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
Processing 99% of U.S. Caselaw for Under $1 in the Common Pile
How Teraflop AI processed 7 million court documents and 40 million pages spanning 365 years of U.S. caselaw for under a dollar using Daft.
Prompting with DataFrames: Massively Parallel LLM Generation is Here
Discover how Daft's prompt function revolutionizes LLM workflows with massively parallel context engineering on DataFrames.
Cutting LLM Batch Inference Time in Half: Dynamic Prefix Bucketing at Scale
Learn how Dynamic Prefix Bucketing reduces LLM batch inference time, improves throughput, and unlocks faster multimodal processing at scale.
Simplifying Voice AI Analytics with Daft: Transcription, Summaries, and Embeddings at Scale
Build a Voice AI analytics pipeline with Daft and Faster-Whisper to convert raw audio into searchable transcripts, summaries, and embeddings at scale.
Using PyTorch DataLoaders to Streamline Multimodal Data
Learn how PyTorch's DataLoader streamlines deep learning pipelines by efficiently loading and shuffling data in batches.
Benchmarks for Multimodal AI: Spark, Ray Data, and Daft
Multimodal AI workloads break traditional data engines. Daft ran 2-7x faster than Ray Data and 4-18x faster than Spark while finishing jobs reliably across audio, video, document, and image workloads.
Introducing Flotilla: Simplifying Multimodal Data Processing at Scale
Flotilla, Daft's new distributed engine, processes terabytes of multimodal data in a single query up to 18x faster than Spark and Ray Data, while running efficiently, reliably, and without manual tuning.
Exploring Daft's Local Execution: The Swordfish Engine
Explore how Daft's Rust-powered engine executes DataFrame and SQL queries. Learn how Swordfish enables fast, streaming image processing at scale.
After the First Run
Using Daft's observability tools to uncover performance pitfalls
Making GPUs Zoom (Part 1)
How Daft is approaching large-scale model inference with advanced GPU optimizations for faster multimodal AI workloads
End-to-End Distributed PDF Processing Pipeline
Build production-ready PDF processing pipelines with distributed computing, OCR, spatial analysis, and GPU embeddings
24 Trillion Tokens, 0 Crashes: How Essential AI Built Essential-Web v1.0 with Daft
Essential AI leveraged Daft's data engine to process a massive web-scale dataset for large language model (LLM) training.
Embedding Millions of Text Documents With Qwen3
Learn how to achieve near-100% GPU utilization processing millions of text documents with Qwen3 embeddings.
Processing 300K Images Without OOM
A Streaming Solution
Eventual Raises $30M to Build the Future of Data Processing
We've raised $30M to build generational technology for simple, reliable, and performant data processing across all modalities and regardless of scale.
We cloned over 15,000 repos to find the best developers
An adventure in AI and data engineering to analyze developers across Github
High-Performance File System Support With DeepSeek 3FS
Learn how Daft integrates with DeepSeek SmallPond 3FS to deliver faster file access and efficient data handling for modern workloads.
Adversarial file reading: from 10,000 small CSVs to massive Parquet files
Learn how adversarial file reading speeds up data ingestion at scale, enabling fast conversion from thousands of CSVs into efficient Parquet files.
Working with the Apache Parquet file format
This guide shows how Apache Parquet boosts read performance, lowers storage use, and supports efficient workflows for large analytical datasets.
Introducing Daft: A High-Performance Distributed Dataframe Library for Multimodal Data
Discover how Daft accelerates multimodal data processing with a high-performance distributed dataframe engine built for modern AI and analytics tasks.