An early path with to make compound AI systems work for data engineering

We've partnered with Continue to launch custom AI code assistants for data engineering, building on our foundation of dlt and dlt+ pipelines. This partnership aims to create a more efficient and trustworthy data engineering workflow by leveraging the Continue Hub's models, context, and other building blocks. Our goal is to enable developers to amplify their work, rather than automating it, as we believe that human engineering time will increase in monitoring, maintaining, and improving AI software development systems. We're working towards a future where compound systems can generate trusted data, unlocking additional data engineering assistants and building blocks. Our focus includes developing custom AI code assistants that integrate with developers' existing workflows, utilizing the Anthropic MCP standard for reliable context, and supporting emerging standards like Iceberg Apache Iceberg for large datasets. By doing so, we aim to address the lack of trust in data, which has been a major factor in enterprise deployment of AI. We invite companies and developers to join us in this community-driven effort to solve some of the problems together.

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