Building an AI Agent for Personalized News Discovery Beyond RSS

TLDR: This article details a practical approach to building an AI agent that proactively scours the internet for relevant news, overcoming the inherent limitations of traditional RSS feeds. By leveraging tools like Perplexity, Make.com, and Google Sheets, users can automate news discovery and receive tailored summaries, saving significant research time.

Summary: The relentless pace of AI news makes it challenging to stay current, with traditional RSS feeds only covering known sources. This article presents an innovative solution: an AI agent designed to actively search the entire internet for information that might otherwise be missed. The agent, built with Perplexity for intelligent web search, records findings in Google Sheets for organized tracking, and utilizes OpenAI to generate weekly summaries delivered via email. This automated system, which runs on Make.com, aims to save 3-5 hours of research per week and uncover insights from previously unknown sources. The article emphasizes that this AI agent acts as a "discovery engine," complementing RSS readers which serve as a "known network."

For architects and teams, this approach offers a robust model for automating information gathering and synthesis. By integrating intelligent search, structured data logging, and LLM-powered summarization, organizations can build custom intelligence pipelines. This reduces information overload, ensures key insights from diverse sources are captured, and frees up human capital for deeper analysis and strategic decision-making. The modular design, utilizing accessible platforms like Make.com, also allows for agile deployment and iteration on various intelligence-gathering tasks.

Key takeaways:

Traditional RSS feeds are limited to known sources and can lead to missed breakthroughs.
An AI agent can proactively discover new information across the internet using tools like Perplexity.
Automation platforms (Make.com) can orchestrate these agents for continuous, low-cost operation.
Structured data logging (Google Sheets) and LLM-powered summarization (OpenAI) provide actionable insights.
This system complements, rather than replaces, existing news consumption methods, acting as a discovery engine.

Tradeoffs:

Gain enhanced news discovery and time savings but sacrifice complete control over initial source selection.
Decision to use cloud services for AI and automation means benefit from scalability and managed infrastructure at the cost of potential vendor lock-in and dependency.

Link: How I Built an AI Agent That Scours the Internet for News I Actually Care About