The JK Jung Times
Special Report: Projects

A comprehensive survey of works produced by this bureau

01 — Mobile App · FILED

Flashcards Alarm

Dispatch

Learning should be habitual, not optional. By embedding a vocabulary quiz into the daily alarm routine, retention becomes automatic—users don’t choose to study, they study because the alarm won’t stop until they answer correctly. A 3-stage backup chain ensures the quiz can’t be bypassed by force-quitting. New users often abandoned the app when their word list was empty; a short AI-driven placement quiz after signup fills that gap with five level-matched starter words so the app feels usable from minute one. Combined with AI-powered content generation via Gemini and spaced repetition, every morning starts with meaningful practice. This entire app was built 100% by AI—from architecture decisions to every line of shipping code, using Claude Opus 4.6 as the engineering team.

After sign-up, new users pick the language they want to study and the app’s main interface language, then complete a five-question vocabulary placement quiz. Each item is AI-generated; difficulty ramps up after correct answers and steps down after mistakes so the session stays challenging without punishing early errors. Results determine five starter words that Gemini recommends at an appropriate level—those cards land in the user’s library immediately so the deck is never empty on first launch. From there, set alarms with time and repeat days. When an alarm fires, a 30-second word quiz appears—letters reveal progressively, and the user must type the correct answer to dismiss. If the quiz isn’t solved, backup alarms fire at 5, 3, and 2 minute intervals. Between alarms, browse flashcards organized by groups, study by swiping cards (right for Know, left for Don’t Know), or use AI Smart Scan to photograph a textbook page and extract vocabulary automatically. Gemini generates definitions in the background. The spaced repetition engine surfaces cards at optimal intervals based on mastery level—6 levels with intervals from 1 to 30 days. Track progress through stats with 7-day charts and mastery distribution.

Flutter handles the cross-platform UI with Provider for state management. Alarms use the native alarm package for reliable background scheduling on iOS 13+ and Android—audio plays even when the app is killed or locked. A 3-stage backup chain (5 → 3 → 2 minutes) persisted to SharedPreferences ensures the quiz can’t be bypassed by force-quitting. Flashcards, groups, and quiz results live in Firestore with unlimited cache enabled for offline-first operation—pending quiz results queue locally (up to 100) and sync when connectivity returns. All AI features—definitions, Smart Scan, word categorization, onboarding placement questions, and starter-word selection—run client-side via the firebase_ai SDK connecting directly to Gemini 3 Flash with no Cloud Functions in the stack, eliminating server costs and cold starts. Spaced repetition uses a custom SM-2-style algorithm with 6 mastery levels and fixed intervals (1, 2, 4, 7, 14, 30 days). Home screen widgets use SwiftUI on iOS and Kotlin on Android, reading shared data through app group UserDefaults and SharedPreferences.

Flashcards Alarm — alarm list screen
Fig. 1: Alarm list with study-skill flashcard triggers
Flashcards Alarm — AI Smart Scan screen
Fig. 2: AI Smart Scan for vocabulary card generation

Key Features

First-session placement quiz: after choosing study language and app UI language, five AI-generated vocabulary questions with adaptive difficulty (harder after correct answers, easier after misses)

Starter deck seeding: AI selects five words matched to the user’s inferred level from the quiz and adds them to the word list so alarms and study work on day one

Alarm-triggered 30-second word quiz with progressive letter reveal to dismiss

AI Smart Scan: camera-based vocabulary extraction from textbooks via Gemini vision

Spaced repetition with SM-2-style algorithm — 6 mastery levels, intervals from 1 to 30 days

AI-generated definitions and automatic word categorization via Gemini

3-stage backup alarm chain (5 min → 3 min → 2 min) to prevent quiz avoidance

Offline-first with Firestore persistence and locally queued quiz results

Home screen widgets (iOS SwiftUI + Android Kotlin) showing review progress and next alarm

Flashcard study with swipe-to-rate — Know / Don’t Know updates mastery

Study analytics with 7-day bar charts, mastery distribution, and streak tracking

Multi-language support across 9 locales (EN, KO, JA, ZH, ES, FR, DE, PT, ZH-TW)

Weak Course and Confusion Pairs for targeted review of struggling words

100% AI-generated codebase via Claude Opus 4.6

Technical SpecificationsTechnologies employed: Flutter, Firebase, Gemini AI, Claude Opus 4.6

Analysis

Flutter over native for single-codebase efficiency across iOS and Android, accepting the trade-off of more complex alarm scheduling via the alarm package’s native bridge.

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02 — Mobile Game · FILED

Number Strike Baseball

Number Strike Baseball — gameplay

Fig. 2

Dispatch

The classic number baseball game (Bulls & Cows) deserves a social, competitive layer. Real-time PvP transforms a simple logic puzzle into a fast-paced daily habit—matches are quick enough to play between tasks, and the 8-tier ranked system keeps players coming back to climb. With AI fallback matchmaking, there’s always an opponent ready within 5 seconds.

Technical SpecificationsFlutter, Firebase, Cloud Functions, Claude Opus 4.6, Gemini AI, Eleven Lab

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03 — Web Platform · FILED

I Love Hwarang

Dispatch

Cultural heritage preservation needs modern fundraising tools. A dedicated platform removes friction between supporters and cultural projects—no generic GoFundMe pages, no manual bank transfers, just a clean experience built for this specific cause.

I Love Hwarang — platform overview

Fig. 3

Technical SpecificationsNext.js, TypeScript, PostgreSQL, Stripe, Firebase, AWS, Claude Opus 4.6

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Developing Stories

Stories currently under investigation by our bureau

Breaking:

Number Sliding Puzzle PvP

Real-time sliding puzzle game with competitive PvP and leaderboards.

Current focus: Real-time state synchronization and Redis-backed leaderboard system.

Status: Developing

Flutter · Node.js · Socket.IO · Redis

Breaking:

Grmpt

AI-powered fashion content creation and curation pipeline.

Current focus: Vertex AI content generation pipeline and cloud infrastructure with Terraform.

Status: Developing

Flutter · FastAPI · Vertex AI · AWS S3 · Terraform

End of Special Report

THE JK JUNG TIMES · All the code that's fit to ship.

Issue #10