AI News Aggregator
AIWeb AppData Analysis
AI News Aggregator
Goals
- AI-driven content classification
- Multi-modal data processing
- Fast content search
Intelligent News Discovery
An advanced news aggregator that pulls content from social networks and utilizes AI to classify and interpret data. It processes audio, video, and imagery for semantic categorization. Designed for high-speed content discovery, it offers a multi-user platform with personal profiles and advanced search. To handle high-velocity multimedia data streams, we implemented an advanced frontend architecture focused on render optimization. The dynamic Masonry grid efficiently handles varying content sizes while maintaining 60fps scrolling.
Process
- 1
Discovery & Product Definition
Worked with Asmeman Co to define user personas, content sources (text, audio, video, imagery from social networks), AI classification categories, and search requirements for the multi-user platform.
- 2
Design System & UX
Created a component-driven design system in Material-UI, including the Masonry grid layouts, personal profile flows, and light/dark/infinity theme specifications.
- 3
Frontend Architecture
Designed a render-optimized React architecture: virtualized lists, aggressive memoization, code-splitting, and a predictable state-management layer that decouples long-running AI analysis pipelines from the UI.
- 4
Iterative Implementation
Built the Masonry feed, multi-modal cards, advanced search, and multi-language i18n support in weekly iterations, integrating AI classification results from the backend services.
- 5
Performance Engineering
Profiled render times and scrolling with browser dev tools, removed unnecessary re-renders, and tuned list virtualization to hold a stable 60 fps while scrolling large feeds.
- 6
QA & Launch
Cross-browser and responsive testing, theme and i18n regression passes, then staged rollout with monitoring of runtime errors and interaction metrics.
