In many discussions around password recovery, the focus seems to be on increasing compute resources and brute-force throughput. However, in practical security and forensic workflows, how much of the performance improvement actually comes from better candidate generation and prioritization? For example, using known password structures, reused patterns, contextual clues, partial user memory, or probabilistic ordering to reduce the effective search space before additional compute is applied. In real-world recovery scenarios, where do practitioners typically see the larger gains: smarter candidate selection or increased compute capacity? submitted by /u/DapperAsi [link] [comments]