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A personal web surfing model is an open niche, but the obvious version, a generative model trained on one person's URL stream, is the wrong one: it would mostly learn habit, distraction included, and the resulting weights would be a privacy-sensitive fingerprint. The defensible design separates "is" from "ought". A local ranker on implicit labels (normalized dwell, revisits, saves, tabs closed unread) captures what the person actually reads; an editable natural-language profile and a per-session intention, applied by an LLM rerank, capture what they want to read. Sharing follows the same logic and should be built from public curation, as Knowledge and Gwern's annotated link database show. The cheapest test is a weekend ought-triage feed over open tabs, started alongside a history archive and tab logger, because every later option, from a ranking MCP tool for research agents to a LoRA reranker, depends on labels that only accumulate once logging begins.
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