Software solutions do not start with code
I can move from idea to code faster with AI, but the real work still starts before that: asking better questions, spotting constraints, and building the right mental model.
Shaping with AI
Section
33 pages
Posts
Switching from doing to directing
As AI takes over more of the mechanical work, engineers at every level need to learn how to direct, verify, and own what gets shipped. This article reflects on why that mindset should start early, not only at senior level.
Shaping with AI
How I used Cortex Code Desktop to assess the risks in Snowflake's upcoming behavior change bundle
Snowflake rolls out semantic and breaking changes through behavior change bundles. Before a bundle becomes the default, there is a window to test how those changes might affect your workloads. In this article, I show how I used Codex and later Cortex Code Desktop to assess the risks, identify likely breakpoints in our Airflow and dbt pipeline, and map out likely fixes before the bundle lands.
Shaping with AI
Effectiveness anti-patterns — part 6: YAGNI: don’t abstract before the second use case
A practical YAGNI story from data engineering: keeping a CI workflow intentionally narrow until the second real use case appeared.
Career notes
From principles to production: starting a new series
Why AI makes engineering fundamentals more important, not less, and why I am starting a series on the principles that still shape real production decisions.
Technical notes