The Job Isn't Writing Code. It's Knowing When the AI Is Wrong.

I use an AI coding agent for almost everything on my job board GlobalRemote . It writes my scrapers, builds my CI pipelines, architects my database schemas. It's written the vast majority of the codebase. After a few months of building this way, I've noticed a pattern: the most valuable thing I do isn't writing code. It's catching where the AI gets it wrong — specifically the cases where the output looks correct but doesn't hold up once you think about it. Here are three recent examples. 1. The Wrong Tool for the Job My pipeline extracts tech stack requirements from job postings using regex. A role showed up on the board with no tech stack listed. The AI investigated, found the regex wasn't matching that posting's format, and proposed expanding the regex pattern. Fair enough. But we already had LLMs classifying and extracting other fields from these same job descriptions. Why maintain a brittle regex when we could use the LLM we're already paying for? The agent agreed and built the LLM