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Fourth, set up basic tracking even if you don't build a comprehensive system immediately. Create a simple spreadsheet listing queries where you want visibility. Test those queries weekly in one or two AI platforms and note whether your content appears. This manual tracking takes just 15-30 minutes weekly but provides feedback on whether your optimization efforts are working.
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I wanted to test this claim with SAT problems. Why SAT? Because solving SAT problems require applying very few rules consistently. The principle stays the same even if you have millions of variables or just a couple. So if you know how to reason properly any SAT instances is solvable given enough time. Also, it's easy to generate completely random SAT problems that make it less likely for LLM to solve the problem based on pure pattern recognition. Therefore, I think it is a good problem type to test whether LLMs can generalize basic rules beyond their training data.
拿部署到飞书机器人举例，甚至不用额外的部署指南，我们只需要点开首页左侧边栏的 MaxClaw 按钮，点击「立即开始」，我们可以选择使用默认配置，或者其他专家。
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Example: "Where should I deploy this?" (Next.js SaaS, Opus 4.5)