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Savanna Grass turns screen use into a suggested amount of time to spend outdoors, then uses short quests to make that break more inviting. Its developer says the debt is calculated with a simple formula, while Twiga—the feature that writes the quest prompt—runs Gemma 3 1B on the device. That means the developer describes the AI inference as local; it does not mean Savanna Grass operates without SavannaTime or that no data ever leaves the phone.
The details below come from developer Alpha Olomi’s project post, not an independent privacy audit or product test. Treat its platform, version, and download details as time-sensitive.
What is Savanna Grass?
Savanna Grass is a companion app for SavannaTime, a screen-time tracker. It is designed to turn a daily screen-use summary into an “outdoor debt”—a suggested amount of time to spend outside—and then offer a small quest as a nudge to take that break. The idea is not that a model decides whether your screen use is good or bad: the developer says a fixed formula calculates the debt, and the AI writes the invitation.
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That distinction matters. A generated quest might make the suggestion feel more personal or actionable, but it does not change the underlying calculation. The project post describes quests as prompts for going outside, not as verified exercise, a health intervention, or a system that confirms you completed an outdoor activity.
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How does Savanna Grass calculate outdoor debt?
According to the developer, the app assigns 10 minutes outdoors for every hour of screen time, counts distracting screen time at 1.5 times the ordinary rate, and caps the daily debt at one hour. The calculation is deterministic; the model is not used to decide how much time is owed.
| Screen-use category | Rule described by the developer | Illustration |
|---|---|---|
| Ordinary screen time | 10 minutes outdoors per hour | One hour corresponds to 10 minutes of debt. |
| Distracting screen time | Counted at 1.5 times the ordinary rate | One hour corresponds to 15 minutes of debt. |
| Daily total | Capped at 60 minutes | Calculated debt above one hour is limited to an hour for that day. |
The post does not fully specify how the app classifies “distracting” use, how it rounds partial minutes, or which categories in SavannaTime’s summary feed into each part of the calculation. So the examples above explain the stated rates, not every possible result the app may display. The useful practical point is that you can check the broad arithmetic without trusting an AI model.
Olomi gives a personal example: he says his displayed use was 2 hours 13 minutes, which produced 23 minutes of debt. He also says two breaks paid 13 minutes of that amount. Those figures describe his own use of the app; they are not a general statistic, a controlled test, or proof that the formula improves anyone’s habits.
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What does one day with the app look like?
- Read the summary. Savanna Grass requests that day’s screen-time summary from SavannaTime.
- Apply the fixed rules. Ordinary and distracting portions are converted into outdoor minutes, then the one-hour ceiling is applied.
- Write an invitation. Twiga uses the resulting context to produce a short quest, while leaving the arithmetic unchanged.
- Take the break. You go outside for the suggested period; the app does not claim to verify your location or activity.
- Record the result. A completed break is posted to SavannaTime, or held locally until synchronization is possible.
This sequence explains why a quest can feel personalized without being a personalized health prescription. The number comes from rules; the wording is the flexible part.
What does Twiga’s AI do, and does it send your day to a cloud service?
Twiga writes the quest prompt. The developer identifies the model as Gemma 3 1B and says it runs locally: with LiteRT on Android and MediaPipe on WebGPU in a browser. In the described design, the model creates an invitation to take a break; it does not calculate the screen-time debt.
Olomi says the AI does not see the user’s day because the model runs on the device. That is a developer’s description of the AI inference path, not an independent privacy audit. It should not be broadened into a claim that the app never communicates with a server: the same post says Savanna Grass requests a daily summary from SavannaTime and sends completed breaks there.
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Those are different data flows. A local model can generate text without sending its prompt to an AI vendor, while the companion app can still communicate with its tracker. The project post does not provide a complete data inventory, retention policy, or independent assessment of SavannaTime’s storage practices. Readers who need those specifics should consult the apps’ current privacy information rather than treating “on-device AI” as a blanket guarantee about every part of the product.
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How does it work with SavannaTime and when you are offline?
The developer describes Savanna Grass as an API client for SavannaTime. It asks for the current day’s summary with GET /stats/summary and records a completed break with POST /time-entries. The break receives a client-generated UUID and is queued on the phone if it cannot be sent immediately; the author says it synchronizes later and that the ID helps avoid duplicate entries after a retry.
That design separates generating a quest from recording the result. The app can offer a prompt, let a break be completed, and queue the record for synchronization rather than requiring the entry to reach the tracker at that exact moment. It does not establish that all features work offline: the author specifically reports that breaks can queue and that quest generation can work without a network connection after the model is available.
What happens if the model is unavailable?
The developer says a hand-written fallback quest appears if the model has not been downloaded or its response cannot be parsed. That means a failed model response is not described as blocking the outdoor prompt entirely. It also means an offline experience can depend on whether the local model is already present; the post does not say that a first-time model download can happen without connectivity.
Olomi says he demonstrated a second quest with the network disabled. This is an account of a specific demonstration, not an independent test of every device, browser, installation state, or offline scenario.
What does the developer’s personal trial show?
Olomi recounts taking two short walks around 1 a.m. and says the breaks appeared in SavannaTime. He reports that those breaks paid 13 minutes against 23 minutes of displayed debt for a day with 2 hours 13 minutes of screen use. This is useful as an example of the intended loop—summary, debt, quest, break, synced entry—but it is a single personal account.
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The post does not establish that Savanna Grass reliably changes screen habits, improves wellbeing, or works equally well for other people. There is no controlled efficacy study or independent evaluation in the material described here. A reader can assess the mechanics and decide whether the nudge is appealing, but should not mistake one anecdote for evidence of a general outcome.
