kruise VS go

kops - Production Grade K8s Installation, Upgrades, and Management silver-surfer - Check ApiVersion compatibility and provide Migration path for Kubernetes objects when upgrading Kubernetes to latest versions Kube-ops-view - Kubernetes Operational View - read-only system dashboard for multiple K8s clusters kubeprompt - Kubernetes prompt info Metalk8s - An opinionated Kubernetes distribution with a focus on long-term on-prem deployments kind - Kubernetes IN Docker - local clusters for testing Kubernetes Clusterman - Cluster Autoscaler for Kubernetes and Mesos Cert-manager - Automatically provision and manage TLS certificates Goldilocks - Get your resource requests "Just Right" katafygio - Dump, or continuously backup Kubernetes objets as yaml files in git Rancher - Complete container management platform Sealed Secrets - A Kubernetes controller and tool for one-way encrypted Secrets OpenKruise/Kruise - Automate application workloads management on Kubernetes https://openkruise.io kubectl snapshot - Take Cluster Snapshots kapp - simple deployment tool focused on the concept of "Kubernetes application" — a set of resources with the same label https://get-kapp.io keda - Event-driven autoscaler for Kubernetes Octant - To better understand the complexity of Kubernetes clusters Portainer - Portainer inside a Kubernetes environment Gardener - Deliver fully-managed clusters at scale everywhere with your own Kubernetes-as-a-Service Kubed - Kubernetes Cluster Operator Daemon Kubestack - Kubestack is the free and open-source GitOps framework to codify your custom platform stack using Terraform.

openkruise/kruise (Go): Automate application workloads management on Kubernetes

A Discussion about including this package in Go as encoding/json/v2 has been started on the Go Github project on 2023-10-05. Please provide your feedback there.

I like the Principles section. Very measured and practical approach to releasing new stdlib packages. https://go.dev/blog/randv2#principles

The end of the post they mention that an encoding/json/v2 package is in the works: https://github.com/golang/go/discussions/63397

There used to be the GO FIPS branch :

https://github.com/golang/go/tree/dev.boringcrypto/misc/bori...

But it looks dead.

And it looks like https://github.com/golang-fips/go as well.

I'm not sure what exactly you mean by acknowledgement, but here are some counterexamples:

- A proposal for sum types by a Go team member: https://github.com/golang/go/issues/57644

- The community proposal with some comments from the Go team: https://github.com/golang/go/issues/19412

Here are some excerpts from the latest Go survey [1]:

- "The top responses in the closed-form were learning how to write Go effectively (15%) and the verbosity of error handling (13%)."

- "The most common response mentioned Go’s type system, and often asked specifically for enums, option types, or sum types in Go."

I think the problem is not the lack of will on the part of the Go team, but rather that these issues are not easy to fix in a way that fits the language and doesn't cause too many issues with backwards compatibility.

[1]: https://go.dev/blog/survey2024-h1-results

Now, I’m not going to use C++ again; I left that chapter years ago, and it’s not going to happen. C++ isn’t memory safe and easy to use and would require extended time for developers to adapt. Rust is the new kid on the block, but I’ve heard mixed opinions about its developer experience, and there aren’t many libraries around it yet. LLRD is too new for my taste, but **Go** caught my attention.

Generative AI development has been democratised, thanks to powerful Machine Learning models (specifically Large Language Models such as Claude, Meta's LLama 2, etc.) being exposed by managed platforms/services as API calls. This frees developers from the infrastructure concerns and lets them focus on the core business problems. This also means that developers are free to use the programming language best suited for their solution. Python has typically been the go-to language when it comes to AI/ML solutions, but there is more flexibility in this area. In this post you will see how to leverage the Go programming language to use Vector Databases and techniques such as Retrieval Augmented Generation (RAG) with langchaingo. If you are a Go developer who wants to how to build learn generative AI applications, you are in the right place!

net/http: add methods and path variables to ServeMux patterns Discussion about ServeMux enhancements

Make sure you have Go installed https://go.dev/.