TrendRadar: A Self-Hosted AI News Monitor That Pushes Alerts to Your Phone

What Problem TrendRadar Solves

TrendRadar targets people who are overwhelmed by the volume of news and social media posts. The README describes it as a tool to stop useless scrolling and see only the news you actually care about. It aggregates hot topics from multiple platforms, adds RSS subscriptions, and lets you filter by keywords. The AI layer then selects relevant news, translates it, and produces an analysis briefing that is pushed directly to your phone. This is aimed at individuals or small teams who want a personal news assistant that runs on their own hardware or cloud account, rather than relying on a closed commercial service.

How the Aggregation and Push Pipeline Works

The core mechanism is a pipeline that pulls data from multiple sources, applies rules and AI, then sends notifications. The README mentions that it uses the newsnow project's API to obtain multi-platform data. That API is a third-party service, and the README thanks its author and asks users to control push frequency so as not to overload the server. From the codebase description, you can infer that TrendRadar fetches hot lists from platforms like Weibo or similar, merges them with user-defined RSS feeds, and then runs keyword filters. The AI features, such as smart filtering, translation, and analysis briefing, were added in versions v6.5.0, v5.2.0, and v5.0.0 respectively, according to the README's section links. The final output goes to a range of push channels: WeChat, Feishu, DingTalk, Telegram, email, ntfy, Bark, and Slack. That is a wide net, and it means you can get the same briefing on multiple devices or apps.

Deployment: Docker and Local Options

The README emphasizes lightweight and easy deployment, claiming you can get it running in as fast as 30 seconds. There are two main paths: Docker and local deployment. The Docker images are published on Docker Hub under the names wantcat/trendradar and wantcat/trendradar-mcp. The latter suggests a separate image for the MCP (Model Context Protocol) component, which is used to expose TrendRadar's data to AI assistants. For local deployment, the README points to the official documentation at trendradar.sandev.cc for detailed steps. There is also a Cloudflare deploy option mentioned in the navigation, which implies you can run it on Cloudflare's edge network. The exact commands are not in the README excerpt, so you would need to check the docs, but the presence of Docker images and a quick-start guide suggests a simple docker run or docker-compose setup.

AI Features and MCP Integration

The AI features are a major selling point. Version v6.5.0 added AI smart filtering of news, which means the system uses a language model to decide which stories are relevant to your keywords, rather than just matching strings. Version v5.2.0 added AI multi-language translation, so you can read foreign sources in your own language. Version v5.0.0 added AI analysis push, which generates a briefing that is sent to your phone. The MCP integration is separate: there is a dedicated Docker image for it, and the README mentions it enables natural language conversation analysis, sentiment insight, and trend prediction. MCP is a protocol that allows AI assistants to call external tools, so this would let a chatbot query TrendRadar's data directly. However, the README does not specify which AI providers are supported, how you configure API keys, or what the cost implications are. That is a gap you need to verify in the official docs before relying on these features.

Push Channels and Notification Configuration

TrendRadar supports a long list of push channels: WeChat, Feishu, DingTalk, Telegram, email, ntfy, Bark, and Slack. This is one of its strengths, because you can route alerts to whatever app you already use. The README shows icons for each, but does not give the configuration keys. You would need to look at the configuration documentation to find the exact settings, such as webhook URLs or bot tokens. The README does warn, in the context of the newsnow API, to control push frequency, which implies there is a setting for how often the system checks for new items and sends notifications. That is a concrete knob you will want to tune, because high frequency could annoy you and also put load on the free API.

Limitations and Failure Modes

The biggest limitation is the dependency on the newsnow API. The README explicitly asks users to go star that project and to use it politely, which means TrendRadar's default data source is a free, third-party service that could change or disappear. If that API goes down, your aggregation stops. The README also notes that the author of newsnow said there is no need to worry about server pressure, but that is based on trust, so there is an implicit risk. Another limitation is that the AI features require external AI APIs, which cost money, and the README mentions rising API costs as a reason for reopening a donation channel. So the total cost of running TrendRadar is not zero: you pay for AI tokens and possibly for the newsnow service if it ever becomes paid. Also, the README is heavily focused on Chinese platforms and users, with the default language being Chinese, so if you need English-first support, you may find the documentation and community thin.

Alternative Approaches and Comparisons

A direct alternative is RSSHub, which also aggregates feeds from many platforms and exposes them as RSS. The difference is that RSSHub is a purely mechanical scraper: it turns websites into RSS feeds without AI filtering or analysis. You would need a separate tool to apply AI or push notifications. Another alternative is a commercial service like Feedly or Inoreader, which offer AI-powered news filtering and integrations, but they are closed-source and cloud-hosted, so you do not control your data. TrendRadar's advantage is that it combines aggregation, AI, and push in one self-hosted package, but it relies on a free API that may not be as reliable as a paid service. For someone who wants full control and does not mind the setup, TrendRadar is more integrated than RSSHub, but less robust than a commercial option.

Maintenance, License, and Upgrade Considerations

TrendRadar is licensed under GPL-3.0, which means if you modify it and distribute it, you must share your changes under the same license. That is a consideration for commercial use. The project is actively developed, with version numbers like v6.5.0 appearing in the README, but there are no recent releases listed in the repository metadata, so you should check the release page for the latest stable version. The README has a changelog section, which suggests that updates are frequent and that you should watch for breaking changes. The Docker images are named with a version tag, so upgrading likely means pulling a new image and restarting. The project also has a website and official documentation, which is a good sign for maintenance, but the README notes that the founder is no longer manually maintaining the thank-you list to focus on code, so support may be informal. You should verify the upgrade path in the docs before relying on it.

Editorial conclusion

Adopt TrendRadar if you want a self-hosted, AI-assisted news monitor that aggregates multiple platforms and RSS feeds into push notifications on your phone or chat apps, and you are comfortable with GPL-3.0 and the dependency on the newsnow API. Do not adopt it if you need a production-grade analytics platform with formal support, or if you cannot accept that the default data source is a third-party service that the README itself asks users to use politely. Before deploying, verify the current state of the newsnow API, check the configuration options for push frequency and keyword filters, and test the AI features with your own API keys, since the README does not specify which AI providers are supported or how costs are met.

Official sources

1. Official documentation
2. Official README
3. Project repository