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docs: complete project research (ecosystem analysis)

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Research files synthesized:
- STACK.md: Flutter + Supabase + Riverpod recommended stack
- FEATURES.md: 7 table stakes, 6 differentiators, 7 anti-features identified
- ARCHITECTURE.md: Offline-first sync with optimistic locking, RLS multi-tenancy
- PITFALLS.md: 5 critical pitfalls (v1), 8 moderate (v1.5), 3 minor (v2+)
- SUMMARY.md: Executive synthesis with 3-phase roadmap implications

Key findings:
- Stack: Flutter + Supabase free tier + mobile_scanner + Open Food Facts
- Critical pitfalls: Barcode mismatches, timezone bugs, sync conflicts, setup complexity, notification fatigue
- Phase structure: MVP (core) → expansion (usage tracking) → differentiation (prediction + sales)
- All research grounded in ecosystem analysis (12+ competitors), official documentation, and production incidents

Confidence: HIGH
Ready for roadmap creation: YES

Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
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# Domain Pitfalls: Food & Household Inventory Tracking Apps
**Domain:** Multi-user food inventory tracking with expiration alerts and community data
**Researched:** January 2026
**Confidence:** MEDIUM-HIGH (ecosystem survey completed, app-specific patterns confirmed via recent reviews and technical documentation)
---
## Critical Pitfalls (v1 Must Address)
These mistakes cause rewrites, data loss, or app abandonment. Cannot defer.
### Pitfall 1: Barcode Data Mismatches and Product Duplication
**What goes wrong:**
- Scanning the same product yields different nutritional data, expiration dates, or categories on different scans
- Duplicate products accumulate in the inventory (e.g., "Milk", "milk", "2% Milk", "Whole Milk" all registered as separate items)
- Barcode APIs return incorrect data; users see wrong expiration dates or nutritional info pop up after scan
- Multiple barcodes for the same product (UPC vs EAN) are treated as separate items
**Why it happens:**
- Barcode databases (USDA FoodData Central, Open Food Facts, commercial APIs) have inconsistent entries, duplicates, and region-specific variants
- Apps don't perform fuzzy matching or deduplication when adding items
- Free barcode APIs (UPC Database, Barcode Lookup) have lower accuracy and less sanitization than paid alternatives
- User behavior: manual entry of product names isn't normalized (capitalization, spacing, abbreviations vary)
**Consequences:**
- Inventory reports become unreliable (user thinks they have 5 liters of milk, but it's split across 3 "products")
- Expiration tracking fails (wrong product linked to wrong expiration date)
- Multi-user conflicts spike (person A can't find the item person B added)
- Users lose trust; many stop using the app after discovering inconsistencies
**Real-world example:**
- FoodShiner and My Pantry Tracker users report that barcode scanning produces incorrect categorization (dry beans marked as refrigerated) and duplicate entries
- NoWaste app crashes when users try bulk operations, losing all entered data
**Prevention:**
1. **Use a normalized product master database** from day one:
- Start with USDA FoodData Central (free, government-verified, includes barcodes)
- Supplement with Open Food Facts (community data, barcode support) for coverage gaps
- Cache results locally to reduce API calls and improve consistency
2. **Implement deduplication at entry time:**
- After user scans/enters product name, show "Did you mean...?" suggestions based on fuzzy string matching
- Compare to user's existing inventory before adding new item
- Merge duplicate items on discovery (with user confirmation)
3. **Normalize product naming:**
- Store canonical product name (from database) separately from user-given name
- Display both on inventory view, but use canonical name for search and merging
- Example: User enters "2% Milk", store as canonical "Milk, 2%"
4. **Validate barcode integrity:**
- Check barcode UPC/EAN check digits before API lookup
- Log failed barcode lookups so you can investigate data quality issues
- Reject lookups that differ wildly from expected product category
5. **Implement conflict resolution for multi-barcode products:**
- If scan returns multiple product candidates, show user the one closest to their search context
- Store all barcode→product mappings (one product can have many barcodes)
**Phase mapping:**
- **v1 (MVP):** Fuzzy matching + deduplication + normalized naming (required for data integrity)
- **v1.5:** Better barcode database selection and caching strategy
- **v2:** Machine learning to auto-merge suspicious duplicates
**Detection (warning signs):**
- Users report "I added milk three times and the app has three separate milk items"
- Expiration reports have items that don't match actual inventory counts
- Search results return multiple hits for clearly the same product
---
### Pitfall 2: Timezone and Expiration Date Logic Bugs
**What goes wrong:**
- User sets "expires Jan 1, 2026" for milk, but app shows it as expired Dec 31, 2025 in another timezone
- Items show as expired when they're not (or vice versa)
- Multi-user households see different expiration dates depending on device timezone
- Off-by-one errors: item expires "Jan 1" but app shows as expired on Dec 31
- Date formats vary by locale (MM/DD vs DD/MM) leading to misinterpretation (1/2 = Jan 2 or Feb 1?)
