Show HN: Neon and Mubit: an agent that cuts its own frontier-model tokens by 73% (neon.com)

Mubit has introduced a middleware solution for AI agents that significantly enhances efficiency by reducing the reliance on resource-intensive frontier models. This innovative approach allows AI agents to learn from each interaction and offload repetitive tasks to smaller, more efficient models, leading to a dramatic 73% cut in token spending on frontier models, such as OpenAI's gpt-5.5, while simultaneously increasing response speeds by 40%. The demonstration, using a data analyst agent querying a Postgres database, showcases how Mubit manages routine functions and adapts to schema changes, a process that traditionally hampers model performance. The significance of Mubit in the AI/ML space lies in its potential to streamline operations, thereby reducing costs and improving performance for developers using AI systems. By effectively functioning as a bridge between the agent and the model, Mubit not only reduces the frequency of calls made to expensive frontier models but also equips agents with memory capabilities that enhance learning and adaptability over time. This enables the AI to avoid previous mistakes and efficiently adjust to modifications in data structures, making Mubit a promising tool for optimizing AI workflows.