Health, home and travel decision

Pattrn

A competent developer can build a useful habit tracker with AI coaching by combining open-source habit apps and an LLM API, but reproducing Pattrn’s polished UX, subscription polish, and tuned AI experience at the same quality is non-trivial, so building a narrow replacement is realistic while matching the full paid product is harder.

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SubscriptionCustom pricing
Initial build46 hours
Monthly upkeep4 hours + $50
Evidence2/3 runs agree

Open-source builds that already do this

Every project below is open source and already does this job today. Fork one, self-host it, or take the parts you need - the build prompt further down assumes an empty file, and this is the shortcut past that. Licences differ; check the one on each card before you ship. All Pattrn alternatives, with the arithmetic →

What a replacement has to do

  • Record daily habit completions → compute streaks/Focus Score → show progress and goal linkage → surface AI recommendations and chat → send notifications and widget updates.

What it still won’t have

  • Polished, consumer-grade UX and visual design
  • App Store trust, existing user reviews and rating
  • Refined AI training/tuning, proprietary prompt engineering and usage telemetry
  • Polished widgets and cross-device polish (iPad, Apple Watch) and subscription management

What remains hard

  • Product polish and ongoing maintenance
Read the build prompt

First-year cost

No published price

Pattrn does not publish a price we could read, so there is nothing to compare against. What building costs is below.

Money you would actually spend

Keep paying

Subscription price × seats × 12

Build it

AI build APIs + hosting

Time you would spend

What you would spend

What we assumed

The verdict above measures whether you could build it. This one is only about money.

Runnable build prompt

Not run yet
Build a minimal cross-platform habit & goal tracking mobile app in Flutter with a small backend (Firebase or Supabase) and the following core features: 1) user accounts (email sign-in) and habit/goal data persisted to Postgres or Firestore; 2) habit creation, daily check-ins, streak and Focus Score calculation logic; 3) goal -> habit linking and milestone breakdown UI; 4) a chat-based AI coach using a hosted LLM API (HTTP integration, conversation state, prompt templates) that can provide recommendations and weekly summaries; 5) local notifications and a simple home-screen widget showing today's habits; 6) subscription management hooks (placeholder endpoints) but full billing out of scope. Out of scope: full App Store subscription UI, advanced gamification, cross-device sync conflicts, analytics dashboard. Include error handling, retry logic for network/API calls, unit tests for scoring logic, and basic end-to-end tests for habit check-in flows.
How we checked5 sources · 2/3 runs agreed · evidence score 60

How the score was reached

  • Partly verdict base52
  • An open-source build was found+5
  • 5 cited sources+3
  • Evidence score60

The base comes from the verdict. Everything under it is a check that either happened or did not, and each one is a fact frozen in this record rather than a judgement made at render time - so the same evidence always produces the same number.

How scoring works →

Cited sources · 5

Every page the run actually retrieved.

Integrity checks

What held up, and what did not.

! 2 of 3 runs agreed; the verdict is the majority✓ Citations limited to fetched pages! 1 moat recorded