Welcome to the Daft blog
Join us as we explore innovative ways to handle multimodal datasets, optimize performance, and simplify your data workflows.
Eventual Raises $30M to Build the Future of Data
Tuning Daft's Distributed UDFs: Lessons from ByteDance
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Announcing Early Access to Daft Cloud
Running model-driven data pipelines reliably at production scale
Why I joined Eventual
Chris Kellogg on his decision to join Eventual
Distributed Model Inference with Daft
A deep dive into Daft’s distributed execution engine, Flotilla, for multimodal data pipelines
Introducing Dynamic Batching: Auto-Tuning for Daft Pipelines
I Got Tired of Tuning Batch Sizes, So I Made Them Tune Themselves
Daft 2025 Year in Review
Daft 2025 Year in Review - Minor Releases, Major Evolution
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 on his decision to join Eventual
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.
Multimodal Structured Outputs: Evaluating VLM Image Understanding at Scale
Leveraging ablation for contrastive image understanding evaluation in Daft
