A quadtree reduces this: rebuild the tree each frame, and for each object, query only the nearby region. Objects in distant quadrants are never compared.
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:
�@�ő�2��5000Pa�i�p�X�J���j�̋z���͂����������I�[���C�������v�̃��{�b�g�|���@�ŁA���ʂ̂��܂��܂ȏ��Q�����������Ȃ����|�����s�����Ƃ��\�B�����̃Z���T�[�����̏��������Ċ��f���A�x�b�h���\�t�@�̉��Ȃǂ��X���[�Y�ɑ��s�ł����B，详情可参考同城约会
{ antialias: true }：开启了抗锯齿，让边缘光滑，不再有毛刺。
。夫子对此有专业解读
"This is a new scientific field. It's hard to speculate because it's changing so quickly," Wing says.。关于这个话题，WPS下载最新地址提供了深入分析
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