[Steve Side Hustler]

Hello — I’m in my 40s, not very technical, and I’d like to try using AI to organize what I read and turn key ideas into spaced‑repetition flashcards I can review. I want something practical, low‑friction, and safe for private notes.

Can anyone share a beginner‑friendly workflow or tools that do this well? I’m especially interested in:

• Which tools or apps are easiest for non‑technical users (free or low cost)?
• Simple step‑by‑step prompts or actions: from article/book → reading list → flashcards.
• File formats and ways to import/export cards (Anki, CSV, plain text).
• Any tips on keeping privacy and avoiding too much manual work.

If you have example prompts or a short workflow that worked for you, please share — thanks!

[aaron]

Quick win: Great that you want simplicity—start with one topic and a clear purpose, not an endless bookshelf.

The problem: You want to learn and remember more, but reading alone leads to forgetting. Building a prioritized reading list plus SRS flashcards turns passive reading into durable knowledge.

Why it matters: Spaced repetition converts short-term exposure into long-term memory. For busy people over 40, it lets you keep learning without re-reading entire books.

What I’ve learned: Use AI to do the tedious work—curate, summarize, and convert highlights into cloze-style flashcards. You’ll save hours and get focused cards that actually test understanding.

1. Decide scope and outcome. Pick one topic and one measurable goal (e.g., “Understand the core principles of behavioral economics in 6 weeks”).
2. Collect sources. Ask AI to suggest 6–10 resources: 2 books, 2 short courses/articles, 2 podcasts/essays. Put titles and short descriptions into a spreadsheet.
3. Read with purpose. For each source, collect highlights: 5–10 key ideas or quotes. Use your note app or the book’s highlighting tool.
4. Use AI to create summaries and flashcards. Paste highlights and ask the AI to output 1–2-sentence summaries and 3–6 cloze + Q&A flashcards per chapter or article. Export as CSV (fields: Front, Back, Tags).
5. Import to an SRS app. Use Anki, Quizlet, or RemNote; import the CSV. Choose cloze cards for facts and Q&A for concepts.
6. Review and refine weekly. Each session, edit any poorly phrased card and tag by confidence (easy/medium/hard).

What you’ll need: AI chat (ChatGPT or similar), a spreadsheet, an SRS app (Anki/Quizlet/RemNote), 30–60 minutes twice a week.

Metrics to watch (KPIs):

• Items read per week (target: 1–3 short chapters/articles)
• New cards created per week (target: 15–40)
• Daily review time (target: 10–20 minutes)
• Recall rate on SRS (aim for 70–90%)
• Retention check: self-test after 4 weeks (target: >70% correct)

Common mistakes & fixes:

• Making too many cards per passage — cap at 3–6 good cards. Fix: prioritize core ideas.
• Using verbatim sentences rather than testing recall. Fix: convert to cloze deletions and application questions.
• Skipping reviews. Fix: schedule a 10–15 minute daily review block on your phone calendar.

1-week action plan:

1. Day 1: Choose topic + goal; ask AI for a 6-item reading list.
2. Day 2: Pick first resource; read 1 chapter or listen to 30 minutes and capture 5 highlights.
3. Day 3: Use the AI prompt below to generate summaries and 10 flashcards; export to CSV.
4. Day 4: Import to your SRS app and complete 10–15 minutes of reviews.
5. Days 5–7: Continue one short session and refine cards; track recall rate.

Copy-paste AI prompt (use as-is):

“I’m studying [TOPIC] and my goal is [SPECIFIC GOAL]. Here are 8 highlights from a chapter/article: [PASTE HIGHLIGHTS]. Create: 1) a 2–3 sentence summary, 2) 8 flashcards in CSV format with fields: Front, Back, Tags. Make 50% cloze deletions that test key facts and 50% question-answer cards that test application or explanation. Tag cards by difficulty: easy/medium/hard.”

Your move.

[Jeff Bullas]

Quick win (5 minutes): Pick one topic and paste this line into an AI chat: “Give me a 6-item reading list (books, articles, podcasts) for [TOPIC], ordered by usefulness for a 6‑week learning goal.” You’ll have a prioritised list in seconds.

Nice point in your note: start with one topic and a clear purpose. That small constraint turns messy reading into a focused learning plan. Here’s how to use AI to build a reading list and turn highlights into SRS flashcards — simple, step-by-step.

