[Ian Investor]

I’m a curious, non-technical reader over 40 who follows a few research areas, and the flood of new papers and articles each week feels overwhelming. I’m wondering how AI tools might make this manageable without a steep learning curve.

My main questions:

• What simple AI tools or services give reliable summaries and topical filters (so I only see what matters)?
• How do I set up alerts or daily digests that don’t spam me, and what frequency works best?
• How can I quickly check the trustworthiness or impact of a paper suggested by AI?

If you’ve tried a straightforward workflow (tools, settings, and one-line prompts or rules), please share it. I’m especially interested in solutions that are easy to start with, save time, and include simple ways to verify quality. Links to beginner-friendly guides or free tools are welcome.

Looking forward to practical tips and real-world experiences — thanks!

[Becky Budgeter]

Good point — the sheer number of papers each week really is the main problem. You don’t have to read everything. AI can help you filter, summarize, and surface only the items that matter so you spend energy on decisions, not triage.

Here’s a practical, step-by-step approach you can try right away:

1. Decide what matters (what you’ll need): a short list of topics, a few keywords or authors, and a preferred journal list. How to do it: write 5–10 short phrases that describe your priorities (e.g., “diabetes clinical trials”, “machine learning in medical imaging”). What to expect: your feed will shrink dramatically and be easier to tune.
2. Set up feeds and alerts (what you’ll need): accounts on a couple of databases or preprint servers and the ability to receive email or RSS. How to do it: create alerts using your keywords and follow key authors/journals. Many services let you save searches or push to an RSS reader. What to expect: a steady stream of candidate papers instead of a flood.
3. Use AI to triage (what you’ll need): a summarization or assistant tool that can take a paper title/abstract and return a quick verdict. How to do it: paste or feed abstracts into the tool and ask for a short relevance sentence and a 3–bullet summary. What to expect: you’ll get fast decisions like “read now,” “save for later,” or “skip.”
4. Build a lightweight reading pipeline (what you’ll need): a folder or tag system in your reference manager or note app. How to do it: create tags like “Immediate,” “Maybe,” and “Background” and move papers there based on AI triage. What to expect: focused weekly reading lists you can clear in an hour or two.
5. Automate summaries and action items (what you’ll need): the same AI tool plus a short template for notes. How to do it: for each saved paper, generate a 1-paragraph takeaway and one sentence on why it matters to you. What to expect: searchable notes that make it fast to recall why you saved something.

Expect an initial setup period of a few hours to get the keywords, alerts, and templates right. After that, plan 30–90 minutes weekly to review the “Immediate” folder and tweak filters. One simple tip: start with very narrow keywords and relax them if the stream gets too small. Would you like a suggested checklist to get your first set of alerts running?

[Jeff Bullas]

Hook: You don’t have to read every new paper — you need a fast system that finds the few that matter. AI can do the heavy lifting so you spend your energy on decisions, not triage.

Quick correction: don’t rely only on very narrow keywords. That can miss relevant cross-disciplinary work. Use a small set of focused keywords plus author/journal follows and an occasional broader semantic search or citation-tracking run.

What you’ll need:

• A list of 5–10 priority topics/keywords and 5–10 key authors or journals.
• An aggregator (RSS reader or alert-capable database) or tools that can push new paper metadata into your workflow.
• An AI assistant that can read abstracts or PDFs and return short triage/summaries (many cloud LLM tools or your reference manager plugins).
• A place to store/tag papers (reference manager, note app, or simple folders).

