World Model Optimizer - Distill frontier models for 40% cost reduction
Show HN: Distill and serve small models with frontier quality for half the cost
World Model Optimizer transforms existing agent traces into a continuous improvement loop, delivering frontier-quality model performance at 40% lower costs. By leveraging world model simulations, meta-harness optimization, and advanced distillation techniques, it enables developers to build and serve optimized small models effortlessly. Whether using the CLI to tune routers on OTel traces or deploying via the hosted platform with E2B sandboxes, this tool ensures every candidate change is rigorously evaluated before becoming the new champion harness. It bridges the gap between expensive frontier models and efficient, high-performance deployment.
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.
- Art9681
The absolute best way to prove this works is by releasing a model that was fine-tuned with this method and then showing benchmarks depicting the improvement delta between the base model and the fine tuned one.
The work is not done. Then release it to the masses and wait a few days for the actual real world anecdotes.
Until then, this is noise.
- adrianco
Local models need to be tuned to work well so this looks useful. Seems to be for general purpose model serving. I’ve been using https://github.com/adrianco/retort to run experiments for coding models across 13 different programming languages to see which frontier and local models work.
- jack_pp
Not sure I get it. The model you're improving is local? If so how do you even calculate cost compared to an API