The first time I implemented Vamana from the DiskANN paper, my approximate nearest neighbor index was slower than brute force. On tiny test fixtures, brute force took 0.27 ms per query. My Vamana implementation took 22.98 ms. That sounds absurd. ANN exists to skip work. The problem was not the algorithm. It was how I mapped the paper's abstractions to actual data structures. The DiskANN pseudocode
Most trading fee calculators show you two numbers: the dollar amount and the percentage of notional. Both are correct. Neither is useful. Here's the problem. Say you're trading Bitcoin perpetuals on Bybit. Taker fee is 0.055% each side. You buy $10,000 notional. Entry fee: $5.50 Exit fee: $5.50 Round trip: $11.00 Does that matter? Impossible to say without knowing one more number: how much are you