Show HN: Distill and serve small models with frontier quality for half the cost

Summary

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. 🌐 Platform | 📚 Docs | Discord 1. Register your providers. pip install world-model-optimizer wmo providers set 2. Tune a router on your OTel traces. wmo build --file traces.jsonl --name my-endpoint # Score every registered model on held-out tasks from your traces wmo optimize route sweep my-endpoint --traces traces.otel.jsonl # Turn those measurements into a routing policy wmo optimize route fit matrix.json --kind knn \ --out .wmo/models/my-endpoint/policy.json 3. Serve it. wmo serve --name my-endpoint See what it bought you against the model you were using before: wmo optimize route report matrix.json .wmo/models/my-endpoint/policy.json \ --baseline gpt-5.5 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. Create an account at platform.experientiallabs.ai, then authenticate the CLI: Copy an agent ID from the platform and run its current champion 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: pip install "world-model-optimizer[e2b]" export E2B_API_KEY=... wmo optimize harness my-agent my-environment --tasks tasks.jsonl --backend e2b Use a world model as an API world-model-optimizer includes world models that can be used to simulate your agent environment for testing and optimization. from wmo import Action, ActionKind from wmo.config.store import WorldModelStore from wmo.engine.loader import load_world_model model_dir = WorldModelStore(".wmo").resolve("airline") wm, _provider = load_world_model(model_dir) session = wm.new_session(task="check out the cart") obs = wm.step(sess...