ScriptsApr 9, 2026·3 min read

TrendRadar — AI-Driven Trend Monitoring System

Deploy an AI-powered trend monitoring system in 30 seconds. Aggregates from 50+ platforms, AI-filters by your interests, pushes to WeChat/Telegram/Slack/email. GPL-3.0, 51K+ stars.

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TL;DR
TrendRadar aggregates trends from 50+ platforms and pushes AI-filtered alerts to your channels.
§01

What it is

TrendRadar is an AI-powered trend monitoring system that aggregates content from 50+ platforms, filters it by your defined interests using AI, and pushes relevant alerts to WeChat, Telegram, Slack, or email. Deploy it in 30 seconds and get personalized trend feeds without manual browsing.

TrendRadar targets developers, researchers, and content creators who need to stay current across many information sources without checking each one manually.

§02

How it saves time or tokens

Manually checking 50+ platforms for relevant content takes hours daily. TrendRadar runs in the background, applies AI filtering to your interest profile, and surfaces only what matters. You read a curated feed instead of scanning dozens of sites.

The push-based model means you do not poll or check dashboards. Relevant trends arrive in your preferred channel.

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How to use

  1. Clone the repository and configure your interest profile in the config file
  2. Set up notification channels (Telegram bot token, Slack webhook, etc.)
  3. Deploy with Docker: docker compose up -d
  4. Receive filtered trend alerts on your configured channels
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Example

# config.yml
interests:
  - topic: 'AI agents'
    keywords: ['agent', 'multi-agent', 'agentic', 'tool use']
    priority: high
  - topic: 'LLM infrastructure'
    keywords: ['inference', 'serving', 'gateway', 'proxy']
    priority: medium

sources:
  - name: hacker_news
    enabled: true
    interval: 30m
  - name: reddit
    subreddits: ['MachineLearning', 'LocalLLaMA']
    interval: 1h
  - name: github_trending
    languages: ['python', 'rust']
    interval: 6h

notifications:
  telegram:
    bot_token: 'your-bot-token'
    chat_id: 'your-chat-id'
  slack:
    webhook_url: 'https://hooks.slack.com/services/...'
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Related on TokRepo

§06

Common pitfalls

  • Broad interest keywords generate too many alerts; start narrow and expand gradually
  • Some source platforms rate-limit scrapers; respect intervals to avoid being blocked
  • The AI filter requires an LLM API key, which adds cost; configure a budget-friendly model for filtering

Frequently Asked Questions

Which platforms does TrendRadar monitor?+

TrendRadar aggregates from Hacker News, Reddit, GitHub Trending, Twitter, Product Hunt, ArXiv, tech blogs, and more. The exact list of 50+ platforms is configurable in the source configuration.

How does the AI filtering work?+

TrendRadar sends each item's title and summary to an LLM with your interest profile. The LLM scores relevance, and only items above your threshold are forwarded. This avoids keyword-only matching and catches semantically relevant content.

Can I self-host TrendRadar?+

Yes. TrendRadar is open source under GPL-3.0. Deploy with Docker Compose on any server. All data stays on your infrastructure. The only external dependency is the LLM API for filtering.

How often does TrendRadar check for new content?+

Intervals are configurable per source. Fast-moving sources like Hacker News can be checked every 30 minutes. Slower sources like GitHub Trending can be checked every 6 hours. You control the balance between freshness and API usage.

Does TrendRadar deduplicate alerts?+

Yes. TrendRadar tracks previously sent items and avoids duplicate notifications. If the same trending topic appears on multiple platforms, it consolidates into a single alert with source links.

Citations (3)
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🙏

Source & Thanks

Created by sansan0. Licensed under GPL-3.0.

TrendRadar — ⭐ 51,000+

Thanks to sansan0 for building a comprehensive trend monitoring tool that saves hours of manual browsing.

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