Case studies, systems notes, and role fit organized as one editorial surface.
Writing
Applied AI needs budget edges
The interesting engineering work around AI is often cost control, validation, and fallback behavior.
The anti-pattern
It is easy to wire an LLM call into a feature and call the project "AI-powered". It is much harder, and more useful, to specify when the feature should run, what it is allowed to cost, and how the product behaves when the model fails.
What I prefer shipping
In the projects I keep in the portfolio, AI is constrained:
- by usage budgets,
- by narrow workflow boundaries,
- by explicit environment requirements,
- and by docs that describe the failure modes.
Why this matters
An AI feature that quietly burns money or creates silent bad output is not a product advantage. It is a hidden reliability problem.
The portfolio signal
When I document AI features now, I try to show the guardrails around them. That is usually a stronger signal of engineering judgment than the prompt itself.