53What are the failure modes you specifically look for when reviewing AI-generated code, and how do you catch each one?▼hardGoogleStripeAnthropic1 replies◆ premiumAI code fails differently from human code: it's confidently plausible. The dangerous defects are the ones that pass a quick read and a happy-path test, missing authorization, hallucinated APIs, hardcoded secrets, unsafe deserialization. Here's the checklist and the automated catch for each.Open full answer →
54Your team opens 50 AI-authored PRs a day. When do you trust the output, and how do you review that volume without becoming the bottleneck?▼hardGoogleOpenAIAnthropic1 replies◆ premiumIf you read every AI PR line by line, you become the throttle the assistant was meant to remove. The staff answer is a risk-tiered trust model: route by blast radius, let gates clear the low-risk volume, and spend human attention only where reversibility is low.Open full answer →
74A customer wants AI code review on every PR. What does it catch, what does it miss, and how do you stop the team turning it off?▼hardNewGreptileCursorCognition2 replies◆ premiumNobody turns off an AI reviewer because it missed a bug. They turn it off because it left nine comments they had to read and three that mattered. Precision is the design variable, and the arithmetic says cutting comments per PR from twelve to four costs you 7% of the real findings and two thirds of the triage time.Open full answer →