
In December 2024, Li Kun developed a unified frontend for the CurtainWallWeb-Frontend repository, integrating device control, alarm records, and an AI analytics bot into a centralized interface. Leveraging Vue.js, TypeScript, and Nuxt.js, Li Kun refactored UI components, updated navigation, and enhanced data visualization across monitoring modules to streamline system management. The architecture emphasized modular design and real-time data handling, consolidating multiple subsystems for improved operational efficiency. By focusing on state management and API integration, Li Kun enabled faster access to actionable insights, reduced context switching for operations teams, and established a scalable foundation for future monitoring and analytics enhancements.

December 2024: Delivered a unified frontend that integrates device control, alarm records, and an AI analytics bot into CurtainWallWeb-Frontend. This release includes UI component refactors, navigation updates, and enhanced data visualization across monitoring modules to deliver a centralized UX for managing and analyzing system data. No major defects reported this period; stability improvements were driven by refactors and consolidation. Impact: streamlined operations, faster access to system insights, and data-driven decision making enabled by AI analytics. Technologies demonstrated: frontend architecture, UI/UX refactoring, modular design, integration of AI analytics, data visualization, and monitoring subsystems. Business value: reduced context switching, improved incident response, and more actionable analytics for ops teams.
December 2024: Delivered a unified frontend that integrates device control, alarm records, and an AI analytics bot into CurtainWallWeb-Frontend. This release includes UI component refactors, navigation updates, and enhanced data visualization across monitoring modules to deliver a centralized UX for managing and analyzing system data. No major defects reported this period; stability improvements were driven by refactors and consolidation. Impact: streamlined operations, faster access to system insights, and data-driven decision making enabled by AI analytics. Technologies demonstrated: frontend architecture, UI/UX refactoring, modular design, integration of AI analytics, data visualization, and monitoring subsystems. Business value: reduced context switching, improved incident response, and more actionable analytics for ops teams.
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