# Lookabout: a photo walk that Gemma checks on your phone, offline > Source: > Published: 2026-10-11 16:51:15+00:00 *This is a submission for the [Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass](https://dev.to/challenges/hacktoberfest-week1-2026-10-05)* Lookabout is an Android app that gives you a reason to go for a walk and look around properly. A little walk. A fresh look. Before you leave, you pick how many pictures you want to find (3 to 6), a topic, and what the sky is doing. Gemma 4, running entirely on your phone, writes a short list of pictures to look for, fitted to the topic, the sky, the time of day and the month. Then you put the phone in your pocket and go. When you spot one, the phone's own camera app takes the photo, and Gemma checks it on the spot. Each picture comes with four yes/no questions. Two or more YES answers and the photo is **Accepted**. Fewer, and the app says **Not yet**, and you can retake it or skip it. The percentage is simply the share of YES answers, not the model's confidence. The theme asks for the screen to be the shortest part of the experience, so most of the work went into the moments when you are *not* looking at it: Everything runs on the phone. After the one-time model download, Lookabout needs no internet connection: the lists, the photos and the checks stay on the device. I took it out on a cloudy afternoon, and the video below is that walk. I chose three pictures and the topic Autumn colour, told the app there was some cloud, and let it use my rough location for the light. Then I switched on airplane mode and asked for the list: I hadn't written any rules about how cloudy weather should shape a photo challenge. Gemma was given "some cloud" and the time of day, and came back asking for a cloudy sky behind the leaves and diffused light on them. **The first picture was easy.** Orange leaves with the grey sky behind them: three of the four questions came back YES, so 75% and Accepted, about four seconds after I took the photo. **The second one made me work.** My first tries at brown and red foliage came back as Not yet, so I kept walking and kept looking. What got through in the end was a line of curled brown leaves along a path. Gemma had answered NO to "Is the light diffused by clouds?". I was standing under those clouds, so I tapped the question to correct it, and that took the photo to two of four: Accepted. **The third went through first time:** an open field under the afternoon sky. The Summary showed three of three accepted and about four seconds per check on my Google Pixel 7a. Gemma and I agreed on 11 of the 12 answers for the final photos. The only cut is at the start, where I shortened the one-time model download (2.6 GB). At 0:25 I switch on airplane mode. From there the phone is offline and everything is shown at real speed, including each photo check, which takes about four seconds. Want to try it? [Download Lookabout 0.1.0](https://github.com/nezhar/lookabout/releases/tag/0.1.0). It needs a real phone running Android 12 or later, and on first start it offers to download the model (2.6 GB, Wi-Fi only). Lookabout is an Android app for photo walks. It sets a challenge of 3 to 6 pictures to find on a walk, and checks each photo on the phone with Gemma 4 E2B, offline. It is for anyone who wants a reason to go outside and look around more closely. An entry for the Hacktoberfest Open-Source AI Challenge, Week 1: Touch Grass. *The designer's screens, with sample photos. Screenshots from a real walk will follow.* Lookabout is open source under Apache 2.0 and uses the openly available Gemma model and LiteRT-LM runtime. **The stack.** Kotlin and Jetpack Compose, with [Gemma 4 E2B](https://huggingface.co/litert-community/gemma-4-E2B-it-litert-lm) running through [LiteRT-LM](https://github.com/google-ai-edge/LiteRT-LM), Google's open-source runtime for on-device models. There is no inference server and no API key anywhere in the project. **Gemma does two jobs.** *It writes the walk.* The prompt carries the topic, the sky, the time of day and the month. If you allow it, the app also works out on the phone where the sun is and adds a line such as "low in the south-west", so the list can ask for long shadows when there are any. Gemma returns each picture together with its four yes/no questions. If an answer comes back incomplete, the gap is filled from a built-in list, which also covers the case where the model is not loaded yet. *It checks the photos.* Each photo goes to Gemma with its four questions, and the app counts the YES answers. I chose yes/no questions over a free-form "does this photo fit?" on purpose: a small model answers narrow questions more reliably, the result is easy to parse, and you can see exactly which answer you disagree with. **Making it hold up on a real phone.** This is where most of the time went. In January, I wrote [Beyond Vendor Lock-In: A Framework for LLM Sovereignty](https://nezhar.com/blog/llm-sovereignty-framework/), looking at how different ways of running language models affect data control, costs and vendor dependency. The framework goes from closed browser applications to fully self-hosted infrastructure. Lookabout gave me a chance to put those ideas into practice, on a much smaller scale. The framework's highest level of control is self-hosting: running the model yourself instead of depending on someone else's API. With Lookabout, the host is the phone in your pocket. That changes a few things. Of course, local inference comes with its own costs. The model is a 2.6 GB download, needs a capable Android phone and takes about four seconds to check a photo on my Pixel 7a. Getting the GPU fallback right and keeping the walk alive when Android closes the app took real work. That's also something I argued in the framework: more control doesn't mean less complexity. It means choosing which complexity you're willing to own. For Lookabout, that trade-off makes sense. The places worth walking to are often the places with no signal. A cloud API would have made the app dependent on connectivity at exactly the moment I wanted people to forget about their phones. The goal was never to build the most powerful image recognition system. It was to build a small, useful experience that works wherever you take it. And on that cloudy afternoon, in airplane mode, it did. Lookabout started as an experiment in running AI on a phone. It ended up giving me a reason to put that phone away.