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

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.

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.

daft.VideoFile decodes only the frames you need. Keyframes, time-sampled, or windowed seek, built for robotics datasets, dashcams, and moderation queues.

Daft now supports native extensions via Apache Arrow's C Data Interface. daft-h3 is the first community extension — 9 Rust-native H3 geospatial functions, 3–16x faster than Python UDFs.

How to transcribe thousands of audio files with Whisper using daft.AudioFile — handling resampling, silence splitting, and worker-resident model loading without the boilerplate.