How an astrophysicist uses Codex to help simulate black holes

The story originates directly from OpenAI's own blog, meaning it functions as a case study marketing piece as much as a research dispatch. There is no independent benchmark, no comparison against prior simulation tooling, and no peer-reviewed validation of the workflow improvements described.

Modelwire has no prior coverage to anchor this to, so it sits largely disconnected from recent activity in our archive. It belongs to an emerging thread worth tracking: domain scientists adopting code-generation models not for app development but for numerical and simulation work, a use case that rarely surfaces in mainstream AI coverage. The honest question this story raises is whether Chan's workflow gains are specific to his familiarity with Codex, his particular codebase, or something genuinely transferable to other computational physicists working in, say, GRMHD or ray-tracing pipelines.

Watch whether any independent computational science group publishes reproducible results using Codex or a comparable model on standard astrophysics benchmarks, such as those tied to the Event Horizon Telescope collaboration's open simulation suites, within the next twelve months. If that happens, the productivity claim has legs; if the only evidence remains vendor-published profiles, treat it as anecdote.