For an alternative you could check out neofetch -- https://github.com/dylanaraps/neofetch -- it's pretty cool. Source: about 1 year ago

Well, yes... they're running on non-Windows systems/alternative operating systems. What are you expecting? Plug-and-play? That's not going to happen with non-Native applications. Just like if you were to install (as an example) neofetch onto Windows, you'd have to recompile it's instructions to run on it (sidenote: You can get neofetch to run on Windows... Via Windows Subsystems for Linux, but that's off topic). Source: about 1 year ago

That's a program called neofetch. Should be in every repository of every GNU/Linux distribution, already just install it with whatever tools you normally use to install software in the repositories. Source: about 1 year ago

For those who don't know, pfetch is a more minimal version of neofetch. I recently rewrote pfetch in Rust and added a few more distro logos, including SteamOS. The project can be found here. Source: about 1 year ago

There are a few ways to do it, but I just used Neofetch. Source: over 1 year ago

In this blog post series, we will demonstrate how to use AWS IoT Greengrass and Hashicorp Nomad to seamlessly interface with multiple interconnected devices and orchestrate service deployments on them. Greengrass will allow us to view the cluster as a single "Thing" from the cloud perspective, while Nomad will serve as the primary cluster orchestration tool. - Source: dev.to / over 1 year ago

AWS IoT Greengrass allows one to manage their IOT Edge devices, download ML models locally, so that inference can then be also be done locally. - Source: dev.to / about 2 years ago

To assist in deployment and management of workloads in your fleet, it's worth taking advantage of a fleet or device management tool such as AWS GreenGrass, Formant or Rocos. - Source: dev.to / over 2 years ago

In some cases, such as with edge devices, inferencing needs to occur even when there is limited or no connectivity to the cloud. Mining fields are an example of this type of use case. AWS IoT Greengrass enables ML inference locally using models that are created, trained, and optimized in the cloud using Amazon SageMaker, AWS Deep Learning AMI, or AWS Deep Learning Containers, and deployed on the edge devices. - Source: dev.to / over 2 years ago

Take a look at Greengrass https://aws.amazon.com/greengrass/ Enables OTA updates and fleet management. Source: about 3 years ago