Nature Bingo

Tanisha Naruka — Sun, 11 Oct 2026 14:56:16 +0000

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

What I Built

Nature Bingo is a web app designed so that the best session is the shortest one: open it, get a quest, put the phone in your pocket, and go outside.

You tell the app what you like (birds, plants, photography, hiking, mindfulness), how much time you have (5 to 60 minutes), and whether you're indoors or out. Gemma, an open-weight model, writes one short, safe nature quest for you, like "Listen and Count: stand still and note every different bird call you hear."

Then you play in the real world:

3x3 Nature Bingo: tick off real finds like a bird, a feather or running water. Completing a line earns bonus XP.
Nature Journal: record what you noticed.
Nature Passport: collect twelve stamps as you play.
Screen-time page: log your minutes against a daily goal.

It's for anyone who wants a gentle nudge outside without another app competing for their attention: students, desk workers, families, and beginner birders or hikers.

Demo

https://drive.google.com/file/d/1Tu0LLBFrjHmqe3Ih2ZnQOzDoN33-pzN7/view?usp=sharing

Code

https://github.com/tanisha-naruka/Nature-Bingo.git

How I Built It

Model: Gemma, Google's open-weight model, running locally through Ollama (gemma3:1b while developing, configurable up to gemma3:4b or larger).
Stack: Next.js 14 (App Router), TypeScript and Tailwind CSS.
Server route: Gemma is only ever called from a server route. It validates input, caps lengths, rate limits requests, and asks the model

Panel Prep

Tanisha Naruka — Mon, 05 Oct 2026 06:57:06 +0000

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

What I Built

I built PanelPrep, a local AI interview-practice coach designed to help someone prepare for important interviews.

The problem wasn't finding another list of interview questions. The difficult part was actually practicing out loud.

PanelPrep turns the user's own study material into an interactive mock interview. Users can upload PDFs, DOCX files, text files, images, or scanned documents, and PanelPrep generates questions based on that material.

Users can answer by speaking or typing, receive follow-up questions, and review measurable feedback such as speaking speed, filler words, answer length, and important terms used.

It supports both English and Hindi, making interview practice more comfortable and accessible.

Demo

https://drive.google.com/file/d/148w7j5kkjxSWfAF4OC9RqslQQr40H3aJ/view?usp=sharing

Code

https://github.com/tanisha-naruka/Panelprep.git

How I Built It

The core of PanelPrep is Gemma, Google's open-weight model, running locally through Ollama.

The user provides their own preparation material, which is processed by the application and used to generate interview questions through the local Gemma model.

The application is built with JavaScript and Node.js. It also includes document extraction, OCR, voice interaction, interview metrics, profiles, history, backups, and progress tracking.

The AI generates the interview questions, while normal application code calculates measurable results such as speaking speed, filler words, answer length, and important terms used.

If the model produces invalid output or Ollama is unavailable, PanelPrep can fall back to a built-in question bank instead of simply breaking.

Why Does Open Innovation Matter?

Interview preparation can involve personal information, resumes, academic records, and private preparation material.

Using an open-weight model locally means the core AI experience can run directly on the user's computer instead of requiring their personal preparation material to be sent to a closed AI service.

This gives PanelPrep:

Privacy for personal preparation material
No per-session cloud AI cost
The ability to change or compare local models
More control over how AI output is handled
An AI experience that can work locally without depending on a paid API

For this project, open innovation wasn't just a technical choice.

It made it possible to build a more private and controllable interview coach for a real person.

Prize Categories

Best Use of Gemma

PanelPrep uses Gemma 3 locally through Ollama as the core AI model for generating interview questions from the user's own material.This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend

DEV Community: Tanisha Naruka

Nature Bingo

What I Built

Demo

Code

How I Built It

Panel Prep

What I Built

Demo

Code

How I Built It

Why Does Open Innovation Matter?

Prize Categories

Best Use of Gemma

What I Built

Demo

Code

How I Built It

Why Does Open Innovation Matter?

My Agent Session

Prize Categories