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Building 8 Android Apps in 4 Days with AI-Assisted Development

By Charles
apps react-native ai claude-code expo typescript

Between January 27 and January 30, we built eight Android apps from scratch to signed production builds. Every app is Play Store-ready with privacy policies, store listings, and signed APKs. Here’s how we did it — and what we learned about AI-assisted development at scale.

The Numbers

MetricValue
Apps built8
Total TypeScript~19,380 lines
Largest appdBLog (5,178 lines)
Smallest appAutoLog (428 lines)
Build time per app2-6 hours
FrameworkExpo 54 + React Native 0.81
LanguageTypeScript (strict mode)

The Apps

  1. WiFi Share ($2.99) — QR code WiFi password sharing. Auto-detects current network, generates scannable QR codes. 2,552 lines.

  2. dBLog ($4.99) — Noise complaint tracker with real-time decibel measurement, GPS-tagged incidents, photo evidence, and court-ready PDF reports. Our most complex app at 5,178 lines. Includes a 25-city noise ordinance database.

  3. HomeKeep ($4.99) — Home maintenance tracker with 32 default tasks across 8 categories. Custom scheduling, completion history, notification reminders. 1,710 lines.

  4. SaleFinder ($4.99) — Garage sale finder with map view, route optimization, 12 categories. Google Maps integration for efficient multi-stop planning. 2,270 lines.

  5. SubWatch ($3.99) — Subscription tracker with monthly/yearly spending dashboards, donut chart breakdowns, renewal reminders, and lifetime cost estimates. 13 categories, 6 currencies. 2,568 lines.

  6. TradeLog ($4.99) — Day trading journal with P&L tracking, win rate analytics, equity curves, per-strategy breakdowns, and daily performance charts. Dark-first trading theme. 2,648 lines.

  7. AutoLog ($4.99) — Vehicle maintenance tracker with multi-vehicle support, 21 service categories, cost tracking, mileage-based reminders, and service history timeline. Dark automotive theme. 428 lines.

  8. PetLog ($4.99) — Pet health and medication tracker supporting 8 species. Tracks medications, vaccinations, vet visits, and weight history with overdue alerts and cost summaries. Dark veterinary theme. 2,031 lines.

The Stack

Every app shares the same foundation:

Expo 54 + React Native 0.81
TypeScript (strict mode)
AsyncStorage (local persistence)
expo-notifications (reminders)
No backend, no cloud, no accounts

This is intentional. By keeping apps local-only, we eliminate:

  • Server costs ($0/month per app)
  • GDPR/privacy compliance headaches
  • Authentication complexity
  • Ongoing maintenance burden

The only recurring cost is the $25 Google Play developer account.

The Process

Phase 1: Research (Day 0)

Before writing code, we ran market research on 9 app ideas using parallel research agents. Each idea got scored on market size, competition, technical feasibility, and monetization potential. The top 3 — noise complaint tracker, garage sale finder, and home maintenance tracker — all scored 7-8/10.

The key insight: look for categories where existing apps have 3-star ratings. Users want better alternatives but incumbents aren’t improving. dBLog targets noise complaint apps (none are comprehensive), SaleFinder targets gsalr (lost Android app in 2025), and HomeKeep targets Centriq/Dwellin (both under-featured).

Phase 2: Build Pipeline

Each app followed the same sequence:

  1. Scaffold — npx create-expo-app with TypeScript template
  2. Core screens — Dashboard, list view, form, detail view, settings
  3. Business logic — Data models, storage utilities, calculations
  4. Polish — Dark mode, haptic feedback, animations
  5. Signing — Generate PKCS12 key, configure Gradle, build APK + AAB
  6. Store prep — Privacy policy, store listing, icon generation

Steps 1-4 are where AI-assisted development shines. Claude Code generates entire screens from a description, handles TypeScript types correctly, and maintains consistency across the codebase. The developer’s role shifts from writing code to reviewing architecture and catching edge cases.

