Reppo.ai - Subnets

The public subnets are currently incentivized by Reppo Foundation, where interaction between publishers and voters is faciliated to gather curated human feedback and preference data. The goal here is to publish open datasets as public goods to showcase how AI training datasets created through on-chain prediction market mechanisms are superior to those created by data vendors and sweat shops. Reppo.ai also collects evals + benchmark metrics on underlying model and platform usage and create a public leaderboard, similar to LM Arena.

On the verification front, Reppo leveragse verification technologies such as Project Vail and Lunal to provide provenance to voters on the AI content being published. Additionally, we plan to build an AVS to further quality of source data for data labelers and annotators in Reppo V2.

Enterprises, startups, and even individuals can permissionlessly spin up new subnets and start collecting curated human feedback on crowdsourced or proprietary content, generating high quality RLHF and DPO training data on-demand for AI models and applications such as self-driving cars, mapping, AR/VR, robotics.

Our mission is to build an open and permissionless network where anyone can contribute to training datasets, provide high signal feedback through their domain expertise, and contribute to making the next gen AI models and systems human aligned.