Capture → Dataset → Training
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This is the Proof-of-Contribution path: useful browsing becomes training material.
A user is researching a topic online and wants to build a high-quality dataset for later BonzAI Desktop training.
The user opens BonzAI+.
They choose Dataset mode.
They capture a text or image target from the current page.
BonzAI+ stores target data, source URL, page context, and local timestamp.
If the target is an image, BonzAI+ stores image data as base64 where available.
Smart Analysis uses local browser AI capabilities to generate richer metadata:
Title.
Caption.
Tags.
Source/context notes.
Image or text description.
Quality hints.
Dataset-ready fields.
The output should be descriptive enough for training, retrieval, evaluation, and provenance.
Each saved sample can be treated as a contribution record. The user can mark it as:
Private only.
Exportable to Desktop.
Publishable contribution.
Training-allowed.
Commercial-allowed.
Royalty-bearing.
BonzAI+ exports a pack containing records, metadata, target data, hashes, and optional signatures. This is still local unless the user shares or publishes it.
BonzAI Desktop's Contribution Studio imports the pack and can:
Validate the schema.
Preview records.
Detect duplicates.
Score quality.
Split train/eval data.
Attach contributor metadata.
Add the imported pack to the local contribution ledger.
The user can train locally with the dataset. If they mint or publish an asset:
Metadata/media is uploaded to IPFS/Pinata where needed.
Hashes, licenses, owners, and revenue routes can be written to registries when configured.
Publishing uses the storage path configured in the app, such as IPFS pinning.
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