[[ anyRcv isNil ifTrue: anyBlock ] -> [ anyRcv ifNil: anyBlock ]] brewrite preview
习近平总书记深刻指出：“我们党为人民而生、因人民而兴，一切工作都是为了人民的幸福。”
，详情可参考搜狗输入法2026
很多人将新闻专业主义，刻板地理解为理性、中立、客观。但在不久的将来，AI 绝对可以写出比人类中立一万倍、四平八稳绝对不会犯错的“八股文”。真正的新闻专业主义，恰恰是 AI 做不到的那些部分：
Scenario generation + real conversation import - Our scenario generation agent bootstraps your test suite from a description of your agent. But real users find paths no generator anticipates, so we also ingest your production conversations and automatically extract test cases from them. Your coverage evolves as your users do.Mock tool platform - Agents call tools. Running simulations against real APIs is slow and flaky. Our mock tool platform lets you define tool schemas, behavior, and return values so simulations exercise tool selection and decision-making without touching production systems.Deterministic, structured test cases - LLMs are stochastic. A CI test that passes "most of the time" is useless. Rather than free-form prompts, our evaluators are defined as structured conditional action trees: explicit conditions that trigger specific responses, with support for fixed messages when word-for-word precision matters. This means the synthetic user behaves consistently across runs - same branching logic, same inputs - so a failure is a real regression, not noise.Cekura also monitors your live agent traffic. The obvious alternative here is a tracing platform like Langfuse or LangSmith - and they're great tools for debugging individual LLM calls. But conversational agents have a different failure mode: the bug isn't in any single turn, it's in how turns relate to each other. Take a verification flow that requires name, date of birth, and phone number before proceeding - if the agent skips asking for DOB and moves on anyway, every individual turn looks fine in isolation. The failure only becomes visible when you evaluate the full session as a unit. Cekura is built around this from the ground up.
咬定青山不放松，一张蓝图绘到底。