Build an Onchain AI dapp with 0G's Storage and Inferencing
summary
ZeroG is a modular L1 for onchain AI with an EVM‑compatible chain, a storage network that shards data with chunking, VRF distribution and Merkle proofs, a scalable data‑availability layer, and a compute marketplace offering verifiable inferencing via TEE and ZK proofs. Claims: 95% cheaper storage, instant retrieval, separation of consensus and execution to reach 11,000+ TPS and sub‑100ms finality, and a $10K hackathon bounty for builders.
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chapters (23)
- 0:00Start
- 0:02Introduction to ZeroG and decentralized AI
- 0:44The problem with closed AI systems
- 1:12Overview of ZeroG’s modular stack
- 1:41Layer 1 chain and storage architecture
- 2:22Data availability and verifiability
- 3:13Compute network: verifiable inferencing explained
- 3:32Alignment nodes and decentralization governance
- 3:58AI service marketplace and future RAG/fine-tuning support
- 4:28EVM-compatible chain architecture & mainnet performance
- 5:27ZeroG Storage: 95% cheaper, instant retrieval
- 6:29Storage mechanism: chunking, VRF distribution, Merkle proofs
- 7:22Data availability issues and ZeroG’s improvements
- 8:48Security, scalability, and Ethereum-aligned standards
- 9:12Use cases for AI, ML, and modular L1/L2 integrations
- 9:44Compute network and decentralized GPU marketplace
- 10:56Example: model selection and cost flexibility
- 11:51Trustless execution vs centralized inference bias
- 12:44Workflow for users and GPU providers
- 13:26Builder Hub and testnet/mainnet resources
- 14:03Hackathon details and $10K bounty
- 14:36Ecosystem roadmap and mainnet launch
- 15:09Closing remarks and builder invitation
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