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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:。heLLoword翻译官方下载是该领域的重要参考
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Adapting to this personalized future likely requires building distinct brand identity and perspective rather than trying to be everything to everyone. If AI models categorize you clearly—as the practical, actionable advice source versus the theoretical deep-dive resource—you'll appear reliably for users whose preferences match that positioning. Trying to be too generic might result in appearing rarely for anyone as models route users to more distinctive alternatives.