Announcing the Masa Bittensor Incentivized Testnet

After selling out the Masa Token Launch earlier this year, we’re excited to collaborate with Masa once again on the launch of their Bittsensor Incentivized Testnet. The launch of the Masa Bittensor Subnet unites the technology and community of two pioneering forces in decentralized AI, Masa and Bittensor.

The Incentivized Testnet is designed to test the Masa Bittensor Subnet’s capacity, resilience, and performance, where miners will provide data and computational resources to the Masa Bittensor Subnet. Masa is offering up to 3,000,000 MASA tokens for the top-performing 256 miners.

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Timeline

The Masa Bittensor Incentivized Testnet runs from July 9th to July 23rd, 2024. The timeline is subject to change at the sole discretion of Masa.

Key milestones

• July 9, 2024: Registration opens, testnet begins, and miners will start earning rewards
• July 23, 2024: Testnet concludes
• August 7, 2024: Masa Bittensor Subnet testnet final rewards will be published on a public Dune Dashboard
• Q4’ 2024: Rewards will be delegated to a validator and distributed at Masa Protocol mainnet launch, target Q4’2024

Rewards and how they’re determined

3,000,000 MASA tokens have been allocated to the rewards pool for the Masa Bittensor Incentivized Testnet:

• Miners supply data and bandwidth to power AI training and processing on the Masa Bittensor Subnet
• The top-performing 256 miners at any given point in time will be eligible to earn a share of the 3,000,000 MASA token rewards pool
• Rewards are tied to performance. The higher your performance, the more rewards you may earn
• Performance is based on the amount of data, quality of data, and computational processing power miners provide to the network. All rewards are calculated programmatically over the entire duration of the testnet campaign. Masa ensures that participants are motivated to continually enhance their contributions
• Final rewards will be published on a public Dune Dashboard on or around August 7, 2024
• Rewards will be distributed upon Masa Protocol mainnet launch in Q4’ 2024. Rewards are delegated to a validator and are subject to to-be-announced lockup terms

About Masa Bittensor Subnet

Masa is the decentralized network for Fair AI, where people earn by contributing data. AI developers are empowered to build anything, anywhere with the world’s data. Masa is backed by Digital Currency Group, Anagram, Animoca, and incubated by Binance and Hashkey. Masa was the first AI project to debut on CoinList Token Launch in 2024, with a viral 17-minute sale.

Bittensor is a decentralized, peer-to-peer artificial intelligence network designed for the creation, sharing, deployment, and training of machine learning models. With its sophisticated TAO economic model that incentivizes the decentralized production of high-value artificial intelligence, Bittensor has become a force to be reckoned with. Since its launch in March 2023, Bittensor has grown into one of the first $10-billion AI ecosystems, with institutional validators staking a total of 5.7 million $TAO, worth over $1billion.

Data is the new currency of the AI economy. With many proprietary and open-source options, general purpose LLMs are converging, becoming commoditized, and are in a race to the bottom. The most valuable AI applications will be the most specialized ones that leverage real-time data for up to date context and subject specialization. Specialized AI applications require huge amounts of specialized AI training data that is constantly up to date.

The Masa Bittensor Subnet provides real-time and static, structured, annotated, and vectorized data from a variety of data sources critical for AI development, such as X (Twitter), Discord, diarized speech (e.g. podcasts, YouTube, TikTok), gated web data (e.g. New York Times), and public web data (e.g. Google Search).

Real-time data can be used to build robust datasets or used directly in system prompts, providing up to date context to LLMs. Static data sets are constantly updated and stored by subnet workers for further processing into vectors to fuel Retrieval Augmented Generation (RAG) in AI agents. These data sets are processed and annotated using agentic data pipelines that employ fine-tuned LLMs trained on JSON and other formats to deliver high-quality outputs from volatile data inputs.

Community Links

• Website
• Twitter
• Technical Whitepaper
• Bittensor FAQ
• Masa Bittensor Subnet Testnet Community

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