Information overload is the default state of the modern internet. You've got RSS feeds, Twitter/X timelines, industry newsletters, government press releases, Reddit threads, and Hacker News — all publishing simultaneously, all demanding attention. The problem isn't access to information. The problem is synthesis. An AI-powered news aggregation platform built on OpenClaw can pull from dozens of sources, deduplicate, summarize, and deliver a unified briefing — all running on a single lightweight server.
Traditional news aggregators (Feedly, Google News, etc.) collect and display headlines. That's useful, but it still leaves the cognitive load on the reader:
A smarter aggregation layer doesn't just collect — it processes. It identifies that three different outlets are covering the same regulatory announcement, merges the key facts, highlights conflicting perspectives, and presents a single coherent summary. This is exactly the kind of task that LLMs excel at, and OpenClaw provides the orchestration framework to make it operational.
A news aggregation platform on OpenClaw consists of three layers:
Each news source gets its own OpenClaw skill — a modular component that handles fetching, parsing, and normalizing content from a specific source type:
For detailed instructions on installing and configuring these skills, refer to Installing OpenClaw Skills and Practical Applications.
Once raw content is ingested, OpenClaw's LLM backbone handles the heavy lifting:
Processed briefings are pushed to your preferred messaging platform. OpenClaw's multi-channel support means you can receive your daily digest on:
Here's what a typical setup looks like. After deploying OpenClaw on your server (follow the one-click deployment guide), configure your source skills:
# news-aggregator config example
sources:
- type: rss
name: "TechCrunch"
url: "https://techcrunch.com/feed/"
poll_interval: 15m
- type: rss
name: "Hacker News"
url: "https://hnrss.org/frontpage"
poll_interval: 10m
- type: api
name: "NewsAPI Tech"
endpoint: "https://newsapi.org/v2/top-headlines"
params:
category: technology
language: en
poll_interval: 30m
processing:
dedup_threshold: 0.85
summary_length: 3_sentences
categories:
- technology
- finance
- ai_ml
- policy
delivery:
schedule: "0 8,12,18 * * *" # 8am, noon, 6pm
channel: telegram
This configuration polls three sources, deduplicates stories with 85%+ similarity, generates three-sentence summaries, and delivers briefings three times daily via Telegram.
Single-source news consumption creates blind spots and bias. Every publication has an editorial lens. By aggregating across sources, you get:
OpenClaw's summarization layer makes this manageable. Instead of reading 50 articles, you read 10 synthesized briefings that capture the essential information from all 50.
A news aggregation platform runs continuously — polling sources, processing content, delivering briefings. It's not compute-intensive, but it must be reliable. A missed polling cycle means missed stories.
Tencent Cloud Lighthouse is the ideal deployment target here. It's simple (no complex cloud networking to configure), high-performance (dedicated resources, not shared), and cost-effective (predictable monthly pricing). For a news aggregation use case, even the entry-level Lighthouse instances provide more than enough headroom.
Grab an instance from the Tencent Cloud Lighthouse Special Offer — the bundled pricing includes compute, storage, and bandwidth, so there are no surprise bills from high-frequency API polling.
What starts as a personal news reader can evolve into a team intelligence platform:
News aggregation is one of those problems that's deceptively simple on the surface and genuinely complex underneath. Collecting headlines is easy. Synthesizing multi-source information into actionable intelligence is hard. OpenClaw's skill-based architecture and LLM processing pipeline handle the hard parts, while Tencent Cloud Lighthouse provides the always-on infrastructure that keeps the whole system humming. Deploy once, configure your sources, and never miss a story that matters again.