Habeas Data
Public portfolio analytics for onchain orgs, powered by Octav API & widgets with x402 payments.
Project Description
Habeas Data is a public-facing analytics application that extends Octav's portfolio data platform. It is designed to bridge the information gap between organizations, and their stakeholders and users.
By leveraging Octav's comprehensive data, APIs and modular display unit (widgets), Habeas helps organizations build transparent, accessible dashboards, widgets and data to showcase their performance and operations. It empowers the organization's stakeholders access and customise real-time financial information through intuitive widgets, charts and tables, which can also be integrated into Octav or exported elsewhere.
Instances of the Habeas app are owned by user organisations, and configured to share appropriate data with the organisation's own users and stakeholders, while reflecting the organisation's own preferences and security practices. To ration API usage and cover the costs of operation, Habeas integrates the x402 payment standard, requiring users to make small onchain payments in Base USDC before querying widgets, tables or data.
How it's Made
The Habeas application forks Jnix2007 (Coinbase)'s x402 Demo repo as a starting point for integrating novel x402 payments. and Coinbase embedded wallets.
On top of this, the app utilises the design pattern of Octav's own UI as a platform for building interoperable widgets for use on Octav and in Habeas' own dashboards and queries. It also relies on the Octav API to extract data to build widgets capable of being integrated into Octav.
The Habeas app is built on Typescript, divided between a client frontend and a server for handling x402 payments and API requests and caching. The client is built using Next.js and React, with Tailwind CSS components. Widgets are constructed using Recharts for data visualization, and are prepared for download and exportation with additional tools like html2canvas. The server is built on Express.js.
The development team's preferred IDEs were (i) Cursor with a mix of Sonnet 4.5 and ChatGPT 5.1, and (ii) Visual Studio Code with CoPilot. This was used together with ChatGPT 5.1 for support with general resources, Adobe Express for design and iMovie for the project video.
