AI agents are moving beyond task automation into the realm of personalized information curation, with developers now building custom news dashboards that filter high-signal content from social media feeds. One builder recently shared a working example of how research agents can automatically compile curated AI news, papers, and projects into an interactive HTML feed that updates every four hours.

What Problem Does This Solve for Information Overload?

The volume of AI research, product launches, and technical discussions happening on social platforms like X has become overwhelming for professionals trying to stay informed. Rather than manually scrolling through thousands of posts, this developer created an automated system that uses AI agents to identify and organize the most relevant content from carefully selected high-signal accounts. The resulting feed functions as a personalized "For You" page tailored specifically to AI news and developments.

The approach leverages Model Context Protocol (MCP) tools, which allow AI agents to connect directly to external services like the X API. By giving agents access to these tools, developers can instruct them to perform complex workflows, such as scanning multiple accounts, filtering for specific topics, and generating formatted output without human intervention.

How to Build Your Own AI-Powered News Dashboard?

• Set Up API Access: Configure the X MCP (Model Context Protocol) connection by accessing the X API documentation and obtaining the necessary credentials to allow your agent to read posts from selected accounts.
• Install the Intelligence Skill: Download and install the x-agent-intelligence plugin from the DAIR Academy GitHub repository, which provides the foundation for agents to understand and process social media content.
• Run the Generation Prompt: Execute a simple command telling your agent to build a self-contained local feed using the x-agent-intelligence skill, specifying your source handles and requesting HTML output saved as feed.html.
• Automate on a Schedule: Set up a recurring automation to regenerate the feed at your preferred interval, whether every four hours, daily, or weekly, ensuring your dashboard stays current without manual effort.
• Customize and Iterate: Tune the feed's appearance, content filters, and source accounts based on your preferences, and optionally store the results as a wiki or other persistent format for team access.

The developer noted that the system works with multiple AI agent platforms, including Hermes, Claude, Codex, and OpenClaw, making it accessible regardless of which agent framework a developer prefers. This flexibility suggests that the underlying approach is model-agnostic, focusing on the workflow design rather than any single AI system's capabilities.

Why Is Agent-Driven Curation Different From Traditional Feeds?

Traditional social media algorithms optimize for engagement and time-on-platform, often surfacing sensational or polarizing content. In contrast, an agent-curated feed can be programmed to prioritize signal quality, relevance to specific domains, and factual accuracy. The developer emphasized that users must manually curate the X accounts that serve as sources, but once that list is established, the agent handles the repetitive work of monitoring, filtering, and organizing.

The builder also highlighted the community aspect of this approach, offering to share the personal feed implementation and planning to conduct tutorials for others interested in reproducing the system. This suggests that AI agent curation is becoming a skill that developers can learn and adapt to their own information needs, rather than a capability reserved for large technology companies.

The practical implication is significant: as AI agents become more capable at connecting to external APIs and processing structured data, professionals across industries can build custom intelligence systems tailored to their specific domains. For AI researchers, this means staying informed without algorithmic distortion. For other fields, the same pattern could apply to financial news, regulatory updates, scientific publications, or industry-specific developments.

The developer's willingness to open-source the approach and support others in implementing it reflects a broader trend in the AI community toward democratizing agent capabilities. Rather than waiting for commercial products to offer these features, developers are building and sharing solutions that put powerful automation tools directly in the hands of end users.