effects-examples VS go

Isn't a language described very similar to the (future) OCaml with effects (https://github.com/ocaml-multicore/effects-examples) added?

Sure. They probably don't mention coeffects so often because their effect system subsumes both effects (actions to be performed) and coeffects (information from the context), and it can do way more than what you're proposing. Here are some examples you may take a look. The dynamic state example in there could be adapted to act as coeffects (contexts) as you suggest. For coeffects in particular, this is a great resource. You may also be interested in Koka's documentation, as it was designed to be a language with effects and coeffects since the beginning (OCaml did only retrofit them recently).

Good question!

https://github.com/ocaml-multicore/effects-examples has links to tutorials and examples for how effects can be used.

There's also some slides from KC's talk on effect handlers https://kcsrk.info/slides/handlers_edinburgh.pdf and materials from the CUFP 17 tutorial: https://github.com/ocamllabs/ocaml-effects-tutorial

https://gopiandcode.uk/logs/log-bye-bye-monads-algebraic-eff... this is also a great introduction

Here's a post outlining the part that people are excited about. Here's the examples list if you'd like more concrete examples.

To be honest, though, despite it being cool that OCaml finally has a concrete multicore release date, I'm more interested in the effect handlers. After reading these slides and this article on the topic I realised OCaml getting support for algebraic effects is way more interesting than the parallelism support.

I think it's not discussed enough how things like language features shape how library APIs are formed. People usually seem to only consider the question "how would I use this feature?" and not "how would the standard library look like with this feature?", which is surprising given how much builtin libraries affect the pleasantness of a language.

One of the things I'm excited to see is the cap-std project for Rust [0] given what Pony [1] has demonstrated is possible with capabilities. I'm also hoping that languages like Koka [2] and OCaml [3] will demonstrate interesting use cases for algebraic effects.

[0] https://github.com/bytecodealliance/cap-std

[1] https://www.ponylang.io/discover

[2] https://koka-lang.github.io

[3] https://github.com/ocaml-multicore/effects-examples

Just randomly stumbled upon this example, which is exactly what you were asking about. It is a strongly-typed fork() that uses first-class effects.

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/.