The NEAR Foundation and Delphi Labs have jointly launched an AI accelerator program and announced the first batch of selected projects. The program aims to support and rapidly scale high-potential projects at the intersection of AI and Web3. Selected projects will receive comprehensive support, including technical guidance, funding opportunities, and access to the expert networks of NEAR and Delphi. Each team will also receive $50,000 in computing credits from computing partner Aethir to ensure they have the computational resources necessary to develop and deploy resource-intensive AI models.

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According to Deep Tide TechFlow, on October 16, the NEAR Foundation and Delphi Labs announced the joint launch of an AI accelerator program and revealed the first batch of selected projects. The program aims to support and rapidly scale high-potential projects at the intersection of AI and Web3. The selected projects will receive comprehensive support, including technical guidance, funding opportunities, and access to the expert networks of NEAR and Delphi. Each team will also receive $50,000 in computing credits from computing partner Aethir to ensure they have the computational resources needed to develop and deploy resource-intensive AI models.
The first batch of selected projects includes:
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Hyperspace: Building a decentralized peer-to-peer AI network, which has attracted over 7,000 unique nodes.
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Almanak: Providing an end-to-end platform for building, testing, training, optimizing, and deploying agents for DeFi, with its agents managing substantial assets for institutional partners.
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Questflow: Developing a decentralized network that coordinates multi-agent AI automation, focusing on the SEO market.
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Q Star Labs: Developing a decentralized generative AI protocol for the large-scale creation of interactive autonomous AI influencers, with its AI influencers already having over 27,000 followers.
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ZettaBlock: Laying the foundation for open and trustless AI, providing infrastructure for unified on-chain and off-chain data.
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Magic: Redefining the use of private data in AI, allowing companies and developers to use private data to train AI models while protecting data integrity and privacy.
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dFusion: Building a consensus-driven framework for curating and validating high-quality data, ensuring the accuracy and credibility of AI model training data.
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Peri Labs: Establishing an open market that leverages underutilized computing resources on billions of consumer devices to support optimized AI models.
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The NEAR Foundation and Delphi Labs have jointly launched an AI accelerator and announced the first batch of selected projects.