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      "description": "Read from the source, not from blog posts about the source. Almost every claim circulating about X's ranking is folklore repeated until it sounded official. The algorithm is open source, so the weights, the filter stack and the spam rules can simply be read.",
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        "Open-source ranking",
        "Retrieval & filters",
        "Account credibility",
        "Node, no deps"
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      "representativeQueries": [
        "write an X post that gets more reach",
        "why did my tweet flop",
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      "displayName": "linkedin-boost",
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      "description": "Separates documented mechanism from folklore, and names which is which. LinkedIn is not one algorithm. Feed, follow recommendations, search, recruiter sourcing, applicant ranking and network growth are separately ranked surfaces with different inputs — so advice aimed at the wrong one is confidently useless. \"My posts get no reach\" and \"I get no interviews\" are different systems.",
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        "Honesty boundary",
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        "Node, no deps"
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      "description": "Two different problems wearing the same complaint. Text reads as machine-written for two separable reasons. Register is surface — em dashes, uncontracted prose, uniformly mid-length sentences — and a script catches it more reliably than a careful read. Genericness is not surface: writing that commits to nothing and could have been about any company in any industry. Most requests to humanise are really about the second, but people notice the first.",
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        "Scored 0-100",
        "Python"
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        "make this sound less like AI",
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        "Design direction",
        "Stack selection",
        "ASCII wireframes",
        "Portfolio-aware"
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        "what should my app's UI look like",
        "help me pick a design direction for a new website",
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