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No tool I found ranks open tabs or a read-later queue against a stated current goal. Existing tools group tabs by similarity or sweep them by age. The two closest pieces of research are BONSAI, an intent-specified social feed, and Intent Assistant, which nudges toward a stated intention. Between them they cover most of the design.
People hoard tabs out of fear, not laziness. In Chang et al. (CHI 2021, n=103), over half said they could not close tabs and 30% called it a problem; only 19% blamed laziness. The driver is a "black hole" fear that anything closed is gone, plus attachment to the tabs themselves. I took these figures from CMU's write-up and ScienceAlert; the paper itself would not load.
Tabs work as reminders, and they rot. In Dubroy & Balakrishnan (CHI 2010), 17 of 21 users kept tabs as reminders, 25% of tabs were never selected and 45% were selected once.
Task bundles plus permission to deprioritize worked in the field. In Tabs.do (UIST 2021), 7 of 10 overwhelmed users still used it daily after 10 weeks. Its "Maybe/Someday" section resurfaces one random item. Due dates went unused.
Arc archives idle tabs after 12 hours, and the archive can be restored. Arc shows that aggressive auto-archive is tolerable when nothing is lost.
Pocket's founder put the eventual read rate at about half, with a range of 10 to 70% by source. One Pocket analysis found 80% of reads happen within 35 days of saving (BuzzFeed, 2013). After a month, an unread item is probably dead.
History drove 0.2% of page requests in Obendorf et al. (CHI 2007), which found history and bookmarks unreliable for long-term rediscovery.
Pocket shut down in July 2025. I found no rigorous study of save versus read rates; the evidence is vendor anecdotes plus older bookmark research.
The theory: Kleinberg, Mullainathan & Raghavan show that when in-the-moment choices diverge from what users want on reflection, engagement can rise while welfare falls. A model trained purely on someone's clicks inherits that problem. This is the core tension in a personal surfing model used as a feed.
Measured on Twitter: in Milli et al. (n=806), engagement ranking amplified anger, and users preferred a timeline ranked by their stated preferences. A companion paper gives a recipe: weight each behavioral signal by how faithfully it reflects value.
LLMs scoring against a written rubric work. In Jia, Bernstein et al. (CSCW 2024), zero-shot GPT-4 scores matched human coders at ρ=0.75, and reranking moved attitudes with no loss of engagement. The Prosocial Ranking Challenge (2026, n=9,386 over 6 months) found real-world effects were small.
BONSAI (2025) is nearest to the ought-feed idea. Users describe a Bluesky feed in plain language, and an LLM scores each post 0 to 10 against that intent. Users said it separated engagement from intent. But 5 of 15 stopped maintaining their intents, and users wanted to see why each item was shown.
Readwise Reader now has a "Triage my inbox" skill and an MCP server, but triage runs only on request and does not rank by goal.
Intent Assistant (Choi et al., CHI 2026) is the best evidence for an LLM as a commitment device. The user states an intention and the LLM asks up to two clarifying questions. Gemini Flash then scores each screenshot and URL every 2 seconds and sends gentle nudges the user can dismiss.
Over a 3-week study (n=22), off-task time fell to 10.4%, versus 16.6% with a timed reminder.
Clarifying the intention gave the biggest accuracy gain. Stating an intention with no AI at all already beat logging alone.
Friction works but wears thin. In one sec logs (Haliburton et al., CHI 2024), app-open attempts fell mostly in the first 3 weeks, and users called hard friction tiring.
Don't ban leisure. In Lukoff et al. (CHI 2021), users with a specific goal wanted control, while users who were unwinding were happy to let the app lead. A recommender should first decide whether to recommend at all, then what.
Start each session from a stated, clarified intention and rank against it. Treat history as noisy evidence, never the objective.
Make letting go safe. Archive aggressively but keep everything searchable, resurface by relevance to the current intention, and show why each item appears.
Answer the distraction impulse with a redirect, not a block. Detect or ask the mode. In "focus" mode, offer the one or two items that serve the goal. In "unwind" mode, offer explicit, guilt-free leisure. Learn from dismissals.
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