**Why it happens:**
- Date fields stored without timezone info (just "2026-01-15" with no UTC offset)
- Comparing local device time against server time in different zones
- Confusion between "date of expiration" vs "expiration timestamp" (e.g., "2026-01-15 00:00:00 in Tokyo" vs "2026-01-15 23:59:59 in New York")
- Frontend/backend timezone mismatches
- Legacy code that worked in one timezone but breaks in others
**Consequences:**
- Expiration alerts fire early or late
- Users can't trust alerts and disable notifications entirely
- Multi-user households disagree on what's expired (person in NYC vs person in London)
- Compliance issues if shared with restaurants/institutions that track food safety
**Real-world example:**
- Atlassian Confluence observed license expiry discrepancies between US Central Time (UTC-6) and Australian Eastern Time (UTC+10)—same "date" was interpreted as different days
- GitHub JWT discussions report tokens expiring unexpectedly due to auth server/resource server timezone mismatch
**Prevention:**
1. **Always use UTC timestamps with timezone awareness:**
- Store all dates as ISO 8601 UTC (e.g., `2026-01-15T23:59:59Z`)
- Never store "date only" without time component
- Store user's local timezone separately if needed (e.g., "America/New_York")
2. **Clarify expiration semantics in code and UI:**
- Document: "Expires at 23:59:59 UTC on the date shown" (item is good until end of that day in UTC)
- OR: "Expires at start of next day in user's local timezone"
- Pick one semantic and stick to it everywhere
3. **Handle locale-specific date formats:**
- Accept user input in their locale (MM/DD or DD/MM) but convert to ISO format immediately
- Store and transmit as ISO 8601 only
- Display to user in their locale on the fly
4. **Test across timezones from day one:**
- Set up devices in multiple timezones or mock timezone offsets
- Test edge cases: midnight UTC, daylight saving time transitions, leap seconds
- Example test: Create item expiring in 5 minutes UTC, verify alerts fire consistently across all user timezones
5. **Implement a "time service" abstraction:**
- All code calls `getCurrentTime()` which returns current UTC moment
- Single place to mock/override time for testing
- Prevents scattered `new Date()` calls with different semantics
**Phase mapping:**
- **v1 (MVP):** Use UTC throughout, store ISO 8601, test with multiple timezones (non-negotiable)
- **v1.5:** Robust timezone conversion helpers; test DST transitions
- **v2+:** Advanced features like "reminder 1 day before expiry in user's local timezone"
**Detection (warning signs):**
- Expiration alerts fire at inconsistent times across devices
- Users in different timezones report different expiration dates for same item
- Off-by-one errors in tests ("item expires tomorrow but app says expired")
---
### Pitfall 3: Multi-User Sync Conflicts and Race Conditions
**What goes wrong:**
- Two users update inventory simultaneously (e.g., both "remove milk" at same time); one update is lost
- Item quantity becomes inconsistent (both think they removed 1 liter, but system shows 0 liters removed)
- Last-Write-Wins silently overwrites important changes (person A marks item as expired; person B removes it from list → person B's removal wins and item reappears)
- Offline users have stale data; when they reconnect, their old changes conflict with newer data
- "Ghost" deletions: one user deletes item, but another user's offline copy still exists and re-syncs it back
**Why it happens:**
- Naive implementation uses timestamps only (whoever wrote last wins)
- No optimistic locking or version tracking
- Offline-first apps don't handle reconnection conflicts properly
- Race condition: "check if item exists" and "decrement quantity" aren't atomic
**Consequences:**
- Inventory counts become unreliable (users don't trust the app)
- Multi-user households stop using shared features
- Silent data loss (users think item was deleted but it reappears unexpectedly)
- Difficult to debug; users report "weird behavior" that's hard to reproduce
**Real-world example:**
- Sylius e-commerce platform documented race condition in inventory tracking: availability checked, then two orders decrement quantity simultaneously, overselling result
- Mobile field service apps: field technician and dispatcher both edit same work order without syncing, last update wins and discards other person's changes
**Prevention:**
1. **Use optimistic locking with version numbers:**
- Every inventory item has a `version` field (e.g., version 3)
- User sends update with `version: 3`
- Server checks: if current version still 3, apply update and increment to 4
- If version changed (someone else updated), reject with 409 Conflict and return current state
