The Bitter Lesson Stops at the Lab Door
The hard part of AI is not getting a model to perform in a demo. It is connecting it to real systems, giving it usable context, and keeping it under control once it matters.
The hard part of AI is not getting a model to perform in a demo. It is connecting it to real systems, giving it usable context, and keeping it under control once it matters.
AI coding agents increase output, but they also increase pressure on code review and branch management. Stacked pull requests and isolated worktrees help keep changes small and …
How to combine Metaxy, Dagster-Slurm, Docling, and Ray to run incremental multimodal pipelines on sovereign AI infrastructure.
Improving developer experience on HPC systems with dagster.
Ranking of OLAP and analytics engines using public benchmarks powered by ELO ratings. Explore the leaderboard
Flexible access to genai models for both API and chat usage
A comprehensive guide to modern data engineering with local-first development practices
Sharing my experience migrating from conda-build to rattler-build for python noarch packages.
data vault knowledge sharing event