Health, home and travel decision
GainFrame
A technical user can realistically build a basic local photo timeline, imports, and simple compare reports, but reproducing the vendor's ML-based body‑fat accuracy and integrated AI Coach (and the polished mobile UX and App Store subscription flow) would be expensive or require proprietary models, so keeping the paid product is reasonable for full feature parity.
View on the App Store↗Not priced
No pricing page we fetched carried a figure, so there is nothing to compare against. The build side is still real.
$100one-off68 h to build
$50/mo6 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 captures or imports progress photos → photos are aligned and stored in a timeline → photos are analyzed for body-fat and muscle-region estimates → user views comparisons/reports and asks an AI Coach grounded in their check-ins
What it still won’t have
- Polished mobile UX and App Store–grade polish
- The vendor’s tuned ML/AI models and any proprietary accuracy improvements
- Built-in subscription handling, trials, and in-app purchases via App Store
- Tight on-device privacy integration and any tested secure photo pipelines
- Ongoing product support and incremental AI feature improvements
What remains hard
- Product polish and ongoing maintenance
First-year cost
No published price
GainFrame 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
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
Build a single-user iOS app (Swift + SwiftUI) with local photo-based progress tracking and a minimal server for optional AI features. Core features in scope: guided pose capture with template overlays and automatic photo alignment; import from camera roll with metadata/date preservation; local encrypted storage of check-ins and a visual timeline; server endpoint (Node.js + Fastify) to accept anonymized, user-approved images for optional ML analysis; integrate a hosted vision model for body-fat and muscle-region estimates (callable via REST); optional LLM-backed AI Coach endpoint that ingests the user's selected check-in data and returns plain-language guidance; connect read-only to Apple HealthKit and implement export/shareable before/after cards (PNG/PDF). Out of scope: training new computer-vision models from scratch, App Store subscription handling (provide hooks instead), and federated multi-user accounts. Include error handling, input validation, end-to-end tests for capture/import/reporting, and CI to run unit/UI tests.
How we checked
How the score was reached
- Partly verdict base52
- 4 cited sources+3
- 3/3 assessment runs agreed+4
- Evidence score59
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 · 4
Every page the run actually retrieved.
- official productGainFrame: Gym Progress Photos App - App Store
- official productGainFrame: Gym Progress Photos App - App Store
- official productGainFrame: Gym Progress Photos App - App Store
- official productGainFrame: Gym Progress Photos App - App Store
Integrity checks
What held up, and what did not.
