Building a one-line integration to post-train open models from feedback

A developer observed that agents often require manual guardrail additions after making mistakes, leading to a repetitive cycle of rule-adding. To address this, they created "middleware.now," a one-line integration designed to enable open models to learn from corrections and failures. The core idea is for the models to use past mistakes and feedback as context for future interactions, thereby improving their performance and reducing the need for constant manual intervention.

This integration offers a novel approach to post-training open models, unlike traditional methods that rely on manual guardrail additions after each agent mistake.