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Step Out is a local AI break planner that creates a short outdoor activity without sending your choices to a cloud model. You select a 5-, 10-, 15-, or 20-minute window, a familiar outdoor space, a goal, and whether you want to walk or sit. A Next.js backend asks Google’s open-weight gemma3:1b model through Ollama, then returns one activity card with instructions, a timed schedule, and a reflection prompt.
The practical result is a deliberately small interaction: generate the plan, save it if useful, and leave the screen. It works offline only after Ollama, the model, and the application dependencies have been installed. The published performance evidence is limited to one Apple M1 laptop, where a cold request took 7.45 seconds and warm requests took 5.45–7.54 seconds.
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What is Step Out?
Step Out is a local, offline-first application by Cynthia Wahome for planning brief outdoor breaks. It is not a general-purpose chatbot and it does not attempt to discover destinations, monitor your movement, or keep a conversation going. Its job is narrower: turn four simple preferences into one activity that fits a short break.
The planner asks for:
- Available time: 5, 10, 15, or 20 minutes.
- A familiar outdoor space: a compound or courtyard, a familiar street, or an outdoor seating area.
- A goal: a quiet pause, gentle movement, or noticing surroundings.
- Mobility preference: walking/standing or seated.
The response is an activity card containing instructions, a schedule, and a reflection prompt. You can save the activity in the browser or download a text copy named step-out-activity.txt. That design keeps the interaction short enough to encourage a real break rather than another long session in front of a screen.
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How does Step Out run an AI model without the internet?
The application separates the user interface, the local model runtime, and the deterministic rules that keep a plan within bounds. The Next.js backend sends the generation request to Ollama at 127.0.0.1:11434, which is the loopback address on the same computer. Ollama loads Gemma 3 1B locally, so the prompt and response do not need to travel to a hosted AI service.
Local does not mean ready instantly. Ollama, the gemma3:1b model, and the Step Out dependencies must be installed while you still have the downloads and package access required for setup. Wahome then generated a fresh plan with Wi-Fi and other internet connections disabled. That is evidence that the configured application can operate without an active connection; it is not evidence that a first-time installation can be completed offline.
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What each part is responsible for
| Part | Role in Step Out | Where it runs |
|---|---|---|
| Next.js application | Collects choices, filters eligible activities, calls Ollama, validates the result, and displays the card. | The user’s computer |
| Ollama | Provides the local model-serving endpoint. | The same computer, at 127.0.0.1:11434 |
| Gemma 3 1B | Turns the constrained activity choice into natural-language instructions and a reflection prompt. | The same computer |
| Browser storage | Keeps saved reflections locally in the browser. | The browser’s local storage |
Because these components are on one machine, a missing model, stopped Ollama process, or unavailable local endpoint prevents generation even when the internet is working. Conversely, an internet outage after setup does not automatically prevent generation.
What do I need before using Step Out offline?
You need a computer capable of running the Next.js app and Ollama, enough memory for the selected model and the rest of the operating system, and the model files already present on disk. The published implementation was run on an Apple M1 laptop. No universal RAM minimum, supported operating-system list, or phone specification is stated, so treat the M1 result as a reference setup rather than a hardware requirement.
Offline readiness checklist
- Install Ollama before disconnecting from the internet.
- Download and verify the
gemma3:1bmodel before an offline session. - Install the Step Out application’s JavaScript dependencies while package access is available.
- Start the local application and confirm that its backend can reach
127.0.0.1:11434. - Run one test generation online, then repeat with Wi-Fi and other internet connections disabled.
- Keep the laptop powered if you expect repeated generations; local inference consumes electricity and can compete with other workloads for memory and processor time.
The last test matters because an application can appear local while a missing asset, package, font, analytics call, or model download still depends on the network. Step Out’s documented offline check concerns generation after installation, not every possible browser request made by a development environment.
How do I create a break plan?
Choose options that describe the break you can actually take, not an ideal location you may not reach. Step Out then returns one constrained activity rather than a menu of alternatives.
