[ Index ]
Everything we know about building AI software that actually ships — written for engineers, no fluff.
How to use AI to plan and execute code migrations reliably — from class components to hooks, callbacks to async/await — without introducing silent regressions.
How to capture, trim, and present compiler errors and test failures to AI coding tools so they fix the right problem — not a plausible-sounding neighbor of it.
Practical patterns for using AI to write complex SQL — window functions, CTEs, EXPLAIN analysis, and index suggestions — without getting burned by hallucinated columns or the wrong dialect.
How to use AI coding assistants to systematically spot injection flaws, auth bypasses, and insecure defaults — with prompts that actually surface real problems.
How to use AI coding assistants to design, implement, test, and clean up feature flags — concrete prompts for each stage of the toggle lifecycle.
AI defaults to generic patterns from its training data. Here's how to anchor generated code to your existing conventions — with examples, explicit constraints, and a drift-detection review.
How to prompt AI tools for reliable IaC — grounding the model in your provider version, avoiding over-permissive defaults, and validating before you apply.
How to structure AI-assisted merge conflict resolution — what context to provide, which conflict types AI handles well, and how to verify the result.
How to use AI assistants to turn cryptic exceptions and terse error strings into clear, actionable messages that save debugging time for everyone.