- Client retries with new version number
2. **Make updates idempotent:**
- Updates should include `operation_id` (UUID)
- Server tracks applied operation IDs; if same operation sent twice, it's a no-op
- Prevents double-decrement if network request is retried
3. **Use CRDTs or operational transformation for offline-first:**
- For fully offline scenarios, use Conflict-free Replicated Data Types (e.g., CRDT) to merge updates automatically
- Example: last-write-wins for quantity, but timestamps for expiry date
- Only use LWW for data where recency truly matters
4. **Implement proper offline sync:**
- When offline, queue operations locally with operation IDs
- When online, send all queued ops with timestamps
- Server applies in order, detecting conflicts and notifying user if needed
- Never silently discard updates
5. **Add audit logging:**
- Log every inventory change: who, what, when
- Multi-user households can see "Sarah added milk at 3:15pm; John removed it at 3:16pm"
- Helps debug conflicts and rebuilds trust
**Phase mapping:**
- **v1 (MVP):** Optimistic locking + version numbers (required for multi-user trust)
- **v1.5:** Idempotent operations; audit log
- **v2+:** Full offline sync with CRDT or operational transformation
**Detection (warning signs):**
- Users report items disappearing/reappearing
- Multi-user households see different inventory counts
- "I added milk but it's not showing up for my roommate"
- Quantity counts don't match actual item counts
---
### Pitfall 4: Setup Complexity Kills Adoption
**What goes wrong:**
- User opens app, sees 15 fields to fill before adding first item (date purchased, quantity, unit, location, category, subcategory, notes, nutritional info, price paid, brand, barcode, expiry method...)
- 50% of users abandon app during onboarding
- Roommates set up the same app differently (one uses "Fridge", other uses "cold_storage"; can't share items)
- App requires account creation, email verification before doing anything
- Dark UX: no clear "add first item" button; users get lost in settings
**Why it happens:**
- Product managers design for power users with full feature set
- Each feature adds a required field
- No progressive disclosure: "required" vs "optional" fields unclear
- No guided onboarding; app assumes users know how to use it
**Consequences:**
- App store reviews: "Looks promising but too complicated" (abandoned after 3 minutes)
- Multi-user adoption fails: roommates give up trying to agree on data structure
- Low feature adoption: users never reach the "aha moment" because they're stuck in setup
**Real-world examples:**
- User research across home organization apps: "complexity was cited as a key reason customers don't engage with features"
- My Pantry Tracker: required fields for scanning barcode, export options too many and confusing
- KITCHENPAL and FoodShiner: users report "too many steps to add an item"
**Prevention:**
1. **Radically simplify MVP onboarding:**
- First item: only three fields - "name", "location", "expiry date" (optional)
- All other fields deferred to settings or item edit screen
- No account creation until user tries to sync with roommate
- No email verification for v1
2. **Add progressive disclosure:**
- "Basic" mode (3 required fields) vs "Advanced" mode (enable all fields)
- User can upgrade mid-session without friction
- Smart defaults: if user scans barcode, offer to fill in category and nutrition (pre-filled, editable)
3. **Use smart onboarding:**
- First 3 screens: "Add your first item" walkthrough (not a settings tour)
- Offer examples: "Here's a milk item someone else started with—you can edit it"
- Then offer: "Add location (fridge, pantry, freezer)" with pre-set options
4. **Standardize shared fields in multi-user setup:**
- When roommate joins household, use exact same location names
- Validate: if roommate enters "cold_storage" and you use "Fridge", warn and suggest merge
- Provide preset location library (Fridge, Pantry, Freezer, Garage, Garden, etc.)
5. **Measure and iterate:**
- Track: % of users who add first item, time to first item, field abandonment rates
- A/B test: 3-field vs 5-field forms
- Survey users who abandoned: "What was confusing?"
**Phase mapping:**
- **v1 (MVP):** 3-field MVP + simple onboarding (non-negotiable for adoption)
- **v1.5:** Progressive disclosure; presets for shared fields
- **v2+:** Advanced mode; user preferences
**Detection (warning signs):**
- 50%+ of installs don't survive first 10 minutes
- Users who add one item never add a second
- "Looks good but too complicated" app store reviews
---
## Moderate Pitfalls (v1 Should Consider, v1.5 Required)
Mistakes that cause delays, frustration, or technical debt.
