As a visual learner (even when learning a new word or the name of a new acquaintance, I must picture how the letters look standing next to each other, like a little painting, or I do not remember it), I actually find this way of learning an algorithm fun and engaging - it's what I end up having to do in my head anyway, and I may not picture the system as correctly as the instructors who drew up these instructions - which will inevitably lead to a TA telling me how I have the "wrong intuition" about a particular theorem when I seek help...

I just understood Fleury’s algorithm in about 5 minutes - I cannot tell you how fast and revolutionary that is for me.

While it's an interesting idea, these are confusing to understand even for someone who knows these concepts.

It seems possible that having to puzzle them out makes it an effective way to learn, at least if you can stand to.

I think the idea can work, however the execution is still far from perfect.

Just on the binary search for example, the "balance" representation is re-used to signify the concept of "comparison", however the tokens put in balance are both of the same size, with only an image to differentiate them. So the link is not implicitly made from their size, weight, and the intrinsic feature which makes the one searched token the right one. This would be confusing for anyone trying to understand the algorithm.

Then, you got a first depiction of the problem, with each object being ordered by size. So the initial assumption is that the choice element would be on their size. Afterward, the target token has a star on it, and the generic, incorrect one, a question mark.

That could work, however, they both have the exact same size.

This is only an example of how the concepts used to explain and signify things visually are only partially followed-through. This greatly impairs the impact of the explanation.

And this is only on the binary search, arguably the simplest algorithm depicted here.

I found these confusing myself, but I would be interested to know if anyone else who didn't understand these algorithms before was able to learn them based on these diagrams?

Excellent use of visual metaphor. But comprehension can be greatly improved with:

1. Some textual callouts 2. Better representation of the sequence the reader is to follow

Cute! This reminds me of Nigel Holmes' "Wordless Diagrams".

The crying figure is super sad!