Show HN: dmx – MCP server for adding configurable, gated loops to coding agents (dmx.deepmodel.ai)

dmx, an open-source AI-native engineering harness, has been introduced as a solution for creating structured and verifiable workflows in AI programming environments. Operating as a Model Context Protocol (MCP) server, it enhances existing tools like Cursor, Claude Code, and Copilot by adding configurable, gated loops to the coding process. By implementing an AI Software Development Life Cycle (SDLC) framework, dmx introduces a five-phase workflow that includes human control points for specifying, planning, building, validating, and releasing code, ensuring that human oversight is integral at every stage. This innovation is significant for the AI/ML community as it transforms the often chaotic interaction between developers and AI models into a systematic approach that emphasizes reliability and accountability. Through its defined phases, persistent job states, and policy-driven validators, dmx not only streamlines coding workflows but also enables teams to maintain version control and historical context. By incorporating structured governance into AI workflows, dmx addresses the limitations of rapid yet unstructured AI coding processes, allowing organizations to foster progressive trust as they gain confidence in their AI tools over time.