Welcome to the Daft blog
Join us as we explore innovative ways to handle multimodal datasets, optimize performance, and simplify your data workflows.
Tuning Daft's Distributed UDFs: Lessons from ByteDance
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
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.
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
Cutting LLM Batch Inference Time in Half: Dynamic Prefix Bucketing at Scale
A new inference backend that maximizes batch inference throughput.
Benchmarks for Multimodal AI Workloads
Spark, Ray Data, and Daft
Introducing Flotilla: Simplifying Multimodal Data Processing at Scale
Daft's new distributed engine
Exploring Daft's Local Execution
The Swordfish Engine
After the First Run
Using Daft’s observability tools to uncover performance pitfalls
