📄 中文摘要
随着高性能本地大语言模型的爆炸式增长和模型上下文协议（MCP）的标准化，开发者的工作站已不再仅仅是编码环境，而是一个复杂的 AI 节点。然而，连接性仍然是一个显著的摩擦点。如何与远程利益相关者共享本地运行的 LLM？云端代理如 Claude 或 ChatGPT 如何通过 MCP 访问本地环境以执行工具？这些问题的解决方案对于促进本地和云端 AI 实验的协同至关重要。
开发者隧道的演变：将本地 AI 实验连接到云端
📄 English Summary
The Evolution of Developer Tunnels: Bridging Local AI Experiments to the Cloud
The rise of high-performance local Large Language Models and the standardization of the Model Context Protocol (MCP) have transformed developer workstations into sophisticated AI nodes. However, connectivity remains a significant friction point. Sharing a locally running LLM with remote stakeholders poses challenges, as does enabling cloud-based agents like Claude or ChatGPT to access local environments via MCP. Addressing these issues is crucial for facilitating collaboration between local and cloud-based AI experiments.