### Pitfall 5: Notification Fatigue and Irrelevant Alerts
**What goes wrong:**
- User gets notified for every expiring item daily, then hourly as expiry approaches
- Items show as "expires today" for 3 days (bad calendar logic or inconsistent alert rules)
- No snooze/dismissal: once alert fires, it keeps firing
- Alerts for items user doesn't care about (notification for baking soda expiring in 6 months)
- Can't customize alert timing per household or per person
**Why it happens:**
- Simple implementation: "if item expires in N days, notify"
- No snooze logic; dismissed notification re-triggers
- No ability to customize which items to alert on (temperature-sensitive vs non-perishable)
- No "quiet hours" or quiet periods
**Consequences:**
- Users disable all notifications
- App becomes useless without notifications; users can't be nudged to use it
- Notification fatigue leads to app uninstall
**Real-world example:**
- General app research shows 46 notifications/day on average smartphone; apps with high-frequency alerts have 3-5x higher uninstall rates
- Your Food app and NoWaste have customizable alert settings, suggesting this is a known pain point
**Prevention:**
1. **Implement smart alert rules:**
- Default: alert 3 days before expiry, once per day
- User can customize: "alert me 1 day before", "alert only for perishables", "no alerts for pantry items"
- Allow per-item overrides: "don't remind me about baking soda"
2. **Implement snooze/dismiss:**
- "Remind me in 1 day" button on alert
- "Don't show again for this item" option
- Dismissed alert doesn't re-trigger for 24 hours minimum
3. **Prioritize alerts:**
- High priority (expires tomorrow) vs low priority (expires in 5 days)
- Only show high-priority in app badge; send low-priority to in-app feed only
- User can configure priority threshold
4. **Respect quiet hours:**
- No notifications between 10pm-7am by default
- User can change quiet hours in settings
5. **Segment by household preferences:**
- In multi-user household, let household admin set alert policy
- Each user gets alerts for items they added or are responsible for
- Prevent notification spam from roommate's items
**Phase mapping:**
- **v1 (MVP):** Basic snooze + configurable alert timing (single alert rule)
- **v1.5:** Per-item customization + priority levels + quiet hours
- **v2+:** ML-based alert timing based on usage patterns
**Detection (warning signs):**
- Users disable notifications
- "Too many alerts" app store reviews
- Uninstall correlates with notification frequency
---
### Pitfall 6: Offline Sync Data Loss and Stale Data
**What goes wrong:**
- User is offline, adds 5 items; when they reconnect, all 5 items vanish (network error during sync)
- User is offline, deletes item; when online, item reappears (server state was newer)
- User adds item while offline, roommate adds same item while online; duplicates appear after sync
- App shows stale data after coming back online ("out of milk" even though you just added 3 liters)
- No indication that data is stale; user relies on outdated info
**Why it happens:**
- Offline queue not persisted to disk (data loss on app crash)
- No retry mechanism or operation tracking
- Naive "sync latest version" without conflict resolution
- No UI indicator that data is syncing or is stale
**Consequences:**
- Users lose trust (work they did while offline vanishes)
- Multi-user households see inconsistent views
- Silent data loss makes app unusable in areas with poor connectivity
**Real-world example:**
- Field service apps report that 30% of conflicts occur during simultaneous updates; nearly 30% of businesses experience sync discrepancies
- Offline-first apps using Last-Write-Wins can lose important updates without user knowing
**Prevention:**
1. **Persist offline queue to disk:**
- Every operation (add/remove/update) stored locally with operation ID before sending
- If app crashes, queue survives
- When online, retry unsent operations
2. **Implement operation-based sync (not state-based):**
- Instead of "send current item state", send "operation: remove 1 liter of milk at 3:15pm"
- Server can apply operation in order, detect conflicts, and notify user
- Prevents loss of concurrent updates
3. **Add retry mechanism with exponential backoff:**
- Failed operations retry every 5s, then 30s, then 5m
- User can manually retry
- Log failed operations so they're not dropped silently
4. **Show sync status to user:**
- "Syncing..." indicator when operations are pending
- "Offline" badge when no connection
- "Out of sync" warning if local data is stale (older than 1 hour)
- Allow manual "refresh" to force sync
5. **Handle merge conflicts gracefully:**
- If offline and online changes conflict, show user both versions
- "You added milk while offline; server shows you removed milk. Which is correct?"
- Don't silently discard either version
**Phase mapping:**
- **v1 (MVP):** Persistent offline queue + operation IDs + sync status indicator
- **v1.5:** Retry mechanism; manual refresh; out-of-sync warning
- **v2+:** Advanced conflict resolution UI
**Detection (warning signs):**
- Users report "I added items while offline and they disappeared"
- Multi-user households see different inventory views
- Sync errors don't surface to user (silent data loss)
---
### Pitfall 7: Bad Search and Item Lookup UX
**What goes wrong:**
- User searches "milk" and gets 15 results: "Milk", "milk", "2% Milk", "Whole Milk", "Skim Milk", "Almond Milk", "Oat Milk"...
