The Snapshot section provides a quick snapshot of user activity and transaction volume on the M1 devnet. It likely includes metrics like total user transactions, unique users, average transactions per user, and block production rate. This snapshot gives you a high-level understanding of how many users are interacting with the devnet and how frequently they're conducting transactions.

• Total User Transactions & Unique Users: These metrics together reflect the overall activity level on the devnet. A high transaction volume suggests active testing, while a growing number of unique users indicates an expanding developer base.
• Average Transactions per User: This provides insights into user behavior. A higher average might suggest users are thoroughly testing functionalities, visualized within the dashboard.
• Average Daily Blocks & Total Blocks: These metrics reflect block production rate and network growth.

• Total Gas Used & Average Gas Used: These metrics provide insights into the overall gas consumption on the devnet and the average cost per transaction.
• Max Gas Incurred on a Transaction: Highlights the highest gas cost for a single transaction, potentially indicating complex interactions or inefficiencies in smart contracts. Gas Usage Trends: Visualizes gas usage patterns over time, revealing trends in daily and running average gas consumption. This can help identify potential areas for optimization. Tx Distribution by Gas Cost Tiers: An area chart depicts how transactions are distributed across different gas cost ranges. This helps understand the prevalence of simple versus complex transactions on the devnet. For the gas levels, objective tiers were used: The mean (average) and standard deviation of gas usage were used.

“Low,” “Medium,” and “High” tiers are objectively defined in the sense that “Low” is defined as transactions with gas usage less than the mean. “Medium” is defined as transactions with gas usage between the mean and mean + standard deviation. “High” is defined as transactions with gas usage greater than mean + standard deviation.

This avoids manual bias and adapts to network changes. It allows for an objective, adaptable, and granular gas tier classification. It ensures that the tiers reflect the statistical distribution of gas usage across user transactions.