Eventual Blog
What I learned re-evaluating VLADBench on the latest VLMs
GPT-6 Astra and Claude Opus 5.5 top the leaderboard, but the cost-performance frontier is a more interesting story.
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.
What if more data alone won't solve robotics? Ken Goldberg on why good old-fashioned engineering still matters
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.
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.
The limiting factor in physical AI isn't compute or architecture - it's data
Robotics is hitting a data wall. The architecture debate gets much of the attention, but the data constraint is more fundamental.
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.
VLAs are dead, long live World Action Models - a summary of Jim Fan's Robotics End Game talk
Jim Fan argues robotics will follow the exact LLM playbook - and VLAs are already being replaced by World Action Models.
What is physical AI - is it more than just hype?
Physical AI has become a real trend, but is there something real here or is it just hype?