- Can't find item despite it being in inventory (search is case-sensitive, needs exact spelling)
- Search is slow (scanning through 500+ items takes 2+ seconds)
- Autocomplete suggests irrelevant items ("mi" brings up "Minimum" instead of "Milk")
- Barcode scanner results in wrong product (scanned a book, got a food item by accident)
**Why it happens:**
- Simple linear search without indexing
- Case-sensitive exact matching
- No fuzzy matching or phonetic matching
- No search analytics to improve relevance
- Conflates "product name" with "brand name" with "user-entered notes"
**Consequences:**
- Users add duplicate items because they can't find the original
- Inventory reports are unreliable (user thinks they have 2 liters, but it's split across "Milk" and "milk")
- Multi-user households can't collaborate (can't find each other's items)
**Prevention:**
1. **Use full-text search with stemming:**
- "2% Milk" should match "milk", "Milk", "2 percent", "two percent"
- Use FTS (SQLite FTS5, PostgreSQL full-text search) with morphological analysis
- Index on canonical product name + user-given name
2. **Implement fuzzy matching:**
- Typos: "mylk" should find "milk"
- Abbreviations: "OJ" should find "orange juice"
- Use Levenshtein distance or similar
3. **Optimize search performance:**
- Indexes on product names
- Autocomplete from local cache (not live search)
- Lazy-load results; show top 5 first
4. **Smart autocomplete:**
- Show items from user's household first ("Milk" you've added before)
- Then show database suggestions (Open Food Facts top products)
- Rank by frequency (items user adds often appear first)
5. **Disambiguate results:**
- Show product type/category alongside name ("Milk - Dairy" vs "Milk - Plant-based")
- Show "you have this" indicator for items already in inventory
- Show quantity/location ("2 liters in fridge")
6. **Add search analytics:**
- Track: which items users search for most
- If "yogurt" search returns no results but user falls back to typing, add "yogurt" to database
- Periodically review failed searches and improve database
**Phase mapping:**
- **v1 (MVP):** Full-text search + fuzzy matching on canonical names
- **v1.5:** Smart autocomplete with user history; search analytics
- **v2+:** Ranking by frequency; ML-based relevance
**Detection (warning signs):**
- Users report "can't find items I know are in my inventory"
- High duplicate rate (users add "Milk" three times)
- Search is noticeably slow (>1 second for autocomplete)
---
### Pitfall 8: Barcode Scanning Mismatches and Failures
**What goes wrong:**
- Barcode scan returns completely wrong product (scanned milk, got yogurt)
- Scan returns expired or discontinued product entry
- Wrong nutritional data, wrong category, wrong location
- Barcode scanner glitches in multi-user app (doesn't work in split-view)
- Camera doesn't focus on barcode; scan fails repeatedly
**Why it happens:**
- Barcode database quality issues (duplicates, wrong mappings, region-specific entries)
- Using free/cheap barcode APIs with poor accuracy
- Barcode scanner library issues (not robust to angled barcodes, low light)
- No fallback when scan fails (forced manual entry)
- No way to report/correct bad scan results
**Consequences:**
- User scans, gets wrong product, wastes time correcting it
- User loses faith in barcode scanning feature
- Manual entry becomes default; barcode feature becomes dead weight
**Real-world example:**
- My Pantry Tracker users report "expiration date section glitch preventing visibility and selection"
- FoodShiner users report "barcode scanner errors when using split-view mode"
- KITCHENPAL and Pantry Check users report scanning returns wrong product frequently
**Prevention:**
1. **Choose a high-quality barcode database:**
- For free: USDA FoodData Central (government-verified, includes barcodes)
- For paid: GS1 database (official UPC/EAN registry)
- Never rely on a single API; fallback to secondary source if primary fails
2. **Validate barcode integrity:**
- Check UPC/EAN check digit before lookup
- Reject if check digit invalid
- Log all failed lookups so you can investigate
3. **Use a robust barcode scanning library:**
- iOS: AVFoundation's barcode detection
- Android: ML Kit barcode scanning (handles rotation, distortion better)
- Test with real-world barcodes: upside-down, angled, damaged, low-light
4. **Implement fallback and manual correction:**
- If scan fails, offer manual entry immediately
- If scan returns product, allow user to "report wrong product" before saving
- Store corrections in local database; future scans of that barcode are corrected
- Example: "Scan returned milk, but you corrected it to yogurt? Save this for future scans of this barcode."
