This visualization demonstrates a three-stage search pipeline that locates content across numerous blog-post dialogs without loading entire files. Using Python's async/await syntax, the code calls three functions—`get_first_notes`, `get_section_summaries`, and `get_section_content`—in bulk loops to filter a folder down to relevant sections, reducing token usage from tens of thousands to roughly six thousand. The transitions between stages are driven by agent analysis of summaries, with each stage executing as a single kernel call.