Cutting dead frames from DROID: 500 hours of robot data in 32 seconds
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.
Turning robot video into training-ready data with daft-physical-ai
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.
How we made our LeRobot video reader up to 15× faster
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.
Finding a Needle in the Haystack: Querying Physical AI Data with Daft
Pose + semantic search over Apple's EgoDex hand-manipulation dataset with Daft: SigLIP embeddings meet hand-pose geometry. Ctrl+F for physical AI data.
Disk is the data plane: Flight Shuffle in Daft
How Daft rebuilt distributed shuffle around Arrow Flight, local disk, and streaming reads to handle multi-terabyte workloads.
First-class observability in Daft
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.
Scaling As-of Joins
How we built, broke, and re-built our ASOF joins — 5.5x faster, half the memory of pandas, and scaled to a distributed cluster.
daft.VideoFile: Seek Lazily, Get Frames
daft.VideoFile decodes only the frames you need. Keyframes, time-sampled, or windowed seek, built for robotics datasets, dashcams, and moderation queues.
Daft Extensions Featuring daft-h3: Native Rust Performance, Community Owned
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.
Newsletter
Get new posts in your inbox
Engineering, product, and company updates from Eventual.