Zixuan Li(@ZixuanLi_):GLM is increasingly helping build AI itself. For GLM-5.3-Flash, a GLM-5.3-powered agent helped bring a production inference system online in less than two weeks, while tripling throughput from the initial baseline. The broader lesson is that stronger coding abilities are only part of the story. To tackle complex engineering work, agents need a way to test ideas, understand failures, and verify improvements. Engineers set the goals and boundaries; a well-designed feedback loop lets the agent keep making progress within them. This is a shift from AI that writes code to AI that helps build and improve the systems it depends on. The model improves the system; the system runs the model.

2026.09.17 07:07

GLM is increasingly helping build AI itself. For GLM-5.3-Flash, a GLM-5.3-powered agent helped bring a production inference system online in less than two weeks, while tripling throughput from the initial baseline. The broader lesson is that stronger coding abilities are only part of the story. To tackle complex engineering work, agents need a way to test ideas, understand failures, and verify improvements. Engineers set the goals and boundaries; a well-designed feedback loop lets the agent keep making progress within them. This is a shift from AI that writes code to AI that helps build and improve the systems it depends on. The model improves the system; the system runs the model.

Z.ai@Zai_org

2026.09.17 07:05

We’re sharing how GLM-5.3 helped build and optimize the inference infrastructure serving GLM-5.3-Flash. The system went from its first successful run to production readiness in less than two weeks, with end-to-end throughput tripling relative to the initial baseline. The key was dense feedback: local correctness tests, execution traces, microbenchmarks, and end-to-end measurements that enabled targeted hypothesis testing rather than reliance on aggregate performance metrics alone.