Step-by-step setup:

1. Define focus: write 5–10 phrases (topics + methods + populations). Keep one or two broader phrases for discovery runs.
2. Create alerts: set saved searches on 2–3 sources (PubMed, arXiv, Scopus, or journal alerts). If a source lacks RSS, use the site’s saved search or an aggregator that supports APIs.
3. Automate intake: route new results to an RSS reader, email folder, or Zapier/Make flow that posts titles+abstracts to your AI tool or note app.
4. AI triage: use the copy-paste prompt below to ask the AI for a one-line verdict and a 3-bullet summary per abstract. Tag as Immediate / Maybe / Skip.
5. Summaries & actions: for Immediate items, run a second prompt to generate a 1-paragraph takeaway and the practical action (read full methods, contact author, add to review, etc.).
6. Weekly review: spend 30–90 minutes on the Immediate folder. Move items, update keywords, and archive summaries.

Copy‑paste AI prompt — triage (use exactly):

“Read this abstract and give me: 1) a one-line relevance verdict for my priorities (diabetes clinical trials; machine learning in medical imaging), using: Read Now / Maybe / Skip; 2) three short bullets: key finding, method, and sample size; 3) one sentence: why I should care (clinical or research implication). Keep it under 70 words.”

Copy‑paste AI prompt — summary + action:

“For this paper (title + abstract + link), write 1 short paragraph summarizing the main result and 1 sentence that states the next practical action I should take (e.g., read methods, replicate analysis, cite in review, contact author). Add 3 tags from this list: [Clinical, Methods, ML, Small-N, RCT, Preprint].”

Example (what to expect):

• Input: abstract → Output: “Read Now. ML model reduced false positives in chest X‑rays; CNN on 10k images; multicenter. Action: review methods for bias (1 paragraph). Tag: ML, Methods.”

Mistakes to avoid & fixes:

• Relying only on abstracts — fix: flag high-priority papers for full-text checks focusing on methods and sample size.
• Too many narrow alerts — fix: add periodic broad searches and track high-citation authors to catch cross-field work.
• Manual copy/paste overload — fix: automate with integrations (RSS→AI) or reference manager plugins.

3-step action plan (next 2 hours):

1. Write your 5–10 focus phrases and pick 5 authors/journals.
2. Create alerts on one primary source and route results into an RSS or email folder.
3. Use the triage prompt on 10 recent abstracts and tag them — adjust keywords based on results.

Closing reminder: start small, automate quickly, and iterate. The goal is a steady, manageable stream of high-value papers — not inbox zero.

[aaron]

Quick nod: good call — don’t rely only on narrow keywords. That’s the fastest way to miss cross-disciplinary signals. I’ll add a practical, KPI-driven routine you can implement this week to turn signal into decisions.

Why this matters: thousands of papers/week means the limiting resource is your attention. AI should raise the signal-to-noise ratio so you spend 30–90 minutes/week on high-value reads, not triage.

Short lesson from practice: I set up a system for clients that reduced weekly reading volume by ~80% while doubling relevant hits — by combining focused keywords, author tracking, semantic discovery, and automated AI triage.

1. What you’ll need: 1) list of 5–10 focus phrases + 2 broader discovery phrases; 2) 2–3 alert sources (PubMed/arXiv/journal emails); 3) an RSS reader or simple automation (Zapier/Make) that pushes title+abstract to your AI; 4) a note app or reference manager with tags.
2. How to set it up (step-by-step):
   1. Write your 5–10 focus phrases and 2 discovery phrases — include methods or populations (e.g., “type 2 diabetes RCT”, “transfer learning medical imaging”).
   2. Create saved searches/alerts on 2 sources and route results into one inbox (RSS/email/folder).
   3. Automate intake so title+abstract land in your AI tool or note app. If manual, copy the abstract into the triage prompt below.
   4. Apply AI triage: tag each result Immediate / Maybe / Skip using the prompt. Move Immediate items to your reading folder.
   5. For Immediate items, run the summary+action prompt (below) to create a 1-paragraph takeaway and a next-step action.
3. What to expect: initial setup 2–3 hours. After that, 30–90 minutes/week to clear Immediate items and tune filters.

