Distributed Storage Intelligence
The Distributed Storage system of the bitGPT Network is akin to a vast, communal library where resources are shared and accessible to all, yet maintained by the community itself. This decentralized approach ensures resilience and efficiency, moving away from the vulnerabilities of centralized storage.
Imagine a system where data, like pieces of a puzzle, is distributed across a network. Using a method similar to BitTorrent, the network breaks down large files into manageable chunks, making it easier for devices—especially those with limited resources—to access and download what they need. This not only speeds up the process but also ensures that data is always available, as multiple nodes (or community members) contribute to hosting these chunks.
In addition to this efficient distribution method, the network employs a decentralized vector store built on WebAssembly. This allows browsers to participate in storing and retrieving high-dimensional data, enhancing the system's capability to handle complex information efficiently. The use of local storage and a clever pagination system ensures that even with large datasets, the system remains performant and accessible.
Participants who contribute storage and bandwidth are rewarded, which not only fosters a robust and reliable network but also encourages active participation. The network utilizes smart contracts to automatically track and reward contributors based on their seeding metrics, ensuring fairness and transparency. Moreover, a reputation system is in place to maintain seeder reliability scores, which helps in identifying and rewarding the most consistent and valuable contributors without compromising their anonymity.
To further incentivize the seeding of rare or high-demand data chunks, the network implements dynamic reward multipliers. This means that contributors who provide access to hard-to-find or frequently requested data are rewarded more generously, creating a dynamic that promotes the availability of diverse and valuable content.
Last updated