riverpod VS pybind11

From a library standpoint, I would recommend https://riverpod.dev over any other state management libraries for productivity purposes.

I have the following code. When I hot reload I get an error like this. Apparently this is fixed in dart 3 and Remi mentions a solve here. I am unclear how to refactor my code to achieve the solve without upgrading Flutter to an unstable version (which I've tried and cannot use because of certain render issues).

I saw this official example but it doesn't use riverpod_generator : https://github.com/rrousselGit/riverpod/blob/master/examples/todos/lib/todo.dart

Looking at the riverpod.dev docs, I saw that they were using Docusaurus which led me to creating this site. You can think of it as a guide or your go-to when trying to build gRPC servers and clients using Dart. It's open source hosted at this repo and everyone is really invited to Collaborate.

Riverpod Docs

I think you might need to use a state management if you want to keep the username and email always in scope in your application. Consider using riverpod or provider

You're right, it's Riverpod.

Sources: Riverpod: https://riverpod.dev REST: https://github.com/codeReview-youtube/flutter_auth_api PROJECT: https://github.com/codeReview-youtube/flutter_rest_riverpod_authentication

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.