Welcome to the Eventual blog
Join us as we explore innovative ways to handle multimodal datasets, optimize performance, and simplify your data workflows.
Daft v0.7.15: Safe Type Conversions, Flight Shuffle Optimizations, and PostgreSQL Support
Daft v0.7.15 ships with try_cast for safe type conversion, Flight shuffle LZ4 compression, UUIDv7 timestamp extraction, and PostgreSQL support.
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
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.