On "How an astrophysicist uses Codex to help simulate black holes"

This is a case study in how large language models excel at scaffolding domain expertise rather than replacing it—Chan already knew what simulations he needed; Codex accelerated the implementation. For SEO practitioners, the lesson cuts both ways: AI handles repetitive code generation and boilerplate faster, but the signal value lies entirely in what problem you're solving first. If you're reaching for Codex (or Claude, or GPT-4) to write your crawler logic or template generators, you're using it correctly. If you're hoping it will tell you what to crawl or why, you've misread the tool's place in the workflow.

The real practitioner shift is this: as AI handles more of the mechanical work, your competitive edge moves upstream to problem definition and downstream to validation. You need to know your technical architecture and audit the output ruthlessly, because a plausible-looking implementation of the wrong thing still crawls the wrong pages. The bottleneck isn't code anymore; it's judgment.