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Abstract:The new program specifically targets privacy-focused projects as more and more users are migrating to Web 3.0.
Automata Networks $20m Incentive Program Backs Projects for Web 3.0
The new program specifically targets privacy-focused projects as more and more users are migrating to Web 3.0.
Owing to the increasing data privacy and security issues on Web 2.0 platforms, more and more users are migrating to Web 3.0. However, the emergence of decentralized finance (DeFi) and decentralized apps (dApps) has led to privacy issues across prominent blockchain networks.
Automata Network addresses this problem, cementing itself as a dApp privacy middleware layer solution compatible across multiple chains on Web 3.0.
The platform, which has been in development since 2019, has achieved a significant milestone today, as it went live on the Mainnet.
To commemorate this achievement and to foster a fair and responsible Web 3.0 ecosystem, the Automata team also launched an Ecosystem Incentive Program.
The Automata Ecosystem Incentive Program aims to promote the privacy-first ethos of Web 3.0. Automata Network will showcase and boost use cases and key products tied to its ecosystem as part of this program.
The programs long-term vision is to bring together a wide range of new solutions, applications, and business models emerging at the intersection of privacy and decentralized finance.
Per Deli Gong, Co-Founder of Automata Network, “In a way to be expected, crypto has shaped and incentivized finance around the strength of communities, but for this human coordination to scale, privacy is sorely needed. With the rollout of our Mainnet and the launch of the Automata Ecosystem Incentive Program, were proud to support DeFi with the privacy it so deserves to truly bring it into the Web 3.0 layer.”
The $20 million Ecosystem Incentive Program will offer support in the form of milestone funding, technical mentoring, and marketing outreach to projects that focus on providing high-quality contributions to the Automata Network.
Eligible participants may include projects building new privacy-focused solutions on top of Automata and projects developing tools, modules, extensions, and libraries for existing Automata products.
Additionally, a portion of the Incentive Program will also support the research and education related to Web 3.0-related privacy topics.
Non-Intrusive Privacy Protection For DeFi
Integrated with industry-leading cryptographic and privacy-protection algorithms, Automata Network facilitates various valuable use-cases synonymous with the ongoing digital revolution.
It offers a decentralized service protocol with privacy-first and frictionless computation that is highly performant, aiming to ensure a privacy-first ethos in Web 3.0 without compromising transparency and efficiency.
Automata not only serves as a privacy middleware layer for dApps across multiple chains, but it also solves DeFis front-running problem. Through its universal front-running prevention solution, Conveyor, which can be easily integrated across DEXes on various chains, and its Fair Liquidity Provisioning Protocol, XATA, Automata Network offers a host of privacy-focused features.
Founded by industry professionals and researchers hailing from brands like LongHash and Zilliqa, Automata Network is among limited projects that worked directly with industry leaders through invitation-only programs such as the Web3 Foundation Grant, Web 3.0 Bootcamp, and the Berkeley Blockchain Xcelerator.
Ahead of its Mainnet launch, Automata successfully raised $3.4 million from prominent investors like Jump Trading, KR1, Alameda Research, IOSG Ventures, Divergence Ventures, and GBV Capital.
As the demand for dApps with seamless privacy increases, Automata Network will play the critical role of offering privacy, high assurance, and frictionless computation to an array of Web 3.0 projects.
Disclaimer:
The views in this article only represent the author's personal views, and do not constitute investment advice on this platform. This platform does not guarantee the accuracy, completeness and timeliness of the information in the article, and will not be liable for any loss caused by the use of or reliance on the information in the article.