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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:
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A piece of insulating foam on Columbia's fuel tank broke loose during launch, damaging the heat shield with devastating results.
08:06, 28 февраля 2026Мир。关于这个话题，51吃瓜提供了深入分析
进展最快的三个行业，报告点了名：游戏、广告、电商。游戏公司用AI生成概念图和场景资产，把原本需要几周的流程压缩到几天。广告公司一个campaign能在几小时内生成几百个个性化版本，以前这需要租场地、配团队、拍好几天。电商更直接——成千上万个SKU都需要产品图、场景图、季节素材，过去是漫长的拍摄和修图周期，现在几个提示词出一套。
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