Developer Tests Multi-Model AI Setup to Build App, Gets Results in 14 Minutes

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A developer experimented with Hermes Agent's Mixture of Agents (MoA) framework, which routes a single prompt to multiple AI models simultaneously before a designated aggregator model synthesizes their responses. In the setup, DeepSeek and MiniMax served as advisory reference models, while GPT acted as the aggregator responsible for tool execution and final output. The experiment involved a practical task: building a Kanban board application tailored for a solo YouTube content creator. Within approximately 14 minutes, the system produced a functional single-page app featuring columns, editable task cards, tags, priorities, due dates, checklists, search, and filtering. The author argues that querying multiple models in parallel — much like consulting several independent experts — can yield more well-rounded decisions than relying on any single AI model alone.

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