Articles
Verification for AI coding, explained
Concepts, methods, and working setups for teams that ship AI-generated code without losing certainty. Every article cites its sources, shows its date, and gets updated only when something actually changes.
Concept
Verification Debt
The gap between how fast AI tools generate code and how reliably teams can verify it before merge – definition, data, and a practical framework to measure your own.
Updated: July 2, 2026Read article →
Method
AI Coding Verification
How teams check AI-generated code against explicit task intent, validation plans, tests, and evidence before a change is accepted – the verification loop, step by step.
Updated: July 2, 2026Read article →
Setup
Local AI Code Review
Reviewing and verifying AI-generated code inside your own environment, without uploading source to an external service – who needs it, and how to set it up.
Updated: July 2, 2026Read article →
Workflow
Works with Claude Code
A real workflow for verifying Claude Code runs: task boundaries before the run, independent validation and evidence after it, a human gate before merge.
Updated: July 2, 2026Read article →
Proof
Evidence Reports
What was intended, what changed, what was validated, what was skipped, what remains uncertain – recorded per run and stored with the code. Structure and a labeled sample.
Updated: July 2, 2026Read article →
Architecture
Local-First Security
Security as verifiable architecture properties instead of marketing guarantees: local-first, advisory by default, no auto-commit, human approval gates.
Updated: July 2, 2026Read article →
Governance
AI Coding Governance
The controls, workflows, approvals, and evidence a team needs to adopt AI coding tools without losing engineering accountability – and where each building block has limits.
Updated: July 2, 2026Read article →
Method
Spec-vs-Implementation Check
Verify AI-generated code against a written statement of intent instead of your memory of the prompt - the five steps, the circularity problem it solves, and where the method ends.
Updated: July 2, 2026Read article →
Method
Code Review vs. Verification
Review judges quality, verification checks a change against written intent - why AI speed broke review-only workflows, what the data shows, and the division of labor that works.
Updated: July 2, 2026Read article →
Method
Machine-Checkable Specifications
Turn prompts into verifiable tasks: goal, boundaries, yes/no acceptance criteria, validation plan - the four building blocks and the rules that make criteria checkable.
Updated: July 2, 2026Read article →
Method
Measuring Verification Debt
Four metrics computable from git and PR data - generation-to-verification ratio, review depth, unverified-merge rate, two-week churn - with formulas, starting thresholds, and a worked example.
Updated: July 2, 2026Read article →
Method
Spec-Driven Development
Spec first, then code: how SDD works with AI agents, what Spec Kit and Kiro actually deliver, and the honest limits practitioners report - including why specs still need verification.
Updated: July 2, 2026Read article →
Method
Two-Pass Review Workflow
Machine pre-check first, human architecture review second: what belongs in each pass, how to keep the machine gate high-precision, and why the human always makes the merge call.
Updated: July 2, 2026Read article →
Method
AI Session Handoffs
Sessions forget - compaction drops details, new sessions start cold. Write state, decisions, and open verification points into a persistent handoff artifact: the method, a template, its limits.
Updated: July 2, 2026Read article →
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