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Solitaire Lab

A card game that is secretly a statistics lab — predict first, then test a solitaire rule change on 20-200 real Klondike deals played BOTH ways by a solver that never over-claims (won / lost / not sure yet). Read an honest at-least/at-most range and a 95% interval, see which deals the rules disagreed on, learn why a bigger search proves more wins of the same deals, and download a CSV for a science-fair chart. On-device, no account. Ages 9-14.

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In planning Swift 6 · SwiftUI · FoundationModels Mathematics / CCSS 7.SP.A.2 - use data from a random sample to draw inferences and gauge the variation in estimates Mathematics / CCSS 7.SP.B.3 - informally assess the degree of visual overlap of two distributions to compare two groups Hero color: #1F7A7A
Engagement: Modes pending

Distributed-narrative cast

Meet the cast

Solitaire Lab's crew each embody one STATISTICS move — the crew IS the method. Twinnie deals every hand twice so the test is fair, Hedgeworth never says more than the data can (at least ... at most ...), Maybelle reminds you an unknown is a maybe, not a no, and Bigsby knows more deals make the answer sharper. Mentor Dealia asks what you predicted before you look.

Dealia portrait

Dealia

(Mentor, a card-dealing raccoon) - asks what you predicted BEFORE you look at the data

Twinnie portrait

Twinnie

Deals every hand twice - the same deals for both rules, so the test is fair

Hedgeworth portrait

Hedgeworth

Never says more than the data can - reports at least ... and at most ...

Maybelle portrait

Maybelle

Knows an unknown is a maybe, not a no - the search ran out of time

Bigsby portrait

Bigsby

Knows more deals make the answer sharper - the interval gets narrower

What's distributed-narrative methodology? →

What's inside

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Learning goal

A card game that is secretly a statistics lab — predict first, then test a solitaire rule change on 20-200 real Klondike deals played BOTH ways by a solver that never over-claims (won / lost / not sure yet). Read an honest at-least/at-most range and a 95% interval, see which deals the rules disagreed on, learn why a bigger search proves more wins of the same deals, and download a CSV for a science-fair chart. On-device, no account. Ages 9-14.

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Question kits

16 curriculum-aligned kits × 25 questions = 400 questions per app, mapped to recognized standards.

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On-device AI mentor

FoundationModels-powered hints, feedback, and adaptive difficulty — all running locally.

Mentored by Dealia — on-device AI, no data leaves the device.

How Solitaire Lab handles your kid's data

  • ✅ All progress, settings, and AI-generated content stays on the device
  • ✅ No analytics, no tracking, no third-party SDKs
  • ✅ No ads, no in-app purchases — you pay once
  • ✅ No personal information collected from children — in line with COPPA (updated by the 2026 FTC amendments)
  • ✅ Parental controls + session limits + content filters built in

Full parent privacy guide →

Built with ForgeKit

Solitaire Lab runs on ForgeKit — the open-source Swift Package Manager framework that powers every Spark & Anvil app. ForgeKit ensures consistent accessibility, COPPA compliance, and design language across the portfolio, so your kid's progress and preferences feel coherent across every app they touch.

Coming to the App Store

Solitaire Lab is in active development. Email us to hear when it ships — no marketing, no spam, just a one-shot launch announcement.

Email me at launch

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