You can get used to landing on your toes. It took me quite a while to get used to, but I managed to run a half-marathon six months after I started doing it.
I disagree about kNN ineffectiveness. It produces satisfactorily results even for gene expression classification, where you have about 100 examples described with 10000 attributes. It does not succumb to the curse of dimensionality that quickly as it evaluates each attribute separately and only combines the results afterwards. The curse of dimensionality is more evident if the learning algorithm tries to deal with attribute interactions of higher orders.
Learning algorithms are just tools. No tool is perfect for every task. Also, you should use the tools you know well, because you can leverage them best.
SVMs are quite hard to be used correctly due to sensitivity to parameters, which do not seem intuitive. You need to know your data well to set them right.
Well, it is academic software. I work at ailab.si and a lot of my work concerns Orange, especially its bioinformatics addon. Its development is slow because we also need to do some other things, like research. New features are usually added when we need them.
It is actively maintained/developed but by only few developers, so its progress can seem slow at times.