Creating black hole simulations with Codex

The detail worth sitting with is not the speed gain itself but what it changes structurally: Chan is using Codex to explore algorithm designs he would never have attempted manually, meaning the tool is expanding the hypothesis space, not just executing a fixed one faster.

This is largely disconnected from recent activity in our archive, as we have no prior coverage to anchor it to. It belongs to a broader pattern, visible across academic computing, where code-generation tools are being adopted not by software engineers but by domain scientists who treat them as research infrastructure. The black hole imaging context is significant because that field already has a high-stakes benchmark in the Event Horizon Telescope data, which means Chan's outputs are testable against real observational constraints rather than synthetic benchmarks. That grounding makes this case study more credible than most vendor-produced demonstrations, though it is still a single researcher's workflow presented by the tool's developer.

Watch whether Chan or the EHT collaboration publishes peer-reviewed results that cite Codex-generated algorithms as part of the method. If that appears in a journal submission within the next 12 months, the workflow has cleared independent scrutiny; if it stays in promotional video form, the scientific weight of this claim remains unverified.