Show HN: Data with logic for modern AI and infra workflows (crates.io)

stof v0.8.39 is a new release of a "smart, declarative runtime for data workflows" aimed at unifying data and logic for modern AI and infrastructure pipelines. The project emphasizes declarative definitions of data transformations and dependencies so teams can describe what they want produced rather than imperatively coding orchestration steps. Although the README failed to load in the provided listing, the project shows significant traction—72,430 total downloads and 141 published versions—indicating active development and community adoption. For the AI/ML community this matters because data-centric engineering and reproducible pipelines are becoming core to model quality and production reliability. A declarative runtime can simplify pipeline composition, dependency tracking, incremental recomputation, and integration with infrastructure automation, reducing boilerplate and human error when managing dataset creation, feature computation, and retraining flows. The release signals a maturing toolset for coupling data logic and infra workflows; teams evaluating solutions for orchestration, data versioning, and repeatable ML workflows should review stof’s docs and changelog (when available) to assess compatibility with their stack.