
Bevis contributed to the DefiLlama/dimension-adapters repository by building and expanding multi-chain data aggregation and fee tracking for the Haiku DEX, enabling accurate analytics across Hyperliquid, Polygon, and Optimism. He implemented adapters and configuration management to support native tokens, gas fees, and new chain integrations, using TypeScript and SQL to ensure reliable data ingestion and query optimization. His work included refactoring SQL queries for data fidelity, standardizing blockchain naming for Dune queries, and resolving data-quality issues. Through backend development and smart contract interaction, Bevis improved data completeness and maintainability, supporting business analytics and reducing operational risk for downstream consumers.

Month 2025-10 monthly summary focusing on key accomplishments, business value, and technical achievements for DefiLlama/dimension-adapters.
Month 2025-10 monthly summary focusing on key accomplishments, business value, and technical achievements for DefiLlama/dimension-adapters.
July 2025 performance highlights for DefiLlama/dimension-adapters: focused on expanding data coverage and improving data reliability. Delivered key feature to recognize Hyperliquid data by adding chain configuration support in Haiku addresses (including contract ID and start time), enabling end-to-end processing of Hyperliquid data. Fixed data retrieval accuracy for Haiku aggregator by refactoring SQL queries and implementing log topic filtering, resulting in more trustworthy datasets for downstream analytics. Overall, strengthened data completeness, reliability, and maintainability, directly supporting business decision-making and analytics readiness. Overall impact and accomplishments: - Expanded coverage for a new Hyperliquid data source with low operational risk due to clear configuration management. - Improved data fidelity in Haiku aggregator, reducing erroneous transaction ingestion and downstream confusion. - Maintained code quality through readability-focused refactors and clear commit messages. Technologies/skills demonstrated: - SQL query refactoring and data filtering by log topics - Configuration management for chain data sources - Change impact assessment and documentation through meaningful commit messages
July 2025 performance highlights for DefiLlama/dimension-adapters: focused on expanding data coverage and improving data reliability. Delivered key feature to recognize Hyperliquid data by adding chain configuration support in Haiku addresses (including contract ID and start time), enabling end-to-end processing of Hyperliquid data. Fixed data retrieval accuracy for Haiku aggregator by refactoring SQL queries and implementing log topic filtering, resulting in more trustworthy datasets for downstream analytics. Overall, strengthened data completeness, reliability, and maintainability, directly supporting business decision-making and analytics readiness. Overall impact and accomplishments: - Expanded coverage for a new Hyperliquid data source with low operational risk due to clear configuration management. - Improved data fidelity in Haiku aggregator, reducing erroneous transaction ingestion and downstream confusion. - Maintained code quality through readability-focused refactors and clear commit messages. Technologies/skills demonstrated: - SQL query refactoring and data filtering by log topics - Configuration management for chain data sources - Change impact assessment and documentation through meaningful commit messages
June 2025: Implemented Haiku DEX integration in DefiLlama/dimension-adapters, delivering volume aggregation and fee-tracking adapters across multiple chains with handling for native tokens and gas fees to ensure accurate data collection of Haiku trading activity and associated fees. This work enhances Haiku data surfaces for volume and fee analytics, enabling better business decisions and revenue visibility.
June 2025: Implemented Haiku DEX integration in DefiLlama/dimension-adapters, delivering volume aggregation and fee-tracking adapters across multiple chains with handling for native tokens and gas fees to ensure accurate data collection of Haiku trading activity and associated fees. This work enhances Haiku data surfaces for volume and fee analytics, enabling better business decisions and revenue visibility.
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