jwplayer VS orchest

JW Player | New York (NYC), NY / UK / REMOTE | Full-time

JW Player pioneered video on the web over a decade ago and continues to innovate as the world’s largest network-independent platform for video delivery and intelligence. Media companies including Apartment Therapy, Business Insider, and Univision, in addition to hundreds of thousands of creators of all types and sizes, rely on JW Player to deliver and monetize their content across all devices.

JW Player’s massive global footprint of over 2 billion unique devices creates a powerful data graph of unique consumer insights and generates billions of incremental video views.

JW Player also started as and continues to be the most popular open-source video player: https://github.com/jwplayer/jwplayer

All our openings can be found here: https://www.jwplayer.com/careers

Overall, we are seeing record video traffic over the past 2 years, and after recently announcing a $100m raise we are looking to expand and tackle a ton of exciting challenges at a huge scale.

If interested in any of these roles, please email me at [email protected]

JWPlayer has a standard CC Non Commercial licence: https://github.com/jwplayer/jwplayer/blob/master/LICENSE

GET https://gxm-jw.cdn.vustreams.com/live/channel01/live.isml/.m3u8 404 bugsnag.js:2428 JW Player Error 102404. For more information see https://developer.jwplayer.com player setup error bugsnag.js:2428 reloading live video

Something to play the video. Modern browsers have basic video playback widgets built in, but if you want auto-switching between encodings based on a client's bandwidth/buffering, you might prefer a fancier javascript playback widget. JW Player might do this; it has been around for quite a while.

Report bugs for JW Player here, the default volume is just an initial value and the player is under no obligation to preserve it.

JW Player | Senior Software Engineer, Sales and Solutions Engineers | New York (NYC), NY and India | REMOTE currently, ONSITE likely | Full-time

JW Player pioneered video on the web over a decade ago and continues to innovate as the world’s largest network-independent platform for video delivery and intelligence. Media companies including Apartment Therapy, Business Insider, and Univision, in addition to hundreds of thousands of creators of all types and sizes, rely on JW Player to deliver and monetize their content across all devices.

JW Player’s massive global footprint of over 2 billion unique devices creates a powerful data graph of unique consumer insights and generates billions of incremental video views.

JW Player also started as and continues to be the most popular open-source video player: https://github.com/jwplayer/jwplayer

All our openings can be found here: https://www.jwplayer.com/company/careers/

Overall, we are seeing record video traffic over the past few months, and with that comes a ton of great challenges to work on at a huge scale.

If interested in any of these roles, please email me at [email protected]

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