Software Engineering
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Agent-ready codebase: how to prepare your code for AI agents
How Git, ACDD, DDD, SOLID, tests, documentation, backlog, and linters help AI agents understand a codebase and safely change legacy systems.
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AI Code Review: What to Check in AI-Generated Code
AI has made code generation cheaper, but not understanding. Review shifts from syntax to intent, scope, architecture, business invariants, failure paths, and whether the code should exist at all.
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Who Is Responsible for AI-Generated Code?
AI changed who produces the text, but not who decides to keep it in the system. Ownership remains with the human who understands the change and accepts the merge.
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Atomic Commits and ACDD: Git Workflow for AI Development
What an atomic commit is, how to define its boundaries, write useful commit messages, keep Git history clean, and stay in control of AI-generated changes.
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How to Introduce AI into a Legacy System Without Rewriting It
Keep the legacy application as the system of record and add AI beside it as a new layer: adapters, read-only scenarios, shadow mode, and gradual expansion of capabilities.