DEV Community: ABHINASH SAHOO

GreenBuddy AI

ABHINASH SAHOO — Tue, 13 Oct 2026 08:39:39 +0000

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle

What I Built

I built GreenBuddy AI, an AI-powered gardening assistant that helps beginners identify plants, get plant-care guidance, and plan a garden based on their location, season, and available growing space.

I wanted to make gardening easier for people who want to grow plants at home but don't always know which plants they have, how to care for them, or what they can grow in limited spaces like balconies.

GreenBuddy AI combines plant image identification with AI-generated gardening guidance to make getting started less intimidating.

The potential customers are beginner gardeners, balcony gardeners, and people who want practical help caring for their plants. In the future, the project could offer personalized gardening plans or additional plant-care features as a possible side hustle. I'm still exploring this opportunity and validating what users find most useful.

Demo

Try it locally: Run GreenBuddy AI on your computer to explore its features.

Project post: https://dev.to/amlan24/greenbuddy-ai-3259

The application is currently being tested locally, so a public deployed demo is not available yet.

Code

GitHub repository: https://github.com/AMLAN-07/GreenBuddyAI

The repository contains the source code for the project. Feedback is collected through an in-app form and saved locally for review.

How I Built It

I built GreenBuddy AI using Python and Flask for the backend, with HTML, CSS, and JavaScript for the user interface.

The project uses an open plant-identification model from Hugging Face to analyze plant images. I also use local AI tooling with Ollama and the Qwen 2.5 3B model for gardening assistance.

The main features include:

Plant identification:

Plant-care guidance: Get assistance with everyday gardening questions.

Gardening planner: Explore planting suggestions based on location, season, and growing space.
User feedback: Collect feedback through a form and save responses to a CSV file.

I tested the application with another person and added a feedback workflow so I can collect suggestions and improve the project based on real usage.

Why Does Open Innovation Matter?

Open innovation made it possible for me to build an AI-powered application without training a large model from scratch or relying entirely on a closed, hosted AI API.

Using open models and tools gives me more control over experimentation and makes it easier to understand how the different parts of my application work together.

Local inference also offers a path toward keeping gardening questions and other inputs on the user's computer when the relevant feature runs entirely locally. Some features, such as downloading models or accessing online resources, may still require an internet connection.

For me, open innovation means that an individual developer can take existing AI research and turn it into something practical, affordable to experiment with, and open to future improvements.

My Agent Session

I explored AI-assisted development tools while building and improving GreenBuddy AI.

I haven't included a saved DevRelay agent session link here because I don't yet have a verified session URL to share.

My First User

I tested GreenBuddy AI with another person and implemented a feedback form to collect suggestions. The feedback is saved locally in user_feedback.csv.

The tester suggested adding a new feature. This helped me recognize the importance of building around user needs instead of relying only on my own assumptions.

I'm reviewing the suggestion to decide which improvement to implement next.

There has been no confirmed payment from this test. My current goal is to validate the problem, improve the application, and understand whether it provides enough value for people to use regularly.

Prize Categories

I'll select the partner prize categories that match the challenge's official rules and the tools actually used in my project before publishing.

GreenBuddy AI

ABHINASH SAHOO — Fri, 09 Oct 2026 13:08:51 +0000

GreenBuddy AI 🌱 — Your Local Gardening Companion

🌿 What I Built

GreenBuddy AI is an AI-powered gardening assistant designed to help beginners identify plants, learn how to care for them, plan seasonal planting, and spend more time outdoors.

I built this project for the Hacktoberfest Open-Source AI Challenge — Week 1: Touch Grass.

✨ Features

🌱 Plant Identification: Upload a plant photo to get likely plant names and model confidence scores.
💧 Plant Care Assistant: Get AI-generated guidance for watering, sunlight, soil, and general plant care.

🪴 Planting Planner: Generate a planting plan based on your location, growing space, and selected period.
🌳 Outdoor Missions: Track simple gardening and outdoor activities to encourage spending more time with nature.
🤖 Local AI Assistant: Use a locally running language model through Ollama for gardening questions and suggestions.

🛠️ Tech Stack

Python and Flask
HTML, CSS, and JavaScript
Ollama with Qwen 2.5 3B
Hugging Face Transformers
PyTorch and Pillow

💡 Why Open-Source AI?

I wanted to explore how AI could support an everyday activity while keeping the project accessible and transparent.

GreenBuddy AI combines local language-model inference with image classification. Once the required models are downloaded, inference can run locally, reducing dependence on hosted AI APIs and helping keep plant photos and gardening questions on the user's device.

The application still needs Ollama and its models installed, and the plant-identification model must be downloaded before its first use. Some gardening recommendations may need verification for local climate and growing conditions.

