Show HN: Distill and serve small models with frontier quality for half the cost
World Model Optimizer 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 Getting started 1.
- ▪World Model Optimizer 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 Getting started 1.
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Story provenance
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/experientiallabs/world-model-optimizer |
| Publication time | Sun, 26 Jul 2026 23:35:15 +0000 |
| Retrieval time | 2026-07-27T00:27:30.798Z |
| Last seen | 2026-07-27T00:27:30.798Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 9kkjmotX2omH |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
World Model Optimizer 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 Getting started 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.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.