QuestSloth: Local Gemma Turns Lecture Notes Into a Dungeon Quiz

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady

What I Built

QuestSloth automates a chore teachers dread — turning dry syllabus notes into quizzes — and makes studying feel like a tiny dungeon crawl.

1. Teacher Forge — paste lecture notes. Open-weight Gemma 2 2B runs locally in the browser via WebLLM and emits a strict quest.json (quest title, boss name, five MCQs, answers, explanations).
2. Student Dungeon — play that pack with no AI inference: HP bars, combos, difficulty modes, consumable powers, achievements, procedural SFX + battle music. Correct answers hurt the boss; misses hurt you and show the explanation.

Slow and steady / International Sloth Day: the teacher stops inventing five MCQs by hand; students get a boss fight instead of a worksheet. Notes never leave the device.

Demo

Live app: https://arjankc.github.io/QuestSloth/

• Forge needs WebGPU (recent Chrome/Edge). First Gemma download is ~1.5GB for the f16 build (or ~2.5GB if your GPU lacks shader-f16 and Forge falls back to q4f32) and caches in the browser.
• Dungeon works without loading any model — try Play sample quest immediately. Unmute the header speaker for lobby pad / battle BGM / SFX.
• Forge Play in Dungeon hands the pack over via sessionStorage (plus a short local quest stash).

Code

Repo: https://github.com/arjankc/QuestSloth

arjankc / QuestSloth

Browser-local Gemma turns lecture notes into dungeon quizzes (Hacktoberfest 2026 Week 3)

QuestSloth

Turn lecture notes into a dungeon-crawler quiz — entirely in the browser.

Built for Hacktoberfest Open-Source AI Challenge: Week 3 — Slow and Steady. Teachers paste notes; local Gemma 2 2B (via WebLLM) forges a quest.json. Students fight the boss by answering — no inference on the play path.

Why open weights?

• Notes never leave the device (Forge runs on-device WebGPU).
• No OpenAI/Claude bill for every quiz.
• Students only need the JSON file — the dreaded “write five MCQs by hand” chore becomes a paste → download.

Stack

• Vite + React + TypeScript + Tailwind CSS
• @mlc-ai/web-llm · prefers gemma-2-2b-it-q4f16_1-MLC-1k (~1.5GB); auto-falls back to gemma-2-2b-it-q4f32_1-MLC (~2.5GB) when WebGPU shader-f16 is missing
• Static host: GitHub Pages (base: '/QuestSloth/', HashRouter)

Modes

Mode	Route	AI?
Teacher Forge	#/forge	Yes — loads Gemma, emits strict quest JSON
Student Dungeon	#/dungeon	No — upload JSON, forged handoff, sample, or

…

View on GitHub

Worth a look:

• src/lib/webllm.ts — Gemma load + f16 feature probe + q4f32 fallback + low-temperature JSON generation
• src/lib/questSchema.ts — Zod validation + JSON Schema for constrained decoding
• src/lib/difficulty.ts / src/lib/powers.ts / src/lib/progress.ts — Easy/Normal/Hard, Hint/Shield/Focus, XP + achievements
• src/lib/audio.ts — procedural Web Audio (no sound files)
• src/pages/Forge.tsx / src/pages/Dungeon.tsx — the two halves of the product

How I Built It

Constraints drove the stack: ~30GB free disk, 4GB VRAM, no Python/Ollama/Docker. Everything is a static SPA on GitHub Pages.

flowchart LR
  notes[LectureNotes] --> forge[TeacherForge]
  forge --> webllm[WebLLM]
  webllm --> gemma[Gemma2_2B]
  gemma --> pack[quest.json]
  pack --> dungeon[StudentDungeon]
  dungeon --> fight[BossFight]

• Vite + React + Tailwind for routing and game state (HashRouter, base: '/QuestSloth/')
• @mlc-ai/web-llm prefers gemma-2-2b-it-q4f16_1-MLC-1k (~1583MB VRAM). On GPUs that don't advertise WebGPU shader-f16, Forge probes adapter.features and loads gemma-2-2b-it-q4f32_1-MLC instead (~2.5GB) — same Gemma 2 2B IT weights, no Canary flags required. If the probe misses, a create failure matching shader-f16 triggers one automatic retry.
• Strict JSON: temperature: 0.15, WebLLM response_format: { type: "json_object", schema }, then Zod parse + one repair retry
• Code-split Forge so the Dungeon route never pulls WebLLM (phone-friendly play)
• Game loop: difficulty tables, one-power loadout (Shield/Focus unlock at level 2), combos, level combat bonuses, achievements, CSS battle FX, lobby pad + battle BGM that speeds up on enrage

Small models love to ignore free-form prompts. Constrained decoding plus a validator kept the dungeon from crashing on bad packs — the same lesson browser-Gemma builders keep rediscovering (Touch Soil is a good peer example of schema-shaped WebLLM output).

The f16 fallback was a live-demo fix: WebLLM's f16-1k build hard-requires shader-f16, and shipping only that ID meant some Chrome/Edge users (especially integrated GPUs) got a Canary flag error instead of a quiz. Probe + q4f32 keeps Best Use of Gemma reachable on more hardware — the same pattern in-browser LLM posts keep hitting (WebGPU present ≠ WebGPU usable; ship a quantization per backend).

Why Does Open Innovation Matter?

• Classroom notes stay on the laptop. Forge never posts syllabus text to a paid API.
• Zero inference bill after the one-time model cache — important if you forge quizzes every week.
• Students only need the JSON file. Schools with locked-down or low-end devices can still play the dungeon.
• Swappable brain. The model id is one string in webllm.ts; any WebLLM Gemma (or other open weight) can replace it.

Lazy payoff: paste notes → download (or hand off) quest → share with the class. The chore of inventing five decent MCQs disappears.

Prize Categories

• Best Use of Gemma — Teacher Forge runs open-weight Gemma 2 2B in-browser via WebLLM to generate every quest pack.