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- Choose a familiar space. Pick a compound or courtyard, a familiar street, or an outdoor seating area. Familiarity is important because the planner does not inspect the location or provide navigation.
- Choose the goal. A quiet pause, gentle movement, and noticing surroundings lead to different activity types.
- Choose mobility. Select walking/standing or seated. Seated choices exclude walking activities.
- Generate the card. The backend narrows the activity collection first, then asks Gemma to produce the response within that eligible set.
- Take the break. Read the instructions, follow the schedule away from the screen, and return only when you need the reflection prompt.
- Save if useful. Keep the result in the browser or download
step-out-activity.txt.
The interface therefore acts more like a short prompt generator than an autonomous coach. It gives you a starting structure while leaving the decision to go outside, choose a safe spot, and stop an activity with you.
How does Step Out keep a plan within five, ten, fifteen, or twenty minutes?
The backend performs the time arithmetic before the model’s wording is used. It allocates preparation and return phases so the schedule totals the selected duration. This is a significant design choice: a language model can describe time plausibly, but it is not a dependable clock.
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In the documented ten-minute example, the schedule assigned one minute to reach the spot, eight minutes to observe sounds, and one minute to return. The three phases total ten minutes even though the generated prose is flexible. If a model sentence is awkward or overly enthusiastic, the application’s timing rule still defines the intended budget.
Why deterministic timing helps
- A five-minute selection cannot silently become a twenty-minute itinerary because the model added extra steps.
- Preparation and return are represented explicitly instead of being treated as free time.
- The same arithmetic can be tested independently of model quality.
- Users with fixed work breaks can trust the selected duration more than a free-form estimate.
Timing does not guarantee that a person will walk at the assumed speed, find the spot immediately, or finish every sentence on schedule. It guarantees only that the generated schedule is constructed to total the selected duration.
How does the activity filter constrain Gemma?
Before inference, Step Out filters a curated collection of five activities by space, goal, and mobility preference. The model is not asked to invent any activity it wants. The backend supplies an eligible activity set, and seated selections remove walking activities before generation.
The implementation uses a Zod 4 response schema. Eligible activity IDs are constrained as an enum, and the returned JSON is validated against the eligible list. If the model emits an ID outside that list or fails the expected structure, the application can reject the response instead of presenting an unchecked activity as valid.
What this control does and does not guarantee
| Control | It helps with | It cannot establish |
|---|---|---|
| Filtering by space, goal, and mobility | Keeping choices relevant to the four selections. | Whether the chosen place is actually available or safe. |
| Enum-constrained activity IDs | Preventing references to activities outside the curated collection. | That every sentence about the activity is suitable. |
| Zod 4 response validation | Detecting malformed or structurally invalid JSON. | That a grammatically valid response is physically practical. |
| Backend time arithmetic | Keeping the planned phases aligned with the selected duration. | How long a real person will take to move or observe. |
These safeguards reduce the model’s freedom in useful ways, but they are not a safety certification. Wahome states that the rules narrow the model’s choices; they do not verify an outdoor location or prove that every generated sentence is appropriate.
How fast is Gemma 3 1B in Step Out?
The only published measurements come from Wahome’s Apple M1 laptop in 2026. A cold request took 7.45 seconds. Warm requests ranged from 5.45 to 7.54 seconds. These figures describe one setup, not a benchmark for every laptop.
| Request state | Observed time | What it means |
|---|---|---|
| Cold | 7.45 seconds | The reported first request on the Apple M1 setup. |
| Warm | 5.45–7.54 seconds | Reported repeat-request range on the same setup. |
Warm requests were not uniformly faster than the cold measurement, which is a reminder that local latency depends on more than model loading. Available memory, competing applications, thermal state, power mode, and the exact request all matter. A laptop with less memory may swap or take longer; a busy machine may produce a different range.
For a break planner, several seconds is usually acceptable because generation happens before the user leaves the screen. It is not proof that the model is suitable for real-time voice interaction or continuous coaching. The app’s purpose is a single card, not a stream of token-by-token guidance.
