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Zhipu automates infrastructure with GLM-5.3’s outer RSI loop in under two weeks

Zhipu AI, developer of the open-weight LLM GLM-5.3, detailed how it used its own model to accelerate internal infrastructure work. The team deployed an Infra Agent powered by GLM-5.3 to optimize GLM-5.3-Flash—a faster, cheaper variant—cutting deployment time to less than two weeks. The agent handled analysis, hypothesis testing, and code changes, while engineers set objectives and the…

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Key points

  • Zhipu’s Infra Agent, powered by GLM-5.3, cut GLM-5.3-Flash deployment to under two weeks, tripling throughput
  • Feedback loop involved engineers defining objectives, the agent analyzing/hypothesizing, and experiments verifying results
  • Company warns human comparative advantage in design may fade as AI systems advance autonomously

The company emphasized three key principles for effective automation: localized feedback (tied to specific system parameters), inexpensive and timely validation, and objective verification (using reference implementations and metrics). While Zhipu’s engineers now rely on GLM-5.3 daily for coding, they caution that human oversight remains critical for high-level design decisions—though they acknowledge this advantage may shrink over time.

Model pages: GPT-6 Astra → · Claude Fable 5.1 →

Full story from Import AI · by Jack ClarkOpen source ↗

Import AI 474: Platonic mindspace; TPUs in space; Zhipu starts an outer RSI loop

Import AI · 28 September 2026

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This text was published by Import AI and written by Jack Clark. It is reproduced here with attribution so you can read it in full; the rights remain with the publisher. Read it at the source ↗

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Zhipu AIGooglePlanetPhysical IntelligencePeriodic LabsGLM-5.3GLM-5.3-FlashKimi 2.6GPT-6 AstraClaude Fable 5.1Periodic NeonMichael LevinPerry DongChelsea Finn

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