nbdev VS dioxus

One example I've seen is ML/DL folks using jupyter notebooks to develop DL libraries in jupyter notebooks, see https://github.com/fastai/nbdev

- https://github.com/fastai/nbdev: Increase developer productivity by 10x with a new exploratory programming workflow.

9. Nbdev: Boost developer productivity with an exploratory programming workflow - https://nbdev.fast.ai/

See if you like nbdev way of working with data through python and jupyter. nbdev is an optional part that will create python packages from jupyter notebooks. Also even the simple tutorials are opinionated and will guide you to unit test your code and write CICD pipelines.

You probably don’t have time for this for the purposes of your task, but I will also throw in the recommendation of nbdev especially if you’re a Python person. I haven’t had a project to use it on yet, but I’ve gone through the docs and the walkthrough and it seems like a great framework for starting potential projects with all the infrastructure needed for if/when they eventually get big and need all the packaging and stuff

nbdev: jupyter notebooks -> python package

Take a look at https://github.com/fastai/nbdev - haven't used it but supposedly the whole if fast.ai library was written that way. It sounds like a natural direction in your scenario - allowing your to keep working in a familiar environment and still producing production ready code (will, at least in paper 😅)

We have a web components example here: https://github.com/DioxusLabs/dioxus/blob/fd21c971038840130f...

Everything should work like normal except: attributes are not typed, custom event listeners must be implemented with web-sys

I think something like https://dioxuslabs.com could deliver native, cross platform apps and win back mobile.

While we took an in-depth look at Dioxus in this tutorial, there is still so much to learn. Luckily, Dioxus provides detailed documentation with references and cookbooks to guide developers. Make sure you check it out, and feel free to comment below with any questions.

You can probably imagine the challenges of integrating such a system in a robust way that does a good job and improves on the status quo. We felt like the Tauri implementation worked well for Tauri apps. But there’s a problem: it was so tightly coupled to Tauri that the work couldn’t be enjoyed by other projects (not even the ones like Dioxus who were using Tauri’s underlying technology of Tao + Wry).

Floneum is a graph editor for local AI workflows. It uses llm to run large language models locally, egui, and dioxus for the frontend, and wasmtime for the plugin system. If you are interested in the project, consider joining the discord, or building a plugin for Floneum in rust using WASI