📰 Dev.to · Philip Hern

openai is pulling its models from cursor following spacex's acquisition of anysphere. here is why model lock-in harms developers.

how to structure your .cursor directory so agents start with the smallest useful context, spend fewer tokens, and produce faster, more focused results

announcing chapter 3 of ai-assisted data engineering, from prototype to production, it moves the book from mindset into the production bar, where ai-assisted wo

how bronze, silver, and gold map to real data vault 2.0 architecture, from RDV raw history to BDV business rules to ID consumer views

announcing chapter 2 of ai-assisted data engineering, the danger of trusting the agent, the darker companion to earned judgment and the ownership habits that ke

announcing philliant on youtube, the channel that will sit beside philliant.com, with videos talking through the posts, ai-assisted data engineering, and the id

why ai makes it feel like you can always do a little more, and why you still need room to breathe when you, not the model, are the one who has to review, unders

the concrete guardrails i put around ai coding agents, including read-only and ask modes, command allowlists, scoped database credentials, branch protection, an

how snowflake dynamic tables provide a declarative, join-supporting alternative to traditional task orchestration and materialized views

snapshots are powerful but expensive. use them only when absolutely necessary, never stack them on top of each other, and let the rest of your warehouse stay de

a short note on getting through a heavy lift by staying with the problem, working in smaller chunks, and reaching a stable enough place to breathe before the ne

a guide on using dbt snapshots to track slowly changing dimensions, including lessons learned from migrating away from custom merge strategies.

an exploration of how artificial intelligence is reshaping data engineering workflows, from automated pipelines to intelligent data modeling.

a practical guide on leveraging cursor and ai agents to automate the tedious parts of data engineering, specifically dbt tests and documentation.

a practical guide to dbt tests, with simple examples that catch common data issues before they leak into dashboards and downstream models

a practical guide to dbt docs, covering what they generate, how to write useful descriptions, doc blocks, persist_docs, and hosting on github pages

an informational primer on sql, including what it is, a short history, why it matters, and where to use it

a beginner-friendly guide for data engineers comparing snowflake to rds and nosql databases, covering scalability, use cases, and learning curves

a small data vault 2.0 sql pattern: pre-filter the effective satellite in a cte, then left join the cte so optional links do not multiply grain