Contribution Packs
A Contribution Pack is a file that carries useful AI work from BonzAI+ into BonzAI Desktop.
Think of it as a dataset export with receipts: what was captured, where it came from, how it was described, what permissions the user chose, and how it can be reused.
Why Packs Exist
Without a pack, a dataset is just loose text and images. With a pack, Desktop can understand:
The original source.
The captured text or image.
The generated caption and tags.
The user rating.
The license or permission choice.
Whether the sample is private, exportable, trainable, commercial, or publishable.
Who should be credited if it becomes part of a larger asset.
What A Record Can Include
Type
Text, image, video note, generation, evaluation, skill, model adapter, provider time, or companion/company action
Content hash
A fingerprint of the saved material
Source URL
Where the material came from
Timestamp
When it was captured
Creator wallet
Optional attribution
License
What the user allows
Quality score
How useful the sample seems
Tags
Search and training labels
Task target
What this record is useful for
Privacy status
Private, exportable, publishable, trainable, commercial, or royalty-bearing
Evidence files
Image data, thumbnails, transcripts, or other attached proof
Signature
Optional proof that the creator approved the record
Contribution role
Data owner, curator, evaluator, voter, trainer, provider, publisher, companion, or company
Contribution weight
Local or onchain score used to estimate how much this record helped
Derivation parents
Earlier records, datasets, models, skills, or companions this record depends on
Royalty route
Optional payout route if the record becomes part of a monetized asset
BONZAI utility snapshot
Optional local record of stake, lock, contribution score, tier, and utility weight at packaging time; live contracts remain authoritative
Local Until The User Acts
Contribution Packs stay local until the user exports, shares, publishes, or mints something from them.
Why Parent Records Matter
The important part is inheritance. If an image dataset trains an adapter, and that adapter helps a company create a sellable output, the final output should still know which records, curators, evaluators, and model builders helped.
That does not mean everything must go onchain immediately. It means Desktop should preserve the local graph first, then publish compact hashes, licenses, ownership, and reward routes only when the user chooses to enter the shared economy.
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