SaltStack VS kubernetes

Of course, booting from a disk is not the only option. If you want to automate installs across servers - you have to boot from the network. That is something you could play around with, in GNS3 for example, using FAI or your own system based on SaltStack. You could skip the installation altogether, and use a Live Image, or a Thin Client.

In the new style, when the tag is longer than 20 characters, an end of tag string is appended to the tag given by the string constant TAGEND, that is, two line feeds '\n\n'. When the tag is less than 20 characters then the tag is padded with pipes "|" out to 20 characters as before. When the tag is exactly 20 characters no padded is done. source: https://github.com/saltstack/salt/blob/master/salt/utils/event.py

https://github.com/saltstack/salt https://github.com/chocolatey/choco https://github.com/nextcloud https://github.com/authelia/authelia https://github.com/grafana/grafana

SaltStack

One thing that really irks me about Salt, though, is that they are very slow to fix bugs. My Salt states are littered with workarounds for bugs that have been open for multiple years. Even in basic things, like ssh authorized_keys management. Other than bug velocity, though, I've been pretty pleased with Salt.

Here are several related GitHub issues: - https://github.com/saltstack/salt/issues/54791 - https://github.com/saltstack/salt/issues/57541 - https://github.com/saltstack/salt/issues/16089

For clarity, here's the issue: https://github.com/saltstack/salt/issues/64111

Nightly builds on supported branches & master running the full test suite, producing fully tested builds. https://github.com/saltstack/salt/actions/workflows/nightly.yml

These calls return structured Protobuf messages containing resource usage data such as CPU, memory, network, process, IO, and per-container stats, depending on the platform and runtime implementation.

The runtime exposes stats through CRI RPC methods.

I run the Jenkins controller in Kubernetes. Helm chart for the deploy, persistent volume for the home dir, a sidecar that injects JCasC config from a ConfigMap. Upgrading Jenkins is just bumping a chart version. Rolling back is rolling back a chart version. Plugin lists are values in a Helm values.yaml file, version-pinned, and reviewed in a pull request like any other change.

Does this scenario sound familiar? It's what happened with containerization before Kubernetes. Kubernetes came along and said: Here's the standard. MCP is doing the same thing for AI tooling.

Building your own runtime layer is the right call in a narrow set of scenarios. The open-source ecosystem has matured enough that deep platform engineering teams can stand up their own orchestration layer on top of the official Model Context Protocol Python or TypeScript SDKs. The SDKs implement the MCP specification over JSON-RPC 2.0 and support both stdio for local process communication and Streamable HTTP for remote execution. Teams wrap MCP servers in adapters provided by frameworks like LangChain or Mastra so agents can invoke them directly, then deploy on Kubernetes using custom Helm charts.

Amazon Elastic Kubernetes Service (EKS) is a fully managed service from Amazon Web Services (AWS) that makes it easy to run Kubernetes on AWS without needing to install, operate, or maintain your own Kubernetes control plane. It automates cluster management, security, and scaling, supporting applications on both Amazon EC2 and AWS Fargate.

Fsnotify is a library that many projects depend on. Kubernetes is one example, and this issue was a concern. arp242's explanation is reasonable, and the concerns seem to have been alleviated. https://github.com/kubernetes/kubernetes/issues/138812

I felt this issue strongly reflects recent trends:

1. Projects are considered "dead" if they haven't changed for a certain period. https://github.com/fsnotify/fsnotify/issues/735

2. People's perceptions are shaped by ambiguous information on social media.

3. People are constantly afraid of dependencies.

4. "AI rewrite" projects are created instantly.

⭐ kubernetes/kubernetes — 122,084 stars, 42,978 forks