This is a submission for the June Solstice Game Jam

TypeForge: Turing-Inspired Intelligent Typing Coach

What I Built

TypeForge is a premium typing coach aligned with Apple's Human Interface Guidelines, built to help typists build speed and accuracy through automated, localized error diagnostics. TypeForge analyzes keystroke performance to isolate specific character transitions that cause delay or accuracy drops, then generates custom, adaptive exercises targeting those weaknesses. Developed for the June Solstice Game Jam, the project celebrates the power of computing and accessibility by turning raw diagnostics into a personalized educational experience. The solstice theme represents the journey of transition: taking a typist from darkness, meaning slow, error-prone typing, into light, meaning fluid, expert flow.

Video Demo

Live Application

https://typing-forge-six.vercel.app/

Code

https://github.com/shogun444/typingforge

Flowchart Architecture

graph TD
    User([User Typer]) -->|Keystrokes| Trainer[Typing Trainer Core]
    Trainer -->|Log Errors| Zustand[Zustand Stores]
    Zustand -->|Query Key Stats| Heatmap[Mistake Heatmap]
    Zustand -->|Identify Weaknesses| Generator[Drill Generator]
    Generator -->|Focus Word Pools| Trainer
    Zustand -->|Calculate Performance| Drawer[Analysis Drawer]

How I Built It

The application was built using Next.js for structure and routing, Tailwind CSS for premium glassmorphism styling, and Zustand for highly responsive state management. Development was accelerated throughout using Google's agentic AI coding assistant, Antigravity. I focused on clean Apple HIG spacing, micro-interactions, responsive scaling, and high-fidelity audio feedback to create a tactile, premium user experience. I also engineered custom canvas-based timeline sparklines to avoid heavy graphing packages and preserve maximum load performance.

# === SYSTEM ARCHITECTURE ===
# app/                    # Next.js pages including Settings and Profile views
# stores/                 # Zustand stores (typing-store, stats-store, drill-store)
# components/             # Apple HIG style UI buttons, input displays, and dialogs
# components/stats/       # AnalysisDrawer (timeline charts) & StatsHistory
# lib/                    # Core typing logic, custom word pools, and audio synthesis

# === DIAGNOSTIC DRILL ENGINE ALGORITHM ===
# 1. Track user errors per key -> populate QWERTY mistake heatmap
# 2. Extract weak character transitions and target key bigrams
# 3. Generate dynamic custom drills targeting user-specific weak keys
# 4. Score run using diagnostic grade: WPM * (ACCURACY / 100)

Prize Category

This project is submitted to Best Ode to Alan Turing because its underlying architecture draws inspiration from code-breaking and cryptanalysis. By modeling typing errors as noise in a transmission channel, TypeForge applies algorithmic diagnostics to target, isolate, and correct user keystroke weaknesses, reflecting Turing's work on computation and machine learning from mistakes. It also qualifies for Best Google AI Usage, since the entire project was built and debugged in collaboration with Google's Antigravity AI agent, maintaining high-quality HIG design standards without introducing regressions during development.