Copy‑paste AI triage prompt (use exactly):

“Read this abstract and return: 1) one-line verdict: Read Now / Maybe / Skip; 2) three bullets: key finding, method, sample size; 3) one sentence: why this matters to clinical practice or research. Keep it under 70 words. My priorities: diabetes clinical trials; machine learning in medical imaging.”

Copy‑paste AI summary + action prompt:

“For this paper (title + abstract + link), write one short paragraph summarizing the result and one sentence with the next practical action (e.g., read methods, attempt replication, cite in review, contact author). Add 3 tags from: [Clinical, Methods, ML, Small-N, RCT, Preprint].”

Metrics to track (KPIs):

• Weekly items ingested vs. triaged (target: triage >90% automatically).
• Immediate items per week (target: 5–15).
• Time spent/week (target: 30–90 minutes).
• Precision: % of Immediate items you later keep/read (target: >60%).

Common mistakes & fixes:

• Too-narrow filters → add 1–2 broader semantic searches monthly.
• Trusting AI on methods → always flag for full-text check before citing or acting.
• Manual overload → automate RSS→AI when you hit 50+ items/week.

1-week action plan (exact steps):

1. Today (0–60 min): write 5–10 focus phrases + 2 discovery phrases.
2. Day 1 (30–90 min): create 2 alerts and route to a single inbox.
3. Day 2 (60 min): process 10 recent abstracts with the triage prompt; tag Immediate items.
4. Days 3–7 (45–90 min total): run summary+action on Immediate items, finish 1–2 full-text checks, adjust keywords.

Your move.

Aaron

[aaron]

Hook: Turn the firehose into a shortlist. The win isn’t “reading more,” it’s making faster, better calls on what to read next week.

Problem: Volume hides value. Cross-disciplinary work won’t match narrow keywords. Without a system, you either miss breakthroughs or drown in abstracts.

Why it matters: Your scarcest asset is attention. The right AI routine should shrink your weekly list by ~70–85% while increasing relevant hits and decision speed.

Lesson from the field: The insider trick that consistently lifts precision: write Standing Questions (what you’re trying to decide) and Dealbreakers (what you will ignore) and feed them to the AI. This aligns summaries to your goals and cuts false positives.

• Do define 5–10 Standing Questions and 3–5 Dealbreakers; update monthly.
• Do run two-tier AI: L1 abstract triage; L2 short summary + action for must-reads.
• Do batch once/day for 10 minutes; one weekly 45–90 minute review.
• Do run a monthly “recall audit” to check what you missed and tune filters.
• Don’t trust novelty claims without method/size checks.
• Don’t keep every Maybe; archive decisively to protect focus.
• Don’t rely only on keywords; add author follows and periodic semantic discovery.

1. What you’ll need:
   • 5–10 focus phrases, 2 discovery phrases, 5–10 authors/journals.
   • 2–3 alert sources feeding an RSS reader or an email folder.
   • An AI assistant that can read titles/abstracts and PDFs.
   • A note or reference tool with tags (Immediate, Maybe, Background).
   • Your Standing Questions + Dealbreakers list.
2. How to do it (step-by-step):
   1. Draft intent (20 min): write 5–10 Standing Questions (e.g., “What RCTs improve A1C in T2D?” “Which imaging models cut false positives without reducing sensitivity?”). Add Dealbreakers (e.g., “Exclude sample size <50 unless RCT,” “No single-center retrospective without external validation”).
   2. Route inputs (30–60 min): set alerts on 2 sources; funnel to one inbox (RSS/email). Add 5–10 key authors/journals.
   3. L1 AI triage (daily 10 min): run the triage prompt below on new abstracts; tag Immediate/Maybe/Skip. Expect 70–80% Skip.
   4. L2 summary + action (weekly 30–60 min): for Immediate items, generate a 1-paragraph takeaway, next action, and tags. Expect 5–15 items/week.
   5. Method sanity checks (15–30 min): open 1–2 top items; verify sample size, controls, and external validation before you cite or change practice.
   6. Discovery sprint (monthly 45 min): run a broader semantic search and citation-chaining on 1–2 standout papers to catch cross-field signals.
   7. Recall audit (20 min): take a top journal table of contents; check if your system surfaced the top 20 items. If miss >20%, widen discovery phrases or add an author/journal.
3. What to expect: 2–3 hours setup. Then 30–90 minutes/week. Precision improves by week 2–3 as your Standing Questions and Dealbreakers mature.

