Smallwild

Sneha Ghosh (Technical_Isopod_075) — Wed, 07 Oct 2026 16:16:54 +0000

Smallwild

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass

What I Built

Smallwild is an urban nature side-quest generator. Choose how much time you have, what kind of outdoor space is nearby, how you feel like moving, and something you are curious about. A local model turns those broad details into one short invitation to step outside and notice the ordinary wild around you.

It is for people whose nature is a street tree, a crack in the pavement, a courtyard, or a pocket of sky between buildings. There is no address field, GPS, map, route, species identification, pollution score, or safety verdict. The point is not to navigate a screen; it is to take one idea outside and look around.

Demo

Run it locally using the steps below. There is no deployed demo or field-test video included yet.

Code

This repository contains a static HTML, CSS, and JavaScript app. The browser sends the selected prompt to Ollama on the user's machine; there is no project backend and no remote AI API.

How I Built It

Smallwild uses Ollama to run an open-weight model locally. The default is gemma3:1b; the model field can be changed to any model already pulled into Ollama. A constrained prompt asks for a compact JSON quest: an invitation, three things to notice, a listening prompt, and a gentle closing thought. The app renders it as a printable field card. A tiny outing count is stored in local browser storage; no observations, locations, or profiles are saved.

To run it:

Install Ollama and pull a model, for example ollama pull gemma3:1b.
Start Ollama and allow the app's browser origin in Ollama's OLLAMA_ORIGINS setting. For a local app, that is http://localhost:8000. For a forwarded preview, use the exact origin shown in the app's connection error. If you run Ollama manually, for example: OLLAMA_ORIGINS="http://localhost:8000,https://<your-forwarded-app-origin>" ollama serve. If Ollama is already running as a service or desktop app, add the origin to its environment and restart it. Avoid OLLAMA_ORIGINS=* on a network-accessible machine.
In this repository, run python3 -m http.server 8000 and open http://localhost:8000.
Enter your local model's name in the form if you chose something other than gemma3:1b.

After downloading the model, inference and the app's assets can run locally. Smallwild does not fetch weather, air quality, maps, or location data. Its suggestions are creative prompts, not verified ecological facts or personal-safety advice; use your own judgment about where to go.

Why Does Open Innovation Matter?

The useful input here is personal but deliberately approximate: what kind of place is nearby and how much time someone has. A local open-weight model means even those broad clues do not need to go to a hosted API. The model is swappable, the prompt is inspectable, and the static app is easy to remix for another language, neighborhood, or model. Once the model is downloaded, generating a quest does not require a per-request service or an internet connection.

Open innovation also makes the limits visible. Smallwild does not pretend a language model can verify local species, weather, or outdoor safety. It makes a small creative suggestion, then gets out of the way. The tradeoff is that users need Ollama, a compatible device, and a locally downloaded model; response quality varies by model.

My Agent Session

Not included.

ExoSight AI Explorer: Discovering Exoplanet Candidates with NASA Data

Sneha Ghosh (Technical_Isopod_075) — Wed, 07 Oct 2026 14:16:27 +0000

This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend

What I Built

I built ExoSight AI Explorer for a friend who loves space but doesn’t have a background in astronomy or machine learning. The goal was to make NASA exoplanet data feel accessible, visual, and exciting instead of overwhelming.

ExoSight AI Explorer is an AI-powered web app that analyzes NASA Kepler/TESS exoplanet data, trains a machine learning model to identify promising exoplanet candidates, and presents the results in a simple interactive dashboard. It helps users explore candidate planets, review key features like orbital period, planetary radius, temperature, and insolation, and understand which objects stand out as high-potential exoplanets.

This project turns raw scientific data into a more intuitive experience for curious people who want to explore the universe without needing to parse complicated research datasets.

Demo

This project is designed to run locally as a Streamlit app:

Launch with: streamlit run app.py
The repo includes the trained model, data processing scripts, and a web interface for exploring exoplanet candidates.

While I don’t have a public deployment link yet, the full app and source code are available in the repository below.

Code

https://github.com/SnehaghoshBarsha444/nasa-space-apps-exosight-ai-explorer

How I Built It

I built ExoSight AI Explorer using open-source tools and public astronomy data.

Data source: NASA exoplanet datasets from Kepler/TESS-style catalog data
Frontend/app interface: Streamlit
Data processing: pandas
Visualization: Plotly
Model training: scikit-learn
Model persistence: joblib

The app processes exoplanet features like:

orbital period
planetary radius
equilibrium temperature
duration
impact parameter
insolation

It then uses a Multi-Layer Perceptron (MLP) classifier to flag exoplanet candidates with high confidence. The model identifies likely candidates from the dataset and stores them in a generated CSV for exploration in the app. The result is an approachable interface that makes it easier to browse candidate exoplanets instead of staring at raw scientific tables.

Why Does Open Innovation Matter?

Open innovation matters here because the foundation of this project is built on public NASA data and open-source software. Without open datasets, transparent model tooling, and community-driven scientific tooling, this kind of project wouldn’t be possible in such a lightweight and accessible way.

Using open-source libraries like pandas, scikit-learn, and Streamlit made it possible to:

train an ML model on real astronomy data
build a user-friendly interface quickly
make the project easy to reproduce and extend
share results publicly with the broader community

A closed API or proprietary system would have made this much harder to build in a transparent, explainable, and reproducible way. Open innovation lets people explore science together, not just consume it.

My Agent Session

Optional, but not included here.

Prize Categories

Open Source AI / Open-Weight Models
Space + Science
Best Use of AI

DEV Community: Sneha Ghosh (Technical_Isopod_075)

Smallwild

Smallwild

What I Built

Demo

Code

How I Built It

Why Does Open Innovation Matter?

My Agent Session

ExoSight AI Explorer: Discovering Exoplanet Candidates with NASA Data

What I Built

Demo

Code

How I Built It

Why Does Open Innovation Matter?

My Agent Session

Prize Categories