xeus-cling VS preplish

More recent activity, but based on clang: https://github.com/jupyter-xeus/xeus-cling https://github.com/root-project/cling

If you like interactive c/c++, how a look at https://github.com/jupyter-xeus/xeus-cling, that allow you to run the c/c++ repl in Jupyter, either in web interface, and terminal interfaces.

There are Cpp intepreters like Cling. There are even cpp notebooks like https://github.com/jupyter-xeus/xeus-cling. If that's an "IDE" it's questionable

Holy shit, its actually a thing for C++ https://github.com/jupyter-xeus/xeus-cling. Now if only there was a C version...

Links I tried and failed:https://github.com/jupyter-xeus/xeus-cling

Are you talking about interpreted C++? Xeus-cling is your friend (i.e., C++ interpreter).

> what's wrong with that?

Why nothing at all, of course. A REPL need not be more than a way to test and explore syntax, functions, and logical structures.

> the user experience is REPL-ish and it can help some people learn the _basics_ of the language

PREPLISH exists for Perl ^_^

https://github.com/viviparous/preplish

If this is for testing of syntax or of trivial code, it sounds like a good use-case for running a local REPL. (Example: https://github.com/viviparous/preplish)

This is a simple REPL project and the readme lists other Perl REPLs.

https://github.com/viviparous/preplish

Perl's concise syntax makes working in a REPL a pleasure. Python has a REPL but the design of the language makes it expand both in length (for loops) and in width (tabs).

I am a recent convert to working in a REPL first to test programming ideas.

I made this solution for some of my simple data wrangling: https://github.com/viviparous/preplish

I tried the module it in a Perl REPL (https://github.com/viviparous/preplish) and got the following ravings that are worthy of a US loony politician:

I agree with you that the immediate start-up and feedback is a great benefit to the coder. This is why I dislike complex, Rube-Goldbergian REPL systems.

There is a use-case for a throw-away interaction with a REPL. For example, how does $builtinFuncX work, or how would $data best be imported into a structure?

A REPL can also be a good initial approach to a more ambitious problem. In this case, a REPL can be good for focus and discipline.

If the second case is going to answer your concern and be constructive, it's necessary to be able to build the code for sharing and cleanly export the code for re-use.

I've had success tackling challenges using REPLs for Python and Perl [1] in both ways. But no tooling is going to solve the problem of a sloppy teammate who claims success just because "it compiles" and "it works on my box". A person who knows how to build good tooling goes further.

[1] https://github.com/viviparous/preplish

It is Jupyter is a Rube-Goldbergian nightmare. Python is a memory hog. There are better solutions, to be sure.

A simple REPL is all that's needed to both do A-type and B-type data exploration. (I won't use the term "data scientist", it's an exaggeration in most cases.)

Python has a REPL, R has a REPL, Perl has PDL and both a simple REPL (https://github.com/viviparous/preplish) and a more complex one (https://metacpan.org/pod/Reply).

Jupyter should not be used as an IDE because it is the wrong tool for development. A-type data explorers just want a painless UI and may not care much about the horrible agglutination of incomplete/slow/broken solutions that Jupyter represents.