cab drivers today lack real-time insight into traffic in areas, identifying hotspots and areas with less competition for rides tried to solve this by taking intuition from social graphs as visualization by representing each area as a node focused on the part where it is easy to infer the info at one glance - green represents high-earning activity, yellow medium, and red low (familiarity) - if there is a surge or high demand, that node pulsates (visual to understand surge) - if relatively more cab drivers are already present, then that node is represented in larger size clicking on every node provides them insights and a readable message to understand in the initial days @Uber_India @rapidoapp_ @nammayatri

Skip method decoding, event parsing, and raw data wrangling. Go from raw onchain data to insights in minute there may have been many scenarios where you wanted to understand insights behind a protocol or a fun on-chain product you built over a weekend but it's always tricky to work around onchain data decoding methods, event and working with raw data writing queries to get the insights onchain wizard automates it for you checkout at: onchainwizard.ai/

- fetch raw data as the main table to work on them - based on functionality, it builds the queries & runs them using @Dune API - fetches the result using the Dune API and visualize it as a dashboard to the user