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Will Step Out work with Wi-Fi turned off?
Yes, after setup, model installation, and dependency installation are complete. The author generated a new plan with Wi-Fi and other internet connections disabled, demonstrating the intended offline workflow. The local endpoint remains available because it is on the same computer.
Offline operation has a precise boundary:
- Works offline: sending the prepared request to local Ollama, running
gemma3:1b, validating the result, and storing reflections in browser local storage. - Requires preparation: obtaining Ollama, downloading the model, installing application dependencies, and updating software.
- Not provided by the app: location search, weather data, route planning, or a live safety check.
If generation fails while disconnected, reconnect only for diagnosis and setup work. Check that Ollama is running locally and that the model files are available; do not assume the outage itself is the cause.
Does Step Out track my location or send reflections to a server?
The described version does not need your location. You select a familiar type of space yourself, and the planner does not inspect coordinates or provide navigation. Reflections remain in browser local storage, while model requests go to Ollama on the same computer.
This is a privacy advantage over a cloud workflow, but it is not an absolute privacy guarantee for every installation. Browser extensions, operating-system telemetry, backups, shared user accounts, or a modified build can introduce other data paths outside the documented Step Out architecture. The narrow claim supported here is that the application processes preferences locally, uses a local model endpoint, and stores reflections in the browser.
What happened in the real-world test?
On 9 October 2026, Wahome tried a ten-minute seated activity in a residential courtyard. Step Out generated “Notice outdoor sounds.” Its schedule gave one minute to reach the spot, eight minutes to observe sounds, and one minute to return.
The reported break lasted 9 minutes 30 seconds, and the author checked the screen zero times. That is useful implementation evidence: the schedule was understandable enough to follow, and the activity did not require repeated phone interaction. It is not an efficacy study. One author, one courtyard, one activity, and one ten-minute session cannot establish that the planner improves focus, mood, productivity, or health for a wider population.
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Use Step Out only in places you already judge to be appropriate. The planner knows the category you selected, not the condition of the pavement, traffic, weather, lighting, privacy, accessibility, or people nearby. Its filters can prevent a seated request from selecting a walking activity, but they cannot see hazards.
Make your own pre-break safety check
- Confirm that the space is open to you and that you can leave easily.
- Look for traffic, uneven surfaces, stairs, heat, rain, poor lighting, animals, or other hazards.
- Keep the activity seated if walking or standing is uncomfortable or unsafe.
- Stop immediately if symptoms, fear, pain, or a changing environment make the plan unsuitable.
- Do not treat a generated reflection prompt as medical, therapeutic, or emergency advice.
The output can be well-formed and still be wrong for a particular person or place. Human judgment remains the final safety layer.
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Is Step Out accessible for different mobility preferences?
Mobility is an explicit input rather than an afterthought. Choosing seated removes walking activities from the eligible set, while walking/standing allows movement-oriented options. That makes the planner more useful for people who need to remain seated, but it does not establish full accessibility.
The available description does not document screen-reader labels, keyboard behavior, color contrast, language support, hearing accommodations, or options for users who cannot reach an outdoor space. Treat those as open implementation questions. If the generated wording conflicts with your needs, follow your own limits rather than trying to satisfy the card.
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Can Gemma run locally on a phone for Step Out?
Not in the described version. Wahome states: “The model runs on the laptop. This version does not run it on a phone.” The browser interface might be viewable from another device in some deployments, but that is different from running Gemma locally on the phone.
Do not infer mobile support from the model’s small name or from the fact that Ollama is local. Phone execution would require a separate runtime, packaging, memory profile, and validation effort that are not documented for Step Out.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should I check when offline generation fails?
Most failures can be isolated by checking the local service, model availability, and application response separately.
Ollama is not reachable
Confirm that the Ollama process is running on the same computer and that the application is targeting 127.0.0.1:11434. A firewall or changed port can make a healthy model appear unavailable to the Next.js backend.
The model is missing
Reconnect long enough to verify that gemma3:1b was downloaded completely, then retry with the network disabled. An installed Ollama service without the model cannot answer a request offline.
