{"slug": "seemore-ai-what-if-ai-made-us-look-up-instead-of-stare-at-screens", "title": "SeeMore AI: What If AI Made Us Look Up Instead of Stare at Screens?", "summary": "A developer built SeeMore AI, an observation-training tool that uses AI to generate indirect clues about photos of everyday surroundings instead of identifying objects outright, prompting users to look at the real world and solve the challenge themselves. The project, submitted to the Hacktoberfest Open-Source AI Challenge's \"Touch Grass\" week, aims to turn screen time into a starting point for real-world attention and pattern recognition rather than instant answers.", "body_md": "*This is a submission for the [Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass](https://dev.to/challenges/hacktoberfest-week1-2026-10-05)*\n\n**🌿 SeeMore AI — Look Closer. See More.**\n\nWhat if AI didn't give you the answer, but gave you a reason to discover it yourself?\n\nWe look at the world every day.\n\nWe walk past trees without noticing their patterns. We sit beside windows without observing their designs. We see everyday objects so often that we stop paying attention to them.\n\nBut what if we could turn these ordinary moments into opportunities to observe, think, and discover something new?\n\nThat question inspired me to build SeeMore AI.\n\n**👀 What I Built**\n\nSeeMore AI is an AI-powered observation training tool that transforms photos of everyday surroundings into interactive observation challenges.\n\nThe idea is simple: instead of directly identifying objects in a photograph, AI creates an indirect clue that encourages the user to observe and figure out the answer.\n\nImagine uploading a photograph containing a house, a tree, windows, and a streetlight.\n\nInstead of saying:\n\n“Find the tree.”\n\nSeeMore could challenge you with:\n\n“Find something in the scene that provides natural shade for pedestrians.”\n\nThe user must connect the clue with the environment and identify the answer independently.\n\nAI gives the clue. Human does the observing.\n\nThe goal is to encourage attention to detail, pattern recognition, and visual reasoning through everyday surroundings.\n\nSeeMore is designed for students, curious learners, and anyone who wants to become more observant.\n\n**💡 Where Did the Idea Come From?**\n\nWhen we think about AI, we often think about getting answers faster, automating tasks, and spending more time interacting with technology.\n\nI wanted to explore a different possibility.\n\nWhat if we used AI to encourage people to interact more with the world outside their screens?\n\nI started imagining an application where a user uploads a photograph, receives a clue, and then has to look carefully at the real environment to solve it.\n\nThe AI would not simply point to an object or reveal the answer immediately. It would create a small challenge that makes the user think.\n\nThat became the central idea behind SeeMore AI.\n\nI chose this approach because observation is a skill we can practise in ordinary places. A classroom, garden, street, or even our own home can become an opportunity to notice details we usually overlook.\n\nAnd that connects directly with the Touch Grass challenge.\n\nI didn't want to build another experience that simply demands more screen time. I wanted to experiment with using technology as a starting point for real-world observation.\n\n**🌱 How SeeMore Connects With Touch Grass**\n\nSeeMore follows a different interaction pattern:\n\nAI gives a clue → You look around → You observe → You think → You answer.\n\nThe screen is only the starting point. The user is encouraged to pay attention to the real world rather than relying entirely on the AI to do the thinking.\n\nFor example, a clue about natural shade could encourage someone to notice trees around their neighbourhood. A clue about repeating patterns could help them discover details in windows, railings, or buildings.\n\nThe intention is to make familiar surroundings feel interesting again.\n\nOf course, the experience depends on the user actually observing their environment. SeeMore is designed to encourage that behaviour rather than claiming that using an application automatically improves attention.\n\nThe goal isn't to keep people staring at a screen. It's to give them a reason to look up.\n\n**⚙️ How It Works**\n\nThe intended experience follows these steps:\n\nChoose a photograph of everyday surroundings.\n\nThe AI examines visual information and looks for useful objects, relationships, patterns, or functions.