AI Code Quality

What We’re Tracking #

Techniques, tools, and practices for producing and maintaining high-quality AI-generated code — correctness, maintainability, test coverage, review process, and preventing quality decay over time. Emphasis on Claude Code and Python, but covers AI coding assistants generally. Focus on concrete practice and evidence (benchmarks, case studies, postmortems) over tool marketing.

Config: journals/topics/config/ai-code-quality.yaml

How this journal works #

Gathered every 5 days: a web search on this topic (8 search terms) skips links already in the journal and writes one dated entry of what’s new, with links to related topics. Preferred sources: simonwillison.net, news.ycombinator.com, newsletter.pragmaticengineer.com, arxiv.org, docs.anthropic.com, github.com.