
DuckWorth
DuckWorth asks a simple question: when someone describes what they want in plain language, which products actually answer that intent, and what are those products worth? The Dive starts with semantic search, then follows the trail from matching products to sales outcomes, so the story moves naturally from discovery to business value. The design keeps that path easy to read. Search controls, KPI summaries, trend views, category mix, and the Semantic Lake each have a clear job, with a layout meant to make comparison feel calm instead of crowded. Its interactivity is practical rather than decorative. Preset queries, shelf-width tuning, date filters, category filters, and product drill-downs let viewers test how meaning changes the selected shelf and immediately see how the revenue picture changes with it. The takeaway is intentionally direct: semantic search is more useful when it connects to measurable outcomes. DuckWorth shows not only which duck-themed products are semantically similar to an idea, but which ones drive revenue, margin, and momentum.
AI Prompts Used
I used a series of prompts on Codex with MotherDuck MCP from synthetic data generation to Dive creation. Find the final repo at https://github.com/na399/Divemaxxing
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