World Dungeon — Your Neighborhood Is the Map, Open-Source AI Is the Dungeon Master

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass

World Dungeon turns your real neighborhood into a walking quest. Before you leave home, the app reads your actual surroundings from OpenStreetMap — the named streets, the parks, the old temples, the unnamed alleys, the bench nobody sits on, the wall that predates the colony — and Gemma2 generates a quest seeded entirely from what is physically there.

Then you put your phone in your pocket and walk.

Your earbuds narrate the story in an ancient, whispering voice. You arrive at real places. The lore unlocks. A burnt manuscript reveals its hidden history as you walk — each waypoint you reach restores another section of text that was hidden under scorched parchment. When you finish, you get a dungeon map made from your actual GPS trace and a quest scroll that is yours alone.

No two quests are the same. No quest exists without walking it.

This is not a "get outside" app with a leaderboard bolted on. The outdoor world IS the game board. The screen is the shortest part of the experience:

Who is it for: Anyone in a city who walks the same streets every day and has stopped seeing them. World Dungeon makes the ordinary feel mythic — a bench becomes a threshold, an unnamed alley becomes a passage, an empty plot becomes an erasure.

🌐 Live app: https://world-dungeon-smoky.vercel.app

The deployed version uses Gemma2-9b-it hosted on Groq for live quest generation. Open it on your phone, allow location access, and tap Generate Quest. A real quest for your actual location will be generated in 8-15 seconds.

git clone https://github.com/HeythisisSud/World-dungeon.git cd World-dungeon cp.env.example.env.local # Add your keys — Mapillary (free), # ElevenLabs (free), Groq (free) ollama pull gemma2:2b npm install npm run dev # Open http://localhost:3000 # Allow location, tap Generate Quest # Put your phone in your pocket and walk Enter fullscreen mode Exit fullscreen mode Code HeythisisSud / World-dungeon How I Built It The Core Idea Every "get outside" app has the same problem: the reward is on the screen, so the screen wins. Pokémon Go solved this by making the outdoor world the game board — you only catch the Pokémon if you physically go there. World Dungeon applies the same principle to storytelling: the lore only exists at the location, the quest only completes if you walk it, the artifact only unlocks if you were there.

The open-source AI stack makes this possible without a corporate backend owning your walks.

Inference: Gemma2 (Google open-weight model)

Gemma2-9b-it running on Groq generates all quest narrative. The model receives real OpenStreetMap data for the player's 1km radius — actual place names, coordinates, landmark types, historical tags — and produces a structured quest JSON: title, premise, four waypoints tied to real OSM coordinates, a 100-150 word lore fragment per waypoint, and a final artifact description.

The prompt explicitly instructs Gemma to:

For local/offline use, the same code runs against Gemma2:2b via Ollama. One environment variable switches between them: USE_LOCAL_OLLAMA=true # offline, private USE_LOCAL_OLLAMA=false # Groq hosted Gemma2

Gemma2-9b-it on Groq wins on every metric. The larger model produces more historically specific lore and more reliable JSON structure. The local 2b model is slower but proves the offline argument — the quest generates on your device with no internet after the initial OSM data fetch.

Map Data: OpenStreetMap via Overpass API

The Overpass query fetches everything OSM has tagged within 1km — not just famous monuments. Benches. Trees. Unnamed walls. Bus stops. Empty plots. Chai stalls. The query deliberately casts wide:

// Fetch benches, shops, historic sites, // natural features, barriers, land use — // anything physically present node [ " amenity " = " bench " ]( around: $ { radius },...); node [ " natural " ~ " tree|rock|spring " ]( around:...); way [ " barrier " ~ " wall|fence|gate " ]( around:...); way [ " landuse " ~ " vacant|brownfield " ]( around:...); // Plus named amenities, historic sites, tourism Enter fullscreen mode Exit fullscreen mode This is what makes every quest genuinely hyperlocal. A quest seeded from Lodi Garden and a quest seeded from Mayur Vihar are not just different waypoints — they are completely different stories because the raw material is completely different.

Mapillary is an open, community-contributed street-level photo platform. When a waypoint unlocks, the real photo of that street location appears — not a stock image of a famous monument, but an actual photograph of the specific street, corner, or alley the player is standing at.

The image reveal is timed to the encounter animation: the player arrives, the screen flashes gold, and the real photo of the place fades in over the darkened encounter panel. The photo makes the lore land differently — you are reading about this specific cracked wall while looking at a photograph of it.

Fallback chain when Mapillary has no coverage:

Voice: ElevenLabs + Web Speech API fallback

The dungeon master voice uses ElevenLabs eleven_multilingual_v2 model. The voice character is deliberately ancient and whispering — like the place itself speaking.

All waypoint audio is pre-generated when the quest loads so there is zero loading delay when the player arrives at a waypoint. The voice fires instantly on encounter trigger.

Web Speech API is the fallback — the game works without ElevenLabs.

Live Map: Leaflet + Stadia Stamen Toner tiles

A real interactive map shows the player's live GPS position and all waypoint locations. The map tiles are color-graded with CSS filters to a dark amber dungeon aesthetic:

.leaflet-tile-pane { filter: invert ( 1 ) sepia ( 1 ) saturate ( 3 ) hue-rotate ( 180deg ) brightness ( 0.4 ); } Enter fullscreen mode Exit fullscreen mode Roads become gold lines on near-black. The map feels like a dungeon map because it IS a dungeon map — just one that updates in real time as the player walks.

The quest scroll starts almost entirely hidden under scorched parchment. As the player walks and unlocks waypoints, the burnt overlay retreats — SVG clip-path animation with ember glow along the reveal edge — and the hidden lore is restored.

This mechanic means the quest scroll is a reward that requires walking to earn. You cannot read the full story without completing the full walk.

