1. I use words for variable names instead of single letters like the textbooks do. I use "priority" instead of "F", "cost_so_far" instead of "G", "heuristic" instead of "H", "cost" instead of "w", "frontier" instead of "OPEN" or "O", "visited" instead of "CLOSED" or "C", "current" instead of "u", "next" or "neighbor" instead of "v".

2. The textbooks use an "open" and "closed" set. But in code, these aren't explicitly stored in set data structures. Instead, they're implicit. The cost_so_far dict(map) contains as keys both the open and closed sets, and the frontier (priority queue) contains the open set. So in my explanation of A* I focus on these data structures (priority queue and dict) instead of the open/closed sets. And when I do talk about the sets, I talk about the combined open and closed sets, calling it "visited" or "reached", because it's the combined set that is actually in the data structures.

3. The textbooks use a priority queue with reprioritization. When you visit a node that has a lower cost than the previously found cost, you go into the priority queue and adjust the cost. In my presentation I don't use reprioritization. Instead, I insert another entry into the priority queue with the lower cost. This makes the priority queue simpler (reprioritization is complicated). And in practice, I think it's faster too.