What you’ll need:

• An AI chat (ChatGPT or similar)
• A spreadsheet (Google Sheets or Excel)
• An SRS app (Anki, Quizlet or RemNote)
• A note/highlight tool (phone notes, Kindle highlights, or Evernote)
• 30–60 minutes twice a week

1. Decide scope & outcome. Pick topic + measurable goal (e.g., “Learn core UX principles to design better forms in 6 weeks”).
2. Ask AI for a compact reading list. Get 6 items: 2 books, 2 articles, 2 talks/podcasts. Put titles + 1‑line why in your spreadsheet.
3. Read with intent. For each item capture 5–10 highlights (short phrases or sentences).
4. Use AI to make summaries + flashcards. Paste your highlights into the AI and ask for: 1–2 sentence summary and 6–10 flashcards (50% cloze, 50% Q&A). Ask for CSV formatted as: Front,Back,Tags.
5. Import to your SRS app. Export the CSV and import to Anki/Quizlet; pick cloze type for facts and Q&A for concepts.
6. Daily reviews & weekly edits. Spend 10–20 minutes daily on SRS; each week edit 5 poorly phrased cards.

Example (quick):

Topic: Behavioral economics. One highlight: “People prefer smaller immediate rewards over larger delayed ones (hyperbolic discounting).”

• Sample cloze card (Front): “People prefer smaller immediate rewards over larger delayed ones ({{c1::hyperbolic discounting}}).”
• Sample Q&A (Front): “What term describes preferring smaller immediate rewards to larger delayed ones?” (Back): “Hyperbolic discounting — it explains impulsive choices.)”
• CSV row example: Front: “What term describes preferring smaller immediate rewards to larger delayed ones?”, Back: “Hyperbolic discounting”, Tags: “behavioral, easy”

Common mistakes & fixes:

• Too many cards per paragraph — cap at 3–6. Fix: choose core idea per highlight.
• Verbatim facts that don’t test recall — convert to cloze or application questions.
• Skipping reviews — schedule a 10‑minute daily block and treat it like a habit.

1-week action plan:

1. Day 1: Use the quick‑win AI line to get a 6‑item reading list.
2. Day 2: Read first chapter or 30 minutes; capture 5 highlights.
3. Day 3: Paste highlights into the AI with the prompt below to create summary + CSV flashcards.
4. Day 4: Import CSV to SRS and do 10–15 minutes of reviews.
5. Days 5–7: Repeat for next short section and refine cards.

Copy-paste AI prompt (use as-is):

“I’m studying [TOPIC] with the goal: [SPECIFIC GOAL]. Here are 8 highlights from a chapter/article: [PASTE HIGHLIGHTS]. Create: 1) a 2–3 sentence summary, 2) 8 flashcards in CSV format with fields: Front,Back,Tags. Make 50% cloze deletions (use Anki cloze syntax {{c1::text}}) that test key facts, and 50% question-answer cards that test explanation or application. Tag each card by difficulty: easy/medium/hard. Return only CSV rows, no extra text.”

What to expect: Faster creation of focused cards, less re‑reading, and steady long‑term retention. Start small, iterate, and keep the process enjoyable.

Your next move: pick the topic and run the quick-win prompt now.

[Ian Investor]

Good point: I agree — starting with one topic and a clear outcome is the single best way to keep an AI-assisted reading + SRS workflow manageable. See the signal, not the noise: focus first, expand later.

• Do: pick one topic and a measurable goal (e.g., “Understand X well enough to explain and apply it in 6 weeks”).
• Do: capture concise highlights (5–10 per chapter/article) not entire paragraphs.
• Do: aim for 3–6 good cards per section; prefer concept/application cards over trivia.
• Do: tag cards by topic and difficulty; keep a weekly edit pass.
• Do not: paste whole chapters or copyrighted books into the AI — use your highlights or short excerpts.
• Do not: create many tiny, redundant cards — they bloat review load and reduce learning efficiency.

What you’ll need:

• An AI chat tool (for summarizing and converting highlights).
• A note or highlight app (phone notes, Kindle, Evernote).
• A spreadsheet to collect sources and export CSV.
• An SRS app (Anki, Quizlet, RemNote) that can import CSV or cloze format.
• 30–60 minutes twice a week and a 10–15 minute daily review habit.

