Under the Hood

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How did we achieve such amazing results?

For the tech nerd and the curious mind — here's what powers every listing.

Advanced AI models, layered together

1

Claude Opus 5

Primary listing brain — reads your photos and writes the title, description, item specifics, condition and pricing rationale.

2

Claude Sonnet 4.6

High-volume bulk work — bucketing thousands of eBay leaf categories into the right niche prompt.

3

Gemini 3 Flash (web-grounded)

Live reference research — collector clubs, catalogs, societies and identification guides for every niche.

4

Grok 4.3 Vision

Independent fallback vision model so listings keep flowing if a primary model is unavailable.

5

On-device TensorFlow (ml5) + QR/OCR

In-browser image classification, marker-card QR detection and text reading — fast, private, zero upload lag.

Prompts, iterations & testing

A library of niche prompts

Every collectible niche — stamps, postcards, comics, advertising, breweriana and many more — gets its own expert prompt mapped to exact eBay leaf categories.

AI authoring AI

Stronger models draft and refine each niche prompt from researched reference sites, then a universal prompt layers shared rules on top.

Many iterations of testing

Prompts were run against real listings over and over, comparing AI output to what sellers actually published, and tuned until they aligned.

A learning loop

Seller edits are analyzed for recurring patterns and turned into reviewed prompt improvements — so accuracy keeps climbing.

By the numbers

100,000+
Lines of code
679
Source files
240
UI components
60
App pages
174
Backend functions
65
Shared engine modules
59
Data models
23
Automated workflows
4
AI agents
5
Layered AI engines