Lexicon Clash

Lexicon Clash is a playful SAT vocabulary showdown that transplants the thrill of my childhood magazine game into the digital arena.

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Inspiration

Lexicon Clash grew out of a simple childhood game I played with my dad. We’d flip open magazines at random, count the number of people on the page, and celebrate whoever landed the larger crowd. That moment of lighthearted competition stuck with me. As a high school senior preparing for the SAT, I wanted to recreate that spark but give it an educational twist. By blending test prep with the unpredictability of Reddit posts, Lexicon Clash transforms rote vocabulary drills into a playful, social challenge.

What It Does

Lexicon Clash: Battle of Reddit Words is a playful SAT vocabulary game that turns test prep into a competitive, social challenge.
Each round:

  1. Displays a random SAT noun with its definition and synonyms.
  2. Presents two face-down Reddit posts.
  3. When a player chooses one, both cards flip to reveal the post’s title, summary, and the count of how often the target word (and synonyms) appear in the post and its top-level comments.
  4. The post with more matches wins the round.

The game tracks streaks, scores, and unique words mastered. A Word Journal saves example sentences for review and allows CSV export. A leaderboard motivates friendly competition, while animations and confetti keep the experience fun.

How We Built It

  • Frontend: React + TypeScript, styled with TailwindCSS and shadcn/ui for a clean UI.
  • Animations: Framer Motion for card flips, Canvas Confetti for celebratory effects.
  • Data: A curated SAT word list with definitions, lemmas, and synonyms.
  • Reddit Integration: Provider layer that fetches random posts and searches for posts containing target words/synonyms. Includes a “guaranteed hit” system to avoid 0–0 rounds.
  • Persistence: Leaderboard and Word Journal stored in localStorage, with CSV export support.
  • Development: Built iteratively using Cursor, Codex, and Replit for rapid prototyping and testing.

Challenges We Ran Into

  • Searching posts with the challenge word in real time using the Reddit API really did not work reliably.
  • Ensuring fairness: handling rounds where neither post contained the target word (solved with search + re-roll logic).
  • Parsing messy Reddit content: stripping markdown, cleaning comments, and reliably splitting sentences.
  • Balancing randomness with educational value—too random felt chaotic, too structured felt predictable.
  • Designing an interface that felt fast and game-like while still surfacing detailed word learning features.

Accomplishments That We're Proud Of

  • Found a workaround by searching posts in advance and hardcoding data for reliable demo gameplay.
  • Built a polished, interactive vocabulary game that blends education with authentic internet content.
  • Developed a flexible provider system so the app can run in a browser now and later integrate with Devvit’s Reddit SDK.
  • Created the Word Journal, which turns gameplay into tangible SAT study material.
  • Learned to leverage AI coding assistants (Cursor + Codex) to speed up iteration without sacrificing design quality.

What We Learned

  • The importance of combining fairness and randomness to keep games engaging.
  • Practical skills in text parsing and regex for real-world messy data.
  • How UX polish (confetti, streaks, smooth flips) directly impacts motivation and retention.
  • That blending philosophy of learning with gameplay mechanics can make test prep genuinely enjoyable.

What's Next for Lexicon Clash

  • Global Leaderboard & Multiplayer: Persist stats across users and allow challenges with friends.
  • Daily Word Challenges: Spotlight one SAT word per day with bonus points.
  • Expanded Vocabulary Packs: Continuously update the word bank to keep practice fresh.
  • Regular Data Updates: Find a way to update post data more frequently so the content always feels live and relevant.
  • Mobile Optimization: Refine layouts for smooth thumb-tap gameplay.
  • Classroom Mode: Allow teachers to assign word sets and track progress.
  • Semantic Scoring: Use embeddings to catch contextually related words, not just exact matches.

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