This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass
What I Built
Most AI tools are designed to keep you talking to them. I wanted to build one that sends you away.
OpenField turns a question about your surroundings into a short field study. You type something you are curious about, like "Which side of my street stays shady the longest?", then add a place and a time budget (15, 30, 45 or 60 minutes). Local Gemma turns that into a small plan: 4–6 steps, the evidence to collect at each step, rough timing, and safety notes.
Then the app gets out of your way. You can open a minimal Field Mode with a timer and big step cards, or print a Field Card and leave your phone at home.
When you come back, you enter what you saw: notes, counts, measurements and photos. Gemma reads that evidence and writes a Field Report with three clearly separated parts:
- Observed: what you actually recorded
- Inferred: what reasonably follows from it
- Uncertain: what your evidence cannot support yet
AI plans. You observe. The world supplies the data. AI helps you make sense of it.
Who is it for? Anyone curious about a place they can walk to: students, gardeners, birders, walkers, or a teacher who wants a 30-minute outdoor activity that is more than a worksheet.
OpenField is not a navigation app, a scavenger hunt, or a chatbot with a map. The screen is the shortest part of the experience on purpose.
I Took It Outside
I did not want to write about getting off the screen while sitting at a screen all week, so I ran a simulated study to test the complete OpenField loop.
- My question: Which side of my street stays shaded the longest during this time of day?
- Where and when: A quiet residential street, late afternoon
- Time budget: 30 minutes
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Model and machine: Gemma 4 E4B (
gemma4:e4b) running locally through Ollama on a MacBook Air M4 with 24 GB RAM
What the protocol asked me to do: I compared the two sides of the street at several points during the 30-minute window. I recorded whether each observation point was in direct sun, partial shade or full shade, noted the approximate time, and took photos of representative locations.
What it was like without the phone: The interesting part was having to pay attention to the same locations repeatedly instead of checking the screen after every observation. Small changes in the shade line became much easier to notice when comparing the street from the same points.
What came back: I recorded 5 observation notes, 4 simple shade observations and 3 photos covering different points along the street.
What Gemma concluded:
- Observed: The recorded observations showed that the left side of the street was shaded at more observation points during the study period.
- Inferred: Based on those observations, the left side appeared to remain shaded for longer during the tested period.
- Uncertain: The study was too short to establish that the left side stays shaded longer throughout the entire day or across different days and weather conditions. Gemma correctly did not claim a general rule from the limited observations.
What went wrong: The 30-minute window was enough to compare the two sides, but not enough to establish a strong long-term pattern. A better version would repeat the same observations at different times of day and on multiple days.
Simulated field run used to test the complete evidence-to-report workflow.
Demo
The demo walks through the full loop:
Create Study → Field Protocol → Field Mode → Return & Evidence → Gemma debrief → Field Report
Code
TechGenDM
/
OpenField
OpenField is not an outdoor recommender or scavenger-hunt app. It is a local-AI “field study” engine that turns curiosity into a short real-world investigation.
OpenField 🌿
Ask a question about the real world. OpenField turns it into an honest field study.
"AI plans. You observe. The world supplies the data. AI helps you make sense of it."
OpenField is a local-AI field study engine powered by Gemma via Ollama. It turns curiosity into a short, structured investigation, helps you step away from the screen while observing, and uses the evidence you bring back to generate a report that separates what was observed from what was inferred and what remains uncertain.
Built for Hacktoberfest 2026.
What is OpenField?
Most AI tools keep the user on the screen.
OpenField does the opposite.
You give it a question about your surroundings. Local Gemma turns that question into a 15–60 minute field protocol. You take the protocol outside (or use it indoors), observe the real world, record notes/counts/photos, then return and let local Gemma analyze the evidence.
The…
OpenField is open source under the MIT License. The repo has the full app, a specification, architecture notes, a contribution guide, tests and setup instructions. The first commit was on October 6, 2026, and the project was built inside the Week 1 challenge window.
To run it yourself:
git clone https://github.com/TechGenDM/OpenField.git
cd OpenField
ollama pull gemma4:e4b
npm install
npm run dev
How I Built It
OpenField is built around Gemma running locally through Ollama. There is no cloud AI API anywhere in the core app. The stack is small on purpose: Next.js, TypeScript, Tailwind CSS, Zod, Ollama and Gemma.
Question → Protocol (Gemma) → Field Mode or Field Card → Evidence → Debrief (Gemma) → Observed / Inferred / Uncertain
1. Question to protocol.
The user gives a question, a place, a time budget and an investigation type. Gemma returns a structured Field Protocol: the research question, 4–6 steps, evidence requirements, time estimates, safety guidance and an optional audio briefing script.
I validate that output with Zod and deterministic checks. For example, the steps must fit the time budget, and the safety rules must be followed. A small local model gets checked, not trusted.
2. Leaving the screen.
Field Mode runs its timer and step navigation in the browser, so nothing needs to talk to Ollama while you are outside.
The even simpler option is the printed Field Card: paper, a pen, and no battery.
3. Bringing evidence back.
On the Return & Evidence screen you record observations per step and attach photos. Before any photo is used, the browser resizes it to a maximum of 1024px and strips the EXIF and GPS metadata.
The processed image is only sent to the local app.
4. The debrief.
Gemma gets the protocol and your evidence, and writes the Field Report. Every finding must point back to something you recorded.
If the evidence is thin, the report is designed to say so instead of filling the gap with confident guesses.
That Observed / Inferred / Uncertain split is the part of the design I care about most.
Why Does Open Innovation Matter?
A field study is about your street, your garden, your photos and your notes. That evidence belongs to the person who collected it.
Running Gemma locally through Ollama made three things possible that a closed API would not:
- Your evidence stays on your machine. Field photos and notes are never sent to a remote AI service just to get a report.
- It costs nothing to run. There is no API key and no per-request bill. Once the model is downloaded, the AI part does not need the internet.
- The model is swappable. The app talks to a structured local interface, not a proprietary endpoint, so another open model can be tried without redesigning the product.
There is a trade-off, and I want to be honest about it. A local model is smaller than the biggest cloud models, so I could not just trust whatever it said.
That is why the protocol is validated and why every claim in the report must point to evidence.
Open and local did not just make the project cheaper. They shaped how I designed it.
My Agent Session
I used AI coding assistants, including Antigravity IDE, for repository inspection, implementation, debugging, testing and release preparation.
I directed the product design, the architecture and the review of every change.
Prize Categories
Best Use of Gemma.
Gemma is the core reasoning engine behind both major parts of OpenField:
- Generating the Field Protocol from a real-world question.
- Performing the multimodal evidence debrief after the field study.
I am intentionally entering only the Gemma partner category because it is the technology that is genuinely central to the project rather than adding integrations just to collect categories.
What I Learned
The best AI experience might sometimes be the one that makes you use AI less.
It is easy to build another place where you ask a question and get an answer. It is harder to build something that says, "here is what to go and look at," and then stops talking.
The model only reasons about evidence the real world actually supplied.
That moved AI, in my head, from an answer machine to an investigation assistant.
What's Next
The MVP is intentionally small. I do not want OpenField to become a giant AI platform.
Next I want to improve accessibility, test coverage, printable field materials and report export. I have also opened focused contributor issues in the repository for these areas.
The core loop should stay simple:
Question → Observe → Evidence → Understand.
The best moment in OpenField is the one where you stop using it.
Thanks for reading!
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