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The asset library

Automatic metadata

Every asset is described, tagged and colour-read on arrival, which is what makes the library searchable at all.

An asset arrives already described. Nobody types a filename, nobody picks tags, and nothing sits in an untitled folder waiting for a tidy-up that never happens.

Written on arrival What it holds
Name Model MM-DD HH:MI, so the list is sortable and readable without being a sentence
Kind and format Image, video, 3D, audio or vector, with the mime type and byte size
Description A written sentence describing what is actually in the picture
Tags The objects, materials and subjects found in it
Colours The dominant colours, as values you can filter by
Style How the image reads
Prompt The exact words that produced it
Model and tool Which engine ran, and which card you ran it from
Settings The full parameter set the run used
Where and when The board, the workspace, who made it, the timestamp

The description, tags, colours and style come from a vision model reading the asset. That is the “AI tagging” part, and it is what turns a library into something you can search by memory rather than by filename.

The vision model writes a description, and the description goes in its own field. It never overwrites the name.

A library where every row reads “A red ceramic vase on a white surface, soft window light from the left” is unusable, because every row looks like every other row until you read a full sentence. Seedream 08-07 14:22 is scannable, and the sentence is one click away when you want it.

Three things read the record:

  • Search, which ranks across name, tags, colours, style, description and prompt at once
  • Filters, by kind and by colour
  • The provenance line on every asset, covered in provenance

Drag in an image from your desktop and it is read the same way. It will not have a prompt or a model, because there was not one, and it will have a description, tags, colours and style.

That means your own reference material becomes searchable alongside what you generated, using the same half-remembered description you would use for either.