Find here DataChain news, findings, interesting reads, community takeaways, deep dive into machine learning workflows from data versioning and processing to model productionization.
In January OpenAI published how it built an internal data agent. Last week Anthropic published how it built one too. Two frontier labs, the same problem, five months apart. Here is the honest side-by-side: what they agree on, where they diverge, and the one assumption they both quietly depend on.
Dmitry Petrov
Neural data like EEG and MRI is never 'finished' - it's meant to be revisited as new ideas and methods emerge. Yet most teams are stuck in a multi-stage ETL nightmare. Here's why the modern data stack fails the brain.
Dmitry Petrov
Parquet is great for tables, terrible for images and video. Here's why shoving heavy data into columnar formats is the wrong approach - and what we should build instead. Hint: it's not about the formats, it's about the metadata.
Dmitry Petrov
LLMs can finally interpret unstructured video, audio, and documents — but they can't do it alone. This post introduces the concept of heavy data and explores how modern teams build multimodal pipelines to turn it into AI-ready data.
Dmitry Petrov
Improved Engineering and Data Management will be what carries GenAI into maturity
Dmitry Petrov
How and Why Generative AI will change the modern data stack.
Daniel Kharitonov
