Accelerate scientific research with Codex
A researcher uses Codex and ChatGPT extensively for scientific research, getting a week's work done in a day, though recent usage limits reduced output.
- What they did
- A theoretical chemist says they used ChatGPT and Codex heavily in a research workflow built around quantum-mechanical simulations of molecules, excitations, and spin behavior. In a follow-up comment, they say they reworked the setup to use one model for most analysis, another as a final reviewer and coordinator, and a cheaper model for routine jobs like starting and stopping calculations, copying files, and parsing data, while they chose when to escalate harder reasoning.
- How it went
- They report unprecedented productivity over the past year, saying the tools let them do far more research than in 10+ years before, but that recently the same work now burns through a weekly allowance in a day. OpenAI later told them a bug affected their usage and gave them $500 in credits; they say they had previously burned through $3,000 in credits quickly too.
- Worth knowing
- The main catch is cost and limits: they now check cost-to-performance before each run and say they have had to scale back usage because the expense is no longer sustainable.
Try it yourself with Codex
I'm researching [scientific question or topic]. Using the data and code in [folder path], reproduce [analysis or result], then run [next experiment or analysis] and summarize the findings with plots saved to [output folder]. Keep a log of assumptions and anything you couldn't verify. You're done when the scripts run end to end and I have a short write-up of the results and open questions.
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Source: Reddit · Undated
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