asdf VS go

The main issue most people have with asdf is that it’s annoyingly slow. Not unusably so, but just enough that it’s irritating.

I identified [0] the source for much of it (sub-shells and pipes) and began a PR [1], but became bogged down with BATS testing, and then found mise / rtx, so kind of lost interest. Sorry. You can always implement these if you’d like.

[0]: https://github.com/asdf-vm/asdf/issues/290#issuecomment-1383...

[1]: https://github.com/asdf-vm/asdf/pull/1441

Or if you need to manage more than just node, asdf has been around for over a decade and works great. You can use a .tool-versions to change runtimes for each project you have, in addition to managing your global runtime versions

https://asdf-vm.com/

Why not just use a tool like asdf (https://asdf-vm.com/) or mise (https://mise.jdx.dev/)?

These tools have the advantage of not being multi-taskers and can manage version for all your tools. You wouldn’t need pyenv and npm and rvm and…

We’ve even started committing the .mise.toml files for projects to our repos. That way, since we work on multiple projects that may need multiple versions of the same tool, it’s handled and documented.

The purpose of a version manager is to help you navigate or install any tools for development easily. Version Manager can be one tool for each dependency (e.g. NVM, g) or One tool for all dependencies (e.g. asdf, mise).

(asdf)[https://asdf-vm.com/] fully supports Python and almost any other language. I've been using it for Ruby, Python, Elixir, and other languages for years and never looked back.

Secondly, our development environments must not drift, because then code may behave differently and a change could pass on our machine but fail in production. There are many tools for locking down environments, e.g nix, pkgx, asdf, containers, etc., and they all share the common goal of being able to lock down dependencies for an environment accurately and deterministically. And that needs to be enforced in our local workflow so we don't have to rely on CI environments for correctness. All developers must have environments that are effectively identical to what runs in CI (which itself should be representative of the production environment).

There are many ways this can be done (e.g nix, pkgx, asdf, containers, etc.), and we won’t get into which specific tools to use, because we'll instead cover the essential essence of preventing environment drift:

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/.