javafx-gradle-plugin VS go

One, I don't understand Java environments really well. All I have done so far is create some GUI applications using JavaFX. Wish I could share my code, but unfortunately, its part of my assignments and can't be open-sourced. Second, the instructions I found in the Contributing docs were bare-minimum, and kinda hard to follow for a beginner. An experienced Java developer would get them really quickly, no doubt about that.

JavaFX (an open source, next generation client application platform for desktop, mobile and embedded systems) has many useful out the box UI controls to build modern interactive desktop apps. These include buttons, checkboxes, list views, labels etc, that can be configured and styled in countless ways. I’ve using them for many years at work building mapping apps!

**Useful resources** _([Full list](/r/JavaFX/wiki/resources))_ * [OpenJFX.io](https://openjfx.io/) * [JavaFX source](https://github.com/openjdk/jfx/) * [JavaFX bugtracker](https://bugs.openjdk.java.net/browse/JDK-8146386?jql=project%20%3D%20JDK%20AND%20component%20%3D%20javafx) * [JFX-Central](https://www.jfx-central.com/) [**Rules**](/r/JavaFX/about/rules/) 1. No NSFW/NSFL content 2. No service requests/offers 3. Must be JavaFX related 3. No politics **Related subreddits** - [/r/java](/r/java) - [/r/javahelp](/r/javahelp) **Other communities** - [JavaFX on Discord](https://discord.gg/yZ3Y3Fd) - [JavaFX on programming.dev](https://programming.dev/c/javafx)

As I'm typing this, I'm realizing that the 4 part webinar actually sets up a lot of it. By this point, you should have installed: your IDE (I used intellliJ), the Java SDK, downloaded the JavaFX file here(that you'll manually add into your project structure), and the MySQL connector J file.

JavaFX works well with Kotlin if you're looking for an imperative toolkit. Just add this plugin in your build.gradle to import it.

You could also download the JavaFX modules separately from https://openjfx.io/

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/.