\n\nInstead of immediately revealing the target, the system presents an observation challenge.\n\nThe user studies the scene and works out the answer.\n\nThe user enters their observation.\n\nThe application is designed to evaluate the response and provide an observation score, with the aim of making each challenge an opportunity to learn.\n\nThe project also includes a dashboard concept for tracking progress and encouraging users to continue practising.\n\n**🛠️ How I Built It**\n\nI built SeeMore AI as a full-stack web application using AI-assisted development.\n\nHere are the main technologies used in the project:\n\nGoogle AI Studio: The environment I used to build and develop the application with AI assistance.\n\nGemma Open Models: The intended core AI model for image understanding and generating indirect observation clues.\n\nReact: For the interactive frontend.\n\nTypeScript: For application logic and type safety.\n\nVite: For frontend development and building.\n\nNode.js and Express: For the backend and API layer.\n\nTailwind CSS: For the user interface styling.\n\nGoogle Gen AI SDK (@google/genai): For integrating the Google AI model API.\n\nGitHub: For source code management and sharing the project.\n\nI designed the interface around a dark visual theme with cyan and violet accents, keeping the focus on the observation experience.\n\nOne important design decision was to make the clue indirect. If the AI immediately reveals the target, the user loses the opportunity to observe and reason independently.\n\nThe challenge is not simply about identifying an object. It is about making the user think about what they see.\n\nNote: The model name and runtime configuration should match the actual working configuration of the deployed application.\n\n**🔓 Why Does Open Innovation Matter?**\n\nFor SeeMore AI, the AI model is central to the experience.\n\nAn open-model approach creates opportunities to experiment with how visual information is interpreted and how observation challenges are generated.\n\nIt also offers flexibility for future development. Different models and deployment approaches could be explored without having to redesign the entire user experience around one particular provider.\n\nIn future versions, I would like to investigate model customisation, different difficulty levels, educational use cases, and privacy-conscious deployment options.\n\nOpen innovation matters because it gives developers room to learn, experiment, adapt, and build on shared technology.\n\nFor a project like SeeMore, that flexibility could help make observation challenges more useful for different users and environments.\n\nI also recognise that using an open model does not automatically make an application offline or guarantee that user data never reaches a server. Those capabilities depend on the actual model deployment and application architecture.\n\n**🚀 What's Next for SeeMore AI?**\n\nThis project began as a Hacktoberfest challenge, but I would love to develop it further.\n\nSome possibilities I want to explore include:\n\nBecause sometimes, we don't need a new place to explore.\n\nWe just need to learn how to look at the world we're already in. 🌿\n\n**🎥 Demo**\n\nLive Demo: [https://seemore-aibyshara.ai.studio](https://seemore-aibyshara.ai.studio)\n\nExplore SeeMore AI and discover how an ordinary photograph can inspire an observation challenge.\n\n**💻 Code**\n\nGitHub Repository: [https://github.com/shraddhakolate30-maker/seemore-ai](https://github.com/shraddhakolate30-maker/seemore-ai)\n\nThe repository contains the application's source code, project structure, configuration example, and documentation.", "url": "https://wpnews.pro/news/seemore-ai-what-if-ai-made-us-look-up-instead-of-stare-at-screens", "canonical_source": "https://dev.to/shraddhakolate30/seemore-ai-what-if-ai-made-us-look-up-instead-of-stare-at-screens-49o", "published_at": "2026-10-09 05:34:48+00:00", "updated_at": "2026-10-09 05:46:35.038777+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-products", "computer-vision", "ai-tools"], "entities": ["SeeMore AI", "Hacktoberfest Open-Source AI Challenge"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/seemore-ai-what-if-ai-made-us-look-up-instead-of-stare-at-screens", "markdown": "https://wpnews.pro/news/seemore-ai-what-if-ai-made-us-look-up-instead-of-stare-at-screens.md", "text": "https://wpnews.pro/news/seemore-ai-what-if-ai-made-us-look-up-instead-of-stare-at-screens.txt", "jsonld": "https://wpnews.pro/news/seemore-ai-what-if-ai-made-us-look-up-instead-of-stare-at-screens.jsonld"}}