SICL VS gophernotes

This is a very approachable paper from 1990 on one way to do it with a C kernel bootstrapping to Common Lisp: https://www.softwarepreservation.org/projects/LISP/kcl/paper... Kyoto Common Lisp (KCL) is the ancestor of today's Embeddable Common Lisp (ECL).

SICL is probably the best modern version of CL written in CL from a design standpoint, even if it's not taking over SBCL's role anytime soon: https://github.com/robert-strandh/SICL It uses some fancy bootstrapping to have the whole language available early, e.g. their definition of class 'symbol is:

    (defclass symbol (t)

That's pretty much the objective of SICL, which is "intentionally divided into many implementation-independent modules that are written in a totally or near-totally portable way, so as to allow other implementations to incorporate these modules from SICL, rather than having to maintain their own, perhaps implementation-specific versions".

   https://github.com/robert-strandh/SICL

Gladly!

https://github.com/robert-strandh/SICL (which I wrote a decent chunk of the compiler backend of.)

I applaud your curiosity and initiative to explore. Are you aware of https://github.com/robert-strandh/SICL?

I mean this Klein and this SICL. Self and Common Lisp are memory-safe, though the implementations need capabilities to manipulate memory; SICL encapsulates them using first-class global environments.

There is significant overlap with SICL and its associated pieces which supply many of the other parts needed to make a Common Lisp. Some of these are Cluster which provides a portable and extensible assembler, Eclector which supplies a portable and extensible reader, Concrete-Syntax-Tree that supports source code tracking during compilation, ctype that implements the Common Lisp type system, and Clostrum that provides first-class environments for e.g. run-time, evaluation, and compilation. The SICL project has as one of its goals the creation of portable infrastructure for implementing Common Lisp, and these pieces are novel building blocks that were created as part of the project.

Not really; you can do it with primitive operations e.g. here is the list in the Cleavir compiler and a paper on "magic" in Jikes RVM. SBCL also has a "virtual op"/vop language for code generation, and vops are written to manipulate objects with assembly snippets.

Both work. I basically never have to touch C or even FFI (cl+ssl being the main use of FFI for me), unless I am poking at SBCL guts in my spare time, and that isn't necessary either. I am sure many Haskell hackers are happy with their IO monad too.

It's used in operating systems, compilers and CLIs.

The Cleavir Common Lisp compiler uses three-address instructions in a control-flow graph, though it is intended more for production use than educational use.

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