orchest VS sourcegraph

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

Sourcegraph | REMOTE | Full-Time | Machine Learning Engineer, Developer Advocate, Enterprise Product Manager, Technical Advisor | https://sourcegraph.com

Sourcegraph is a code AI platform that makes it easy to read, write, and fix code–even in big, complex codebases.

We are building Cody, an AI coding assistant that uses code search and code intelligence to help devs quickly understand what's happening in code and generate new code that matches the best practices in your codebase. Cody supports AI-enabled autocompletion, fixing bugs, refactoring, test generation, code explanation, and answering high-level questions. You can read Steve Yegge's post on why Cody's code context engine differentiates it from the fast-moving field of AI dev tools: https://about.sourcegraph.com/blog/cheating-is-all-you-need.

Apply here: https://grnh.se/0572f98b4us

That's pretty much what https://sourcegraph.com/ are selling, is it not?

Despite their shitty rug-pull <https://github.com/sourcegraph/sourcegraph/pull/53345>, I do really like Sourcegraph and one doesn't (currently?!) need to be logged in to use it: https://sourcegraph.com/search and they have a handy rewrite pattern such that one can just plug the repo path into the URL for quick searching e.g. https://sourcegraph.com/github.com/JetBrains/intellij-commun...

- https://sourcegraph.com is pivoting and building a copilot application (named Cody). This is pretty good, since sourcegraph is great at understanding your code

While a readable Dockerfile can work as documentation, there are a few caveats:

* the application needs to be designed to work outside containers (so, no hardcoded URLs, ports, or paths). Also, not directly related to containers, but it's nice if it can be easily compiled in most environments and not just on the base image.

* I still need a way to notify me of updates; if the Dockerfile just wgets a binary, this doesn't help me.

* The Dockerfiles need to be easy to find. Sourcegraph's don't seem to be referenced from the documentation, I had to look through their Github repos to find https://github.com/sourcegraph/sourcegraph/tree/main/docker-... (though most are bazel scripts instead of Dockerfiles, but serve the same purpose)

We use Sourcegraph, which is a tool that searches through code in repositories. We leverage this tool in order to understand the adoption curve of our components across all of Reddit. We have a dashboard for each of the platforms to compare the inclusion of RPL components over legacy components. These insights are helpful for us to make informed decisions on how we continue to drive RPL adoption. We love seeing the green line go up and the red line go down!

SourceGraph: https://github.com/sourcegraph/sourcegraph/pulls?q=is%3Apr+a...