StableBrowse

StableBrowse sits in a narrow but growing category: data infrastructure for physical AI. The thesis on the homepage is the right one — internet video doesn't contain enough examples of hands using tools, objects changing state, or bodies moving through space, and robots need that data collected in the world rather than scraped from it. Strengths. The post-processing depth is what separates StableBrowse from a camera crew. Every episode is converted into structured signals — stereo depth, hand tracking, hand mesh, camera trajectory, object state, action boundaries, and sensor QC — and delivered with synchronized timestamps, calibration, schema-valid labels, review UI assets, and output manifests. That is the layer most teams underestimate when they commission a capture. The sensor stack covers egocentric RGB, stereo depth, IMU, audio, tactile gloves, and hand pose, which is enough breadth for manipulation work and long-horizon tasks. The temporal label vocabulary (grasp, lift, move, place, adjust, inspect, recover, repeat) is fine-grained enough to train policies on subtask boundaries rather than whole-episode labels. The annotation layer is model-assisted but human-reviewed with schema validation, and the delivery format includes traceable JSON and per-episode review pages — meaning your team can audit data quality instead of trusting a summary. The MDD pedigree matters: 620 minutes of motion-capture data with 10K+ fine-grained descriptions is a reasonable proxy for whether a team can keep multiple sensors synchronized and labels dense. Weaknesses and fit boundaries. StableBrowse is not a model provider and does not sell a product you can just log into. The homepage contact section asks you to describe the physical skill your model needs; the team then defines the sensor stack, collects episodes, post-processes signals, and delivers the dataset in a format your training pipeline can consume. That means scoping time, sensor decisions, and pipeline alignment are part of every engagement. Teams that want off-the-shelf data, immediate access, or a quick pilot on a small budget are a poor fit — synthetic or public corpora will get you further per dollar. The domain coverage on the site leans toward cooking, assembly, packaging, sewing, measuring, machine work, and inspection; if your task sits far outside hand-object manipulation and tool use, the value is less proven. Where it fits. If you are a frontier lab or research team that has hit the ceiling with public corpora, and your model needs a physical skill, environment, tool, object class, or sensor stack that no existing dataset covers, StableBrowse's custom data programs are aimed exactly at that. The auditability story (provenance, manifests, review UI, calibration) is what you want when training data quality has to be defensible to reviewers or customers. Bottom line. Treat this as a data procurement engagement rather than a tool subscription. Scope the physical skill, the sensor stack, and