Core Insights - The article discusses the increasing need for customized customer management systems as companies undergo digital transformation, highlighting the limitations of open-source CRM systems and advocating for self-developed solutions [1][3]. Group 1: Limitations of Open-Source CRM - Open-source CRM systems often have fixed functionalities, making it difficult for companies to customize features without risking system stability and data security [3]. - Different industries have unique business logic that standard open-source CRMs cannot accommodate, necessitating tailored solutions [3]. Group 2: Advantages of Self-Developed CRM - Self-developed CRM systems can be customized to meet specific business needs, allowing for direct integration with existing systems and full control over data [3][12]. - Companies can utilize low-code platforms, full-stack development frameworks, or cloud-native solutions to create tailored CRM systems without needing extensive coding expertise [6][10][11]. Group 3: Development Strategies - The article outlines three key steps for successful self-developed CRM implementation: focusing on core requirements, validating prototypes, and adopting an iterative development approach [12][13][14]. - Companies are advised to avoid common pitfalls such as underestimating hidden costs, accumulating technical debt, and failing to comply with regulations [17][18][19]. Group 4: Industry Benefits - Various industries have successfully leveraged self-developed CRM systems to enhance customer engagement and operational efficiency, such as retail, manufacturing, and professional services [20][21].

"我自学编程三个月，明明做得像模像样，却老觉得自己像个骗子。" 基于此，他不禁产生质疑："我到底是在学编程，还是只学会了跟 AI'对话'？" 最近，自学 Django 三个月的开发者 kekda_charger在技术社区Reddit抛出了一个引发热议的困惑——靠着 AI 辅助，他已经能做出带用户认证的 Web 应用、实时更新功能的系统，甚至完成 API 集成；但一旦脱离 AI，连简单的登录模块都要折腾两三天。 从 "快速出活" 到 "空白恐慌"：一个自学开发者的困境 如开头所说，kekda_charger 的编程之路，始于三个月前自学 Django。 借助 AI 工具，他的学习进度堪称 "飞速"：不仅完成了数据库驱动应用的开发，还实现了实时交互功能——这些在传统学习路径中可能需要半年 才能掌握的技能，他用三个月就落地成了项目。 但光鲜的成果背后，是难以言说的底气不足。kekda_charger 坦言，自己项目中 80%-90% 的代码来自 AI 生成，而他自己的工作只是 "理解、修改 和调试"。 久而久之，这种 "AI 依赖" 带来的副作用逐渐显现：他能轻松读懂复杂代码的逻辑，也能在现有功能上做修改、加新特性 ...

Core Points - The article discusses the architecture and technology choices for social applications, emphasizing the importance of selecting the right frameworks and services for development [5][8][9]. Group 1: Frontend Development - The frontend of a social app consists of mobile (iOS/Android) and web applications, utilizing frameworks like React.js, Vue.js, and Angular for single-page applications [3][5]. - Mobile app development can be native (using Swift for iOS and Kotlin for Android) or cross-platform (using React Native, Flutter, uni-app, or Taro), each with its own advantages and disadvantages [6][8]. Group 2: Backend Development - The backend handles business logic, data storage, user authentication, and API interfaces, with popular frameworks including Spring Boot for Java, Django for Python, and Express.js for Node.js [9]. - Java is noted for its high performance and stability, making it suitable for large-scale applications, while Python offers rapid development capabilities for smaller projects [9]. Group 3: Database and Storage Solutions - Relational databases like MySQL and PostgreSQL are commonly used for structured data, while NoSQL databases like MongoDB and Redis are preferred for unstructured data and high-speed access [9]. - Object storage services from providers like Alibaba Cloud and Tencent Cloud are essential for managing user-generated content such as images and videos [9]. Group 4: Cloud Services and Compliance - For the Chinese market, compliance with local regulations, including ICP filing and app registration, is crucial, along with the selection of domestic cloud service providers like Alibaba Cloud and Tencent Cloud [8]. - The article highlights the importance of integrating third-party SDKs for functionalities like instant messaging and content moderation, with a focus on local providers [8][9]. Group 5: Development Tools and Technologies - The use of message queues (e.g., Kafka, RabbitMQ) and search engines (e.g., Elasticsearch) is recommended for system decoupling and enhancing user experience through personalized content [9]. - Containerization technologies like Docker and Kubernetes are suggested for efficient application deployment and management [9].