yaegi VS gophernotes

Yes. There are long standing feature requests for (e.g.) the reflect package that simply don't get done because they'd break this assumption and/or force further indirection in hot paths to support "no code generation at runtime, ever".

Packages like Yaegi (that offers an interpreted Go REPL) have "know limitations, won't be addressed" also because of these assumptions.

https://github.com/golang/go/issues/4146

https://github.com/golang/go/issues/16522

https://github.com/traefik/yaegi?tab=readme-ov-file#limitati...

There is always https://github.com/traefik/yaegi - a Go interpreter written to make it easy to write plugins.

I think https://github.com/traefik/yaegi comes as close as using the python interpreter in you CLI, but for Go

There's a go interpreter: https://github.com/traefik/yaegi It could run programs without compiling them, but there're some limitations.

I'd recommend looking into a different approach for plugins such as hashicorp/go-plugin (which uses multiple process PIDs and RPC communication between them) or traefik/yaegi (which implements a Go-compatible scripting language that can be interpreted at runtime and which still supports most Go modules).

Why do we need a language like Rust when we have Rust. Why not just create a Rust interpreter. (There's such an interpreter for Go, BTW, https://github.com/traefik/yaegi )

A cool solution I saw was Traefik's yaegi module. They basically created an interpreted scripting language with Go compatible syntax (turning Go into an interpreted, not compiled, language). I haven't tried this but it sounds like it brings the better parts of dynamic languages like Python's plugin support to Go - plugin writers can still write "Go" code, which can load dynamically.

https://github.com/gopherdata/gophernotes

I've had this bookmarked for some time and just havent gotten around to it.

Gopher Notes Kernel for jupyter notebooks? Could be useful :)

I started this because gophernotes was not working for another project I'm slowing working on -- it is interpreted, and not up-to-date (generics, etc).

A go kernel is available at https://github.com/gopherdata/gophernotes

> You do know that statically typed languages have REPLs too? Like the ML family, including Haskell.

I do, but that I don't see how that relates to the bit of my post which you've quoted. I certainly didn't claim or imply that REPL and static type systems were mutually exclusive, only that REPLs are a poor substitute for many static analysis tasks.

> And when using something like a Jupyter notebook with a kernel for your compiled language https://github.com/gopherdata/gophernotes you can do similar interactive programming.

Yeah, I'm aware. I operate a large JupyterHub cluster (among many other things) at work. :)

> Lisp REPLs take that a step further, as you interact with and in your whole actually running program.

That sounds nice, but it's too abstract to persuade IMHO.

If you are coming from python,jupyter notebook, gophernotes is a great library to setup your own playground.

Why can't you just build libraries to make Go a better language for data science? There's already Go support for a Jupyter Notebooks kernel: https://github.com/gopherdata/gophernotes