Code is cheap. Confidence is not. Make correctness easier to prove.
AI can generate implementation faster than teams can build confidence in it. Make intent, constraints, and evidence of correctness clear before code arrives.
Shaping with AI
Categories
17 pages
Technical notes
Data engineering, system design, architecture
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
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
From principles to production
Why AI makes engineering fundamentals more important, not less, and how they shape real production decisions.
Technical notes
How I improved AI-generated Python code by setting clear instructions
A reflection on turning one AI-generated Python prototype into reusable coding-agent guidance: clearer implementation boundaries, stronger quality expectations, and a shared quality bar before prototypes become real tools.
Shaping with AI