go-sumtype VS go

I find this implementation to be quite minimal and less clumsy than alternatives. Sure, you don't get nice exhaustive pattern matching. Also, type inference gets in the way when instantiating UserKey (though you can wrap it in constructor functions). But expressing your intent using types still makes your code much more convenient and easier to understand.

The call out to sum types is something I feel. I've been using Rust daily for almost 10 years now, and sum types are absolutely still one of the things I love most about it. It's easily one of the things I miss the most in other languages. I'm usually a proponent of "using languages as they're intended," but I missed exhaustiveness checking so much that I ported a version of it to Go[1] as a sort of lint.

[1]: https://github.com/BurntSushi/go-sumtype

A Google search for golang sum types currently shows my project as a second hit: https://github.com/BurntSushi/go-sumtype

I've been writing Go and Rust nearly daily for about a decade now (Go is more than a decade, Rust is about 8 years). You are not going to teach me anything about the pros and cons of either language in a reddit comment. I do not need to be taught about the "iota mess" when I've written tooling for exhaustiveness checking in Go.

https://github.com/BurntSushi/go-sumtype forces exhaustive type switches for interfaces specifically annotated to need that.

We can fix these two problems by relying on static analyzers such as go-sumtypes

See also https://github.com/BurntSushi/go-sumtype

go-sumtype[0] has completeness checking for sealed interfaces.

[0] https://github.com/BurntSushi/go-sumtype

Pretty easy to achieve outside of the compiler: https://github.com/BurntSushi/go-sumtype

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/.