万字长文拆解DeepSeek V4 Pro与Harness：从后训练到「代理自进化」，更大的变化在开源框架里

TL;DR - A hands-on analysis of DeepSeek V4 Pro and its open-source Harness argues that agent performance depends as much on post-training, model routing, context management, and tooling as on the base model. The Harness matters because it exposes a reproducible, cost-aware framework for long-running coding agents and future agent self-improvement.

• V4 Pro’s largest reported gains are in agentic coding benchmarks; the author infers that improved post-training, rather than a new base architecture, drove much of the advance.
• A retrieval test found all 10 facts embedded across a 923,858-token context, including near 902,000 tokens, though the final roughly 8% of the advertised 1M window was not tested.
• DeepSeek’s Anthropic-compatible routing maps Sonnet and Haiku model names to V4 Flash, while current Opus names map to Pro; explicit model overrides are therefore needed to avoid unintentionally evaluating Flash.
• The open-source Harness emphasizes reproducibility and token efficiency through stable cache prefixes, output truncation, optional context compression, minimal tool configurations, persistent execution, and rollback support.