AI Code Has a Conversation Debt
The risk of AI-generated code is not simply that it can be wrong. It is that teams lose the learning conversation that turns questionable choices into better engineering judgment.
AI Systems
6 articles in this category.
Git Worktrees and Scoped Commit Hooks Are the Safety Rails for Agentic Coding
Branches record intent, but worktrees and scoped commit hooks make parallel local agent work isolated, reviewable, and safer to integrate.
Senior Developers Are Becoming Calibration Systems
In an AI-native engineering organization, seniority is increasingly the ability to calibrate trust: knowing what to delegate, what to verify, and where a plausible answer becomes a dangerous one.
AI-Augmented Ticket Creation Without Outsourcing Thinking
AI can improve ticket clarity and completeness when used as a structuring layer. It should not replace human ownership of intent, boundaries, and risk decisions.
Git Blame Is Missing the Most Important Information
Git blame preserves authorship, but engineers often need intent. Git Explain uses deterministic local git retrieval and a bounded tiny local model pass to reconstruct why a line exists.
Reimagining Grep: Searching Code by Meaning Instead of Text
Grep is excellent at exact strings and weak at intent. A local semantic retrieval primitive lets developers search code by meaning under strict constraints: local-first, secure-by-default, CPU-friendly, and minimal dependencies.