Health, home and travel decision

Arc

A single developer can reproduce the core diagnostic and local coaching features in a few months, but the full shipped product's mobile polish, content, and App Store distribution make a complete replacement harder and justify continued subscription for many users.

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Built by Pritam, who ships 3 products in this index

You pay

Not priced

No pricing page we fetched carried a figure, so there is nothing to compare against. The build side is still real.

You’d pay instead

$100one-off140 h to build

$0/mo3 h/mo upkeep

No published price to break even against.

No open-source build does this yet

Nothing published replaces this one, so a replacement starts from an empty file. Here is what it would have to cover.

What a replacement has to do

  • User answers onboarding questionnaire → app computes chronotype and parameters → schedule engine produces daily protocol (peak focus windows, caffeine cutoff, light windows) → local storage of entries and history → UI shows live ring, charts, and sends timed notifications

What it still won’t have

  • App Store-managed subscription handling and ecosystem discoverability
  • Polish of the shipped mobile UX (animations, accessibility, edge-case polish)
  • Curated science content, guides, and branded copy
  • Established user reviews and trust/brand recognition
  • Cross-device cloud sync (the product advertises local-first zero-cloud; any optional cloud sync would be missing)

What remains hard

  • Product polish and ongoing maintenance
Read the build prompt

First-year cost

No published price

Arc 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
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Subscription price × seats × 12

Build it
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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 an iOS app (SwiftUI + Combine) with a local SQLite (GRDB) database that implements: (1) a 32-question onboarding flow storing responses locally; (2) a chronotype and scheduling engine that computes chronotype, caffeine-decay curve, morning light window, peak focus windows, and nightly wind-down time; (3) a local-only daily protocol screen with a live countdown ring, single-action briefing, and push/local notifications; (4) time-series charts and a single progress score; (5) export/import of local data (JSON). Out of scope: cloud sync, server-side analytics, and App Store submission. Include error handling, unit tests for the algorithm and storage layer, and UI tests for the main flows.
How we checked2 sources · 2/3 runs agreed · evidence score 53

How the score was reached

  • Partly verdict base52
  • 2 cited sources+1
  • Evidence score53

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 · 2

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