/orchest/orchest

The code example is part of our ELT/data pipeline tool called Orchest: https://github.com/orchest/orchest/

First want to say congrats to the Patterns team for creating a gorgeous looking tool. Very minimal and approachable. Massive kudos!

Disclaimer: we're building something very similar and I'm curious about a couple of things.

One of the questions our users have asked us often is how to minimize the dependence on "product specific" components/nodes/steps. For example, if you write CI for GitHub Actions you may use a bunch of GitHub Action references.

Looking at the `graph.yml` in some of the examples you shared you use a similar approach (e.g. patterns/openai-completion@v4). That means that whenever you depend on such components your automation/data pipeline becomes more tied to the specific tool (GitHub Actions/Patterns), effectively locking in users.

How are you helping users feel comfortable with that problem (I don't want to invest in something that's not portable)? It's something we've struggled with ourselves as we're expanding the "out of the box" capabilities you get.

Furthermore, would have loved to see this as an open source project. But I guess the second best thing to open source is some open source contributions and `dcp` and `common-model` look quite interesting!

For those who are curious, I'm one of the authors of https://github.com/orchest/orchest

Haven't tried it. In its favor, Argo is vendor neutral and is really easy to set up in a local k8s environment like docker for desktop or minikube. If you already use k8s for configuration, service discovery, secret management, etc, it's dead simple to set up and use (avoiding configuration having to learn a whole new workflow configuration language in addition to k8s). The big downside is that it doesn't have a visual DAG editor (although that might be a positive for engineers having to fix workflows written by non-programmers), but the relatively bare-metal nature of Argo means that it's fairly easy to use it as an underlying engine for a more opinionated or lower-code framework (orchest is a notable one out now).

If you’d like to try something for you data workflows that’s vendor agnostic (k8s based) and open source you can check out our project: https://github.com/orchest/orchest

You can find it here: https://github.com/orchest/orchest Convenience install script: https://github.com/orchest/orchest#installation

We automatically provide container level parallelism in Orchest: https://github.com/orchest/orchest

For people in this thread interested in what this tool is an alternative to: Airflow, Luigi, Kubeflow, Kedro, Flyte, Metaflow, Sagemaker Pipelines, GCP Vertex Workbench, Azure Data Factory, Azure ML, Dagster, DVC, ClearML, Prefect, Pachyderm, and Orchest.

Disclaimer: author of Orchest https://github.com/orchest/orchest