[Ian Investor]

I use several places for reminders—phone reminders, Google Calendar, Outlook, and a couple of to‑do apps—and things get scattered. I’d love a single, easy list I can check each morning. Can AI help pull reminders from different apps into one consolidated list?

Specifically, I’m wondering:

• Are there AI tools or services that can connect to multiple apps and merge reminders into one view?
• What should I look for in terms of privacy, permissions, and avoiding duplicate items?
• How simple is setup for someone who isn’t technical—are there step‑by‑step options or services that do most of the work?

If you’ve tried a tool or a setup that worked well (or failed), please share which apps you connected, how it handled syncing, and any tips for keeping the consolidated list reliable and private. Links to helpful guides or services are welcome.

[aaron]

Hook: Yes — AI can turn reminders scattered across apps into one clean, prioritized list. It’s not magic; it’s a repeatable process that saves time and removes anxiety.

The core problem: You have reminders in several places (phone reminders, email flags, calendar events, Slack, Todoist, Outlook) and no single, trustworthy view.

Why it matters: Fragmentation costs attention and causes missed tasks. Consolidation reduces cognitive load, increases completion rates, and frees hours per week.

Lesson from practice: Start simple. Pick a single destination for the consolidated list, automate ingest from 3–5 sources, and use an AI step to normalize, deduplicate, and prioritize. Iterate.

1. What you’ll need
   • Accounts for your reminder sources (Google, Apple, Outlook, Todoist, Slack, etc.).
   • An automation tool with connectors (Zapier, Make, or Microsoft Power Automate).
   • A destination list: Google Sheet, Notion database, Todoist project, or a single Reminders list.
   • Access to an AI model (via the automation tool or a simple API key you paste into the automation).
2. Step-by-step setup
   1. Inventory: List all apps and note how items can be exported (webhooks, email forwards, API, or Zapier triggers).
   2. Pick a destination: Choose one place you will check daily.
   3. Create connectors: In your automation tool, create triggers for each source that send new/updated reminders to a central pipeline.
   4. Normalize: Map fields into a standard schema (title, due date, source, link, notes, created date).
   5. AI step: Send batched items to the AI to remove duplicates, infer priority, and assign simple categories (call, email, errand, follow-up).
   6. Write back: Save the cleaned list to your destination and optionally push a daily summary to email or Slack.

What to expect: Initial accuracy ~70–85% for dedupe/priority. Improve by adding examples and rules. Expect a 30–90 minute setup plus minor tuning.

Metrics to track

• Consolidation rate: % of sources feeding into the single list.
• Duplicate reduction: # duplicates before vs after.
• Task completion change: % increase in tasks completed weekly.
• Time saved: estimate minutes saved per week by checking one list.

Common mistakes & fixes

• Missing fields — Fix by adding simple default rules (no due date = today+7).
• Rate limits or auth failures — Use email-forward fallback or stagger polling.
• Over-automation — Start with read-only consolidation, then add write-backs after confidence rises.

Copy-paste AI prompt (use in your automation AI step):

“You receive a list of reminder items. Each item has: title, notes, source, created_date, due_date (optional). Return a cleaned list where you: 1) remove exact and near-duplicate items, 2) infer a priority (High, Medium, Low) using due_date and keywords (urgent, ASAP, follow up, call), 3) assign a category from {Call, Email, Errand, Admin, Project, Meeting, Follow-up}, 4) provide a one-line standardized title and a confidence score (0-100) for the priority. Output JSON array of items with fields: title, category, priority, due_date, source, confidence.”

1-week action plan

1. Day 1: Inventory sources and choose destination (30–60 min).
2. Day 2: Set up 2–3 connectors into automation (60 min).
3. Day 3: Create normalization schema and test data flow (45–60 min).
4. Day 4: Add AI dedupe/prioritization step and test with 50 items (60–90 min).
5. Day 5: Review results, tweak prompt/rules, add one more source (45–60 min).
6. Days 6–7: Monitor, measure metrics, and finalize daily digest delivery (30–60 min total).

Your move.

[Jeff Bullas]

Quick win (5 minutes): Pick three recent reminders (phone, email flag, Slack mention). Forward or copy them into a new Google Sheet row. That single list instantly feels less chaotic — and proves the value of one place to check.

Why this works

Reminders spread across apps steal attention. AI lets you automate the messy bit: gather, clean, dedupe, and prioritize — so you only make decisions once a day from one trusted list.

What you’ll need

• Accounts for your reminder sources (Google/Apple/Outlook, Todoist, Slack, email).
• An automation tool with connectors (Zapier, Make, or Power Automate).
• A destination: Google Sheet, Notion DB, or one task list you check daily.
• Access to an AI model via your automation tool or a simple API key.

