Claude Code创始人Boris Cherny近日在播客中分享了Opus 5模型的应用经验与系统优化策略。1他建议开发者每隔六个月删除claude.md、Skills和Hooks等系统提示词与工具集合，由模型根据自身能力重新决定所需功能。1这一策略的效果显著——Claude Code在Opus 5发布后删除了超过80%的系统提示词。1
Opus 5在ARC-AGI-3基准测试中达到30%的成绩，可连续运行数月而无需额外的技术支撑。1Boris强调，使用AI编程工具的方法论已从"依赖理论的学问"转变为"不断试错的经验科学"，需要通过实际实验而非预先设计来优化模型使用。1Dynamic Workflows框架能够协调数千甚至数万个智能体完成复杂任务，其中Anthropic已通过Routines实现代码库的自动化维护，每天运行数百至数千个智能体。1
现实应用中已涌现多个成功案例。1Bun团队用Opus 5花费11天时间将整个代码库从Zig重写为Rust，并已投入生产环境。1Boris启动的Swift重写实验运行已超过两周，其背后可能调用数千至数万个智能体。1
Boris Cherny, creator of Claude Code, recently discussed best practices for leveraging the Opus 5 model in a podcast, revealing an unconventional approach to prompt engineering and agent orchestration.1
Cherny recommends deleting core system components every six months, including claude.md, Skills, and Hooks, allowing the model to independently determine what capabilities it requires.1 This strategy reflects a broader shift in how developers interact with advanced AI models—moving away from theoretical design principles toward empirical experimentation.1 Following Opus 5's release, Claude Code removed more than 80% of its original system prompts, demonstrating Cherny's confidence in the model's self-directed capability evolution.1
The Opus 5 model has demonstrated remarkable performance, achieving 30% accuracy on ARC-AGI-3 benchmarks and operating continuously for months without additional scaffolding support.1 Cherny shared concrete examples of this capability in action: the Bun team successfully rewrote an entire codebase from Zig to Rust in 11 days using Opus 5, with the resulting code already deployed to production.1 Cherny's own Swift rewrite experiment has been running for over two weeks, likely orchestrating thousands to tens of thousands of autonomous agents working in parallel.1
Dynamic Workflows technology enables coordination of thousands or even tens of thousands of agents on complex tasks, according to Cherny.1 At Anthropic, the Routines feature automates codebase maintenance by running hundreds to thousands of agents daily.1 Cherny's advocacy suggests that successful AI-powered programming increasingly depends on experimentation and observation rather than predetermined theoretical frameworks.1
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