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?
Image Embeddings: Tutorial & Examples
Learn about the concept of image embeddings, their various use cases, and best practices for handling them in data processing workflows.
Multimodal Embeddings: Tutorial & Examples
Learn multimodal embedding techniques for cross-modal search, recommendation systems, and content moderation applications.
Open-sourcing 43 Billion Tokens of SEC EDGAR
Datamule, Teraflop AI, and Eventual collaborated to release the SEC-EDGAR dataset containing 590 GB of data, spanning 8 million samples and 43 billion tokens from all major filings in the SEC EDGAR database.
Tuning Daft's Distributed UDFs: Lessons from ByteDance
Learn from the ByteDance Volcengine LAS Team on how to optimize Daft UDFs on Ray. Discover the formula to evenly distribute data across actors.
Why I joined Eventual
Chris Kelloggs shares why he joined Eventual to build open-source, distributed systems for large-scale AI and multimodal data workloads
Introducing Dynamic Batching: Auto-Tuning for Daft Pipelines
Manually tuning batch sizes is hard. So I implemented dynamic batching to never deal with it ever again.
How We Use AI Coding Agents
Our engineering team's best practices for working with AI coding agents.
Why I Joined Eventual
Sam Stokes shares why he joined Eventual, the company behind Daft, and what excites him about helping build our large scale data processing platform.
Multimodal Structured Outputs: Evaluating VLM Image Understanding at Scale
Leveraging ablation for contrastive image understanding evaluation in Daft