Turning raw RSS feeds into narrow, custom-formatted news briefings (github.com)

A new project, News Editor Agent, has been launched to transform raw RSS feeds into tailored news briefings by employing a multi-step consensus process. Unlike traditional single-inference models, this initiative uses a Mixture-of-Agents approach that reduces biases and enhances output quality through peer review among multiple Editor models. The tool enables users to filter and curate content from various RSS feeds based on specific topics, while also allowing the customization of output formats to meet diverse needs, including forwarding summaries to Telegram. This development is significant for the AI/ML community as it showcases innovative ways to handle news analysis, enabling professionals to monitor niche sectors or track specific entities effectively. The three interconnected workflows—parsing, editorial, and posting—utilize advanced models such as Google’s Gemini and OpenAI’s GPT for generating and refining content. Additionally, the cost-efficient operation, at under $0.20 per run for processing up to 200 articles, emphasizes its practicality for organizations and individuals alike looking to streamline information digestion and enhance their engagement with the latest developments in AI.