---
id: personal-web-surfing-models/01-summary
title: 'Personal web surfing models and an ought-driven tab feed: 1. Summary'
report: https://ai.hiina.space/reports/personal-web-surfing-models.md
section: 1
version: 1
updated: '2026-10-27'
author: Hiina
---

Index of everything on this site: https://ai.hiina.space/index.md

# Personal web surfing models and an ought-driven tab feed: 1. Summary

- Section 1 of 9 of the report "Personal web surfing models and an ought-driven tab feed" by Hiina (v1, updated 2026-10-27)
- Report overview and contents: https://ai.hiina.space/reports/personal-web-surfing-models.md
- Next: https://ai.hiina.space/reports/personal-web-surfing-models/02-gwern-s-guardian-angels-and-how-browsing.md

## 1. Summary

No one has published a personal web surfing model: a model trained on one person's open-web history and used to rank links or tabs for them or an agent. The neighbours are mature: next-page prediction, session-based recommendation, web agents and personal LLMs. A useful version is buildable on commodity hardware now. It just shouldn't be a generative model trained on a person's URL stream.

- **History encodes what you did, distraction included.** Kleinberg et al. formalize how optimizing for revealed behavior can raise engagement while lowering welfare. Gwern himself warns that passive lifelog data mostly encodes routine. A pure imitation model of someone's browsing would learn their "please distract me" scrolling.

- **The workable architecture has two layers.**

  - A local ranker trained on implicit labels (dwell, revisit, saved, closed unread) supplies the "is".

  - An editable natural-language profile (Gwern's `PRINCIPAL.md`), applied by an LLM rerank against the current task, supplies the "ought".

  - The literature on LLM user profiles and retrieval favors this over raw-history prompting. TALLRec shows a LoRA reranker can learn from under 100 labeled pairs once the labels exist.

- **Sharing works best through public curation, not models trained on private history.** Browsing histories are about 99% unique, so they act as fingerprints. A "Gwern view" is buildable from his public link-annotation database, tags and newsletters as retrieval plus a profile-prompted judge. [Knowledge](https://knowledge-web.org) already does this for about 450 people as an MCP server.

- **The ought feed has close precedents but no tab version.**

  - BONSAI (an intent-specified Bluesky feed) and Intent Assistant (CHI 2026: state an intention, an LLM nudges toward it) both worked in small field studies. Compass did the same for YouTube Shorts.

  - Every tab tool groups tabs by similarity or age; none ranks by a stated goal.

  - The evidence says: clarify the intention each session, make archiving safe so the pile can shrink, and answer "distract me" with a redirect and an explicit leisure option, not a block.

**Most promising first build:** a weekend ought-triage feed. A local 7 to 8B model scores the open tabs against a goals file and a per-session intention. It returns "now", "archive" and one "unwind" pick. Pair it with a tab logger that starts collecting labels for a later personal-ranker MCP tool. Details are under Prototype options.

One thing to do today: Chrome's history may expire after about 90 days, and Firefox's per-visit interaction data is off by default (`browser.places.interactions.enabled`). Start archiving now if you want training data later.

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Text licensed CC BY 4.0 (attribution: Hiina, ai.hiina.space). Source: https://github.com/hiinaspace/ai-hiina-space