go-cloud VS go

Even when going multi-cloud you can employ different strategies. Vault is definitely one of them, but you can also use federation to exchange one cloud's credentials for another's, giving you the ability to centralize secrets in one of them. You can use a layer of abstraction like GoCloud [0]. You can also build for each cloud separately and decide either not to centralize secrets at all, or build some trivial bespoke tooling to synchronize some of them. I'm not endorsing any of the options, just pointing out that Vault isn't the only one.

https://github.com/google/go-cloud

> If Google started adding Google Cloud specific primitives natively to Go would you call that forward thinking as well?

Go actually ships with a quite forward thinking SQL interface. It's an abstract interface over a DB, and you just import the "driver" that powers it. The driver conforms to a standard interface, so all of them behave roughly the same.

I think this is what everyone wants from Deno/etc - why can't there also be a KV interface that's universal, or a Queue interface that's universal?

People attempted this w/ go [1], where it attempts to use the same nice experience of the SQL logic, but it never seemed to gain traction.

https://gocloud.dev

github.com/google/go-cloud/pubsub package provides a set of interfaces and tools to work with publish/subscribe messaging. This package allows easy communication between independent components by decoupling the sender and the receiver.

I'm guessing this is a reasonable example of what they're using it for? server.go

https://github.com/google/go-cloud probably out of context, but not IaC but agnostic backend development with Go across multiple clouds

The plan is to have a process for generating AWS CDK targeting Lambda (pluggable providers, but start with AWS CDK, because it's what I use), and to use the Google Cloud Development Kit (also called CDK, but not the same) https://github.com/google/go-cloud to abstract the services.

In this post, I will talk about an exciting project maintained by the team that develops the Go language: the Go Cloud Development Kit, also known as the Go CDK.

The API of gocloud.dev, is stable. We are at ariga.io, already use gocloud.dev for internal service, and even in the public for easy adopt multi-clouds provider: https://github.com/ariga/atlas/commit/ef0b0eae65a61375482497ceb9ed9790a469b56e

On Go, which has a community focused on the cloud, there is even GoCloud, a library with a single, common, and high-level API that allows an application to support any of those clouds and even on-premise alternatives for those services. All can be configurable at deploy time by the infrastructure team.

The description indicates it is not production ready, and is archived at the same time.

If you pull all stops in each respective language, C# will always end up winning at parsing text as it offers C structs, pointers, zero-cost interop, Rust-style struct generics, cross-platform SIMD API and simply has better compiler. You can win back some performance in Go by writing hot parts in Go's ASM dialect at much greater effort for a specific platform.

For example, Go has to resort to this https://github.com/golang/go/blob/4ed358b57efdad9ed710be7f4f... in order to efficiently scan memory, while in C# you write the following once and it compiles to all supported ISAs with their respective SIMD instructions for a given vector width: https://github.com/dotnet/runtime/blob/56e67a7aacb8a644cc6b8... (there is a lot of code because C# covers much wider range of scenarios and does not accept sacrificing performance in odd lengths and edge cases, which Go does).

Another example is computing CRC32: you have to write ASM for Go https://github.com/golang/go/blob/4ed358b57efdad9ed710be7f4f..., in C# you simply write standard vectorized routine once https://github.com/dotnet/runtime/blob/56e67a7aacb8a644cc6b8... (its codegen is competitive with hand-intrinsified C++ code).

There is a lot more of this. Performance and low-level primitives to achieve it have been an area of focus of .NET for a long time, so it is disheartening to see one tenth of effort in Go to receive so much spotlight.

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/.