5. **Allow user-reported corrections to feed back to database:**
- Crowdsourced corrections (with moderation) improve database over time
- After N users report correction, flag for admin review
- For v1, keep crowd data separate from official database
6. **Show what was scanned:**
- "Scanned: [barcode number]" in UI
- User can verify: "Did I scan what I thought?"
- Makes it clear if user mispresented barcode
**Phase mapping:**
- **v1 (MVP):** Use robust scanning library + validate check digits + fallback to manual entry
- **v1.5:** User correction UI; local correction database
- **v2+:** Crowdsourced corrections; secondary barcode API fallback
**Detection (warning signs):**
- Users report "scanned item is completely wrong"
- Barcode scanning feature has low usage (users default to manual entry)
- Support requests for "why does scan show wrong product?"
---
### Pitfall 9: Insufficient Permission and Sharing Controls
**What goes wrong:**
- Roommate deletes another person's food items without consent
- No audit trail; can't see who ate the milk
- Items shared with wrong permission level (guest can delete, admin can't override)
- Family member can see all nutrition/allergy data meant to be private
- No concept of "ownership"; unclear who is responsible for item
**Why it happens:**
- Simple binary permissions (can/can't view)
- No write vs delete distinction
- No audit logging
- Roles not well-defined (what's the difference between "admin" and "owner"?)
**Consequences:**
- Multi-user households distrust the app; stop using shared features
- Arguments: "Who ate the last milk?" with no way to know
- Privacy concerns: nutrition data shared with people who shouldn't see it
**Prevention:**
1. **Define clear permission model:**
- Owner: can add, edit, delete items they own; can transfer ownership
- Editor: can add, edit items; can't delete others' items
- Viewer: can only read
- Guest: can add items only (no edit or delete)
- Admin: can manage permissions and delete any item (with audit log)
2. **Implement audit logging:**
- Every change logged: who, what, when
- Visible in app: "Sarah removed milk at 3:15pm"
- Immutable log (can't delete history)
3. **Ownership and responsibility:**
- Each item has an owner (person who added it or is responsible)
- Owner can edit/delete freely
- Non-owner can edit but not delete (requires owner approval or admin override)
- Roommate deletion requires explicit confirmation: "Remove Sarah's milk?"
4. **Role-based defaults:**
- New household members default to "Editor" role
- Can be changed to "Viewer" if trusted with less responsibility
- New guests default to "Viewer"
5. **Privacy controls per field:**
- Nutritional info visible to owner and household admin only
- Allergy flags visible to household only (not to guests)
- Price paid visible to admin only (housemates don't need to know)
**Phase mapping:**
- **v1 (MVP):** Basic permission model (Owner/Editor/Viewer) + audit log
- **v1.5:** Ownership transfer; delete confirmation
- **v2+:** Fine-grained field-level privacy; guest roles
**Detection (warning signs):**
- Multi-user households report "someone deleted my items"
- Conflict: "I didn't delete that" with no audit trail to prove it
- Users prefer separate accounts over shared household (breaks feature adoption)
---
## Minor Pitfalls (v1 Nice-to-Have, v1.5 Recommended)
Issues that cause annoyance but can be worked around or deferred.
### Pitfall 10: API Cost Surprises and Vendor Lock-In
**What goes wrong:**
- Nutrition API charges $0.001 per user; at 10K users, costs $100/month unexpectedly
- Free barcode tier limited to 1,000 lookups/day; app hits limit at 2pm daily
- Switching barcode APIs requires rewriting scanner integration
**Why it happens:**
- Paid APIs have hidden volume limits on "free" tier
- Per-user or per-API-call pricing scales unexpectedly with user growth
- Using closed proprietary APIs with no fallback
**Prevention:**
1. **Use only free APIs in MVP:**
- USDA FoodData Central (free, unlimited, no key required)
- Open Food Facts (free, community-sourced, barcode support)
- Google Cloud Vision (free tier: 1,000 requests/month; paid after)
- No paid tier dependencies in v1
2. **Plan for self-hosted fallbacks:**
- Can you cache USDA data locally?
- Can you run barcode scanning on-device without API?
