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SignalForge

Run a channel from the operator's chair: read the retention curve, A/B-test a thumbnail, chase a learnable algorithm — and see safely why rage-bait and misinformation self-destruct. You cannot grow without reading the statistics honestly. Statistics & media literacy, ages 9–14. On-device, no accounts, no ads.

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In planning Swift 6 · SwiftUI · FoundationModels CCSS Math — Ratios & Proportional Relationships, Statistics & Probability (6.RP–8.SP) NCSS ISTE Hero color: #0ea5a3

SignalForge teaches statistics and media literacy by putting you in the operator's chair of a simulated creator channel. You read a retention curve to find exactly where viewers leave, run fair A/B tests one variable at a time, tell a real growth rate from a lucky spike, and re-derive a learnable algorithm from its patch notes — and along the way you watch, safely, why rage-bait and misinformation spread fast but self-destruct. The 'Analytics Crew' each embodies one number or one trap, so the knowledge is the power system: you cannot grow the channel without reading the statistics honestly.

Distributed-narrative cast

Meet the cast

SignalForge's 10-member 'Analytics Crew' each embodies ONE statistics or media-literacy skill (R-DN-PARITY): Chief (trust > clicks — the credible-growth north star & mentor) · Retention (read a retention curve) · Sample (small samples are noisy) · Split (A/B test one variable) · Curve (percent growth compounds) · Loop (the algorithm amplifies what it measures) · Bait (rage-bait spends trust — a cautionary inoculation) · Base (base rate / survivorship — the hidden failures) · Ledger (trust builds slow, crashes fast) · Sponsor (ROI / cost-benefit). Media literacy taught by operating a channel, not lecturing about one; outrage is shown collapsing, never glamorized.

Chief portrait

Chief

Trust is the real metric — read every statistic by asking 'does this build trust that lasts?', not 'did it spike today?' (the editor-in-chief & mentor)

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Retention portrait

Retention

A retention curve shows where viewers leave — the steepest drop marks the exact moment something went wrong, so the shape of the line tells you what to fix

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Sample portrait

Sample

A small sample is noisy — a result from a few viewers can swing by luck; wait for enough data before you trust a difference

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Split portrait

Split

A fair test changes only ONE thing — keep everything the same except the single variable, or you can never tell which change caused the difference (A/B testing)

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Curve portrait

Curve

Percent growth compounds — growing by the same percent each week bends upward faster and faster, so a small steady rate can overtake a big one-time jump

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Loop portrait

Loop

A feedback loop amplifies whatever it rewards — learn what the algorithm measures and you can read the feed like a designed machine, not random weather

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Bait portrait

Bait

Outrage spreads fast but spends trust — rage-bait travels quickly, then burns credibility and collapses an audience; a number bought with anger is a debt (cautionary)

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Base portrait

Base

Judge by the base rate, not the standout — for every viral hit you see, thousands of similar attempts failed and stayed invisible, so ask 'out of how many tries?'

Read chapter →

Ledger portrait

Ledger

Trust is a ledger that builds slowly and crashes fast — many honest acts to earn it, one dishonest act to lose it; the math is not symmetric

Read chapter →

Sponsor portrait

Sponsor

Weigh return against cost — a deal is only worth it if what you gain exceeds what you give up, and the cost includes money AND trust (ROI / business math)

Read chapter →

Browse all chapters → · What's distributed-narrative methodology? →

What's inside

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Learning goal

Run a channel from the operator's chair: read the retention curve, A/B-test a thumbnail, chase a learnable algorithm — and see safely why rage-bait and misinformation self-destruct. You cannot grow without reading the statistics honestly. Statistics & media literacy, ages 9–14. On-device, no accounts, no ads.

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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 Chief — on-device AI, no data leaves the device.

How SignalForge 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
  • ✅ COPPA compliant under the 2026 FTC amendments
  • ✅ Parental controls + session limits + content filters built in

Full parent privacy guide →

Built with ForgeKit

SignalForge 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

SignalForge 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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