A persistent progress screen shows every completed quest as a stamp on a winding road — Candy Crush style, but the stamps contain real photos from the player's actual walks. The road is generated algorithmically with a sine-wave layout. Each stamp is a circular polaroid of the Mapillary photo or the player's own camera photo from that quest.

Frontend: Next.js 14, TypeScript, Tailwind Backend: Fastify, Node.js, TypeScript, Zod Inference: Gemma2 via Groq (hosted) or Ollama (local) Map data: OpenStreetMap / Overpass API Photos: Mapillary API Voice: ElevenLabs API Deployment: Vercel (frontend), Render (backend)

This project is only possible because of open infrastructure at every layer. That is not an accident — it is the architecture.

Every quest generation prompt contains your precise GPS coordinates and a detailed map of your immediate surroundings — every named place within 1km of where you are standing. With a closed API (GPT-4, Gemini API), that data goes to a server you do not control, is logged, and is potentially used for training or advertising targeting.

With Gemma2 running locally via Ollama, the prompt never leaves your device. The quest is generated in your own RAM. Switch USE_LOCAL_OLLAMA=true and your walks are completely private.

OpenStreetMap is community-built and freely licensed under ODbL. Google Maps charges per API call, restricts derivative use, and owns the data. OSM data means the lore of your neighborhood is generated from a map that your neighbors helped build — the bench tagged by someone who walks that park every morning, the unnamed alley that a local mapper added because they knew it mattered.

Mapillary images are contributed by real people walking real streets. The photograph that appears when a player unlocks a waypoint was taken by someone who walked that exact spot. A closed alternative (Google Street View) would cost money at scale and send the player's location to Google's servers on every image load.

Want darker, more literary lore? Fine-tune Gemma on Gothic fiction. Want quests in Hindi or Urdu? Drop in a multilingual open-weight model. Want faster generation on a low-end phone? Switch to a 1B quantized model. Want to run it entirely offline on a trail with no signal? Set USE_LOCAL_OLLAMA=true and pull the model before you leave.

A closed API makes none of these choices yours to make. The model is the product, not a component you can inspect, modify, or replace.

After the initial OSM data fetch and model pull, the quest generates on your device. On a trail, in a park with no signal, in a neighborhood where data is expensive — the quest still runs. The walk is yours regardless of connectivity.

Google Places API + GPT-4 + Google Street View

The core engineering challenge was the prompt that turns raw OSM data into compelling lore. Here is the iteration that made the biggest difference:

What failed first: Generic fantasy tropes. Gemma kept writing about "ancient warriors" and "mystical energies" regardless of location. A bench in Delhi got the same lore as a castle in Scotland.

What fixed it: Explicit cultural grounding in the prompt: `Do NOT use European fantasy tropes. Draw from: Mughal court politics, the 1857 uprising, the 1947 partition, DDA housing colony history, Delhi monsoon cycles, auto-rickshaw culture, chai stall regulars.

The lore must reference the specific place name at least twice. Make the walker feel that THIS EXACT SPOT has a story only they now know. `

The difference in output quality was immediate and significant. The same bench that generated "ancient mystical energy" started generating: "The bench was placed here in 1978 by a DDA worker whose name appears in no other record from that project. The colony's oldest resident says he came back every monsoon season to repaint it. Nobody saw him arrive. Nobody saw him leave."

The second key insight was splitting lore into two layers — surface observation (what you see right now) and buried history (what happened here). The surface layer grounds the player in the physical present. The buried layer is revealed on tap, rewarding curiosity. This structure came from noticing that the best quest lore always does two things: describes and reveals.

Honest note: The submission deadline caught me before I could complete a full outdoor field test. Here is what I know from testing the GPS system indoors and what the planned test would have covered:

GPS system: watchPosition with maximumAge:0 fires correctly on desktop simulation. The 20-meter proximity threshold triggers the encounter animation as expected.

Planned test location: Mayur Vihar, East Delhi.

I will update this post with real field test results within 24 hours of submission. The code is live and the quest generation works — the walk is next.

Gemma (Google) — Featured ($200) Gemma2-9b-it is the core inference model. It generates all quest narrative from real OSM data. Benchmarked against Gemma2:2b (local) and Phi3:mini. The project is built around the open-weight Gemma2 model family — swappable between local and hosted with one environment variable, demonstrating the model-as-component argument concretely.

ElevenLabs — Partner ($100) The dungeon master voice is the primary UI of the walk experience. Without narration, players look at a screen. With ElevenLabs eleven_multilingual_v2 at tuned stability and pitch settings, the place itself speaks. All waypoint audio is pre-generated at quest start so narration fires instantly on arrival with zero loading delay. Web Speech API provides graceful degradation when offline.

Render — Featured ($200) Backend (Fastify + Node.js) deployed on Render. Handles the full quest generation pipeline: Overpass API fetch → Gemma2 prompt build → Groq inference → Zod validation → Wikimedia image fetch → response. Free tier instance with environment-variable-driven configuration.

SerpApi — Partner ($100) Optional landmark enrichment layer. When SERPAPI_KEY is configured, the backend pulls real-world context about each OSM landmark before building the Gemma prompt. Recent news or historical facts about a location are injected as [REAL CONTEXT: "..."] into the prompt, grounding the AI lore in verifiable reality. Degrades gracefully when key is absent.

GitHub Copilot — Partner ($100) GitHub Copilot assisted throughout development — autocompleting TypeScript types, suggesting Zod schema structures, and accelerating the Fastify route handlers. The entire codebase is on GitHub with a documented commit history showing the

Built in 3 days for Hacktoberfest Week 1: Touch Grass. The neighborhood has always had stories. We just needed an open dungeon master to read them.