🚀 Try the Project

GitHub repository: https://github.com/AMLAN-07/GreenBuddyAI

The project currently runs locally. Follow the setup instructions in the repository's README to install the dependencies and start the application.

🌱 What I Learned

Building GreenBuddy AI helped me explore how to connect a local language model to a Flask application, integrate an image-classification model, and create a frontend that brings multiple AI features together.

I also learned that AI-generated gardening advice should be treated as a starting point and checked against reliable, location-specific information.

🔮 What's Next?

Improve plant-identification accuracy and display clearer results.
Add weather-aware planting recommendations.
Improve gardening advice for different regions and seasons.
Add tests, documentation, and more beginner-friendly setup instructions.
Welcome contributions from the open-source community.

🤝 Contributions

Suggestions, bug reports, and contributions are welcome! Please visit the GitHub repository to explore the code and contribute.

🏆 Challenge

Built for Hacktoberfest 2026 — Week 1: Touch Grass.

Thanks for checking out GreenBuddy AI. Let's use AI to learn about plants, grow greener spaces, and reconnect with nature! 🌿

Building Kairos AI: My 10-Day Journey Building a Voice Coding Mentor with Murf Falcon & LiveKit

ABHINASH SAHOO — Sat, 15 Aug 2026 11:34:04 +0000

🔗 Project Repository
The complete source code for Kairos AI is available on GitHub:
https://github.com/AMLAN-07/murf-livekit-starter
Meet Kairos AI
Kairos AI handling a voice conversation with the user.

Specialist Agent Handoff
Kairos handing a DSA problem to the dedicated DSA Specialist Agent.

Call Analytics Dashboard
Kairos AI call analytics showing call outcomes.

LiveKit signal connection
↓
Timeout
↓
Tested network connectivity
↓
IPv6 connection failed
↓
IPv4 connection succeeded
↓
Adjusted network configuration
↓
LiveKit worked

Challenges Along the Way

"The project definitely didn't work perfectly on the first attempt."
LiveKit timeout → IPv6 investigation

Building Kairos was not always smooth. I faced issues with LiveKit connection timeouts, networking, agent handoffs, and tool integration. Debugging these problems helped me understand that building a voice agent requires not only good code, but also careful testing of real-time communication and system configuration.

What Didn't Go as Planned

Kairos was not built in one smooth run.

I had issues with LiveKit connectivity, IPv6 networking, agent handoffs, tool execution, and outbound calling. Some features worked in isolated tests but behaved differently when connected to the complete voice workflow.

Those problems were actually some of the most useful parts of the challenge. They taught me to debug the entire system instead of assuming every problem was caused by the Python code.

Building a voice agent is as much about debugging real-time systems as it is about writing prompts and integrating AI models.

The Problem I Wanted to Solve I wanted Kairos to provide a more natural interface: Talk → Think → Discuss → Practice
Meet Kairos AI Kairos can also communicate in English, Hindi, and Hinglish, allowing users to interact in a more natural way.
Murf Falcon — Giving Kairos a Voice tts=murf.TTS( voice="Samar", locale="en-IN", style="Conversational", tokenizer=tokenize.basic.SentenceTokenizer( min_sentence_len=2 ), text_pacing=True, )
Human Help / Escalation An AI mentor shouldn't pretend it can solve every situation.
Specialist Agent Handoff Instead of making Kairos an expert at everything, I created a separate specialist whose only responsibility is DSA.
Outbound Calling I also added an outbound-call capability.
Final Architecture After 10 days, Kairos evolved into something much more interesting than the simple voice agent I started with. ┌─────────────────┐ │ User │ └────────┬────────┘ │ Voice / Phone │ ▼ ┌─────────────────┐ │ LiveKit │ └────────┬────────┘ │ ▼ ┌─────────────────┐ │ Kairos AI │ │ Main Agent │ └───────┬─────────┘ │ ┌──────────────────┼──────────────────┐ │ │ │ ▼ ▼ ▼ Memory Tools DSA Specialist │ │ │ │ ┌───────┴──────┐ │ │ │ │ │ │ Codeforces Human Help │ │ │ │ │ └──────────┴───────┬──────┘ │ │ │ ▼ │ Analytics / Calls ◄────────┘ │ ▼ Murf Falcon TTS │ ▼ 🔊 User Conclusion

When I started this challenge, the goal was simply to build a voice agent.
Ten days later, I had built Kairos AI, a voice coding mentor with:

🎙️ Real-time voice interaction
🇮🇳 Indian English voice
🧠 Persistent memory
💻 Coding-problem tools
🛡️ Guardrails
🧑‍💻 Human escalation
📞 Outbound calling
📊 Call analytics
🤝 Specialist-agent handoffs
🌐 Multilingual interaction

The biggest takeaway for me is that voice AI is not just about making an LLM speak.
It's about designing an entire interaction system around the user.

And that's what made these 10 days so valuable.