Build with context and proof
Software Change
Practical thinking for specifying, testing, reviewing and changing enterprise software with AI assistance.
Reading path
Move from the defining idea to practical decisions.
When AI Writes the Code, What Does the Engineer Own?
Verification, technical judgment, delegation and communication become more valuable as generating an implementation becomes easier.
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Measure AI Delivery Gains from Request to Reliable Release
A practical scorecard for separating faster generation from better delivery, using review effort, rework, reliability and total cost.
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The Specification Becomes the Control Plane
AI can accelerate delivery only when intent, boundaries, evidence and failure behavior are explicit enough to govern the work.
Read field noteAIDLC: Designing the Delivery System Around AI
An AI-native lifecycle changes the system of work around evidence, review gates and accountability.
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AI-Driven Testing Must Prove the Behavior
Generated tests create value when they challenge behavior, boundaries and failure using an independent definition of correctness.
Read field noteAI Code Review Changes the Bottleneck
As generation accelerates, review capacity, architecture coherence and accountable judgment become the constraints that matter.
Read perspectiveBrownfield Development with Codex: Map Before You Change
The safest use of coding agents in mature systems begins with context recovery, a focused change and a tested path back.
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