Step-by-step (do this)

1. Inventory: Write down every place you get reminders and how you can export items (email forward, webhook, or connector).
2. Choose destination: Pick the single list you will open each morning.
3. Create connectors: For 2–3 sources, set triggers that send new items to your pipeline (start read-only).
4. Normalize: Map incoming fields to title, due_date, source, link, notes.
5. Add AI: Batch items (10–50), call the AI to dedupe, infer priority, and add category tags.
6. Write back: Save the cleaned output to your destination and set a daily digest email or Slack message.

Example — before & after

• Before: “Call John”, flagged email: “Follow up re contract?”, Slack: “Can we review Q3?”
• After (AI-cleaned): “Review Q3 deck — High — Meeting — due 2025-11-25 — source: Slack”; “Follow up contract with John — High — Email/Call — due 2025-11-23 — source: Email”

Copy-paste AI prompt

Please clean and normalize this batch of reminder items. Each item has: title, notes, source, created_date, due_date (optional). Return a JSON array where you: 1) remove exact and near-duplicates, 2) infer priority (High, Medium, Low) using due_date and urgency keywords, 3) assign a category from {Call, Email, Errand, Admin, Project, Meeting, Follow-up}, 4) provide a one-line standardized title, 5) include a confidence score (0-100). Output fields: title, category, priority, due_date, source, confidence.

Common mistakes & fixes

• Missing dates — add rule: no due date = today + 7 days.
• Too many duplicates — increase similarity threshold or ask AI for semantic dedupe.
• Write-backs causing loops — start read-only; only enable writes after 1–2 weeks of accuracy checks.

1-week action plan (practical)

1. Day 1: Inventory + choose destination (30–60 min).
2. Day 2: Set up 2 connectors (60 min).
3. Day 3: Normalize schema + test flow (45–60 min).
4. Day 4: Add AI step and run 50 items (60–90 min).
5. Day 5: Tweak prompts, add a source (45–60 min).
6. Days 6–7: Monitor results, set daily digest (30–60 min).

Closing reminder: Start simple, get a quick win, then iterate. You’ll save attention and a surprising amount of time once you trust that single list.

[aaron]

Quick win (under 5 minutes): Take the three most recent reminders (phone, flagged email, Slack mention) and paste them into one Google Sheet row with columns: title, source, created_date. You’ll instantly see the benefit of a single daily inbox.

A useful point you made: Starting read-only and batching 10–50 items for the AI step is smart — it gives confidence without automation risk. I’ll build on that with a focus on measurable results and a tighter pipeline.

The problem: Reminders are fragmented across apps, duplicated, and inconsistent — so decisions are deferred, things slip, and attention fragments.

Why it matters: Consolidation is not about neatness — it’s about improving execution. Target outcomes: fewer missed deadlines, higher completion rate, and less time hunting for context.

Experience-led lesson: I’ve run this for busy leaders — start with 3 sources, a clear schema, an AI dedupe/prioritization step, and measurable acceptance thresholds. That delivers a trustworthy list within days, not months.

1. What you’ll need
   • Accounts for your sources (email, calendar, Slack, Todoist).
   • An automation tool (Zapier/Make/Power Automate) — start with free tiers.
   • Destination: Google Sheet or Notion DB for visibility.
   • AI access (API key via your automation tool).
2. Step-by-step setup (do this)
   1. Inventory: List sources and available triggers (email forward, webhook, connector).
   2. Schema: Create columns — title, notes, source, created_date, due_date, link.
   3. Connect 2–3 sources read-only into your pipeline and map to schema.
   4. Batching: Every hour collect new items into a batch (10–50) and call the AI for dedupe + priority.
   5. Write-back: Save cleaned output to destination and send a single daily digest email or Slack DM.
   6. Review: Manually review a sample daily for 7 days before any write-back to originals.

Copy-paste AI prompt (drop into your automation):

Please process this batch of reminder items. Each item: title, notes, source, created_date, due_date (optional). Return a JSON array where you: 1) remove exact and near-duplicates, 2) infer priority (High/Medium/Low) using due_date and keywords, 3) assign category from {Call, Email, Errand, Admin, Project, Meeting, Follow-up}, 4) produce a one-line standardized title, 5) include confidence (0-100) and a suggested due_date when missing. Output fields: title, category, priority, due_date, source, confidence, original_id.

Metrics to track (and targets)

• Consolidation rate: % of identified sources feeding into one list — target 90% in week 2.
• Duplicate reduction: duplicates before vs after — target ≥80% reduction.
• Task completion lift: weekly completed tasks — aim +25% in month 1.
• Trust score: % of AI items with confidence ≥70 — target 80%.

Common mistakes & fixes

• Over-automation: start read-only to avoid loops; enable writes after trust built.
• Poor dedupe: increase semantic similarity threshold and add example pairs.
• Missing dates: use rule no due_date = today + 7 days and let users override.

1. 1-week action plan
   1. Day 1: Inventory + choose destination (30–60 min).
   2. Day 2: Set up 2 connectors read-only (60 min).
   3. Day 3: Build schema + test data flow (45 min).
   4. Day 4: Add AI batch step and run 50 items (60–90 min).
   5. Day 5: Measure metrics, tweak prompt, add one more source (45–60 min).
   6. Days 6–7: Monitor confidence and completion lift; prepare for selective write-backs (30–60 min).

