There is a help button in the top right that shows you need to focus on the circle node connectors to "solve' the problem.

At least for the first example:

You have a blue box labeled xt with a single node connector at the top. You have a purple box labeled ht with a node connector at the top and bottom. You have a green box labeled Yt with a node connector at the bottom.

The game tells you at the top you have 3 edges remaining.

Dragging a line from one node to another, releasing, and it turning green means you have placed a "correct" connection.

i.e. xt -> ht [bottom] will give you a green line.

Repeat until you have all edges solved for.

It's not spelling it out for you, but once you complete the "game" you'll at a very high level understand the moving pieces within the network, and the "flow" of data.

The help didn’t exist originally: https://news.ycombinator.com/item?id=40430064

I guess "decades of clicking things" is a domain one can be knowledgeable in? Usually boxes with draggable things on the top are inputs, on the bottom are outputs.

Hmm, my decades of clicking things lead me to assume a flow from top to bottom, so something with a connector on the bottom is a source that will output data through that connector, and something with a connector on the top is a sink that will accept input data through that connector.

It flows in the reverse direction of what I’d expect (out is at the top, in is at the bottom, the opposite of any visual programming or diagram I’ve ever seen). It’s also represented in a way I’ve personally never seen ANN’s drawn. I thought you had to connect the dots in the middle and thought “huh? It mustn’t work on mobile” until I read the comments and tried again. And this is with decades of clicking things domain knowledge, and a small bit of neural networks knowledge.

I didn't encounter the issue you described with adding an already existing edge though -- the counter stays the same

the answer to RNN is non-trivial, but perhaps Recurrent in RNN might be of some use

Even knowing what the R in RNN stands for requires some pre-existing knowledge of neural networks. Which isn't something that's helping _me_ learn about them, particularly.