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Last Light is a local-first dusk-walk ticket, not another app designed to keep you on-screen. You choose a city or use your location, select how long you want to walk, and describe conditions such as the sky and ground. It then prepares a compact brief with a leave-by time, sunset direction, destination-free route suggestion, estimated distance, an experimental walk-quality score, four sensory prompts, and one instruction: “Phone in a pocket that is not your hand.”
The intended handoff is the product: spend a short time preparing, print or remember the ticket, and put the phone away. Pocket Mode provides a countdown, optional voice narration can read the brief aloud, and a private field note remains in your browser for later reflection.
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Last Light is an open-source experiment that converts sunset geometry and local environmental signals into a short walking brief. It is deliberately narrower than a navigation, social, or habit app. There is no feed to scroll, streak to preserve, destination to optimize, or notification loop to pull you back.
Its design principle is summed up by project author Varnit Kumar: “The screen prepares the walk. Then it gets out of the way.” The ticket succeeds when you stop using the product and continue outside with your phone put away.
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What the ticket contains
- A leave-by time based on the selected date and local sunset.
- The direction of sunset, expressed as an azimuth rather than a turn-by-turn destination.
- A destination-free route suggestion, such as following the brighter western edge of a park or choosing a quiet loop.
- An estimated walking distance tied to the selected duration.
- A walk-quality score generated from the project’s experimental model.
- Four sensory prompts that direct attention to sound, light, texture, temperature, or movement.
- A single phone-away rule: keep the phone in a pocket that is not your hand.
How does the AI walk ticket work?
The ticket is assembled in stages. Solar calculations establish when and where dusk happens, a scoring model estimates walk quality, and an optional language model turns those facts into a concise field brief.
- Set the place and walk. Pick a city or allow the browser to use your location, choose a walk length, and describe the sky and ground. These inputs establish the context without requiring an account.
- Calculate the light. Last Light uses latitude, longitude, date, and time-zone offset to calculate sunrise, sunset, golden hour, civil dusk, solar noon, sunset azimuth, and day length.
- Estimate distance and quality. The selected duration is converted to an estimated distance, while the scoring engine evaluates environmental and seasonal features.
- Write the brief. If a compatible Gemma endpoint is available, it receives structured facts and returns JSON containing a title, lead sentence, four sensory cues, a route, and the phone-away rule. The response is validated before it is shown.
- Hand off to the walk. Use Pocket Mode as a countdown, print the ticket, or have the optional voice narration read it once. Then put the phone away.
Why the calculations do not need a map or weather service
The astronomical portion is deterministic. Given coordinates, a date, and the correct time-zone offset, the application can derive the sun’s timing and direction without loading map tiles or calling a weather API. That keeps the core ticket usable even when external services are unavailable.
Does Last Light work offline?
Yes, with an important distinction: the core calculations and fallback writing can run without an AI endpoint, while a hosted or local model adds more varied language when available.
| Operating mode | What supplies the text | What remains available |
|---|---|---|
| No model endpoint | Deterministic offline field recipes | Solar timing, direction, route suggestion, prompts, score, printing, and Pocket Mode |
| Local Ollama-compatible setup | A locally available Gemma endpoint | The same ticket, with model-generated title, lead, cues, route, and rule |
| Hosted compatible endpoint | Gemma reachable through the configured service | Model-written brief after structured facts are validated |
| Vercel deployment | Ridge fallback rather than the full TabPFN stack | Scoring and ticket generation without oversized serverless dependencies |
The project’s full TabPFN backend can run locally and on Render. Vercel uses a lighter Ridge implementation because TabPFN’s PyTorch dependencies exceed the serverless function size limit. This is an implementation trade-off, not a requirement for readers: a person using the ticket does not need to install PyTorch or configure a model.
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What is the walk-quality score based on?
The score is an experiment, not a safety rating. It is trained on a generated dataset of 1,400 dusk-walk rows identified by the project as “the Last Light project, 2026.” The rows combine environmental, geographic, seasonal, and timing signals.
Features used by the model
- Temperature, rain, wind, cloud cover, and humidity.
- Latitude and day of year, which help represent seasonal and daylight differences.
- Minutes until sunset and the selected walk duration.
- Foliage season and urban surroundings.
TabPFN and Ridge roles
| Model | Role | Deployment note |
|---|---|---|
| TabPFN | Primary predictor for the walk-quality score | Full backend available locally and on Render |
| Ridge regression | Lightweight baseline and fallback predictor | Used on Vercel where PyTorch dependencies exceed the function-size limit |
A high score means the model found a combination of conditions that resembles more favorable rows in its generated data. It does not verify pavement, lighting, traffic, flooding, air quality, wildlife, or crime conditions in your actual neighborhood.
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What does the optional AI add?
Gemma is an optional language layer, not the source of the astronomical facts. The application sends structured values—such as timing, direction, duration, and modeled conditions—and asks for a short JSON response. Validation happens before display so malformed or missing fields do not become a ticket.