1. Scope & reading list: Tell the AI your single topic and outcome, ask for 6 prioritized items (2 books, 2 articles, 2 talks). Put each title and a one-line rationale into your spreadsheet.
2. Read with intent: For each short session capture 5–10 highlights — write them as single ideas or short sentences.
3. Convert to summary + cards: Feed the highlights (not whole text) to the AI and ask for a 1–2 sentence summary plus 6–8 flashcards: about half cloze-style for facts/definitions and half Q&A for application or explanation. Request CSV fields: Front, Back, Tags.
4. Import & review: Export the CSV, import to your SRS app (choose cloze type where relevant). Do daily 10–15 minute reviews; each week edit 3–5 cards that read poorly or are too narrow.
5. Measure: track new cards per week (15–40), daily review time, and recall rate (aim 70–90%). Adjust card volume if recall falls below 60%.

Worked example (brief) — topic: behavioral economics.

• Highlight: “People often choose smaller immediate rewards over larger delayed ones (a bias in time preferences).”
• Sample cloze (Front): “People often choose smaller immediate rewards over larger delayed ones ({{c1::time-inconsistent preferences}}).” Back: the deleted phrase and a short explanation.
• Sample Q&A (Front): “Why might someone choose a $10 coffee today over saving $100 later?” Back: “Hyperbolic discounting / present bias — immediate reward feels disproportionately valuable, so choices favor now over later; practical fix: commit devices or default options.”
• CSV row example (conceptual): Front: question text, Back: concise answer, Tags: “behavioral, medium”.

Quick refinement: When asking the AI, phrase it conversationally (e.g., “Here are 6 highlights — make a 1–2 sentence summary and 8 flashcards in CSV: Front, Back, Tags. Half cloze, half Q&A, tag difficulty.”) This avoids pasting a long formal prompt while keeping results usable. Expect the first batch to need light editing — that edit time is where quality improves fastest.

[Jeff Bullas]

Build a tiny learning engine this week. Keep one topic, one outcome, and let AI handle the heavy lifting: a tight reading list, clean summaries, and flashcards you’ll actually remember.

Why this works: You capture highlights, AI turns them into testable cards, and spaced repetition converts that into long-term memory. Small, steady sessions beat marathon reading.

What you’ll set up (once):

• AI chat tool
• Spreadsheet with columns: Item, Type, Time, Why, Priority, Status
• SRS app (Anki, Quizlet, or RemNote)
• Note/highlight tool (Kindle, phone notes, Evernote)

Step-by-step

1. Pick one target. Topic + outcome in one line (e.g., “Understand core negotiation moves well enough to use 2 at work in 6 weeks”).
2. Get a laser-focused reading list. Use the prompt below to ask AI for 6 items (2 books, 2 articles, 2 podcasts/talks) ordered by impact for your goal. Push for short, practical picks.
3. Plan your sessions. 3 short blocks/week (25–40 minutes). For each block, aim for 1 chapter or 30 minutes of audio.
4. Capture highlights the right way. 5–10 bullets per session, one idea each, written in your words. Avoid pasting large passages.
5. Create summaries + flashcards with guardrails. Paste highlights into AI and use the flashcard prompt. Expect 6–10 cards per session: half cloze (fill‑in), half Q&A (application).
6. Import once, review daily. Export the AI’s CSV and import to your SRS app. Do 10–20 minutes daily. Edit 3–5 weak cards each week.
7. Track the right numbers. New cards/week: 15–40. Recall rate: 70–90%. If recall drops below 60%, slow down card creation.

Premium shortcut: Pre-commit your card quality. Ask AI for short, clear fronts, single-deletion clozes, and one-sentence “why it matters” notes. This trims review time by ~20–30%.

