apiclarity VS go

We'll install APIClarity into a Kubernetes cluster to test our API documentation. We're using a Kind cluster for demonstration purposes. Of course, if you have another Kubernetes cluster up and running elsewhere, all steps also work there.

If you go to APIClarity, the first thing you’ll see is the source code (Figure 1), followed by some documentation at the bottom.

This feature is available in the open-source tool APIClarity, as part of the OpenClarity initiative.

Hi, I would also like to add another tool I'm contributing to at work (cisco) called APIClarity [1]. It aims at reconstructing swagger specifications of REST microservices running in K8S, but can also be run locally.

This is a challenging task and we don't support OpenAPI v3 specs yet (we are working on it).

Feel free to have a look, and get ideas from it :)

We'll also be presenting it at next Kubecon 2022.

[1]: https://github.com/openclarity/apiclarity

For example, APIClarity is a tool that observes all of the API traffic within your Kubernetes environment. Based on traffic observation, APIClarity infers an OpenAPI description for those APIs. This is especially helpful if the API creator never defined or provided such a description. It also surfaces potential problems with existing APIs, such as requests made to undocumented, shadow APIs or continued use of deprecated, zombie APIs. If you’re getting started on the path toward OAS compliance, then tools like APIClarity can be a great source of insight and observability.

The fundamental first step to solving this problem is to create an API spec and use it to audit and document the APIs your apps use. Ideally, we would create an API spec simply by observing API traffic in real-world applications. In the past, there was no simple, scalable, and open-source tooling capable of doing this. Now, we have APIClarity—an open-source API traffic visibility tool for Kubernetes (K8s) clusters. It’s purpose-built to address the gap and enable API reconstruction through observation.

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/.