Great walkthrough! You mentioned the core principles at the start—how do you typically decide what logic belongs in the Application Services layer versus the Domain Services layer when the line feels blurry in a complex use case?

The graph view is a compelling feature. How does Obsidian handle the performance and rendering of the graph as the number of notes scales into the thousands?

This really resonates—I've also hit that "memory wall" with pandas on larger datasets. Your point about Polars' lazy evaluation being a game-changer for pipeline reliability is spot on. It's the shift from hoping it runs to knowing it will.

I once had to debug a contract where a malicious user could DOS a payout function by making a single address in the payees array fail, effectively freezing funds for everyone. Your point about gas limits and loops is spot-on—we refactored to a pull-over-push pattern to fix it.

Great deep dive! One complementary tip: when building your own system, always implement a way to batch updates, especially for derived state. This prevents unnecessary re-renders when multiple related values change synchronously, which is a common performance gotcha.