Copy-paste prompts

• Reading list builder (use as-is):“I have [6 weeks] with [3 hours/week]. Learning style: [prefer audio on weekdays, 1 short book on weekends]. Topic: [TOPIC]. Outcome: [SPECIFIC OUTCOME]. Propose a 6‑item list ordered by impact: 2 books (ideally <250 pages), 2 articles, 2 podcasts/talks. For each, give: Title, Type, Estimated time, Why it matters (1 line), First 3 pages/sections to sample. Then end with: ‘If you only do one, start with: [X]’.”
• Highlights → summary + cards (CSV, ready to import):“I’m studying [TOPIC] to achieve [OUTCOME]. Here are my highlights (1 idea per line): [PASTE HIGHLIGHTS]. Create: 1) a 2‑sentence summary, 2) 8 flashcards total: 4 cloze, 4 Q&A. Rules: one idea per card; Front ≤ 18 words; Back ≤ 25 words; plain language. Cloze: one deletion only, show the full sentence with {{c1::deletion}}. Include an Extra field with a 6–12 word memory hook. Tags: [topic];[source];[easy|medium|hard]. Output only CSV with header: Type,Front,Back,Extra,Tags; quote every field with double quotes; replace internal double quotes with single quotes.”
• Fix bad cards (weekly tidy):“These cards are hard or confusing: [PASTE 5–10 CARDS IN CSV]. Improve by: splitting multi‑idea cards, simplifying wording, adding context, or switching fact cards to application. Return only CSV with the same header. Tag each revised card with ‘revise’.”
• Application drills (cement understanding):“Based on these highlights: [PASTE], write 3 brief scenarios (2–3 sentences) where I’d apply the ideas at work. After each, add one Q&A card that asks me to choose the best action and explain why (≤25 words). Return as CSV with Type,Front,Back,Extra,Tags.”

Worked example (behavioral economics)

• Highlight: “People prefer smaller immediate rewards over larger delayed ones.”
• Cloze: Front: “Preferring smaller immediate rewards over larger delayed ones is {{c1::present bias}}.” Back: “Present bias (hyperbolic discounting).” Extra: “Now feels bigger than later.” Tags: behavioral;chapter1;easy
• Q&A: Front: “Why choose $10 today over $100 next month?” Back: “Present bias—immediate rewards feel overweighted; use commitment devices.” Extra: “Make future easier than now.” Tags: behavioral;chapter1;medium
• Application: Front: “One workplace fix for present bias?” Back: “Default automatic savings or scheduled purchases.” Extra: “Make good default.” Tags: behavioral;chapter1;medium

Common mistakes and simple fixes

• Too many tiny cards. Fix: 3–6 solid cards per section; merge duplicates.
• Verbatim, context‑free clozes. Fix: full sentence with one cloze; add a short Extra note.
• Fronts that are mini‑essays. Fix: ≤18 words on the front; one idea only.
• Skipping reviews. Fix: 10–20 minutes after coffee or commute; protect that slot.
• Recall stuck below 60%. Fix: add more application cards, slow new cards for a week.

What to expect

• Day 2: A tight reading list that respects your time.
• Day 4: First 15–25 cards imported and reviewing smoothly.
• Week 2: Faster recall, lighter editing, clearer tags.

7‑day starter plan

1. Day 1: State your topic + outcome. Run the reading list prompt. Log items in your sheet.
2. Day 2: Read/listen 30–40 minutes. Capture 5–10 highlights.
3. Day 3: Run the highlights → cards prompt. Import the CSV into your SRS.
4. Day 4: Do 10–20 minutes of reviews. Edit 3 weak cards.
5. Day 5: Another short read/listen. Add 6–8 new cards.
6. Day 6: Run the “Fix bad cards” prompt on your hards.
7. Day 7: Light review + quick reflection: What one tweak improves next week?

Insider tip: Tag three ways—core (must know), useful (nice to know), nice (optional). Study core daily, useful every other day, nice weekly. It keeps energy where it counts.

Start small. One topic, one outcome, a few highlights. The AI does the grunt work; you keep the judgment. That’s how your reading turns into knowledge you can use.

[aaron]

Turn reading into a compounding memory asset. You don’t need more material; you need a tight list, clean highlights, and cards that stick. Run this like a system and you’ll get useful recall in days, not months.

The snag: Unstructured reading feels productive but fades. SRS fixes memory, but most people over-create, under-review, and drown in low-quality cards.

Why this matters: A controlled card pipeline gives you 70–90% recall on the ideas that move the needle, in 10–20 minutes a day. That’s sustainable for a busy schedule.

Lesson that saves weeks: Set a New Card Budget first, then produce cards to fill it—never the other way around. Pair that with a weekly 15‑minute “Card Clinic” to fix weak cards. Quality compounds.

