Built an AI news agent that stops information overload (reckoning.dev)

A developer has built an intelligent AI news agent designed to combat information overload by transforming massive, diverse news streams into personalized, high-quality intelligence. Leveraging an adaptation of the reactive agent pattern initially created for academic research synthesis, this system continuously monitors dozens of user-defined topics and uses adaptive search strategies, multi-engine orchestration, and semantic deduplication to deliver nuanced, relevant updates. Unlike traditional aggregators, it prioritizes knowledge synthesis over mere headline collection, merging related stories into comprehensive summaries that preserve source authority and avoid redundant content. Key technical innovations include the use of semantic embeddings via Ollama's BGE-Large model for intelligent deduplication beyond simple keyword matching, a configurable topics.yaml for explicit interest declaration and topic grouping, and context-aware processing that adjusts coverage depth and retention times based on the domain’s pace (e.g., politics vs. scientific research). The system integrates multiple search engines selectively—academic databases for research papers, news APIs for real-time updates, and social media for emerging trends—allowing a flexible, resilient approach to information retrieval under API rate limits. This architecture not only respects cognitive bandwidth and prioritizes meaningful insights but also highlights cross-topic patterns and coverage gaps, enabling a form of personal AI infrastructure that amplifies human understanding rather than replacing it. This work signifies a shift in AI-driven news consumption, emphasizing configurability and synthesis to address real-world challenges in continuous information processing. It offers a scalable model for personalizing knowledge across complex domains and lays the groundwork for broader applications like market intelligence and competitive monitoring, illustrating how sophisticated AI behaviors can be achieved through adaptive configuration rather than extensive custom programming.