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@tonbistudio

Today's YouTube video is a longer look at MoA in Hermes Agent, trying to answer some of the common questions: How does using MoA impact cost, speed, and quality? I created an open weights MoA using GLM-5.2, Kimi K2.6 and Minimax M3 to find out, then made a three-headed Grok with GPT-5.5 as the aggregator to see if that added some much-needed style to the GPT model. Check it out! https://t.co/lQMfbuI1Ix

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    "text": "Nous Research just dropped MOA (Mixture of Agents) presets inside Hermes Agent. I made a quick video showing how to set it up and create your own MOA.\n\nThe idea: mix multiple models to get capabilities beyond any single model you can use right now.\n\nHow it works:\n\nNormally Hermes sends your conversation + tools to one model.\n\nWith MOA you get several reference models plus one aggregator. The references read the conversation and offer thoughts and suggestions, but they get no tool access and never reply to you directly.\n\nThe aggregator is the one that actually acts. It sees the normal conversation plus the private advice from the references, then makes the tool calls and writes the final response.\n\nFrom Hermes's side, the aggregator's output IS the model's response, so you can use /goal or anything else like that. Cool idea, curious to see how it really performs!",
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