--- id: personal-web-surfing-models/07-prototype-options title: 'Personal web surfing models and an ought-driven tab feed: 7. Prototype options' report: https://ai.hiina.space/reports/personal-web-surfing-models.md section: 7 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: 7. Prototype options - Section 7 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 - Previous: https://ai.hiina.space/reports/personal-web-surfing-models/06-tab-triage-and-an-ought-not-engagement.md - Next: https://ai.hiina.space/reports/personal-web-surfing-models/08-conclusion.md ## 7. Prototype options Start with 1 and 2 together on one weekend. The ought-triage feed is useful on day one, and the logger starts collecting the labels that every later option needs. Options 3 to 5 build on that data. | \# | Prototype | Effort | Needs | |-----|---------------------------------------------------|--------------------------------|-------------------------------| | 1 | Ought tab triage and feed | 1 to 2 days | A goals file, the open tabs | | 2 | History archive and tab logger | 1 weekend, then runs passively | Nothing, starts the clock | | 3 | Personal ranker as an MCP tool for research agents | 1 to 2 weeks | 4 to 8 weeks of data from 2 | | 4 | "View as Gwern" (and others) | About 1 week | Scraped public link data | | 5 | LoRA-trained personal reranker | 2 to 4 weeks | A few hundred pairwise labels | ### 7.1 Ought tab triage and feed (weekend hack) This tests the ought-feed idea directly. - **What it does.** - A script or extension reads the open tabs, using URL, title and, where cheap, page text. - A local LLM scores each tab 0 to 10 against a `GOALS.md` kept in the user's notes plus a one-line "right now I'm working on" intention. - It returns three lists: "now" (top 3 that serve the intention), "archive" (stale or off-goal, closed into a searchable archive so nothing is lost), and one deliberate "unwind" pick. - A new-tab page shows the feed and asks "focus or unwind?" before showing anything. - **Stack.** - Firefox `recovery.jsonlz4` or a small WebExtension. - A 7 to 8B instruct model on a single 24 GB consumer GPU through vLLM or Ollama. - Archive to markdown notes or to SQLite. - Shuffle candidates and score in small batches to dodge LLM position bias. - **Plugs in.** A coding agent or a scheduled agent run can do a nightly triage pass and write the result into the notes. - **Risks.** The goals file goes stale: BONSAI users stopped maintaining their intents. Mitigate by asking for the intention fresh each session, which Intent Assistant found was the biggest win. Show a one-line reason per item. ### 7.2 History archive and tab logger (do now) The history behind 3 to 5 disappears if nobody starts archiving it. - **What it does.** - Cron-copies the Chrome `History` database (including `context_annotations`) and Firefox `places.sqlite` to a self-hosted machine, or uses Promnesia. - Turns on Firefox's `browser.places.interactions.enabled`. - Adds a roughly 100-line extension that logs tab created, activated, closed and foreground time to a local endpoint. - **Output.** One SQLite table of visits joined with labels: dwell normalized per domain, revisited within 30 days, bookmarked or saved to notes, tab closed unread. - **Risks.** - Label noise: tabs are kept out of fear, and dwell favors long pages. - It is among the most sensitive datasets a person owns. Keep it on local hardware, strip query strings, and drop sensitive domains at ingest. ### 7.3 Personal ranker as an MCP tool for research agents This is the personal surfing model in its most useful form. - **What it does.** An MCP tool `rank_links(task, candidates)` returns candidates ordered by how likely the user is to endorse them, each with a reason. A coding-agent research run calls it before choosing which result to open next. - **Stack.** - Stage 1: a local embedding model, plus LightGBM or logistic regression trained on the labels from 2. - Stage 2: an LLM rerank of the top-k against a `PRINCIPAL.md` (an LLM-generated profile of the user's interests and source preferences, edited by hand) and the agent's task. - Only the profile and candidate titles ever reach a hosted model. - **Evaluation.** - A time-split offline test: do links later revisited or saved rank above links closed unread? - Then an A/B test of research reports with and without the ranker, graded blind by the user. - **Risks.** It learns habits, including distraction clicks. Keep the "is" score and the "ought" rerank separate and visible. ### 7.4 "View as Gwern" (and others) This tests the sharing idea cheaply. - **What it does.** It scrapes Gwern's link bibliographies, annotations, tags and newsletters, and embeds them. It generates a Gwern `PRINCIPAL.md` and exposes `rank_as(person, topic, candidates)`. A side panel shows the user's ranking against his on the same search results, flagging what he would surface and what he would skip. - **Shortcut.** Before scraping, check whether [Knowledge](https://knowledge-web.org)'s MCP server already covers the people of interest. - **Risks.** - The data shows curation, not browsing, and has no negatives. - Scrape politely: his robots.txt blocks some fetchers. - A model of a named person deserves care in how it is labeled and shared. ### 7.5 LoRA-trained personal reranker This is the Guardian Angel route, applied to links. - **What it does.** - Collects pairwise judgments ("which of these two would you rather read now, given X?") through the feed in 1 and the agent loop in 3. Dismissals and corrections count as labels too. - LoRA-tunes a 7 to 8B model on a single 24 GB consumer GPU once there are a few hundred pairs. TALLRec showed gains from under 100. - Optionally runs nightly dynamic-evaluation updates, as in Gwern's design. - **Payoff.** A small, local model that ranks in the user's voice, including their source preferences, that can be versioned and, if they choose, published without the raw history. - **Risks.** - Gains over option 3 may be small. PRISM-X found per-user tuning only marginally beat a pooled model. - Evaluating it takes the user's own time, since only they can grade it. - Worth building only once 3 shows the ranker is used. --- Text licensed CC BY 4.0 (attribution: Hiina, ai.hiina.space). Source: https://github.com/hiinaspace/ai-hiina-space