Uncle Bob says he no longer reads the code generated by his AI agents to maintain productivity. Instead, he surrounds them with strict tests and metrics—unit tests, Gherkin tests, QA procedures, mutation testing, coverage—to ensure high confidence in their output.

• Uncle Bob Martin no longer reads code written by his AI agents, treating their output as a black box.
• He relies on a strict testing gauntlet—unit tests, Gherkin tests, QA procedures, mutation testing, and coverage thresholds—to catch problems instead.
• Code that fails any of these checks (e.g., coverage drops or a mutation test breaks) doesn't get merged, letting the test suite act as gatekeeper rather than manual review.

Quodeq is an AI agent that inspects your codebase using read-only tools, scores it against the six ISO 25010 quality dimensions, and maps issues to CWE classifications. It rewards good code as well as flags violations, then generates exact fixes you can paste into your IDE or AI assistant. You can run it offline with Ollama or connect to cloud models without sending your code offsite.

• Quodeq scores code with a Q² formula that rewards good patterns instead of just penalizing violations, mapping issues to ISO 25010 dimensions and CWE identifiers with exact fixes
• Runs fully local via Ollama or connects to cloud models (Claude, Codex, Gemini) without sending code offsite
• MIT licensed and fully open for inspection, extension, and custom quality criteria beyond the built-in ISO 25010/CWE checks
• Builds a structural model of the codebase across multiple languages/frameworks rather than acting as a simple linter

This article discusses the dangers of accumulating technical debt, especially in the context of rapid AI advancements. While it may seem beneficial to defer debt repayment for future improvements, this approach can lead to an overwhelming complexity that even AI tools can't manage. Developers must balance short-term gains with long-term sustainability.

• AI-assisted coding tempts developers to defer technical debt indefinitely, betting that future model improvements will make it cheap to fix later, with some codebases growing past 40,000 lines/day.
• This bet assumes AI capability growth continues indefinitely, but once it plateaus, accumulated debt becomes unfixable by any human or AI.
• The result is "subprime" technical debt—debt whose risk was mispriced on the assumption of endless future gains, setting up an eventual reckoning.