Join us as we explore innovative ways to handle multimodal datasets, optimize performance, and simplify your data workflows.

A Python library built on Daft for turning robot video into training-ready data, starting with hand tracking and reward scoring as UDFs, with more to come.
The robot state recorded next to the video tells you which frames matter before you decode any pixels. Trimming all of DROID takes 32 seconds on a laptop.

Ken Goldberg argues that robotics needs more than data - modularity, algorithms, and metrics still matter. A breakdown of his MIT talk on the 100,000 year data gap.

A Python library built on Daft for turning robot video into training-ready data, starting with hand tracking and reward scoring as UDFs, with more to come.

LeRobot has emerged as the dominant open format for robot learning data, but decoding frames is expensive. Here's how we made Daft's native LeRobot reader up to 15× faster.

Pose + semantic search over Apple's EgoDex hand-manipulation dataset with Daft: SigLIP embeddings meet hand-pose geometry. Ctrl+F for physical AI data.

How Daft rebuilt distributed shuffle around Arrow Flight, local disk, and streaming reads to handle multi-terabyte workloads.

Robotics is hitting a data wall. The architecture debate gets much of the attention, but the data constraint is more fundamental.

A new dashboard, per-operator memory attribution, and OTel endpoints for your existing collector. Everything you need to see what Daft is doing with your query.

How we built, broke, and re-built our ASOF joins — 5.5x faster, half the memory of pandas, and scaled to a distributed cluster.