> This doesn't sound very practical advice to me. It seems to be saying: Do a bit of breadth-first mixed with some depth-first. But whatever you do don't do onl

> This doesn't sound very practical advice to me. It seems to be saying: Do a bit of breadth-first mixed with some depth-first. But whatever you do don't do only depth-first. It seems to be saying that to me too. Given the tree and maze visualizations, I'm a bit surprised that there was no corresponding visualization of the recommended strategy of switching back and forth between depth-first and breadth-first search (at regular or perhaps random intervals). Regarding the optimality of various strategies, there is a distinction to be made between the optimal strategy for an individual learner/searcher, and the optimal individual strategy for searching collectively. Also, there is a difference between strategies for acquiring existing knowledge that is new to you, vs. expanding the boundary of existing knowledge. Regarding the "expanding the boundary of knowledge collectively" problem space specifically, it is worth noting that a pure depth-first approach isn't really possible in practice. Even in fields that reward depth-first individual search strategies such as math, an individual searcher's search is bounded by the length of their career, and each new searcher starts semi-randomly somewhere along the then-current boundary of collective knowledge.