Defi, Data, Degen

This article presents an AI-powered, LLM-enabled data pipeline for smart contract analytics on the Sui blockchain. Using Snowflake, it curates structured event and object catalogues from 95k+ Move packages, enabling LLMs to perform on-chain data analysis at scale. The pipeline automates SQL generation for DeFi metrics like trading volume, funding rates, DAUs, and open interest, showcasing a scalable approach to blockchain data pipelines.

GaussianMove uses the AAA algorithm to generate near-optimal rational approximations offline, then evaluates them on-chain via Horner's method—achieving CDF error of 3.35×10⁻⁹ and PPF error of 3.11×10⁻¹³ with predictable gas costs. Sui's native sui::random then makes Gaussian sampling operationally simple inside a single transaction. This article describes the constraints, the approximation methods, and several applications once these functions are available as ordinary library calls.