Fraction AI Launches Mainnet on Base, Ushering in Decentralized Auto-Training for AI Agents

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Using this, the protocol moves from the testnet stage to a live, scalable deployment, which allows open and decentralized reinforcement study to create, train, and change AI agents.
Users can now deploy AI agents to the base thanks to the mainnet debut, which provides live “spaces” lives.

The mainnet of Fraction aia decentralized auto-training platform for AI agents, launched to Basea network of Ethereum Layer 2 consumed by Coinbase. Using this, the protocol moves from the testnet stage to a live, scalable deployment, which allows open and decentralized reinforcement study to create, train, and change AI agents.

Users can now deploy AI agents to the base thanks to the mainnet debut, which provides live “spaces” contests that cover topics such as financial review, generation code, and copying. By limiting real-world activities, these settings allow agents to specialize through performance-based reinforcement. In addition to evaluating agent's effectiveness, each competition serves as a training field, converting a closed-lab reinforcement to a user-driven user, without permission to feedback.

Part of AI is based on the development of beneficial agents in human guidance. If there are no clear instructions based on human intuition and context, models can produce content or make crunch numbers, but outcomes are common. Users have assigned tasks to agents in small parts, try them in competitive environments, and make adjustments based on actual feedback. Over time, this cycle increases the expert and effectiveness of the agents.

The Fraction AI has had a huge growth and adoption since its testnet. More than 30 million data sessions have resulted from the creation of 1.1 million agents of more than 320,000 users. More than 90% of the full amount of weth in Sepolia Testnet is now processed through the wise contract of the platform, which shows the scalability and stability of its early infrastructure.

Shashank Yadav, CEO of Fraction AI stated:

“The AI landscape today is defined by centralization, where access to top-tier training methods is restricted to some corporations with massive compute budgets. We have set up Fraction AI to challenge the paradigm-through decentralized education of reinforcement and empowering anyone to guide smart agents with a unique perspective.”

Through continuous contact and competition, thousands of freely created agents can thrive thanks to the innovative study of modern AI protocol study from agent feedback (RLAF). By accumulating experience points, platform agents can govance and get features such as premium operating, ongoing identity, and even token release.

As the protocol develops, users get fractals, which is evidence of contribution that influences frac token allocations. To promote decentralization and protect the network, the system incorporates staking mechanisms.

The mission of Fraction AI is based on widespread access to and technical sovereignty, and it has the support of leading investors including Spartan, Borderless, Anagram, and symbolic capital in addition to polygon, close, and 0g counselors. Developers, creators, and builders can now take their agents from the idea to continued improvement in a vibrant, open intelligence market thanks to the Mainnet launch.

Users can create and own AI agents on the decentralized auto-training platform part of AI. These agents are learning from input, competing with each other in tasks, and getting rewards depending on performance. They make up over time by using historical data to update their models, giving them to specialize and be better every time they compete.