AuraFlora

Navneet Sinha — Mon, 12 Oct 2026 02:39:51 +0000

🌿 An AI Companion That Tells You When to Put Your Phone Down

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

What I Built

AuraFlora is a bioacoustic grounding companion designed to help people disconnect from their screens and reconnect with the physical world.

We spend hours looking at screens, listening to playlists, and consuming digital content—even when we're surrounded by nature. Most wellness applications add more screen time to the very problem they're trying to solve.

AuraFlora takes a different approach: use AI to understand the sounds around you, give you one meaningful physical activity, and then get out of your way.

The experience follows a simple loop:

🎙️ Press: Tap a single button to begin recording.
🌿 Listen: Capture 5–10 seconds of ambient environmental audio.
🧠 Analyze: Extract acoustic features and distinguish environmental sound characteristics.
🗣️ Ground: Generate a short, actionable grounding instruction using AI.
⬛ Disconnect: Deliver the instruction through spoken audio, then transition to a pitch-black screen.

Imagine receiving a prompt such as:

"Notice the distant hum, then find one quieter sound nearby."

Instead of opening another feed or searching for something to watch, you have a small, tangible reason to pay attention to the world around you.

The design follows a strict philosophy: no feeds, no streaks, no leaderboards, and no unnecessary notifications. The goal is to make the interaction brief enough that you can return to the real world.

Demo

🌐 Live Demo: [APPLICATION URL]

🎥 Video Demo: [YOUTUBE LINK]

The project is designed around a mobile-first grounding experience, with a minimal interface and an intentionally screen-free ending.

Code

🌱 GitHub Repository: [GitHub Repo Link]

AuraFlora combines a React-based web experience with a Rust backend and a Flutter mobile application.

The repository explores how acoustic analysis, AI-generated instructions, and spoken guidance can be combined into a short, purposeful interaction.

How I Built It

I wanted to explore a different use of AI: not generating more content for people to consume, but helping them become more aware of their surroundings.

The technology stack

React + TypeScript: Web experience and user interaction.
Rust + Axum + Tokio: Backend API and asynchronous service orchestration.
rustfft: Acoustic signal processing and feature extraction.
TabPFN: Soundscape classification integration, with a heuristic fallback where configured.
Gemma: Grounding-instruction generation.
ElevenLabs: Text-to-speech generation.
Flutter + Riverpod: Native mobile application and interaction-state management.

The architecture

The intended processing pipeline is:

Ambient Audio → Acoustic Feature Extraction → Soundscape Classification → Grounding Instruction → Speech Generation → Blackout

The Rust backend is responsible for coordinating the processing pipeline and external AI services. The frontend handles recording, interaction state, audio delivery, and the transition into the blackout experience.

The acoustic-analysis stage can examine characteristics such as RMS energy, spectral centroid, spectral entropy, and frequency-band energy ratios. These features can inform the grounding instruction, although they do not independently guarantee accurate identification of individual sound sources.

The project also explores explicit state management, fallback behavior, and a minimal interaction model to keep the experience focused.

Why Does Open Innovation Matter?

Open innovation matters because the future of AI should not be limited to building applications that maximize engagement or keep users inside a platform.

With open-weight models, open-source tooling, and self-hostable infrastructure, developers can experiment with AI applications that prioritize different values: privacy, user autonomy, transparency, and control.

For AuraFlora, this creates several opportunities:

More control over the AI pipeline: Different models and inference environments can be evaluated without redesigning the entire product.
Self-hosting possibilities: Components can be deployed on infrastructure controlled by the project, reducing dependence on a single provider.
Privacy-conscious experimentation: Local or edge processing can reduce the need to transmit raw recordings when the architecture supports it.
Community-driven improvement: Contributors can improve DSP methods, prompts, error handling, accessibility, and model integrations.
A different relationship with technology: AI can be used to encourage meaningful offline activity rather than endless digital consumption.

Open innovation does not automatically make an application private or offline-capable. Those properties depend on the actual deployment and data flow. But open tools give developers greater freedom to investigate, build, and improve these capabilities.

That is what makes this challenge exciting to me: building technology that knows when to step back.

My Agent Session

This project also involves experimenting with AI-assisted development to improve implementation quality, architecture, and debugging workflows.

Agent session: You can inspect the complete execution flow, prompts, and task breakdowns in the [.task directory]

Prize Categories

Best Use of Gemma: Used Gemma as the core open-weight model for generating context-aware grounding instructions.

Best Use of TabPFN: Integrated TabPFN for soundscape classification features to analyze acoustic properties.

Best Use of ElevenLabs : Utilized ElevenLabs text-to-speech to deliver spoken audio guidance right before the screen transitions to blackout.

🌿 AuraFlora — Listen to the world. Leave the screen.

Built for Hacktoberfest 2026 · Touch Grass Challenge

Hacktoberfest #OpenSource #AI #Rust #React #Gemma #TouchGrass

Ghost-Hunter: An Air-Gapped, Temporal-Powered Follow-Up Sentinel with Local Gemma 3

Navneet Sinha — Mon, 05 Oct 2026 04:27:34 +0000

Ghost-Hunter is an open-source, local-first, air-gapped follow-up sentinel built to eliminate forgotten customer requests, unresolved operational tickets, and missed follow-ups. Powered by Local Gemma 3 via Ollama and orchestrated using Temporal, Ghost-Hunter acts as an intelligent background agent that continuously monitors, tracks, and triggers proactive follow-ups without relying on external cloud APIs or exposing sensitive user data.

Key features include:

Local-First AI Execution: Operates entirely offline using Ollama and Gemma 3 to analyze conversation logs, detect open action items, and draft responses.
Temporal Workflow Orchestration: Reliable state machine handling for scheduling, retries, and time-based triggers.
Air-Gapped & Secure: Zero third-party cloud data transmission, ensuring strict data privacy and compliance.
Interactive Sentinel Dashboard: Real-time monitoring of pending follow-ups, sentiment tracking, and response approval workflows.

Demo & Source Code

GitHub Repository: Neet2516/Ghost-Hunter

Architecture

+-------------------------------------------------------------------+
|                        Ghost-Hunter Engine                        |
|                                                                   |
|  +--------------------+    +-------------------+    +----------+  |
|  | Event Triggers     | -> | Temporal Workflow | -> | Local    |  |
|  | (Tickets / Logs)   |    | Engine            |    | Gemma 3  |  |
|  +--------------------+    +-------------------+    +----------+  |
|                                                          |        |
|  +--------------------+    +-------------------+         v        |
|  | Follow-Up Action   | <- | Approval Queue    | <-------+        |
|  | (Email / Slack)    |    | & Sentinel UI     |                  |
|  +--------------------+    +-------------------+                  |
+-------------------------------------------------------------------+

Ingestion Layer: Captures incoming tickets, user inquiries, or log entries requiring trackable follow-ups.
Temporal Engine: Schedules workflow timers and manages execution state across days, weeks, or months.
Local LLM Processing: Queries Gemma 3 locally to summarize state, evaluate necessity of follow-up, and generate draft text.
Action Queue: Places generated follow-ups in an operator review queue or dispatches automated alerts.

Key Capabilities

Automated Follow-Up Detection: Automatically classifies whether a user query requires follow-up action.
Context-Aware Response Generation: Contextual drafts synthesized using Gemma 3.
Resilient Scheduling: Built-in retry mechanisms and failure recovery via Temporal workflows.

DEV Community: Navneet Sinha