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The solution is building your own tracking system using no-code automation tools. This approach requires more initial setup but provides ongoing monitoring at a fraction of commercial tool costs. The system I built uses Make.com, a no-code automation platform, to query AI models systematically, analyze responses, and track mentions over time. Make offers 1,000 operations monthly on their free tier, making it possible to start tracking without any monetary investment.

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This works, but it has a vulnerability: it hardcodes the native code string manually. If fermaw’s integrity check was especially paranoid and compared the spoofed string against the actual native code string retrieved from a trusted reference (say, by calling Function.prototype.toString.call(originalFunction) on a cached copy of the original), the manually crafted string might not match precisely, particularly across different browser versions or platforms where the exact whitespace or formatting of [native code] strings varies slightly.

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