Flare Network to Hold Hackathon on March 7th

Flare Network is hosting a hackathon in collaboration with Google Cloud focusing on Verifiable AI using Trusted Execution Environments (TEEs). The event is scheduled to start at UC Berkeley with Blockchain at Berkeley starting on March 7.The hackathon features a total prize pool of $100,000, with $60,000 designated for in-person participants and $40,000 for virtual participants. Refer to the official tweet by FLR: Flare is hosting a hackathon with on Verifiable AI using Trusted Execution Environments (TEEs).It's at UC Berkeley with , starting March 7.Compete for $100K: $60K in-person, $40K virtual.Apply now: FLR InfoFlare Network is a distributed protocol that incorporates the Ethereum Virtual Machine (EVM), enabling the execution of Turing-complete smart contracts. Turing-complete indicates that any computational problem can be addressed given adequate memory. As such, the Flare Network can foster an ecosystem of decentralized applications (DApps), with the ultimate objective of scaling smart contract networks.Flare Network employs a consensus algorithm named Avalanche, customized to function with Federated Byzantine Agreement (FBA), a consensus mechanism utilized by networks such as XRPL and Stellar. Uniquely, Flare Network's consensus protocol doesn't depend on economic mechanisms like Proof of Stake (PoS) to maintain network security. In contrast, many networks, including Ethereum (which will fully transition to PoS with Ethereum 2.0), rely on token-staking validators for network security.The Spark token (FLR) is the native token of the Flare Network, primarily used as a safeguard against spam attacks by placing a cost on network transactions. Additionally, the token serves as collateral within decentralized applications (DApps), contributing to the robustness and security of various applications on the network. The Spark token also provides data to an on-chain oracle and enables users to participate in protocol governance, strengthening the democratic and decentralized nature of the network.