sniffnet VS nbdev

Sniffnet is an open source, Rust-based network monitoring tool I’ve been working on for almost two years now.

As described in this issue the GUI library used by Sniffnet doesn't support yet text selection, but they are working on it and hopefully soon it will.

I’ll be personally talking about Sniffnet, the Rust-based network monitoring tool I’m working on: I’m so excited to share it with the world!

I'm the creator and maintainer of Sniffnet, an open-source network monitoring tool developed in Rust.

Sniffnet is a Rust-based network monitoring tool to help everyone inspect their Internet traffic.

Sniffnet, a cross-platform app to comfortably monitor your Internet traffic written in Rust, has now a website. See the related discussion on Github.

Another option is https://github.com/GyulyVGC/sniffnet if you are just trying to see what packets are flowing and who's talking. It's nowhere near as powerful as Wireshark (nor is it designed to be) but it might not be as confusing for someone who is new to network packet sniffing. I think the biggest downside is the Windows install file doesn't come with the NPCAP driver, I think they really need to bundle that with the installer the way Wireshark does, otherwise people may not get it to work.

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 😅)