A lightweight, fast-to-deploy AI-driven public opinion monitor that aggregates trending topics and RSS feeds across multiple platforms. It applies keyword, regex, and natural language filtering to news, delivering multi-channel alerts with optional AI translation via external models.
Project overview
The project targets information overload by combining multi-platform data aggregation with AI-driven relevance scoring, offering a centralized monitoring workflow deployable with minimal coding effort.
Project type
MCP
Use cases
Data Analysis
Deployment
Refer to project documentation
License
GPL-3.0
Best for
General users seeking a lightweight application to aggregate, filter, and monitor trending public opinion and news across multiple platforms.
Key capabilities
Aggregates trending topics and public opinion from multiple platforms using external APIs.
Supports RSS/Atom feed fetching with keyword grouping.
Filters news using precise keywords and regex syntax with display name support.
Uses natural language interest descriptions for AI to automatically tag and score news relevance.
Translates pushed content to any language with smart batching.
Limitations and risks
AI analysis and translation require an external LLM accessed via LiteLLM, which may involve paid services.
Fetching multi-platform trending data depends on the external newsnow API.
Getting started
The setup is documented as easy, designed for deployment in 30 seconds as a lightweight target, with coding requirements marked as optional.
Alternatives and comparisons
Aggregates 500+ curated news feeds across 15 categories and tracks military, economic, disaster, and escalation signal convergence.