- Design API dependency as pluggable (today USDA, tomorrow custom)
3. **Monitor API usage:**
- Log all API calls with user context
- Alert when approaching rate limits
- Weekly cost estimate (if paid API is used)
4. **Defer to v2:**
- Commercial API integrations (better data quality)
- Advanced features like image recognition
- In v1, use free/open alternatives only
**Phase mapping:**
- **v1 (MVP):** Free APIs only; estimate future costs
- **v1.5:** Implement caching to reduce API calls
- **v2+:** Optional paid integrations; no required paid dependencies
**Detection (warning signs):**
- API bill is growing faster than user base
- Free tier hit during testing; unexpected charges in production
---
### Pitfall 11: Poor Search Performance and Battery Drain
**What goes wrong:**
- Item search takes 2+ seconds to return results (app is searching every item linearly)
- App battery drains 10% per hour (full-text search indexes aren't optimized)
- On older phones, autocomplete feels sluggish
- Barcode scanning drains battery (constant camera polling)
**Why it happens:**
- No indexing or caching of search results
- Syncing happens too frequently (every 10 seconds instead of every 60 seconds)
- Camera-based features not optimized for low-power modes
- Large result sets loaded into memory at once
**Prevention:**
1. **Index aggressively:**
- SQLite FTS5 or similar on product names
- Cache autocomplete results locally
- Pre-compute common searches
2. **Optimize syncing:**
- Batch updates: sync every 60 seconds, not every change
- Only sync if there are pending changes
- Respect device low-power mode (disable non-essential syncs)
3. **Optimize camera and connectivity:**
- Camera polling only when scanning (not always-on)
- Disable background updates in low-signal areas (don't drain battery searching for network)
- Use WorkManager (Android) or BackgroundTasks (iOS) to schedule updates during charging
4. **Profile and test:**
- Benchmark search latency on low-end devices
- Measure battery impact of syncing, searching, camera
- Set performance targets: search <500ms, sync <10% battery/hour
**Phase mapping:**
- **v1 (MVP):** Basic indexing and caching
- **v1.5:** Battery profiling; sync optimization
- **v2+:** Advanced performance tuning; background task optimization
**Detection (warning signs):**
- Search takes >1 second
- App uses >20% battery in 1 hour of usage
- Autocomplete is noticeably slow on older phones
---
### Pitfall 12: AI Purchase Prediction Overfitting and False Positives
**What goes wrong:**
- ML model trained on single user's data makes terrible predictions for another user
- Model predicts you'll buy milk weekly because you did so for 3 months; then predicts forever even after you stop
- Predictions clog the shopping list (10 irrelevant items for 1 relevant)
- Model doesn't account for seasonality (Christmas cookies predicted year-round)
**Why it happens:**
- Small sample size per user (N=30 purchase events) leads to overfitting
- Training data becomes stale (model doesn't account for changing habits)
- No concept of "recency" (old data weighted equally with recent data)
**Consequences:**
- Users turn off predictions; feature goes unused
- Cluttered shopping list is worse than no predictions
**Prevention:**
1. **Start simple; add ML only if needed:**
- v1: Manual shopping list only
- v1.5: Suggest items from user's past purchases (heuristic, not ML)
- v2+: ML predictions only if you have 500+ prediction-worthy items per user
2. **Use simple heuristics instead of complex ML:**
- "Items purchased in last 6 months" → suggest them again
- "Items purchased on Mondays" → suggest on Sunday
- No model needed; explainable and less prone to overfitting
3. **If using ML, use robust techniques:**
- K-fold cross-validation to detect overfitting
- Regularization to prefer simpler models
- Use recency weighting (recent purchases > old purchases)
4. **Account for data drift:**
- Retrain model monthly (not once and forget)
- Monitor: are predictions accurate for recent purchases? If not, retrain
- Use Population Stability Index to detect when model is stale
5. **Limit prediction spam:**
- Show top 3 predicted items, not all 20
- Require user confirmation before adding predicted item to shopping list
- "Do you want to add milk?" instead of automatically adding it
6. **Make predictions explainable:**
- "Suggested because you buy milk every Monday"
- "Suggested because you bought this 4 weeks ago"
- User can provide feedback: "I don't buy this anymore" → don't suggest
**Phase mapping:**
- **v1 (MVP):** No predictions; manual list only
- **v1.5:** Simple heuristic suggestions (items from last 6 months)
- **v2+:** ML predictions only with strong safeguards; limited to 3-5 items
**Detection (warning signs):**
- Users turn off predictions
- Shopping list clogged with irrelevant suggestions
- Predictions don't match actual purchasing behavior
---
### Pitfall 13: Community Data Spam, Privacy Leaks, and Moderation Burden
**What goes wrong:**
- Sales/price database polluted with spam ("BEST DEALS CLICK HERE")
- Bad actors add thousands of fake products
- Real user data (household names, allergies) accidentally exposed in shared databases
- No moderation; requires hiring team to handle reports
- Privacy: food data is personal; can infer health conditions, pregnancy, diets
**Why it happens:**
- Community contributions not validated before publication
- No identity verification (same person spamming with 100 accounts)
- No privacy controls on shared databases
- Moderation not built in from day one; becomes firefighting later
**Consequences:**
- Community data becomes unusable (too much spam)
- Regulatory risk (GDPR, health privacy violations if personal data exposed)
- Cost scaling issue (need moderation team as user base grows)
**Prevention:**
1. **Don't build community features in v1:**
- v1: App-only, no community sales database
- v1.5: Internal sales database curated by team only
- v2+: Consider community contributions with strong safeguards
2. **If you build community features:**
- Require user identity verification (not anonymous)
- Karma/reputation system (new users' contributions require approval)
- Report and flag system; flagged items quarantined
- Automatic spam detection (duplicate entries, suspicious patterns)
3. **Privacy controls:**
- Never share household names or personal health data
- Only share product names and prices
- User can opt-out of price/sales sharing entirely
4. **Moderation scalability:**
- Build moderation UI from day one
- Automated filters catch 90% of spam before human review
- Community mods (trusted users) can review and approve
- Clear escalation path if needed
**Phase mapping:**
- **v1 (MVP):** No community features; internal database only
- **v1.5:** Internal sales database; team-curated only
- **v2+:** Community features with identity verification and moderation
**Detection (warning signs):**
- Community data has spam
- Privacy concerns from users
- Moderation requiring full-time team member
---
## Phase-Specific Recommendations
### For v1 MVP (Must Handle)
Focus on these pitfalls to launch with:
1. **Barcode Data Mismatches** → Fuzzy matching + deduplication
2. **Timezone/Expiration Bugs** → UTC timestamps from day one
3. **Multi-User Sync** → Optimistic locking + version numbers
4. **Setup Complexity** → 3-field MVP onboarding
5. **Notification Fatigue** → Snooze button + configurable timing
These five are non-negotiable. If you skip them, app won't be usable in multi-user households.
### For v1.5 (Stabilization)
After MVP feedback, address:
- Offline sync with persistent queue
- Barcode scanner fallback and correction UI
- Better permission model with audit logging
- Permission model with audit logs
- Improved search performance and UX
- Advanced alert customization
### For v2+ (Growth Phase)
Only after v1 is stable:
- ML-based predictions (with safeguards)
- Community features (if privacy/moderation plan is solid)
- Advanced permission roles
- Performance optimization for 1M+ items
---
## Summary: Implementation Priority Matrix
| Pitfall | Severity | Effort | Phase | Notes |
|---------|----------|--------|-------|-------|
| Barcode mismatches | Critical | Med | v1 | Kills data integrity; users abandon after discovering duplicates |
| Timezone bugs | Critical | Low | v1 | One-time fix; becomes tech debt otherwise |
| Sync conflicts | Critical | Med | v1 | Makes multi-user unusable; cascades to trust issues |
| Setup complexity | Critical | Low | v1 | 50% onboarding dropout without it |
| Notification fatigue | High | Low | v1 | Users disable notifications; feature dies |
| Offline sync | High | Med | v1.5 | Causes data loss; impacts adoption |
| Search UX | High | Med | v1.5 | Drives duplicate creation; frustrates users |
| Barcode failures | High | Med | v1.5 | Falls back to manual entry; undermines scanner value |
| Permissions | High | Med | v1.5 | Required for multi-user trust |
| API costs | Medium | Low | v2 | Can defer but plan for it |
| Performance | Medium | Low | v1.5 | Impacts older devices; battery drain |
| AI predictions | Low | High | v2 | Nice-to-have; don't build until v1 stable |
| Community spam | Low | Med | v2 | Defer; address when you have users to moderate |
---
## Sources
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- [KITCHENPAL: Pantry Inventory - Google Play](https://play.google.com/store/apps/details?id=fr.icuisto.icuisto&hl=en_US)
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- [How Distributed Systems Avoid Race Conditions - Medium](https://medium.com/@alexglushenkov/the-art-of-staying-in-sync-how-distributed-systems-avoid-race-conditions-f59b58817e02)
- [AI for Food Safety - ScienceDirect](https://www.sciencedirect.com/science/article/pii/S0924224425002894)
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- [NoWaste: Food Inventory List App - App Store](https://apps.apple.com/us/app/nowaste-food-inventory-list/id926211004)
- [My Pantry Tracker Mobile & Web App](https://mypantrytracker.com/)
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