TrendRadar: what it is, what problem it solves & why it's gaining traction

TrendRadar: what it is, what problem it solves & why it's gaining traction

What it solves

TrendRadar is a lightweight, easy-to-deploy "hotspot assistant" designed to eliminate mindless scrolling by aggregating and filtering news and trending topics from multiple platforms. It helps users stay informed about specific interests without being overwhelmed by irrelevant information.

How it works

The system collects data from various hot-list platforms and RSS/Atom feeds (via the newsnow API). It then processes this data through several layers:

  1. Filtering: Users can filter content using traditional keywords, regular expressions, or an AI-powered intelligent screening system where users describe their interests in natural language (e.g., "I want to see news about AI and new energy"), and the AI scores and filters the content.
  2. Analysis: It integrates with LLMs (via LiteLLM, supporting 100+ providers like DeepSeek, OpenAI, and Gemini) to provide deep insights, trend summaries, sentiment analysis, and potential impact assessments.
  3. Delivery: The processed information is pushed to various channels including Telegram, Slack, DingTalk, Feishu, Email, and custom Webhooks.
  4. MCP Integration: It includes a Model Context Protocol (MCP) server that allows AI agents to search news, read article contents via Jina AI Reader, and perform cross-platform aggregation.

Who it’s for

  • Individuals who want a curated, automated news feed based on specific professional or personal interests.
  • Researchers or analysts who need to track trending topics and sentiment across multiple platforms.
  • AI enthusiasts who want to integrate real-time web trends into their AI agent workflows via MCP.

Highlights

  • AI-Driven Filtering: Move beyond keywords to natural language interest descriptions for smarter content selection.
  • Multi-Channel Push: Supports a vast array of notification platforms (Telegram, Feishu, Slack, etc.).
  • Deep AI Analysis: Generates summaries, sentiment analysis, and trend predictions based on aggregated data.
  • MCP Server Support: Enables AI agents to interact with the news data, read full articles, and perform historical comparisons.
  • Flexible Scheduling: A unified timeline system to control when to collect, analyze, and push data.
  • Rapid Deployment: Supports Docker, GitHub Actions, Cloudflare Pages, and local installation.

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