TouchGrass AI turns a few outing choices into a short outdoor quest, then is designed to let you put your phone away. Its defining design boundary is simple: a server generates and validates quests, while the browser keeps saved quests, progress, and photo evidence on the device.
What TouchGrass AI does
Project creator Shashank Chakraborty describes TouchGrass AI as a small companion for walks and nearby exploration. A user selects an outing type, available time, and effort level; the app returns a handful of prompts, such as noticing nature, exploring a familiar area, or taking a photo walk. Some prompts ask for photo evidence and remain incomplete until that evidence is saved.
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The goal is not to keep the user interacting with an AI during the outing. As Chakraborty puts it: “A model can suggest the next small thing to notice; it should not need a permanent place in the user’s pocket once the walk starts.”
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In the creator’s described implementation, the browser sends the selected activity, duration, and difficulty to a same-origin server route. That route requests structured JSON from Ollama and checks the response against the app’s quest rules. If generation fails or the result is unusable, the route can return a ready-made quest instead.
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This arrangement keeps the model request and any hosted API credentials on the server side rather than embedding an API key in browser code. The project account says local development uses Ollama on the developer’s computer, while the hosted demo uses Ollama’s API configured with a Gemma model. These are implementation details reported by the creator; they are not an independent audit of the repository or deployment. Ollama has documented an OpenAI-compatible API and local-endpoint examples, but that general documentation does not verify this particular hosted setup: Ollama’s OpenAI compatibility overview.
Local development and hosted generation
- Local development: the creator says the app uses an Ollama instance running on the developer’s computer.
- Hosted demo: the creator says generation calls Ollama’s API using a Gemma model configured on the server.
The distinction matters: “local-first” describes where saved activity data lives, not a model running on every visitor’s phone. The creator’s account says hosted generation sends the chosen quest settings and prompt to Ollama. Generating a new quest through that hosted flow therefore depends on network access.
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What stays in the browser
According to the creator, saved quests, task progress, and photo evidence are stored in the browser on the device. Photos are stored as blobs in IndexedDB, can be previewed or removed, and are excluded from the service worker cache. The app does not request or send the user’s location, and the creator says photo evidence is not uploaded.
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IndexedDB is a browser API for storing structured client-side data, including files and blobs. Its data is scoped to an origin, and available storage limits and eviction behavior vary by browser; a local save should not be treated as a permanent backup. See MDN’s IndexedDB API documentation (last modified April 3, 2025) for the platform-level details. That documentation explains browser behavior generally; the app-specific storage claims above come from the creator.
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What works offline—and what does not
The offline boundary is narrower than “the app works offline.” The creator says that after the app shell has been visited, it can load offline, and a previously saved quest can accompany the user without a signal. The stored quest, task progress, and local photo evidence are intended to remain usable on the device. Creating a new hosted AI quest still requires connectivity.
- Before going out: open the app and generate or save a quest while connected.
- During an outing without signal: use that saved quest and its locally stored progress and evidence.
- When you want another generated quest: reconnect to use the hosted generation path.
Why the save flow matters
The creator describes a persistence bug in which quests disappeared after returning home. The fix connected the home flow to local saving so quests appeared on the quest shelf. It is a useful implementation lesson: choosing a browser database is not enough. An app must also save at the right points in its navigation and lifecycle, then make the saved item visible where the user expects to find it.
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Chakraborty reports generating a quest on a phone, taking a photo for one of its tasks, and finding that photo still attached to the saved quest. This is the creator’s report of a phone test, not an independently verified product test.
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What this design does—and does not—promise
- It separates responsibilities: the server handles AI generation and validation; the browser holds the user’s saved outing state and photo evidence, as described by the creator.
- It limits the intended data flow: the creator says location is not requested or sent and photo evidence is not uploaded. Hosted generation settings and the prompt do go to Ollama.
- It is not fully offline: offline use is for the visited app shell and already-saved quests, not for hosted generation.
- It is not a guarantee of permanent storage: IndexedDB data can be affected by browser-specific storage limits and eviction behavior.
The project is therefore best understood as a brief online setup step followed by a locally saved, low-interaction outing—not as an offline AI model or a cloud-synced photo journal.
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