Groundfall: The Offline Field Naturalist & Eyes-Up Sensory Walk Companion

Aditya Kumar Jha — Wed, 07 Oct 2026 19:10:23 +0000

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

What I Built

Most outdoor apps demand that you stare at a screen in the forest: following digital map lines, maintaining streaks, or logging stats. Groundfall is built on the inverse principle: make the screen the shortest part of the experience.

Groundfall is an offline field naturalist and sensory walk companion that actively encourages you to keep your phone in your pocket. Before you step outside, it crafts a sequence of tactile and acoustic micro-observations. Once you are out, it uses a unique "Eyes-Up" Screen-Time Auditor to measure how much time your screen was actually locked or hidden versus active. Every glance penalizes your Eyes-Up Naturalist Ratio, rewarding you for truly experiencing nature.

Demo

Check out Groundfall in action here:

aditya-prog-bit.github.io

Code

You can view the complete source code and run it locally:

aditya-prog-bit / groundfall

The Offline Field Naturalist & Sensory Walk Companion (Hacktoberfest Week 1: Touch Grass)

🌿 Groundfall: The Offline Field Naturalist & Sensory Walk Companion

"Build something with open-source AI at its core that gets people off the screen and into the world."
Submission for Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass (#hf26challenge, #devchallenge)

🌐 Live Interactive Demo

👉 https://aditya-prog-bit.github.io/groundfall/
Anyone can open and test Groundfall immediately on any device (phone or desktop) with zero installation, zero server required, and 100% on-device client privacy.

🍂 Why Groundfall?

Most "outdoor" apps do the exact wrong thing: they ask you to look at a screen while you're standing in front of an ancient oak tree. They flash notifications, show turn-by-turn map lines, and track gamified streaks that keep your gaze glued to glass.

Groundfall is built on the inverse principle: Make the screen the shortest part of the experience.

Before you step outside, Gemma crafts a tailored sequence of tactile, acoustic, and botanical…

View on GitHub

How I Built It

Groundfall relies entirely on local, on-device intelligence and native web APIs to function deep in the backcountry:
Google's Gemma (Open-Weights): Acts as the core sensory engine. Before departure, Gemma crafts custom tactile and acoustic micro-observations (e.g., noticing leaf chlorosis transitions or identifying high-frequency bird calls).
Backboard Memory: Used for the Field Notebook feature, seamlessly summarizing walk debriefs, awarding botanical specimen badges, and storing observations in a persistent .backboard/field_memories.json format.
Page Visibility API (document.visibilityState): Powers the "Eyes-Up" auditor to track and penalize active screen time.
Web Audio API & Real-Time FFT: Synthesizes zero-asset trail bells (587 Hz & 880 Hz) directly in memory to avoid network downloads. It also monitors environmental sound frequencies to compute an ambient Acoustic Green Index, filtering out mechanical hums.
FastAPI & Python: Bridges the local Ollama inference with the frontend PWA.

Why Does Open Innovation Matter?

Open innovation is the only way a project like Groundfall can exist. Challenging the cloud-API-first orthodoxy, local inference proves that intelligent applications don't need subscriptions, credit cards, or continuous network connectivity.

For backcountry tools and privacy-first outdoor companions, zero-network open-weight models represent a genuine paradigm shift. By utilizing open-source tools, I was able to build an AI application that respects user privacy and actually works when you are miles away from the nearest cell tower, keeping walkers connected to nature rather than their screens.

Prize Categories

I am submitting Groundfall for the following prize categories:

Best Use of Gemma
Best Use of Backboard

Hacktoberfest Build: A Persistent Coding Companion using Backboard's Vector Memory

Aditya Kumar Jha — Sun, 04 Oct 2026 18:28:40 +0000

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

What I Built

Who I Built This For: My friend, who is currently enrolled in a fast-paced coding bootcamp.

The Problem & Solution: When bootcamp students get stuck, they often paste errors into ChatGPT and get the final code—bypassing the learning process completely. To help my friend actually learn, I built a Socratic "Rubber Duck" tutor using Backboard's Stateful Vector Memory and Semantic RAG. Instead of just giving the answer, it acts as a patient tutor, retrieving relevant coding concepts and asking guiding questions while tracking their progress across sessions.

Demo

When my friend asks for help with a math error in their code, the agent uses a live Backboard RAG citation to guide them rather than writing the fix:

🧠 Backboard RAG: Concept: Mean Squared Error (MSE)... (Score: 0.53)
"Let's look at how you are calculating squared_errors. What happens mathematically when you subtract targets ** 2 from the differences, rather than squaring the differences themselves?"

Code

aditya-prog-bit / rubber-duck-companion

Socratic Coding & Debugging Companion built with Backboard R-CLI and Vector Memory RAG for Hacktoberfest 2026

🦆 The "Rubber Duck" Coding Companion

Built with Backboard R-CLI & Socratic Agent Harness for Hacktoberfest 2026
"Ship something that solves a real problem for a friend or someone you love."

🎯 Overview

The Rubber Duck Coding Companion is an interactive, Socratic AI debugging companion built to help beginners and friends learn how to code and debug on their own—without generic AI immediately spoiling the answer.

Generic AI models usually rewrite the whole function the moment you paste an error. While that gets past the compiler, it deprives learners of the essential "aha!" moment.

By leveraging Backboard's Vector Memory & RAG Infrastructure along with its custom agent harness, we built a stateful debugging companion that:

Requires NO paid LLM chat tokens: Runs entirely on Backboard's free vector memory tier.
Stores persistent student memories: Logs student learning milestones, mistakes, and eureka moments across sessions.
Retrieves pedagogical…

View on GitHub

How I Built It

Socratic Sub-Agent: I configured .backboard/agents/rubber_duck.md with strict rules: NEVER write the final code. Ask guiding questions one at a time.
Vector RAG Knowledge Base: I loaded Backboard's vector database with debugging concepts. Backboard's semantic search retrieves the right hints without using paid LLM chat tokens.
Persistent Student Memory: When my friend solves a bug, Backboard automatically saves a persistent cloud memory, remembering their achievements for future sessions.
Memory & RAG Studio UI: I built a local dashboard at 127.0.0.1:8000 where my friend can inspect their live vector memories and test semantic searches.

Why Does Open Innovation Matter?

Closed chatbots force you into their default "eager-to-solve" behavior. Open innovation frameworks like Backboard allowed me to strip that out and inject a strict pedagogical framework tailored to how a student actually needs to learn. Furthermore, utilizing accessible vector memory locally in their terminal ensures my friend's bootcamp assignments remain completely private.

Prize Categories

Best Use of Backboard

DEV Community: Aditya Kumar Jha

Groundfall: The Offline Field Naturalist & Eyes-Up Sensory Walk Companion

What I Built

Demo

Code

aditya-prog-bit / groundfall

The Offline Field Naturalist & Sensory Walk Companion (Hacktoberfest Week 1: Touch Grass)

🌿 Groundfall: The Offline Field Naturalist & Sensory Walk Companion

🌐 Live Interactive Demo

🍂 Why Groundfall?

How I Built It

Why Does Open Innovation Matter?

Prize Categories

Hacktoberfest Build: A Persistent Coding Companion using Backboard's Vector Memory

What I Built

Demo

Code

aditya-prog-bit / rubber-duck-companion

Socratic Coding & Debugging Companion built with Backboard R-CLI and Vector Memory RAG for Hacktoberfest 2026

🦆 The "Rubber Duck" Coding Companion

🎯 Overview

How I Built It

Why Does Open Innovation Matter?

Prize Categories