About Reppo

Planetary Scale AI Training Data through Prediction Markets.

Reppo is a decentralized blockchain network designed to create Ai training data at scale. Reppo leverages prediction market mechanisms to align economic incentives with truthful and high-quality evaluation on crowdsourced source data. The network coordinates data owners, data labelers, data annotators and data consumers to interact, validate and get rewarded for their efforts. Reppo can thought of as "Bittensor for AI training Data" for a broad strokes understanding of the broader vision, although the underlying incentive mechanism, consensus rules, and architecture are entirely different. The "miners" on Reppo network are primarily source data contributors who participate in subnets of their choice and provide licensed and proprietary human-generated input. On the other end, data labelers and annotators i.e. voters (VeREPPO holders) who can be compared to "validators" who redirect network emissions to miners based on the quality of their work.

Reppo Network is made up of public and private subnets, each of which a specialized mini-network powered by Reppo's incentive framework and focuses on a specific type of AI training data through specific tasks. Some examples are listed here.

Different subnets leverage different collection methods and Reppo's platfrom provides the necessary enablement tooling. A public subnet allows permissionless contribution from miners and validators (content guardrails permitting).

Today, Reppo does not operate its own L1 or L2 blockchain. It is a generalized Data-AI coordination layer to crowdsource on-demand human feedback at planetary scale.

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Our flagship product is a self-serve platform, deployed on Base, that allows anyone to start curating domain-specific AI training data. the public subnet is maintained by Reppo Foundation, where we facilitate interaction between GenAI content creators and voters to gather curated human feedback and preference data. We are also collecting evals on underlying models as well as benchmarking the various AI tools/platforms being used by miners on Reppo.

Enterprises, startups, and even individuals permissionlessly start 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.