intellij-community VS go

Also, no BuSL stupidity, they're all Apache 2 AFAIK: https://github.com/JetBrains/intellij-community/blob/idea/23...

And the "all you can eat" toolbox license is just a staggeringly good deal, IMHO, which also comes with a "you can keep your license forever, just no updates" which is way different from setting subscription-based licensing money on fire when your license expires. Whoever came up with that should be applauded because it really drives down my "what about" anxiety of paying subscription money for IDEs

I readily admit I am not familiar enough with fossil to know about the impedance mismatch, but I'll point out that https://github.com/JetBrains/intellij-plugins/tree/idea/241.... https://github.com/JetBrains/intellij-community/tree/idea/24... https://github.com/JetBrains/intellij-community/tree/idea/24... https://github.com/JetBrains/intellij-community/tree/idea/24... may a long way toward finding how they think about those operations

There is quite a bit going on in our view’s class, so we'll take it slow and go through its functions one by one, according to their importance. The first thing we need to do is to create the structure our items will fit into. com.intellij.ui.treeStructure.Tree seems to best match our needs, and that’s what we’ll use. In order to prepare it for what is coming, we need to configure it.

I think it's time to wrap it up. We've made a pull request to the IDEA developers, and I've accomplished the tasks I set out to do. I'm really happy to help the developers of my favorite IDE.

I offer, again, my JetBrains GrammarKit counterpoint from the last time that assertion came up <https://news.ycombinator.com/item?id=38192427>

>>>

I consider the JetBrains parsing system to be world class and they seem to hand-write very few (instead building on this system: https://github.com/JetBrains/Grammar-Kit#readme )

- https://github.com/JetBrains/intellij-community/blob/idea/23... (the parser I'll concede, as they do seem to be hand-rolling that part)

- https://github.com/JetBrains/intellij-community/blob/idea/23... (same for its parser)

- https://github.com/JetBrains/intellij-community/blob/idea/23... and https://github.com/JetBrains/intellij-community/blob/idea/23...

- https://github.com/JetBrains/intellij-plugins/blob/idea/233.... and https://github.com/JetBrains/intellij-plugins/blob/idea/233....

I had the same experience with OmniGraffle, https://www.omnigroup.com/omnigraffle

It just worked. There was support. I wouldn't dig a hole in the ground with my bare hands, why wouldn't I use good tools. Of course I would like to use F/OSS for various reasons.

The model I absolutely love is Jetbrains, their core product is OSS, Apache licensed. The whole thing, totally usable. https://github.com/JetBrains/intellij-community

The money I send their way does both, it pays for developers and it puts an amazing artifact in the world that others can use and learn from. If they weren't open source, I wouldnt pay for it. I don't know how many others are the same as me, but Jetbrains really deserves credit here.

# https://github.com/JetBrains/intellij-community/blob/idea/233.9802.14/json/src/jsonSchema/schema.json#L52

and also FOSS (Apache 2): https://github.com/JetBrains/intellij-community (as well as PyCharm found in the "python" subdirectory)

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/.

The Go Programming Language

When Go first shipped, it was already well-documented that the only stable ABI on some platforms was via dynamic libraries (such as libc) provided by said platforms. Go knowingly and deliberately ignored this on the assumption that they can get away with it. And then this happened:

https://github.com/golang/go/issues/16606

If that's not "getting burned", I don't know what is. "Trying to provide a nice feature" is an excuse, and it can be argued that it is a valid one, but nevertheless they knew that they were using an unstable ABI that could be pulled out from under them at any moment, and decided that it's worth the risk. I don't see what that has to do with "not being as broadly compatible as they had hoped", since it was all known well in advance.

Sadly, I think that is indeed radically different from Go’s design. Go lacks anything like sum types, and proposals to add them to the language have revealed deep issues that have stalled any development. See https://github.com/golang/go/issues/57644

I've been writing a lot about Go and gRPC lately: