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Pocket Field Guide is a browser-based nature app designed to help you notice more outdoors, not spend longer looking at a screen. Choose a 15-, 30- or 60-minute outing and a place such as a park; the app uses AI to make a short observation checklist. During the walk, you can photograph one find for a match against its built-in guide, then read a brief field note suggesting what to check in person and what related thing to look for. Your finds are saved in an on-device journal that can be exported as Markdown. The project is designed to work offline after its initial model download, but that first download is about 900 MB, and its identifications are prompts for observation—not certified scientific or safety determinations.
What is Pocket Field Guide?
Pocket Field Guide is a browser-based nature app created by Arsh Goyal. It is aimed at beginners who want an easy reason to walk and pay attention to plants, birds, bugs, mushrooms and leaves. Its central idea is to use a phone briefly to set up an outing and help interpret an observation, then encourage the user to put the device away and continue looking at the real environment.
The experience has three broad phases: plan a short walk, use a checklist to guide attention, and record a find if something catches your eye. For planning, choose one of three durations—15, 30 or 60 minutes—and a place, such as a park. The app asks its language model to produce a short checklist of things to notice. Once the outing starts, the interface switches to a timer and checklist intended to make the phone less central.
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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteIf you spot something you want to investigate, you can take one photo and compare it with the app’s built-in guide, which contains about 150 common species. The app then generates a short field note: not just a label, but a suggestion for a visible trait to check yourself and a related thing to look for next. Finds can be saved in the local journal, with location optional, and exported in Markdown format.
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The author’s stated design has no account, API key or server. Models are downloaded to the browser and run on the device, and the author says the app is intended to work in airplane mode after that initial download. That combination is meant to suit parks and trails where connectivity is unreliable, while keeping the outing and journal on the user’s device.
How does a walk work?
The app is organized around a walk rather than an open-ended chatbot session. The AI creates a starting point for attention; the checklist and timer then provide a reason to leave the screen alone until something in the surroundings is worth investigating.
- Choose an outing length. Select 15, 30 or 60 minutes, depending on how much time you have.
- Set the kind of place. Choose a location description such as a park. The project description does not specify a complete list of place choices.
- Read the generated checklist. Gemma produces a short list of things to notice during the outing. Treat it as a set of observation prompts, not a guarantee that a particular species will be present.
- Start the walk and put the phone away. The interface changes to a timer and checklist, intended to guide the walk without requiring continuous screen use.
- Photograph a find only when useful. A single photo can be matched against the built-in guide. The result is a candidate to investigate, not proof of identity.
- Use the field note as an observation prompt. Check the suggested trait on the organism itself, then follow the suggested related observation if it is present.
- Save or export what you want to keep. The journal is stored on-device, location is optional, and entries can be exported as Markdown.
This flow is deliberately modest: it does not require documenting every sighting or keep a running conversation on-screen. The author describes the desired outcome as more time outdoors rather than more time in the app.
How does Pocket Field Guide identify a photo?
Photo matching uses SigLIP base, an open model described as licensed under Apache-2.0. The project runs an approximately 95 MB, 8-bit vision model on CPU through WebAssembly (WASM). Rather than asking a generative model to invent an identification from scratch, the app compares an image with text representations of the species in its own guide.
For each species label, the developer prepares three text descriptions. Their embeddings are averaged during the build process. At runtime, the photograph is compared with those prepared representations in one vision pass. In practical terms, the result is a best match among the guide’s represented options; it is not an exhaustive search across all species in the world.
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The built-in guide contains about 150 common species, so coverage is inherently bounded by that guide. An unfamiliar organism, an unusual life stage, a poor photograph or a species not represented in the guide may lead to an unhelpful or misleading match. The project description does not establish scientific identification accuracy, a certification, or coverage beyond the built-in set.
The author reports that identification took about 1–2 seconds on the author’s laptop CPU. In an informal comparison, SigLIP ranked 15 of 16 Wikipedia lead photos correctly as the top guess. Those are developer-reported observations, not an independent benchmark or evidence that the same result will hold for other phones, browsers, photographs or field conditions.
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What role does Gemma play?
Gemma handles walk planning and the short written notes, while SigLIP handles image matching. For planning and note generation, the main model is Gemma 3 1B instruct, quantized to 4-bit and run using WebGPU. The reported model file is approximately 785 MB. A smaller Gemma 3 270M fp16 model is the CPU fallback.
The writing model is constrained with hand-written, one-line trusted traits for the species. The author says an earlier version allowed Gemma to recall facts freely and produced a false claim about a robin’s throat patch. The current approach is meant to make the note turn known traits into things a person can verify outdoors, rather than invite the model to supply unsupported natural-history facts.
Generated notes are also checked for required sections, wording based on the trusted traits, and prompt echo. If a response fails those checks, the app falls back to a plain note built from the traits. These safeguards reduce certain ways a generated answer can go wrong, but they do not make every identification or field note authoritative. A note can only be as useful as the candidate match and the traits available for it.
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Does it work offline, and what happens to location data?
The author describes the app as static files built with Vite and vanilla JavaScript, with Transformers.js and ONNX Runtime Web running in a Web Worker. The models are downloaded once and cached locally. The developer reports using a custom Origin Private File System cache because the browser Cache API failed for the large Gemma weights.
