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We want to nest splitters, with different editors. A script that parses a structured input might make this faster and more accessible.
The goal is that a parent splitter would be the fee recipient for the cluster, and would be editable by Obol or others to contribute the appropriate amount of reward to the retroactive fund, the remainder would go to the child splitter, or any extra addresses if included.
The child splitter would contain all of the customers of the operator's addresses proportionally. The editor would be the operator address or a nominated party/multisig. This model would smooth rewards across all customers, increasing regularity of payment and average payout. Further iterations might include stealth addresses to minimize doxxing of recipients, but they likely can still be probabilistic-ly clustered with CL analysis.
🛠️ Proposed solution
A script to parse a structured data format, to create these nested splitters would be helpful.
Approved design doc: link
🧪 Tests
👐 Additional acceptance criteria
We should decide if splitsV2 are worth using considering
❌ Out of Scope
The text was updated successfully, but these errors were encountered:
Also worth noting that we mostly haven't used splits's v2 splitter (i think because there was something about their push vs pull nature). We should confirm they work fine, and script deploying them rather than v1 splitters.
🎯 Problem to be solved
We want to nest splitters, with different editors. A script that parses a structured input might make this faster and more accessible.
The goal is that a parent splitter would be the fee recipient for the cluster, and would be editable by Obol or others to contribute the appropriate amount of reward to the retroactive fund, the remainder would go to the child splitter, or any extra addresses if included.
The child splitter would contain all of the customers of the operator's addresses proportionally. The editor would be the operator address or a nominated party/multisig. This model would smooth rewards across all customers, increasing regularity of payment and average payout. Further iterations might include stealth addresses to minimize doxxing of recipients, but they likely can still be probabilistic-ly clustered with CL analysis.
🛠️ Proposed solution
A script to parse a structured data format, to create these nested splitters would be helpful.
🧪 Tests
👐 Additional acceptance criteria
We should decide if splitsV2 are worth using considering
❌ Out of Scope
The text was updated successfully, but these errors were encountered: