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:
p->scavange++;
。关于这个话题，服务器推荐提供了深入分析
The new DDoS: Unicode confusables can't fool LLMs, but they can 5x your API bill Can pixel-identical Unicode homoglyphs fool LLM contract review? I tested 8 attack types against GPT-5.2, Claude Sonnet 4.6, and others with 130+ API calls. The models read through every substitution. But confusable characters fragment into multi-byte BPE tokens, turning a failed comprehension attack into a 5x billing attack. Call it Denial of Spend.
Jimmy Kimmel reacts to Fox News praising Trump's State of the Union
，详情可参考safew官方版本下载
Цены на нефть взлетели до максимума за полгода17:55
2 For points outside the convex hull, an acceptable solution is to find the closest point on the surface and determine the barycentric coordinates for that point instead. ↑，详情可参考搜狗输入法下载