According to OpenAI on Twitter, the organization released a new preprint on arXiv and is submitting it for journal publication, inviting community feedback. As reported by OpenAI’s tweet on February 13, 2026, the preprint link is publicly accessible via arXiv, signaling an effort to increase transparency and peer review of their research pipeline. According to the arXiv posting linked by OpenAI, enterprises and developers can evaluate reproducibility, benchmark methods, and potential integration paths earlier in the research cycle, accelerating roadmap decisions for model deployment and safety evaluations. As reported by OpenAI, the open feedback call suggests immediate opportunities for academics and industry labs to contribute ablation studies, robustness tests, and domain adaptations that can translate into faster commercialization once the paper is accepted.

According to @godofprompt on Twitter, interviews with 12 AI researchers from OpenAI, Anthropic, and Google reveal a shared set of 10 operational prompts used to ship products, publish papers, and break benchmarks, as reported by the original tweet dated Feb 12, 2026. According to the tweet, these prompts emphasize systematic role specification, iterative refinement, error checking, data citation, evaluation harness setup, constraint listing, test case generation, failure mode analysis, chain of thought planning, and deployment readiness checklists. As reported by the source post, teams apply these prompts to accelerate model prototyping, reduce hallucinations with explicit constraints, and align outputs with research and production standards, creating business impact in faster feature delivery, reproducible experiments, and benchmark gains.

According to Jeff Dean, the referenced demo is “incredibly impressive,” signaling a meaningful advance worth industry attention; however, the tweet does not identify the model, company, or capability, and no technical details are provided in the post. As reported by the embedded tweet on X by Jeff Dean, the statement offers endorsement but lacks verifiable specifics on the underlying AI system, performance metrics, or deployment context. According to standard sourcing practices, without the original linked content context, there is insufficient information to assess practical applications, benchmarks, or business impact. Businesses should withhold operational decisions until the original source of the demo and peer-reviewed or benchmarked results are confirmed.