Which platforms and versions are described?
At the time of the developer’s post, the latest version was reported as 1.2.0, with a signed Android APK of 58 MB and an Android 7.0-or-later requirement. The post also describes a browser implementation using MediaPipe on WebGPU. These are publication-time details, not confirmation of the current release, download source, browser compatibility, or current minimum requirements.
The developer says version 1.1 made breaks persist in the pocket and added reminders, while version 1.2 made numbers explorable. Check the project’s current release information before installing or relying on those version-specific details. The cited post does not establish iPhone support or provide a current official store listing.
What does it cost, and what do you need?
The project description establishes software components but does not state a subscription price, one-time purchase, paid tier, or verified referral program. The APK and code repository mentioned by the developer are not proof of a merchant relationship or affiliate offer. No special watch, outdoor sensor, phone model, or physical gear is identified as necessary.
Practically, you need access to SavannaTime, a supported Android installation or compatible browser, and enough storage and connectivity to obtain the local model initially. The reported 58 MB APK size is separate from any model files or future updates, so it should not be treated as the total storage footprint. Confirm the current package, permissions, model-download size, and account requirements from the project’s release information.
How should you compare it with alternatives?
No competing product or comparative trial is established by the developer’s account, so a winner cannot be declared on effectiveness. A useful comparison is to check concrete behaviors instead: whether a companion reads usage locally or uploads it, whether AI inference happens on-device, whether the debt formula is visible, whether breaks queue offline, which platforms are supported, and what the underlying tracker stores.
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For example, an app with cloud-generated suggestions may offer broader models but a different privacy trade-off. A tracker with no debt formula may provide more customization while making its recommendations harder to audit. Savanna Grass’s distinctive combination, as described, is the transparent 10-minute rule, a 1.5 multiplier for distracting use, a 60-minute cap, and a local quest writer—not proof that it is more accurate or more motivating than another tool.
Who is Savanna Grass for—and what should you check first?
It is most relevant to someone already using SavannaTime who wants a lightweight prompt to step away from screens. The fixed formula makes the basic debt easier to understand than a recommendation whose logic is hidden inside a model; the locally run quest writer is intended to add a little variety without sending the prompt to an AI service, according to the developer.
Before relying on it, consider these practical checks:
- Confirm the companion relationship. The described app reads a SavannaTime summary and logs breaks there, so SavannaTime is part of the stated workflow.
- Decide whether the formula suits you. A daily cap and a flat time-to-time ratio are product rules, not personalized health guidance. The post does not describe a setting to change the rates or cap.
- Separate model privacy from tracker privacy. Local inference addresses the AI prompt path; the tracker still receives summary requests and completed-break entries as described.
- Check your device and the current release. Android and browser details in the post may have changed, and the cited account does not verify current availability on other platforms.
- Keep expectations modest. A quest can be a nudge, but the developer’s personal example does not show that the app will reliably change your routine.
- Plan for edge cases. A partial-day total, an unfamiliar “distracting” category, a missing model, or a failed parse may produce a fallback or a result you cannot reproduce by hand. Treat the displayed number as the app’s rule-based output, not a clinical measurement.
Common limitations and pitfalls
The one-hour cap can make very heavy-use days look similar once the ceiling is reached, so the display is not a complete accounting of every minute you spent on screens. The 1.5 multiplier also depends on SavannaTime’s classification, which the project post does not define in detail. If that category matters to you, inspect how SavannaTime labels apps and activities.
Offline support is similarly specific rather than absolute. A queued time entry is not the same as a completed synchronization, and a local quest may still require the model to have been downloaded earlier. A fallback sentence proves continuity of the prompt, not that the full model is available. Finally, a logged break is a record of an entry, not GPS evidence that you were outdoors.
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Does Savanna Grass measure screen time by itself?
The described implementation reads a daily summary from SavannaTime rather than documenting its own screen-monitoring system. The project post does not explain how SavannaTime collects or categorizes usage on each platform.
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Does Savanna Grass use GPS to check that I went outside?
The developer’s description says breaks are recorded as time entries, but it does not establish GPS tracking, location verification, or any other method for proving where a break happened. Do not assume a logged break is independently verified outdoor time.
Is there a Savanna Grass version for iPhone?
The cited project post reports an Android APK and a browser implementation, but does not establish iPhone support. Check current official release information for any newer platform options.
Can I change the 10-minute rate or the one-hour cap?
The post describes those values as the app’s rules but does not say whether users can customize them. If changing the calculation is important to you, verify the current app controls rather than assuming the rates are configurable.
Does the app require an internet connection?
The developer says completed breaks can queue on the phone and synchronize later, and reports generating a quest with the network disabled after the model was available. Initial model download, daily summary retrieval, and later synchronization may still require connectivity.
What happens if Twiga fails?
If the model is not downloaded or its response cannot be parsed, the developer says a hand-written fallback quest is shown. The post does not promise identical behavior on every device or browser.
Is Savanna Grass a health or exercise app?
The described feature is a screen-time nudge and time-entry logger. The developer does not present a controlled health intervention, exercise verification system, or medical guidance.
Is there a subscription or required accessory?
The project description does not identify a subscription price, paid tier, affiliate offer, special phone, wearable, or outdoor accessory. Verify current release and account terms because those details can change.
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Savanna Grass is best understood as a SavannaTime companion: a visible formula turns screen use into capped outdoor minutes, while on-device Gemma 3 1B writes the quest. The developer describes queued offline logging and a fallback prompt, but the privacy, platform, pricing, and effectiveness claims should be checked against current official information rather than treated as independently verified facts.
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