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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.

The benefit extends beyond AI optimization. Structured content is easier for human readers to scan and comprehend too. People increasingly skim content rather than reading every word, and clear structure helps them extract key information quickly. When you optimize for both AI processing and human scanning through better structure, you improve the experience for all visitors while increasing AI citation rates.

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Quadtree-based image compression formats and level-of-detail systems all work this way. Satellite imagery, terrain rendering, and geographic information systems use quadtree decomposition to serve data at varying resolutions: zoomed out, you see large coarse blocks; zoomed in, you see fine-grained tiles. The same principle extends to three dimensions (octrees) for volume rendering and 3D spatial indexing.

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