Homing

Home, before you unpack.

Inspiration

Every one of us has moved to a city where we knew nobody. The first few weeks are the same disorienting blur: Which neighborhood should I live in? Is that commute actually bearable? Where do people like me hang out? Is this area my speed or not? Tourist apps answer "what should I see this weekend" but nobody builds for the person who has to figure out a whole life in a place they've never lived.

Google Maps shows you streets. Zillow shows you listings. Niche shows you a wall of statistics. None of them let you feel a city before you arrive. We wanted the thing we all wished we'd had on day one: a local friend who flies you through your new city in 3D, talks you through each neighborhood, and helps you find where you actually belong.

That's Homing.

What it does

Homing turns "I'm moving to a new city" from a spreadsheet of unknowns into a guided, explorable experience.

Fly through your new city in 3D. Real photorealistic 3D tiles, not a flat map. Orbit, pan, and zoom over actual buildings and terrain.
Take a narrated tour. A camera-on-rails ride glides you stop-to-stop along real transit lines while a voice (powered by Deepgram) narrates each place like a local showing you around.
Explore neighborhoods with real context. Click a district to see its character, population, density, median income, dominant industries, and climate — the stuff that actually decides whether you'd live there.
Check your commute. Drop in two addresses and compare drive / transit / bike / walk times and distances, with the route drawn on the 3D map.

Everything the UI renders comes from a single City data object, so a new city is one data file away.

How we built it

Frontend: React 19 + TypeScript, Zustand for state, Tailwind for styling, Vite for the build.
3D engine: CesiumJS rendering Google Photorealistic 3D Tiles through the Map Tiles API.
Voice: Deepgram text-to-speech streams narration that stays in sync with the camera as it moves between tour stops.
Routing: Google Routes API for multimodal commute comparison, with results geocoded and drawn as polylines on the globe.
Architecture: a fully data-driven city model — places, tours, districts, and quests are pure data, so the picker, map, explore mode, and tour mode, all light up automatically for any new city.

The guided tour is the technical heart. Instead of teleporting the camera between stops, we build a smoothed spline through the route's control points and interpolate the camera along it. For control points $Not disclosed$ and a local parameter $t \in [0,1]$, we evaluate a Catmull–Rom segment: Not disclosed $$

which gives the camera a continuous, train-like glide instead of robotic jumps. Commute distances use the haversine formula over the WGS-84 sphere: Not disclosed

Challenges we ran into

Syncing voice to motion. Getting Deepgram narration to start, pace, and finish in lockstep with a moving camera — without audio cutting off mid-flight or lagging behind the visuals — took real tuning of the playback and tour-advance lifecycle.
Taming the camera. Photorealistic 3D tiles look incredible but make camera control unforgiving: pitch, heading, and height all interact, and a naive path through waypoints felt nauseating. The spline interpolation above was our fix.
3D tile performance. Streaming gigabytes of 3D tiles while keeping the UI responsive meant being careful about when we fly, preload, and render overlays.
Graceful degradation. The app depends on third-party keys (Cesium Ion, Google Maps). Rather than show a broken globe, we built a friendly "add your keys" state so the experience never hard-crashes.
Designing for relocators, not tourists. Our hardest product challenge was resisting the tourist framing. Every feature had to answer "does this help someone decide where to live?" which is why neighborhoods carry demographics and commute math, not just pretty photos.

Accomplishments that we're proud of

A genuinely cinematic 3D city tour with synchronized AI voice narration that feels like a friend, not a robot.
A data-driven architecture where adding an entire new city is one file no UI changes.
Real, decision-grade neighborhood context (income, density, industries, climate) fused with live multimodal commute routing on the same 3D map.
We shipped all of it as a small team in a hackathon timebox.

What we learned

CesiumJS + Google 3D Tiles from the ground up, camera math, splines, and the art of making 3D navigation feel good rather than dizzying.
Streaming TTS is a UX problem, not just an API call - timing and lifecycle matter more than the audio itself.
Constraints make products. Narrowing from "explore any city" to "help someone relocate" sharpened every design decision.
Data modeling is leverage - investing early in a clean City schema paid for itself the moment we wanted to scale beyond one city.

What's next for Homing

More cities, fast - our data-driven model means national coverage is a content problem, not an engineering one.
A "neighborhood fit" engine - tell us your budget, commute, and lifestyle, and we recommend where to live, then learn from whether the fit was right. That outcome data is something no map or listings site has, and it's the moat we're building toward.
End-to-end relocation workflow - from "where should I live" through housing, commute, and a settle-in checklist, so Homing becomes the system of record for an entire move.
Voice-first concierge - ask "where should someone like me live in this city?" out loud and take the tour that answers it.
Embedded distribution - partnering with employers, universities, and relocation services to reach people at the exact moment they're moving.