The response is rejected
A schema or enum failure means the backend received output that did not match the eligible activity structure. Retry the request; if failures persist, inspect the model prompt, the eligible activity IDs, and the Zod 4 validation error rather than displaying unvalidated JSON.
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The plan takes much longer than expected
Close memory-heavy applications, connect the laptop to power, and compare a cold request with several warm requests. The 5.45–7.54-second warm range was measured on one Apple M1, so a different computer may legitimately be slower.
The activity does not fit the place
Regenerate with a different space, goal, or mobility selection, or disregard the card. Filtering improves relevance but cannot inspect the actual environment.
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What would a careful implementation need to reproduce?
A comparable local planner should make the model the language layer, not the authority over timing or eligibility. Step Out’s architecture offers a practical blueprint:
- Represent the available activities as a curated collection with stable IDs and metadata for space, goal, and mobility.
- Filter that collection before inference, including explicit exclusion of walking activities for seated requests.
- Calculate preparation, activity, and return phases in application code so their total equals the selected duration.
- Pass only eligible IDs to the model and constrain the response schema with a typed validator such as Zod 4.
- Reject any returned ID that is not in the eligible enum and handle malformed JSON visibly.
- Keep storage behavior explicit; in Step Out, saved reflections stay in browser local storage.
- Test with the network disabled after installation, then record cold and warm timings on the target hardware.
This approach makes failures diagnosable. A bad sentence is a model-quality issue, an impossible duration is a timing bug, and an out-of-set activity ID is a validation failure. Mixing all three responsibilities inside one prompt would make the application harder to trust.
How should I judge whether an offline break planner is worth using?
Step Out is a good fit if you want a quick, low-distraction suggestion, have a computer that can run Ollama, and prefer local processing over a cloud request. It is less suitable if you need phone-only operation, location-aware recommendations, weather adaptation, medical guidance, or independently validated wellbeing outcomes.
| Decision factor | What Step Out provides | What remains your responsibility |
|---|---|---|
| Model/runtime | Gemma 3 1B through Ollama on the laptop. | Installing, storing, and updating the runtime and model. |
| Offline behavior | Generation after setup with internet connections disabled. | Testing your own installation for hidden network dependencies. |
| Privacy and storage | Local preferences, local inference, and browser-local reflections. | Securing the computer, browser profile, and backups. |
| Time enforcement | Backend arithmetic that totals the selected duration. | Allowing for real walking speed and unexpected interruptions. |
| Safety controls | Curated filtering, mobility exclusion, enum IDs, and schema validation. | Checking the location and rejecting unsuitable instructions. |
| Accessibility | Walking/standing and seated choices. | Determining whether the interface and activity fit your needs. |
| Evidence quality | One Apple M1 timing report and one author field test. | Not treating those observations as independent evaluation. |
The strongest case is convenience with local privacy: one short card, no cloud model, and a schedule that is calculated in code. The weakest case is any expectation that a small language model can understand your environment or replace personal judgment.
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Can I use Step Out on a computer with no internet at all?
Only after the initial setup has been completed while downloads are possible. The model files, Ollama runtime, application dependencies, and any required browser assets must already be stored locally; a brand-new installation cannot obtain missing components from an offline connection.
Does choosing a familiar street make the planner aware of traffic or weather?
No. That choice is a category used to filter the activity collection. Step Out does not query maps, weather services, sensors, or live reports, so you must inspect conditions yourself before starting.
Where are saved reflections kept?
The described implementation keeps reflections in the browser’s local storage. Clearing site data, changing browser profiles, or using a different device can make those locally stored reflections unavailable.
Why can a local request be slower on my laptop than the published timing?
The 5.45–7.54-second warm range and 7.45-second cold result came from one Apple M1 laptop. Memory pressure, power mode, thermal throttling, background processes, and different hardware can all change latency.
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No. It is a short outdoor-break prompt, not a clinical tool. Stop if an instruction feels unsafe or uncomfortable, and seek qualified help for medical, mental-health, or emergency concerns.
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