Phase 3: Store Assets

Every app needs:

  • Privacy policy (HTML page hosted on our website)
  • Store listing (title, description, screenshots, tags)
  • App icon (512px + 1024px adaptive icon)
  • Feature graphic (1024x500px)
  • Signed AAB for Play Store upload

We templatized the privacy policies — all our apps share the same privacy stance (no analytics, no network, no tracking), so each policy follows an identical structure with app-specific data sections.

What AI Does Well

Boilerplate generation. React Native screens have a lot of repeated patterns: SafeAreaView wrappers, FlatList configurations, modal sheets, form inputs. AI generates these correctly and consistently.

TypeScript interfaces. Defining data models, storage schemas, and prop types is tedious but important. AI handles this well, including edge cases like optional fields and union types.

Cross-platform styling. Writing StyleSheet objects that look good on both iOS and Android requires knowing platform quirks. AI knows these patterns from training data.

Utility functions. Date formatting, currency conversion, statistical calculations, color manipulation — these are well-defined functions where AI generates correct implementations quickly.

What AI Does Poorly

State management at scale. For simple apps, useState + AsyncStorage works. But dBLog (our largest app) required careful coordination between the decibel meter, incident logger, and report generator. We had to manually architect the data flow.

Platform-specific bugs. Expo’s file system API changed between versions (v17 vs v19). AI generated code for the wrong version, causing crashes on export. These bugs only surface during testing on a real device.

Design decisions. AI can implement any design you describe, but it can’t tell you what the design should be. Layout, color choices, information hierarchy, and interaction patterns still require human judgment.

Store listing optimization. Writing compelling Play Store descriptions that balance keywords with readability is a creative task. AI drafts are functional but not persuasive. We rewrote most descriptions.

Architecture Decisions

Local-First

Every app stores data on-device using AsyncStorage. No accounts, no sync, no cloud. This is both a feature (privacy) and a limitation (no cross-device sync). For utility apps in the $3-5 price range, we believe privacy wins over convenience.

Shared Patterns

All apps use the same navigation pattern: bottom tabs or stack navigation with a consistent header. Settings screens share the same layout: theme toggle, currency selector, data management options. This consistency speeds up development and reduces bugs.

No Ads, No Tracking

We chose paid-upfront pricing instead of ad-supported free. The math: a $4.99 app with 100 downloads earns $424 (after Google’s 15% cut). An ad-supported free app with 1,000 downloads might earn $50-100 from ads. The paid model wins unless you’re building for mass market.

Revenue Expectations

We’re realistic. Niche utility apps at $3-5 aren’t going to generate Silicon Valley returns:

ScenarioMonthly Revenue
10 downloads/app/month$240
25 downloads/app/month$600
50 downloads/app/month$1,200

At 25 downloads per app per month across 8 apps, that’s $600/month — enough to cover infrastructure costs and fund future projects. The key is volume: each additional app adds another revenue stream with zero marginal cost.

Lessons Learned

  1. Template everything. Privacy policies, store listings, signing configs, icon generation — do it once, then copy-paste for every app.

  2. Ship fast, iterate later. v1.0 doesn’t need every feature. dBLog shipped without audio recording (added in v1.1). Getting to the store matters more than feature completeness.

  3. AI is a multiplier, not a replacement. We built 7 apps in 4 days, but a solo developer without AI assistance could reasonably build one app in that time. The 7x speed-up comes from AI handling boilerplate while the developer focuses on architecture and testing.

  4. Privacy sells. Every app listing leads with “no ads, no tracking, no accounts.” In a market full of data-harvesting free apps, paid privacy-first alternatives are a genuine value proposition.

  5. The store is the bottleneck. Building the app takes hours. Getting through Play Store review, uploading screenshots, writing descriptions, and waiting for approval takes days. Start the store submission process as early as possible.

What’s Next

All 8 apps are signed, privacy policies are hosted, store listings are drafted. We’re waiting on domain registration (griswoldlabs.com) to publish the privacy policy URLs, then it’s a straight shot to Play Store submission.

The pipeline works. The next batch of apps — whatever they are — will be faster.