Taipy is 2 years old. It was developed not only for Data scientists but also with end users in mind.
First, Taipy was created because we failed miserably when we tried Python GUI tools on real data/AI projects. We tried with tools like Streamlit or even with Gradio. These are ok for having a quick pilot for data scientists not for real projects.
In addition, we wanted to embed the concept of scenarios in our backend to easily perform what-if analysis, compare your executions, etc.
We can go into more details if you are interested to delve deeper into the advantages of Taipy: performance, support for large data in your gui objects, style kit customization, gui events, sync/async calls, etc.
I am not in the market for any of this stuff, but as a piece of advice HN readers respond much better to technical marketing than anything that smells like sales copy.
If you were you going to rewrite this, I would expect to understand WHY your thing is different or better. Does it render faster? Does it have a cleaner API? Is it built on different primitives?
"These are ok for having a quick pilot for data scientists not for real projects." <- this sentence doesn't mean anything.