sol2 VS pybind11

As someone said, make the game data-driven is a good first step but I will say, also have some sort of way to add additional game logic. For C++ games, lua is really easy to embed the interpreter in your C++ binary, read in the files from a directory (like /mods) with the C++ filesystem api new in C++17, and it's very easy to use SoL to write an API for lua specific to your game. Many games use lua in this way and it's probably the most common mod path setup.

I've only ever done this from C++, but it's using the same lua C library, so should be durable from C as well. You can look up how sol2 or any other wrapper libraries do it.

Consider sol2

This version relies on a Lua C++ wrapper called sol2 to hide Lua stack management from the developer, so creating new bindings can be done by adding a few lines of human-readable C++. It still has to be done manually, but at least sol2 is able to automatically figure out types of objects being bound, so it's not much different from our de-/serialization code.

Here's an example: sol2

Here is link number 2 - Previous text "Sol"

Have you considered using sol2? https://github.com/ThePhD/sol2 Or if you don't want to switch over, you can at least look at their code and see how they handle this.

It is half of a pun as I was inspired by [sol3](https://github.com/ThePhD/sol2) which is a lua <-> c++ wrapper. Sol means sun and the julia c-api prefixes all it's functions with jl, luna means moon so it is pronounced "jay luna"

If you want to be portable I'd recommend C++ and Lua, I used those for years and it runs on everything and there's this most amazing wrapper API which was a huge inspiration

I'm investigating using C++ to build a REST server, and would love to know of people's experiences with Crow-- or whether they would recommend something else as a "medium-level" abstraction C++ web server. As background, I started off experimenting with Python/FastAPI, which is great, but there is too much friction to translate from pybind11-exported C++ objects to the format that FastAPI expects, and, of course, there are inherent performance limitations using Python, which could impact scaling up if the project were to be successful.

I have a C++ function computing a large tensor which I would like to return to Python as a NumPy array via pybind11.

This is based on the Google Ink Stroke Modeler C++ library, and using pybind11 to make it available on python.

pybind11 makes it easy to call C++ from Python if you want to mix.

If you've never used Pybind before these pybind tests[1] and this repo[2] have good examples you can crib to get started (in addition to the docs). Once you handle passing/returning/creating the main data types (list, tuple, dict, set, numpy array) the first time, then it's mostly smooth sailing.

Pybind offers a lot of functionality, but core "good parts" I've found useful are (a) use a numpy array in Python and pass it to a C++ method to work on, (b) pass your python data structure to pybind and then do work on it in C++ (some copy overhead), and (c) Make a class/struct in C++ and expose it to Python (so no copying overhead and you can create nice cache-aware structs, etc.).

[1] https://github.com/pybind/pybind11/blob/master/tests/test_py...

I have a python module for which the logic is written in C++ and I use pybind11 to expose the objects and functions to Python.

Reading from Python requires a wrapper, using pybind11 this is fairly done.

Also I can highly recommend starting using C++ to augment your Python code, i.e. find the parts that are slow or undoable in Python and write those in C++ then expose them as Python functions. You can use https://github.com/pybind/pybind11 to call C++ code from Python.