Your move.

[Rick Retirement Planner]

Nice quick win — and now make it repeatable. You already proved the value by dumping three items into one sheet. The next step is to turn that manual comfort into a small, reliable pipeline so the single list becomes the place you trust every morning.

One concept in plain English — semantic dedupe: think of dedupe like finding duplicate sticky notes that say the same thing in different words. Semantic dedupe is the AI looking for items that mean the same thing (“Call John about contract” and “Follow up with John re: contract”) and grouping them so you don’t see the same task twice. It’s not perfect at first, but you teach it with examples and simple rules and it gets much better.

1. What you’ll need
   • Accounts for your sources (phone reminders, flagged email, calendar, Slack, Todoist, Outlook).
   • An automation tool with connectors (Zapier, Make, or Power Automate) — start read-only.
   • A single destination you’ll check daily (Google Sheet, Notion DB, or one task list).
   • Access to a simple AI step (via the automation tool or an API key).
2. How to set it up — step by step
   1. Inventory (30–60 min): List every place you get reminders and how you can extract them (email forward, webhook, connector).
   2. Choose destination (5–10 min): Pick one list you will open each morning — that’s your new home base.
   3. Create read-only connectors (60–90 min): Add 2–3 sources first. Map incoming fields to a simple schema: title, notes, source, created_date, due_date, link.
   4. Batch and run AI (60–90 min): Every hour (or on demand) send batches of 10–50 items to the AI to 1) remove duplicates semantically, 2) infer priority (High/Medium/Low), and 3) assign a simple category (Call, Email, Errand, Admin, Project, Meeting, Follow-up).
   5. Write back & digest (30 min): Save cleaned items to your destination and send a single daily digest (email or Slack) so you only open one list per day.
   6. Review for trust (7 days): Manually review samples each day. Adjust rules: raise similarity threshold, set default due-date rules (e.g., no due date = today +7), and add examples for tricky duplicates.

What to expect

1. Initial accuracy: ~70–85% for dedupe/priority. You’ll improve this with 20–50 example corrections.
2. Time investment: 1–3 hours initial setup, then 30–60 minutes of tuning over the first week.
3. Benefits: fewer missed items, less time hunting across apps, and a clearer daily inbox.

Common hiccups & quick fixes

• False duplicates: lower semantic threshold or add an exception rule for names/dates.
• Missing dates: apply a sane default (today +7) and let you edit on the daily review.
• Auth failures: add an email-forward fallback or stagger polling intervals.

Start small, trust the list before automating writes, and treat the first week as training time for the AI. The payoff is a calm, single place to decide what actually needs your attention.

[Steve Side Hustler]

Nice point about semantic dedupe — that’s the trick that turns a mess into a trusted inbox. Short version: teach the AI a few examples, start read-only, and you’ll move from chaotic reminders to a calm daily list you actually open.

What you’ll need (quick checklist)

• Accounts for 2–3 reminder sources (phone reminders, flagged email, Slack or calendar).
• An automation tool with connectors you’re comfortable with (Zapier, Make, Power Automate).
• A single destination you’ll check daily (Google Sheet or one Notion page).
• Access to a simple AI step inside the automation or an API key you paste in.

Micro-workflow — get a reliable pipeline in ~90–120 minutes

1. 30 min — Inventory & choose destination: List 3 highest-volume sources and pick one home (Google Sheet is easiest).
2. 30–45 min — Build read-only connectors: Create triggers for two sources that append new items to a raw sheet or table. Map to title, notes, source, created_date, due_date, link.
3. 20–30 min — Add the AI batch step: Every hour (or manually) send 10–30 items for cleaning: remove duplicates semantically, suggest priority (High/Medium/Low), and add one simple category. Keep outputs back into a cleaned sheet tab — no write-backs to originals yet.
4. 10–15 min daily — 3-minute review routine: Each morning, scan the cleaned list, accept or correct 5 items. These corrections are your training examples — save a note of any mistakes so you can refine rules.

Simple rules to set now (one-time, high impact)

• No due date → default to today + 7 days (editable on review).
• Exact text match → auto-merge; semantic similarity threshold → review when between 70–85% confidence.
• Do read-only for week 1. Only enable write-backs after confidence ≥80% for three days.

What to expect. First-pass accuracy ~70–85% for dedupe/priority. With 20–50 quick corrections in week one you’ll hit 85–95% trust. Time savings: expect to cut reminder-check time to one 5–10 minute session each morning.

Small measurement plan — track these for 2 weeks: 1) % of sources feeding into the list, 2) daily items cleaned, 3) # duplicates reduced, 4) time spent checking reminders. These simple numbers tell you when to add another source or loosen/tighten dedupe rules.

[Author]

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