Hi HN, we built world-model-optimizer, an open-source tool for continually improve models specialized to agents. Today we are launching `wmo serve`, a tool to route repetitive task…

wmo turns agent traces you already collect into continuous improvement. Start with a model endpoint at frontier quality with 40%+ lower cost. Keep improving it with world model simulations, meta-harness optimization, and model distillation. Distill your own small model into the pool with wmo optimize model, serve a single model with no routing via wmo optimize route pin, or build an optimized harness for your agent with wmo optimize harness. Hosted agents already run in platform-managed E2B sandboxes. To evaluate a local optimization in E2B, install the extra and provide an E2B key: world-model-optimizer includes world models that can be used to simulate your agent environment for testing and optimization. Or over HTTP (same code path), namespaced by model name: GET /world_models, then POST /world_models/{name}/sessions and POST /world_models/{name}/sessions/{id}/step. After wmo login, the same wmo run command can open a hosted world model or run an agent's current champion harness in E2B. The platform manages model and sandbox credentials, so hosted runs do not need local API keys. Workspace upload is opt-in with -u: WMO live-syncs changes and preserves concurrent local edits. Long-running agents can detach, continue in the platform, and be messaged or reattached later. WMO can run the real pi worker inside isolated E2B sandboxes while the world model supplies the environment. Optimization and evaluation rollouts run in parallel, and model credentials stay outside the sandbox.