Update applying_neos.md

applying_neos.md

@@ -15,7 +15,7 @@ If you need some inspiration for a HistFactory-based solution, there are a coupl
 - [`dilax`](https://github.com/pfackeldey/dilax) is a slighly more mature version of this, but does not use the same naming conventions as `pyhf`. It's a nice first attempt at what could be the right way to go about this in future.
 
 ## long-term plans
-I'm not working very actively in the field right now, but I've tried my best to indicate the direction I think things should go in [this discussion on the `pyhf` repo](https://github.com/scikit-hep/pyhf/discussions/2196). The key ingredient is PyTrees (read the issue for more details). If you're interested in working on this, I'd be happy to help out, but I don't have the time (or the HistFactory expertise) to do it fully myself. I think it's a really important thing to do, though -- probably essential if this is going to be truly used in HEP!
+I'm not working very actively in the field right now, but I've tried my best to indicate the direction I think things should go in [this discussion on the `pyhf` repo](https://github.com/scikit-hep/pyhf/discussions/2196) -- if this is important to you, maybe leave a reaction or a comment there! The key ingredient is PyTrees (read the issue for more details). If you're interested in working on this, I'd be happy to help out, but I don't have the time (or the HistFactory expertise) to do it fully myself. I think it's a really important thing to do, though -- probably essential if this is going to be truly used in HEP!
 
 I've just released [`relaxed` v0.3.0](https://github.com/gradhep/relaxed), which has been tested with dummy PyTree models to work. It's designed for a `pyhf` that doesn't exist yet, and may never exist at all. But it will work with any PyTree model, so if you can write a PyTree model, you can use `relaxed` to do your fits, then backpropagate through them.