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Hemera Secures $2.6M in Funding, Unveils AI-Powered Platform for On-chain Intelligence

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Hemera, an AI-powered platform for Web3 data management, has made waves in the industry with the unveiling of its flagship offerings to the public. Founded by Stanford Ph.D. Arthur Meng, Hemera aims to revolutionize on-chain data querying using AI-powered solutions.

The Hemera Protocol, developed in collaboration with industry experts, offers a decentralized indexing network optimized for Web3 environments, providing users with real-time insights and personalized experiences. With the public beta release of the Hemera Protocol, developers now have access to a cutting-edge Large Language Model (LLM) operating system tailor-made for Web3.0 environments.

One of the standout features of Hemera’s ecosystem is SocialScan, an AI-driven platform that promises to democratize access to AI-driven experiences, empowering users to customize their interactions through Hemera AI Agents. These agents offer a glimpse into the future of internet experiences, characterized by unprecedented levels of personalization and utility.

The launch of Hemera comes on the heels of a successful funding round, with the company securing $2.6 million in funding led by LIF Capital and Nomad Capital. Notable angel investors, including founders from ZetaChain, Sending Labs, and Microsoft’s Harry Shum, have also contributed to Hemera’s funding round.

With partnerships already in place with leading projects and public chains such as Polygon, Linea, and Chainlink, Hemera is well-positioned to catalyze industry-wide adoption of decentralized intelligence. As the decentralized web continues to evolve, Hemera’s innovative approach to AI-driven protocols promises to unlock new possibilities for developers and users alike.

Overall, Hemera’s vision for the future of Web3 is one of decentralization, inclusivity, and vibrancy. With its groundbreaking offerings and visionary leadership, Hemera is set to chart a course towards a more dynamic and equitable Web3 ecosystem.

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