Hi HN, we built world-model-optimizer, an open-source tool for continually improving models specialized to agents. Today we are launching `wmo serve`, a tool to route repetitive tasks to distilled smaller models. Agent traces you already capture are opportunities to get signal on how to make your model cheaper, faster, better.

We continuously improve your specialized model through distillation from open source models, model routing to frontier + custom models, and token compaction to remove noise and save tokens. Pass in traces and an OpenRouter key, and wmo starts a local OpenAI-compatible endpoint to run with your model at a lower cost with equivalent quality. Behind the scenes a router decides which tasks should go to the frontier versus your model. Tinker continually trains as new traces arrive.

We also offer a hosted solution for anyone that just wants a frontier quality endpoint with self-improvement over time at a 40%+ lower cost. Sign up for the waitlist at experientiallabs.ai and give the repo a star. The GitHub repository is experientiallabs/world-model-optimizer.