Welcome to the Eventual blog
Join us as we explore innovative ways to handle multimodal datasets, optimize performance, and simplify your data workflows.
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.
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.
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?
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.
Audio transcription at scale with daft.AudioFile
How to transcribe thousands of audio files with Whisper using daft.AudioFile — handling resampling, silence splitting, and worker-resident model loading without the boilerplate.
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.
daft.File: Lazy Metadata Filters
Filter millions of files by path, size, and content type before opening any of them. Cheap operations first, expensive operations on the survivors.
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.