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.
Announcing Early Access to Daft Cloud
Early access to Daft Cloud for running model-driven AI pipelines reliably at production scale. Built on Daft OSS for continuous, resilient execution.
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.
Daft 2025 Year in Review - Minor Releases, Major Evolution
In 2025, we shipped 56 releases and introduced features that changed how teams run multimodal AI pipelines at scale.
Knowledge curation (not search) is the AI big data problem
Google was Information Retrieval. Wikipedia is Knowledge Curation.
How We Use AI Coding Agents
Our engineering team's best practices for working with AI coding agents.
How Sourcetable Built the World's First AI Spreadsheet with Daft
Sourcetable CTO Andy Grosser discusses their data infrastructure choices and why reliability and scale drove their architecture decisions.
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