DeepSeek：希望 Harness 成为 Agent 时代的操作系统

The practical shift here is that agent debugging becomes a first-class engineering discipline, not an occasional chore: slow runs, poor outputs, high token costs and non-deterministic execution paths all need systematic diagnosis. Practitioners building on agent frameworks can use tools like these two plugins to inspect decision points, fork alternative paths, and refine context organisation or harness architecture. Compared with traditional breakpoint debugging on live processes, this reframes debugging around decisions rather than code.

小胡子哥@Barret_ChinaSource

Routing configuration — active parameters, top-k and shared-expert design — directly affects inference cost and throughput, yet specs are scattered across model cards and configs. This consolidation lets engineers pick models by routing efficiency rather than headline parameter counts. It also highlights how differently DeepSeek, Qwen and gpt-oss trade expert granularity against compute per token.

官方一手marktechpost@Michal SutterSource