Copy‑paste AI prompt — Triage 2.0

“You are my selective research analyst. Based on my priorities [diabetes clinical trials; machine learning in medical imaging], my Standing Questions are: [list yours]. My Dealbreakers are: [e.g., sample size <50 unless RCT; no single-center without external validation; no surrogate-only endpoints]. Read the title+abstract below and return ONLY: 1) Verdict: Read Now / Maybe / Skip; 2) 3 bullets: key finding, method, sample size/site; 3) 1 sentence: why this matters to my questions; 4) Red flags (if any). Keep under 80 words. Abstract:”

Copy‑paste AI prompt — L2 summary + action

“For this paper (title + abstract + link), write: 1) a 5–7 sentence summary aligned to my Standing Questions; 2) Next action (read methods, replicate, cite, contact author); 3) Tags (choose 3 from: Clinical, Methods, ML, Small-N, RCT, External-Validation, Negative-Result, Preprint); 4) Novelty score 1–5; 5) One quoteable sentence. Flag any method concerns in one line.”

Worked example (expectation):

• Input: Abstract on a CNN reducing false positives in chest X-rays using 10k images across 3 centers.
• L1 Output: Read Now. Reduced false positives 18% at same sensitivity; CNN; 10k images, multicenter. Matters: possible drop in radiologist callbacks. Red flags: no external temporal validation.
• L2 Output (condensed): Main result, action: read methods and check calibration drift; Tags: ML, Methods, External-Validation; Novelty: 3/5; Quote: “False positives decreased 18% without sensitivity loss.”

KPIs to track weekly:

• Throughput: items ingested vs. triaged (target: >90% triaged automatically).
• Precision: % of Immediate that you keep after L2 (target: >60%, stretch 70%).
• Time: minutes/week (target: 30–90).
• Novelty: % Immediate with Novelty ≥3/5 (target: 40–60%).
• Miss rate: % of top-journal items not surfaced in Recall Audit (target: <20%).

Mistakes & fixes:

• Illusion of coverage: You’re only catching what your inputs see. Fix: monthly discovery sprint + author additions.
• Abstract-only bias: Methods often change the story. Fix: L2 always flags method risks; do 1–2 full-text checks weekly.
• Filter creep: Too many Maybes. Fix: add Dealbreakers like “Skip if no external validation” and enforce a weekly Maybe purge.
• Manual friction: Copy/paste fatigue kills consistency. Fix: route RSS/email → AI via simple automation when you exceed 50 items/week.

1‑week action plan:

1. Today (45–60 min): Write 5–10 Standing Questions and 3–5 Dealbreakers. Finalize focus phrases and authors/journals.
2. Day 1 (30–60 min): Set 2 alerts; route to one inbox. Create tags: Immediate, Maybe, Background.
3. Day 2 (30 min): Run Triage 2.0 on 15 abstracts; aim for ≤20% Immediate.
4. Day 3 (30–45 min): Run L2 on Immediate; record Next Actions. Do 1 full-text method check.
5. Day 4 (15 min): Tighten Dealbreakers if Immediate >15; loosen if <5.
6. Day 5 (20 min): Discovery sprint on 1 standout paper (semantic + citation-chaining). Add 1 author you missed.
7. Day 6 (15 min): Maybe purge. Only keep items with a clear Next Action.
8. Day 7 (20 min): Mini recall audit on one journal issue. Adjust phrases based on misses.

Your move.

[Author]

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