Welcome to the Daft blog
Join us as we explore innovative ways to handle multimodal datasets, optimize performance, and simplify your data workflows.
GPU Inference with @daft.cls
Run GPU models on millions of rows without OOM. Real patterns from ByteDance, Essential AI, and more.
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
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.
Prompting with DataFrames: Massively Parallel LLM Generation is Here
Discover how Daft's prompt function revolutionizes LLM workflows with massively parallel context engineering on DataFrames.
Agentic systems are just query engines for unstructured data
Explores how agentic AI systems act as declarative query engines, revealing how reasoning and orchestration transform unstructured data.
Fall 2025 Review: OSS Updates | UDFs, Functions, & daft.File
Daft Fall 2025: AI Functions, improved UDFs, faster vLLM inference, and new daft.File VideoFile subtype - plus Bigtable sink and Common Crawl loader.
Cutting LLM Batch Inference Time in Half: Dynamic Prefix Bucketing at Scale
Learn how Dynamic Prefix Bucketing reduces LLM batch inference time, improves throughput, and unlocks faster multimodal processing at scale.
Simplifying Voice AI Analytics with Daft: Transcription, Summaries, and Embeddings at Scale
Build a Voice AI analytics pipeline with Daft and Faster-Whisper to convert raw audio into searchable transcripts, summaries, and embeddings at scale.
Using PyTorch DataLoaders to Streamline Multimodal Data
Learn how PyTorch's DataLoader streamlines deep learning pipelines by efficiently loading and shuffling data in batches.