When no compatible endpoint is configured, deterministic field recipes produce the brief instead. This keeps the experience predictable and avoids making a network connection a prerequisite for a dusk walk. The language model therefore changes phrasing and variety, while the underlying ticket structure remains the same.
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How do I print and use a Last Light ticket?
Printing is the simplest way to make the screen handoff physical. A small pocket notebook can serve as an optional companion for carrying the brief or recording observations after the walk; the project does not require any particular notebook or printer.
- Open Last Light and choose your city or approve location use.
- Select the length of the walk and enter the sky and ground description that best matches current conditions.
- Review the leave-by time, sunset direction, estimated distance, prompts, and score. Check that the date and time zone are correct.
- Choose the print option in the ticket view and print the compact brief. If you do not have a printer, read it once or copy the essentials into a pocket notebook.
- Start Pocket Mode if you want a visible countdown while getting ready. Optional voice narration can read the ticket without requiring you to keep looking at the screen.
- Before stepping outside, put the phone in a pocket that is not your hand. Treat the printed brief—not the handset—as your reference.
Where are field notes stored?
Field notes are saved with the browser’s localStorage. They are not sent to a database and are not used to build a profile. Because storage is local to that browser, notes are not automatically synchronized to another device or browser; clearing site data can remove them.
Location is used to issue the ticket. The project does not require an account, social graph, map tiles, weather API, or recommendation feed, which limits the amount of personal data involved in the basic workflow.
How is Last Light different from an ordinary outdoor app?
The difference is the intended direction of attention. Most outdoor products are built for continued interaction—checking a map, browsing recommendations, logging a habit, or sharing an activity. Last Light is built for a short preparation phase followed by deliberate disengagement.
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| Comparison axis | Last Light | Typical navigation or outdoor app |
|---|---|---|
| Screen-time goal | Handoff: prepare, then stop looking | Continued checking during the activity |
| Dependency model | Local calculations and deterministic fallback, with optional model endpoints | Often dependent on live maps, accounts, feeds, or network services |
| Output | Printed-style brief with timing, prompts, direction, and a phone-away rule | Map, route tracking, recommendations, or activity history |
| Privacy posture | Browser-only notes and no required account | Varies; many services synchronize activity to an account |
| Safety posture | Experimental score plus the user’s own judgment | Navigation or safety features may provide different, but not guaranteed, coverage |
“Destination-free” is intentional. The route suggestion gives a direction or type of loop to try; it is not turn-by-turn guidance and does not promise that a path is open, lit, accessible, or safe.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Is the walk-quality score a safety guarantee?
No. The project explicitly labels the score experimental. A favorable value cannot account for a sudden storm, an unlit crossing, unsafe traffic, a mobility barrier, a closed path, or a personal risk that only you can see.
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- Check current weather and daylight yourself before leaving.
- Choose routes with appropriate lighting and traffic conditions.
- Adapt the distance and surface to your mobility, fitness, and available time.
- Tell someone where you are going when that is appropriate.
- Stop or change course if conditions feel unsafe, regardless of the score.
Think of the score as a transparent prompt for reflection on modeled conditions, not as permission to ignore ordinary outdoor judgment.
Who is Last Light best suited to?
It fits people who want a structured reason to take a short evening walk without turning the walk into a data-collection project. It is especially useful when you want sunset timing and a few prompts but do not want to browse a map, maintain a streak, or create an account.
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It is less suitable when you need reliable turn-by-turn navigation, verified accessibility information, live hazard alerts, emergency monitoring, or a guaranteed forecast. Those needs require services and judgment beyond this ticket’s deliberately small scope.
What should you remember before trying it?
- The practical value is the pre-walk handoff: timing and direction first, phone away second.
- Solar calculations do not require a map tile or weather API.
- Gemma is optional; local recipes keep a ticket available without an AI endpoint.
- The score comes from a 1,400-row generated dataset and two model paths, so it is useful as an experiment rather than a verdict.
- Field notes stay in browser storage, while location is used to issue the ticket.
Frequently Asked Questions
Can I choose a city instead of sharing precise location?
Yes. The workflow allows a city selection as well as browser-based location use. A manually chosen city is useful when you prefer not to grant precise geolocation, but you should confirm that the city and time-zone setting match where you will actually walk.
Does the route suggestion provide turn-by-turn directions?
No. It is a destination-free idea for a direction or loop, not navigational guidance. If you need exact turns, crossing information, or rerouting, carry a separate navigation plan and use Last Light only for the pre-walk brief.
Can I move my field notes to a new phone?
Not automatically. Notes live in the original browser’s local storage, so changing devices, browsers, or cleared site data can leave them behind; export or copy anything you want to keep before switching.
What happens if sunset timing looks wrong?
Check the selected date, city or coordinates, and time-zone offset first. A stale offset can shift every light-related value, especially around daylight-saving changes, even though the underlying solar calculation is deterministic.
Can I use the ticket for a morning walk?
The ticket is designed around dusk signals—sunset, golden hour, civil dusk, and sunset direction. You could reuse its prompts informally at another time, but its leave-by logic and score are not presented as a morning-walk mode.
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