After the initial model download, the app is intended to run in airplane mode. This is particularly relevant on trails and in parks with weak signal: the model work is local rather than dependent on an active request to a remote AI service. The journal also stays in the browser, and recording location is optional. The author presents this local processing as avoiding transmission of walking routes to a remote server.
“Local” should be understood in the project’s described design, not as a guarantee about every aspect of a person’s device or browser. The available description does not specify browser-by-browser storage behavior, how long browser data survives, or a separate synchronization or backup service. If you want to keep journal entries, use the Markdown export rather than assuming browser storage is a permanent archive.
What device requirements and download should you expect?
The reported first download is approximately 900 MB in total. That is a substantial initial transfer for a web app, especially on a metered connection or a device with limited free storage. The large Gemma 3 1B instruct model accounts for most of the reported model size; the vision model is approximately 95 MB.
The 4-bit Gemma path requires WebGPU. If that path fails, the worker restarts and uses the smaller CPU model. A CPU fallback means the project has an alternate path, but it does not establish equal speed or output quality on every device. Exact browser support, minimum hardware, memory requirements and performance on specific phones are not stated in the project description.
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The author also reports that cross-origin-isolation headers enable multithreaded WASM. In one CPU-path measurement, this improved processing time from about 2.7 seconds to about 1.7 seconds. This is a developer-reported measurement, not a device-independent guarantee; it explains why the app’s hosting configuration can affect the CPU path.
| Component or behavior | Reported detail | What it means for a user |
|---|---|---|
| Photo-matching model | SigLIP base, 8-bit, approximately 95 MB, CPU/WASM | Image matching is designed to run locally; the guide limits the possible species matches. |
| Walk-planning and writing model | Gemma 3 1B instruct, 4-bit, approximately 785 MB, WebGPU | This is the main model path and requires WebGPU. |
| CPU fallback | Gemma 3 270M fp16 | Used if the WebGPU path fails; comparative speed and quality are not stated. |
| Initial model download | Approximately 900 MB total, as reported by the developer | Plan to download before relying on the app away from a connection. |
| Reported identification timing | About 1–2 seconds on the developer’s laptop CPU | This is not a promised speed for other devices. |
| Reported informal image comparison | 15 of 16 Wikipedia lead photos ranked correctly as the top guess | This is a small developer-run comparison, not independent accuracy validation. |
Is Pocket Field Guide safe to use for species or mushroom identification?
Use it as a learning aid, not as a scientific authority or a basis for a safety decision. The project does not establish scientific identification accuracy or medical or safety certification, and its guide covers about 150 common species rather than all possible organisms.
The mushroom warning is explicit and should remain prominent: “Never eat a wild mushroom identified by an app.” Mushroom identification can be consequential, and a photo-based match is not a safe way to decide whether a mushroom is edible. Do not eat a wild mushroom based on this app’s result or generated note.
For less consequential observations, use the result to decide what to inspect: compare visible traits, consider whether the setting makes sense, and leave the identity uncertain if the evidence does not fit. A useful note can direct your attention, but it cannot guarantee that the initial match is correct.
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Pocket Field Guide makes a clear trade: a large first download and device-dependent local processing in exchange for an experience designed to work without an ongoing cloud connection. Whether that trade is worthwhile depends on how you walk and what you expect from a nature app.
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- Less screen dependence: The short checklist and timer support a walk where the phone is put away between observations. Taking photos or reading notes still involves screen time when you choose to use those features.
- Offline use after setup: Local model files are meant to make the app usable in airplane mode, but you need to complete the initial download first.
- Local journal and optional location: The stated design keeps journal entries in the browser and makes location optional. Export entries if you want a separate copy.
- Bounded coverage: The built-in guide’s approximately 150 common species make the tool more focused than a universal field reference, but leave many possible species outside its stated coverage.
- Grounded notes rather than unrestricted recall: Hand-written traits and output checks address a failure mode the author encountered, though they cannot eliminate errors in matching or interpretation.
- Variable performance: WebGPU is required for the larger 4-bit writing model, with a smaller CPU model as fallback. The experience may therefore depend on the browser and device.
Who is Pocket Field Guide for?
It is best suited to a beginner who wants a low-pressure prompt for a short walk and is comfortable treating an AI match as a starting point. Someone who enjoys observing first and looking up details selectively may find the checklist-and-note format more appropriate than an app built around continuous logging or conversation.
It is a weaker fit if you need comprehensive species coverage, independently validated identification accuracy, safety-grade identification, a lightweight download, or a guaranteed performance level on a specific phone. The project description does not establish those capabilities. For a walk where a mistaken identification could affect a decision—especially any decision about eating a wild organism—use an appropriate qualified source instead of relying on an app.
Frequently Asked Questions
Can I use Pocket Field Guide without creating an account?
The project is described as having no account or API key. That is the author’s stated design; the description does not provide a browser-by-browser setup or sign-in compatibility list.
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The project offers Markdown export, which gives you a file you can keep outside the browser. The description does not establish automatic syncing or a direct import process on another device.
Does Pocket Field Guide cover insects and fungi as well as birds and plants?
The author presents it for noticing plants, birds, bugs, mushrooms and leaves, but the guide is described as containing about 150 common species. The description does not publish a complete species inventory or say how many entries belong to each group.
Will Pocket Field Guide run in every mobile browser?
The project description does not name a supported-browser matrix or minimum device specifications. Since the main writing model uses WebGPU and the fallback uses a different CPU model, support and performance should not be assumed to be identical across browsers.
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