storage VS go

Check here: https://github.com/containers/storage/blob/main/docs/containers-storage.conf.5.md

One example that keeps popping up over the years is containers and ZFS or more specifically Linux kernel namespaces and ZFS. First LXD in 2016, podman in 2020 and 2021. There is docker issues in the past as well with the ZFS storage driver or overlayfs. These issues are fixed rather quickly by ZFS (because they are very good at what they do) or by upstream, but bugs keep happening. It is something I do not want to deal with. As I expect future problems with ZFS and projects that depend on specific features of the linux kernel, I prefer using something else. In this case Stratis, LVM and XFS, or LVM and ext4.

I tried using NFS because I know it well, and it is easy to do using ZFS. This Red Hat blog post says NFS should work and it does not work at the same time. I decided to just try. The ZFS server has no idea about the subuids on the podman host, so I had to mess around with --uidmap and --gidmap. That worked, as long as I did not use a pod. To keep things neat and simple, I tried to put all my Nextcloud containers into one pod. However, the id-mapping features cannot map multiple container IDs to the same host IDs. So, I cannot map the www-data (70) user and the postgres (82) user to localadmin (1000) on the podman host. Next, I tried directly mounting the NFS share as a volume using the '--opt type=nfs4' option when creating the volumes. Right away, I learned that rootless containers can't mount network shares. Makes a certain kind of sense and is also documented in the man page. But I first tried using root containers, to prove out the concept. The volumes mounted without complaint, but I landed back at square one because the id-mapping is not applied anywhere now. Appears to me that, NFS is a complete dud for this kind of application.

Docker is properly attributed to, see https://github.com/containers/storage/blob/a4cc7aa79e050c976...

I think OP wanted to say that Podman hates Docker what is not I feel when I'm interacting with the community there. People who use Podman do it because of it's additional features that Docker does not have, like starting an Container from a rootfs or mounting the currect directory in a container using "." as path. It's a lot of small things that make Podman better.

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/.