Welcome to the Daft blog
Join us as we explore innovative ways to handle multimodal datasets, optimize performance, and simplify your data workflows.
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
Agentic systems are just query engines for unstructured data
A systems engineer’s view of the new AI stack
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
Making GPUs Zoom (Part 1)
A deep dive into GPU optimizations for production-scale multimodal data processing
End-to-End Distributed PDF Processing Pipeline
OCR, Spatial Analysis & GPU Embeddings with Python
