hebel. today · 13 November 202650 / 80

Technology · Frontend Web Development

Developers build local-only dashboards using multi-agent teams

Developers generate complete, responsive web applications by assigning specialized AI agents to distinct project files for interface design, application logic, and automated testing. Using a master prompt that specifies features, data structures, and constraints,…

Developers generate complete, responsive web applications by assigning specialized AI agents to distinct project files for interface design, application logic, and automated testing (Open Cloud AI). Using a master prompt that specifies features, data structures, and constraints, teams build functional dashboards like an editorial planner that calculates priority scores and saves data to browser localStorage without external dependencies (Open Cloud AI). This workflow treats the agent as a team of independent workers, where each agent maintains ownership of specific modules like scoring rules or UI components to ensure project maintainability.

  • Omnia migrated an 800-page bilingual site from Webflow to a Git-based Astro setup in 23 hours using AI agents to improve page load speed by 2.7x (SWIPE).
  • RevenueCat improved paywall performance by refactoring component state resolution to prevent unnecessary recalculations during every user tap (RevenueCat Blog).
  • Sentry captures Core Web Vitals for single-page application soft navigations to provide visibility into the 62% of user sessions occurring after the initial page load (SWIPE).
  • Uber secures AI agents with provisioning checks and runtime enforcement to ensure authorized actions match the user's request (TLDR DEV).

Try today · 30 min

Build a local-only editorial planning dashboard using the provided master prompt.

Open your AI agent interface and paste the master prompt from the source to initialize the project structure.

You need

  • Antigravity or similar AI agent environment
  • Empty project folder

Steps

  1. Create a new project folder named cloud-ai-swarm-lab.
  2. Launch the swarm agent with the master prompt provided in the source.
  3. Verify the generated files include index.html, styles.css, and app.mjs.
  4. Run the automated tests using node --test to validate the scoring logic.
  5. Test the dashboard in your browser to confirm data persistence via localStorage.

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Sources

  1. Open Cloud AI on hebel. →This guide details the master prompt and file structure for building a complete, local-only editorial dashboard with AI agents.

  2. SWIPE on hebel. →The Omnia case study demonstrates a 23-hour migration from Webflow to Astro using AI agents for site-wide updates.

  3. RevenueCat Blog on hebel. →RevenueCat explains how to optimize component state resolution in Compose to avoid redundant calculations on user interaction.

  4. TLDR DEV on hebel. →Uber details their approach to securing AI agents through runtime enforcement and behavioral controls.

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