Show HN: Distill and serve small models with frontier quality for half the cost (github.com)

A new tool, the World Model Optimizer (WMO), has been introduced to help developers distill and serve smaller AI models efficiently and cost-effectively, achieving frontier-quality outputs at a 40% lower cost. WMO leverages continuous improvement techniques, including world model simulations and meta-harness optimization, allowing users to enhance AI performance by registering data providers, tuning routing algorithms based on observable traces, and comparing new models against established benchmarks like GPT-5.5. This tool holds significant implications for the AI/ML community, particularly for organizations seeking to optimize model deployment without incurring high costs. The technical implementation involves straightforward commands for registration, optimization, and serving, making it accessible even to those with limited experience. Additionally, by utilizing anonymous usage telemetry for performance insights, WMO ensures privacy while enabling developers to refine their models with actionable data, paving the way for more responsive and economical AI solutions in various applications.