TouchGrass

Rajanikant Singh — Mon, 12 Oct 2026 07:00:10 +0000

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

unScroll — Stop Scrolling. Start Living. 🌱

What if the best app was the one that made you close it?

We open our phones for a minute and somehow lose an hour scrolling. Meanwhile, the world outside keeps waiting.

That's the problem I wanted to tackle with unScroll — an AI-powered application designed to help people step away from endless scrolling and reconnect with the real world.

What I Built

unScroll turns digital detox into an experience built around real-world missions.

Its core philosophy follows three stages:

Attract: Discover a mission that encourages you to do something meaningful.
Release: Put your phone away and experience the moment.
Reward: Return, reflect on the experience, and build your progress.

Whether it's spending time in nature, exploring your surroundings, or connecting with people, the goal is simple: less time consuming content, more time creating real-life experiences.

Instead of competing for your attention, unScroll gives you a reason to reclaim it.

Demo

🌐 Try unScroll

Code

💻 Explore the GitHub repository

How I Built It

I approached unScroll with one principle: AI should encourage action in the real world, not keep people glued to another screen.

The project uses Gemma 2 and open-weight AI as part of its mission-driven approach. The application is designed around generating real-world challenges, applying safety rules, and encouraging users to step away from their screens.

The experience follows a simple loop: discover a mission, step away from the screen, and return to reflect on the experience.

Why Does Open Innovation Matter?

If AI is going to shape how we live, we should have the freedom to shape how AI works.

Open-weight models make it possible to experiment, customize model behavior, and build experiences around specific goals rather than relying entirely on closed APIs.

For unScroll, that goal is different from the usual engagement-driven application: help people spend less time online and more time living offline.

Open innovation gives developers the freedom to explore that idea and build technology around human well-being.

My Agent Session

I built unScroll using Gemini 3.1 Pro in the Antigravity IDE, leveraging AI-assisted development to help build and refine the application.

🔗 Project repository and code

Prize Categories 🏆

Overall Grand Prize ($250): Using Gemma 2 and open-weight AI to rethink the role of technology—helping people step away from screens and reconnect with the real world.
Best Use of Render ($200): Hosting the application on Render, bringing together the FastAPI backend and React frontend.
Best Use of ElevenLabs ($200): Voice-driven mission briefings and audio playback that help users engage with their missions while minimizing screen interaction.

We have enough apps fighting for our attention.

Maybe it's time to build one that helps us give that attention back to the world.

unScroll. Stop scrolling. Start living.

🌍 Plan Your Next Adventure with TripSort

Rajanikant Singh — Mon, 05 Oct 2026 04:43:36 +0000

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

What I Built

I built TripSort, an Agentic AI-powered trip planner that helps travelers plan complete trips without the hassle of manually searching and coordinating flights, destinations, activities, and travel information.

Instead of relying on a single AI agent, TripSort uses 5 specialized AI agents that work together to handle different parts of the planning process. It integrates real-time flight data and web search to provide more relevant and practical recommendations, while using LangGraph for agent orchestration and PostgreSQL for persistent workflow state.

I built it to solve a common problem for travelers: turning a vague idea like “plan a trip to Bali” into a structured, personalized, and actionable itinerary.

Demo

Live: https://tripsort.tech/
Github: https://github.com/rajanikant04/tripsort

Code

rajanikant04 / tripsort

AI agent for International Trip Planning

✈️ TripSort AI — A Multi-Agent Travel Planner with LangGraph

An open-source AI travel planner that turns a natural-language trip request into a practical travel plan with flight suggestions, hotel ideas, and a day-by-day itinerary. The project uses a multi-agent workflow built with LangGraph, LangChain, and FastAPI.

Why this project?

Planning a trip usually means jumping between multiple websites, tools, and spreadsheets. This project brings that flow into one experience by combining:

a flight-search agent,
a hotel-research agent,
an itinerary-planning agent, and
a final response agent,

all coordinated through a LangGraph workflow.

Features

✈️ Flight research using AviationStack
🏨 Hotel suggestions using Tavily search
🧠 Multi-agent orchestration with LangGraph
📝 Structured travel itinerary generation
🌐 FastAPI backend with a simple web interface
💾 Conversation state persistence using PostgreSQL
⚡ LLM-powered responses with Groq

Tech Stack

Python 3.10+
FastAPI
Jinja2 + HTML/CSS/JavaScript frontend
LangGraph
LangChain
Groq LLMs
PostgreSQL
Tavily API
AviationStack…

View on GitHub

How I Built It

TripSort is built as a multi-agent AI system using LangGraph and LangChain to orchestrate specialized agents and manage the overall trip-planning workflow.

I used Groq-powered LLMs as the reasoning engine and designed 5 specialized AI agents, each responsible for a specific part of the travel-planning process. LangGraph coordinates these agents, manages state, and controls how information flows between them.

The system integrates AviationStack for real-time flight information and Tavily for web search, allowing the agents to work with up-to-date travel data instead of relying only on the model's knowledge.

The backend is built with FastAPI, with PostgreSQL used for persistent LangGraph checkpointing and workflow state. The complete application is containerized using Docker and deployed through Vercel.

The core idea is to combine an LLM with specialized agents, external tools, persistent state, and a structured workflow to create an end-to-end autonomous trip-planning experience.

Why Does Open Innovation Matter?

Open innovation made it possible to build TripSort as a flexible, modular, and extensible agentic AI system rather than a simple application built around a single closed API.

Using open-source frameworks like LangGraph and LangChain gave me control over how agents are orchestrated, how state is maintained, and how different tools and models interact. I could design the system around 4 specialized agents, customize their workflows, and integrate external services such as AviationStack and Tavily based on the application's needs.

A closed API could provide powerful AI capabilities, but it would limit the level of control and customization over the underlying agent workflow. Open innovation allowed me to experiment, combine different technologies, and build the AI system around the problem rather than around a single vendor's ecosystem.

Most importantly, it made TripSort more transparent, modular, and easier to extend with new agents, models, tools, and capabilities in the future.

Prize Categories

For now, TripSort isn't targeting a specific prize category — the goal was to build something useful, practical, and genuinely fun to use.

Planning your next trip? Give TripSort a try! 🚀

TripSort — Your AI-powered travel companion. ✈️🌍 by Rajanikant

DEV Community: Rajanikant Singh

TouchGrass

unScroll — Stop Scrolling. Start Living. 🌱

What I Built

Demo

Code

How I Built It

Why Does Open Innovation Matter?

My Agent Session

Prize Categories 🏆

🌍 Plan Your Next Adventure with TripSort

What I Built

Demo

Code

rajanikant04 / tripsort

AI agent for International Trip Planning

✈️ TripSort AI — A Multi-Agent Travel Planner with LangGraph

Why this project?

Features

Tech Stack

How I Built It

Why Does Open Innovation Matter?

Prize Categories