The latest articles on DEV Community by Ken (@ken_22b3). https://dev.to/ken_22b3 https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F3943788%2F7e814fb9-f3de-4136-92c4-badd3d6e080b.png https://dev.to/ken_22b3 en Ken Thu, 21 May 2026 10:21:42 +0000 https://dev.to/ken_22b3/my-ai-code-passed-all-tests-then-pinned-my-cpu-at-100-help-3e0g https://dev.to/ken_22b3/my-ai-code-passed-all-tests-then-pinned-my-cpu-at-100-help-3e0g <p>Hey everyone! 👋</p> <p>I recently used an AI assistant to write a function. The source code looked perfectly clean and passed all my unit tests. But the moment it was deployed, it spiked my CPU to 100%.</p> <p>The issue: AI models are great with syntax, but they are hardware-blind. They don't understand how the compiler translates code into binary/assembly, leading to unexpected cache misses and instruction bloat on the actual silicon.</p> <p>Looking for guidance: How do we catch these hardware-level regressions in CI/CD before deploying? Does anyone know of a tool or workflow—perhaps something that analyzes compiled binaries or uses an assembly-aware model—to flag these performance issues?</p> <p>I'd love some pointers if you've solved this! </p> ai performance softwareengineering architecture