I then added a few more personal preferences and suggested tools from my previous failures working with agents in Python: use uv and .venv instead of the base Python installation, use polars instead of pandas for data manipulation, only store secrets/API keys/passwords in .env while ensuring .env is in .gitignore, etc. Most of these constraints don’t tell the agent what to do, but how to do it. In general, adding a rule to my AGENTS.md whenever I encounter a fundamental behavior I don’t like has been very effective. For example, agents love using unnecessary emoji which I hate, so I added a rule:
这次发布的核心逻辑，是把 Claude 变成可以深入企业不同部门的专业智能体，同时允许管理员创建私有插件市场，在组织内部统一分发和管理这些工具。，详情可参考爱思助手下载最新版本
"countDelta": -1，推荐阅读heLLoword翻译官方下载获取更多信息
最终，Anthropic 选择支付 15 亿美元和解金，在 AI 版权诉讼史上创下纪录，但细看之下，账算得并不亏。按照美国版权法，每件作品的法定赔偿上限可达 15 万美元，而此次和解折算下来，每本书约赔 3000 美元，仅为上限的 2%。
encrypting and unlocking crypto wallets