标题： 基于大型语言模型的实时股票分析无服务器架构：一种迭代开发与调试案例研究 显示英文标题

标题： A Serverless Architecture for Real-Time Stock Analysis using Large Language Models: An Iterative Development and Debugging Case Study

摘要： 随着强大且易于使用的大型语言模型（LLMs）如谷歌的Gemini的出现，为金融数据分析的民主化带来了新的机遇。本文记录了一个新型无服务器系统的设计、实现和迭代调试过程，该系统用于实时股票分析。该系统利用Gemini API进行定性评估，通过GitHub Actions自动化数据摄入和处理，并通过解耦的静态前端展示结果。我们详细描述了系统的架构演变，从最初的构思到一个健壮的事件驱动流程，强调了在部署过程中遇到的实际挑战。本文的很大一部分内容专门用于案例研究，涵盖了常见的软件错误、平台特定的权限问题以及罕见的环境级平台错误。最终的架构运行成本接近于零，展示了个人构建复杂AI驱动的金融工具的可行模式。操作应用是公开可访问的，完整的源代码可供审查。最后，我们讨论了LLMs在金融分析中的作用、稳健调试方法的重要性，以及软件开发中人机协作新兴范式。 显示英文摘要

摘要： The advent of powerful, accessible Large Language Models (LLMs) like Google's Gemini presents new opportunities for democratizing financial data analysis. This paper documents the design, implementation, and iterative debugging of a novel, serverless system for real-time stock analysis. The system leverages the Gemini API for qualitative assessment, automates data ingestion and processing via GitHub Actions, and presents the findings through a decoupled, static frontend. We detail the architectural evolution of the system, from initial concepts to a robust, event-driven pipeline, highlighting the practical challenges encountered during deployment. A significant portion of this paper is dedicated to a case study on the debugging process, covering common software errors, platform-specific permission issues, and rare, environment-level platform bugs. The final architecture operates at a near-zero cost, demonstrating a viable model for individuals to build sophisticated AI-powered financial tools. The operational application is publicly accessible, and the complete source code is available for review. We conclude by discussing the role of LLMs in financial analysis, the importance of robust debugging methodologies, and the emerging paradigm of human-AI collaboration in software development.