gleam VS go

I haven't had time to really try to write anything in it, but https://gleam.run/ looks really good too. Like Elm for backend + frontend!

Want a friendly language for building safe systems at scale? Gleam is here for you. It features modern and familiar syntax, that's reliable and scalable. Gleam runs on an Erlang virtual machine, and can run plenty of concurrent tasks. It comes with a compiler, build tool, formatter, editor integrations, and package manager all built in so you can get started right away. Congrats to the team on shipping your first major version 🙌.

While I love Clojure, I have to agree about tooling. I recently started using Gleam* and was impressed at how easy it was to get up and running with the CLI tool. I think this is an important part of getting people to adopt a language.

* https://gleam.run/

If you use languages that compile to WASM (such as Gleam https://gleam.run), and can also run Postgres via WASM, then it opens very interesting offline scenarios with codebases which are similar on both the client and the server, for instance.

Recently, Gleam has gained more popularity, and a lot of developers (including me) are learning it. At the time of this writing, it has exceeded 14k stars on GitHub; it grew really fast for the last month.

Oh, strange that github had a date of 2016 on this one: https://github.com/gleam-lang/gleam/issues/2

I was just going by that, though I do remember checking out gleam 5 years ago or so.

Re: macros, I really do think they’re a big deal and all the other newer languages I’ve used, such as Rust have some kind of macros or powerful meta programming features.

For older languages, a few, like Ruby have enough meta programmability to make nice DSLs, but many others don’t. Given the choice, I’d much rather have Elixir/Clojure style macros than other meta-programming facilities I’ve seen so far.

I had been only following this language with some interest, I guess this was born in gitlab not sure if the creator(s) still work there. This is what I'd have wanted golang to be (albeit with GC when you do not have clear lifetimes).

But how would you differentiate yourself from https://gleam.run which can leverage the OTP, I'd be more interested if we can adapt Gleam to graalvm isolates so we can leverage the JVM ecosystem.

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/.