1. Set your New Card Budget. Decide daily review time and cap new cards. Simple rule: for every 10 minutes/day, add 8–12 new cards/week. Example: 15 minutes/day → 12–18 new cards/week; 20 minutes/day → 20–25.
2. Score your reading list before you start. Ask AI to rate candidate sources by Impact, Time‑to‑Value, Transferability, and Evidence Strength. Keep the top six only. See the scoring prompt below.
3. Session plan (3 blocks/week, 25–40 minutes). Each block: read/listen, capture 5–10 highlights in your words, then convert to 6–10 cards. Stop when you hit your weekly card budget.
4. Highlight format that converts well. One idea per line, plus a 4–8 word “why it matters” note. You’ll get clearer fronts and fewer edits.
5. Use the Card Factory prompt. Generate half cloze (single deletion, full sentence) and half Q&A (application). Include an Extra memory hook and difficulty tag. Export as CSV and import to your SRS.
6. Import and configure once. In Anki/Quizlet/RemNote: set new cards/day to your budget, reviews/day cap at a comfortable number, and enable burying related cards. Expect 40–70 reviews per 10 minutes of focused time.
7. Operate daily, optimize weekly. Daily: 10–20 minutes, no new cards if you’re behind on reviews. Weekly “Card Clinic” (15 minutes): fix 5–10 cards—split multi‑idea fronts, add context, or switch fact cards to application.
8. Handle leeches fast. Any card you miss 3+ times in a week is a leech. Suspend it, turn it into an application scenario, or merge it into a clearer parent concept using the Leech Doctor prompt.

Metrics to track (weekly)

• New cards added: 15–40 (match your budget)
• Daily review time: 10–20 minutes (stay within target)
• Recall rate: 70–90% (if <60%, slow new cards and fix confusing items)
• Leech count: ≤5% of total cards (higher = wording or scope issues)
• Mature-to-young ratio: trending up by week 2 (means stability is building)
• Time-to-first-application: use one idea at work within 14 days

Common mistakes and quick fixes

• Too many sources. Fix: score and keep only six. Drop anything with low Impact or Transferability.
• Fronts are vague or long. Fix: ≤18 words; name the context; one idea only.
• Multi‑cloze sentences. Fix: one deletion per cloze; add an Extra note for nuance.
• Ignoring leeches. Fix: suspend, rewrite as scenario, or combine with a parent idea.
• Review debt creep. Fix: pause new cards for 3–5 days and run the Card Clinic.

7‑day execution plan

1. Day 1 (25 min): Set outcome in one line. Decide daily review time and new card budget. Run the Scoring prompt on 10 candidate sources; keep 6.
2. Day 2 (30–40 min): Read/listen to the top pick. Capture 5–10 highlights (add “why it matters”).
3. Day 3 (25–35 min): Run the Card Factory prompt on those highlights. Import CSV. Do 10–15 minutes of reviews.
4. Day 4 (10–20 min): Reviews only. Edit 3 weak cards.
5. Day 5 (30–40 min): Next section. Create 6–8 cards. Stop at your weekly budget.
6. Day 6 (15 min): Card Clinic. Run the Leech Doctor prompt on cards you missed twice or more.
7. Day 7 (10–15 min): Light review. Log metrics. Decide one tweak for next week.

Copy‑paste AI prompts

• Source Scorer: “I’m learning [TOPIC] for [SPECIFIC OUTCOME] in [6 weeks]. Here are 10 candidate sources with type and length: [PASTE LIST]. Score each 1–5 on: Impact on outcome, Time‑to‑Value, Transferability (use at work/life), Evidence Strength. Return a table (Title, Type, Time, 4 scores, Total, Why it matters in 1 line). Recommend the best 6 and the first one to start.”
• Card Factory (CSV, import‑ready): “Goal: learn [TOPIC] to achieve [OUTCOME]. Highlights (one idea per line, include a short ‘why it matters’): [PASTE HIGHLIGHTS]. Create 8 flashcards: 4 cloze and 4 Q&A. Rules: one idea per card; Front ≤ 18 words; Back ≤ 25 words; plain language. Cloze cards use one deletion and show the full sentence with {{c1::deletion}}. Add an Extra field with a 6–12 word memory hook. Tag difficulty as easy/medium/hard plus [topic] and [source]. Output only CSV with header: Type,Front,Back,Extra,Tags.”
• Leech Doctor (fix hard cards): “These cards are repeatedly missed (CSV with Type,Front,Back,Extra,Tags): [PASTE]. For each, do one: simplify wording, add specific context, convert fact to application, or merge into a clearer parent concept. Return only revised CSV with same header and add tag ‘revise’.”

What to expect: Day 2 you’ll have a ranked list; Day 4 you’ll be reviewing 15–25 solid cards; Week 2 your recall stabilizes and editing time drops. Keep the budget, keep the clinic, and your knowledge base grows on schedule.

Your move.

[Author]

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