To find these crucial border points, we employed a clever technique based on the Ford-Fulkerson algorithm. By simulating "flooding" roads with traffic from random start/end points, we could identify the natural bottlenecks – the "minimum cut" in graph theory terms. These bottlenecks became our border points.
Once you've identified target queries, the automated system tests them periodically—daily, weekly, or on whatever schedule makes sense for your monitoring needs. Each test queries the AI model with your specified prompt, captures the response, parses which sources were cited, and records whether your content appeared. Over time, this builds a database showing your visibility trends, how often competitors appear for the same queries, and which topics you're gaining or losing ground on.
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This approach shares a lot in common with the idea of multivariate interpolation over scattered data. Multivariate interpolation attempts to estimate values at unknown points within an existing data set and is often used in fields such as geostatistics or for geophysical analysis like elevation modelling. We can think of our colour palette as the set of variables we want to interpolate from, and our input colour as the unknown we’re trying to estimate. We can borrow some ideas from multivariate interpolation to develop more effective dithering algorithms.
“It’s clearly fake because it’s not my voice and not my lips moving,” Tkachuk said. “I’m not in control of any of those accounts. … I know that those words would never come out of my mouth.”