Hi, I am Simon Sun

MiroThinker 1.7 has achieved SOTA results in the field of long-horizon question reasoning. These outstanding results stem from the combination of a powerful Model and a solid Harness. This article documents key engineering optimizations in its Harness implementation. Prerequisite Background MiroThinker is a deep research Agent — given a complex question (“what are the titles of the cs papers on arxiv today”), it autonomously breaks down the task, searches, scrapes webpages, runs Python for verification, and finally outputs the \boxed{answer}. The foundation is classic ReAct: each turn involves LLM reasoning + tool calling, the results are written back to the history, looping 200~300 times until convergence. ...

Source: Anthropic Engineering — Demystifying evals for AI agents Good evaluations help teams ship AI agents more confidently. Without them, it’s easy to get stuck in reactive loops—catching issues only in production, where fixing one failure creates others. Evals make problems and behavioral changes visible before they affect users, and their value compounds over the lifecycle of an agent. ...

We deployed OpenIM version 3.5.1 in our Kubernetes environment for our company’s IM scenarios. During development and operation, we encountered some issues. Here, I’ll document the details of the problems and the resolution process. Problem Trigger Scenario We wrote a stress testing program to simulate typical user scenarios: Establish connection (Online) Send message Receive reply Disconnect (Offline) Test settings: 100 concurrent user accounts, with each account continuously repeating the above process to simulate high-frequency online/offline scenarios. ...

We deployed OpenIM version 3.5.1 in our Kubernetes environment for our company’s IM scenarios. During development and operation, we encountered some issues. Here, I’ll document the details of the problems and the resolution process. Problem Trigger Scenario 300 users online simultaneously, sending one-on-one messages irregularly. Problem Phenomenon From the monitoring metrics, we can see: A large number of goroutines in openimserver-openim-push and openimserver-openim-msggateway. A large number of socket connections in openimserver-openim-push and openimserver-openim-msggateway pods. ...

We deployed OpenIM version 3.5.1 in our Kubernetes environment for our company’s IM scenarios. During development and operation, we encountered some issues. Here, I’ll document the details of the problems and the resolution process. Problem Description Simply put: Users are clearly online but cannot receive real-time messages. They can only receive push notifications, and messages only appear in the chat interface after a delay. This problem only occurs after scaling out. Everything works fine with a single node. The root cause is an inconsistency in OpenIM’s internal load balancing mechanism, causing messages to be sent to the wrong service node. ...