Stera: Open-Source Infra That Turns iPhones into Spatial Data for World Models
FPV Labs has launched Project Stera, an open-source pipeline that transforms consumer iPhones into high-fidelity spatial data capture devices for embodied AI research. The release includes the Stera-10M dataset, comprising over 200 hours of data and 10 million frames collected using the system. By providing accessible, standardized tooling, Stera aims to democratize access to multimodal real-world data and reduce fragmentation in AI data collection.
- ▪Project Stera is an open-source, end-to-end pipeline that enables iPhones to capture high-fidelity spatial data for AI training.
- ▪The Stera-10M dataset contains over 200 hours of data and more than 10 million frames captured using the Stera system.
- ▪Stera captures multimodal data including RGB, depth, IMU, 6DoF poses, hand movements, and semantic task information.
- ▪The Stera SDK allows users to read, process, and export data for evaluation and model training.
- ▪Stera aims to eliminate reliance on proprietary hardware like Aria and enable widespread, standardized data collection for embodied AI.
2 outlets in our directory ran this story, first to last over 1 hour. All of the coverage we found sits in one bucket: centre. That one-sidedness is itself worth noticing.
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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 | Ycombinator |
| Canonical URL | https://news.ycombinator.com/item?id=48160091 |
| Publication time | Sat, 16 May 2026 13:27:39 +0000 |
| Retrieval time | 2026-05-16T13:40:18.659Z |
| Last seen | 2026-05-16T13:40:18.659Z |
| 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 | s_SapfUqfURN · 2 stories |
| 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
We are releasing Project Stera - an open source, end-to-end pipeline that turns a commodity iPhone into a research-grade capture system for embodied AI training data.Today, we're open-sourcing the whole stack, along with Stera-10M, a 200+ hour dataset, and 10M+ frames captured entirely through it.FPV Labs began with one bet - the scaling law for embodied AI will need high-fidelity, multimodal real-world data, and the underlying infrastructure that produces this at scale without compromising downstream quality will determine how fast we build a general-purpose model.Over the last 12 months, we've seen how high-fidelity data is locked behind gated hardware like Aria, which is out of reach for researchers, builders, and startups that want to work with high-quality multi-modal data, and how…
Excerpt limited to ~120 words for fair-use compliance. The full article is at Ycombinator.