AI code review tools are genuinely useful for catching syntax errors, obvious bugs, and common anti-patterns. They are also systematically unable to tell you that the feature you built was the wrong call, that the abstraction is off, that the naming reveals confused thinking, or that the correct review comment is “delete this.” Here is what AI reviews find, what they miss, and why human judgment still has no substitute.
Source generators are powerful. They are also running on every single build, blocking IntelliSense, breaking Hot Reload, and multiplying their cost across every target framework you support. Nobody mentions this in the getting started guides. Here is how to measure the damage, find the culprits, and decide when source generators are actually worth it.
Claude is indexing your bin/ and obj/ directories right now. You asked it how to stop that. It told you about .claudeignore. You added it, committed it, and felt responsible. There is just one problem: .claudeignore does not exist. Claude invented it, the internet spread it, and your secrets were never protected. Here is what actually works.
Spreadsheet-based privacy audits examine yesterday’s system while today’s code deploys undocumented PII. Build .NET CLI tools that discover all personal data, catch expired consents, and verify deletions. Then fail builds when compliance breaks.
Your security tests run. They pass. But can you prove when they ran and against which code version? Most security testing lives in Word documents, Postman exports, and screenshot folders on SharePoint. The tests themselves might be valid. The evidence trail is not. This article shows how to build CLI-based test suites using xUnit and WebApplicationFactory that generate their own proof: structured logs with timestamps, commit hashes, and correlation IDs captured automatically in CI/CD pipelines. No more quarterly reports that could have been written yesterday. Instead, 847 test executions across 23 deployments, each linked to a specific commit and preserved for 90 days.
