AI News Briefing Assistant: An Automated Information Aggregation System Based on RSS and Large Models

The AI News Briefing Assistant is a highly automated AI information aggregation and summarization tool designed to solve the reading dilemma caused by information explosion in the AI field. It uses multi-threaded concurrent crawling of RSS feeds, combines Map-Reduce architecture with Gemini/Qwen large models for intelligent condensation and global analysis, and finally generates beautifully formatted HTML email briefings to help users efficiently track the latest developments in the AI field.

The AI field is developing rapidly, with a large number of papers, technical blogs, product releases, and other content emerging every day. Manually tracking multiple information sources is time-consuming and prone to omissions. Traditional methods have limitations: email subscriptions easily flood the inbox, social media has an information cocoon effect, manual access is inefficient, and understanding core content requires a lot of time investment.

The system adopts Python modular design, with core components including: 1. Multi-threaded concurrent crawling engine, supporting OPML feed format, greatly improving content acquisition efficiency; 2. Map-Reduce intelligent long text processing, which condenses article key points in batches and then performs global summary to solve the token limit of large models; 3. Supports large model backends such as Google Gemini and Alibaba Tongyi Qianwen (Qwen).

The system implements an intelligent deduplication mechanism, avoiding duplicate pushes through local caching of historical links and content similarity recognition; it automatically extracts the first image of news and generates adaptive HTML emails. The template design includes clear hierarchy, reasonable spacing, and action buttons, adapting to reading on desktop and mobile devices.

It provides interactive guided configuration (setup.py script) to generate .env and config.ini files, supporting the setting of AI API keys, email SMTP information, feeds, and sending time; it supports scheduled task scheduling (daily/weekly), can execute the first crawl immediately, and runs automatically in the background without manual intervention after configuration is completed.

Sensitive configurations (API keys, email authorization codes) are only stored locally in .env files, protected by gitignore to avoid leakage; data processing is carried out between the local and the user's email server, with no third-party storage; it uses the GPL-3.0 open-source license, and the modular design supports secondary development (custom parsing logic, AI prompts, email templates, integration with Slack/Discord and other channels).

Applicable to AI researchers (tracking the latest papers), technical practitioners (aggregating product/blog information), investors/analysts (monitoring industry trends); it shortens information acquisition time, provides structured insights, and helps users establish systematic industry cognition.

In the future, it will introduce personalized recommendations (based on reading history), multi-language translation, and social sharing functions; integrate video/audio content processing (YouTube technical explanations, podcast interviews); and promote the transformation of information consumption from active search to intelligent push.