Visual UI snip tool for A/B variant generation — drag to select a component, get design variants instantly
Wellknown found it in public sources; nobody has proven control of it yet. Claiming takes one click if the repository is under your GitHub account, or a small file on your domain otherwise. Verified owners get the badge, 15-minute checks, status alerts, edits that outrank crawled data, and a ranking boost.
Agents can do it too: POST https://wellknown.network/api/v1/claims with {"agent":"orpheus-mcp","method":"well_known_file"} — machine-readable steps at claim.json, guide at /docs/claim.
Everything here was measured by our prober or read from a registry. Nothing is self-reported.
Attributed to the source that supplied each field. Treated as claims, not facts.
<p align="center"> <img src="images/orpheus_logo.png" alt="Orpheus Logo" width="200"> </p> # Orpheus — Technical Design Document **Version:** 1.0 **Date:** 2026-03-19 **Status:** Draft --- ## Table of Contents 1. Executive Summary 2. Problem Statement 3. System Overview 4. Component Design 5. Data Models 6. Retrieval Pipeline 7. Learning-to-Rank System 8. Attribution System 9. Human-in-the-Loop Flow 10. Multi-Metric Ingestion 11. Corpus Design 12. Corpus Quality Validation 13. Corpus Improvement Loops 14. Integration Architecture (PostHog, GA4) 15. Limitations and Known Tradeoffs 16. React SDK Expansion Plan 17. One-Week MVP 18. 4-Week Build Plan (Compressed Rollout) 19. Project Structure --- ## 1. Executive Summary Design decisions in UI/UX work are currently made through a combination of intuition, tribal knowledge, and periodic A/B testing — but these three information sources rarely talk to each other. A designer proposes a variant, engineers run the test, results go into a spreadsheet, and the next designer starts from scratch. The A/B UI Variant Recommender closes this loop: it generates design variants informed by what has actually worked, improves from every test result and user selection it observes, and always explains exactly why it proposed what it proposed. The system operates as an MCP (Model Context Protocol) server, integrating directly into AI coding tools like Claude Code, Cursor, and Windsurf. When a developer or designer asks for UI variants for a hero section, pricing table, or CTA button, the system retrieves the top candidates per domain from a curated corpus (style, color, typography, layout, UX patterns), anchors Variant 1 on the `ui-reasoning.csv` expert recommendation for that product/page context, then selects Variants 2 and 3 by maximizing embedding distance from already-chosen docs — guaranteeing visual diversity without pigeonholing into pre-defined strategy buckets. Strategy labels (e.g. "bold", "layered") are assigned pos…
Mapped onto the structured taxonomy from declared text and observed tool names. Confidence shown for derived entries.
Every source is kept verbatim. Field changes are logged as events.