DEV Community: Shraddha Kolate

LUNORA — Your Space, Your Universe | A Little Space to Think, Feel & Choose

Shraddha Kolate — Sat, 24 Oct 2026 07:17:39 +0000

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady

🌌 LUNORA — Your Space, Your Universe | A Little Space to Think, Feel & Choose

LUNORA — Your Space, Your Universe.

LUNORA represents a cosmic-inspired personal universe—a space where people can explore their feelings, reflect on their thoughts, and make their own choices.

What if we could spend less time overthinking small decisions and more time actually living? 🌙

That question inspired me to build LUNORA.

Sometimes, even small everyday choices take more time and mental energy than they should. I wanted to create something that makes these little moments feel lighter—not by making every decision for people, but by giving them a space to pause, explore, and choose for themselves.

That's how LUNORA began: a personal, cosmic-inspired space for everyday choices, feelings, and reflection.

✨ What I Built

LUNORA brings together a few simple experiences:

🌠 Cosmic Choices & Secret Chit: Make everyday choices more playful, with a little mystery and surprise.
💜 Feelings: Give emotions a space to be acknowledged.
⭐ My Stars & Cosmic Journal: Keep thoughts and personal reflections in your own universe.
🎧 Cosmic Atmosphere: Add to the immersive, space-inspired experience. Reflection Companion: An AI companion that asks a short question to help you think through your options, rather than deciding for you.

The idea is simple: your choices remain yours.

How I Used Open-Weight AI

For the Reflection Companion, I integrated Qwen3-0.6B, an open-weight model, using Transformers.js.

The model runs locally in the browser, allowing it to generate reflections without sending the user's written input to a server. The model download is approximately 400 MB, so the first launch requires a download and a compatible device.

During testing, the first launch was interrupted by a worker-startup issue. After debugging, I verified that the real model could generate reflections locally. I also tested offline responses and cancellation.

This taught me that integrating AI means more than making a feature appear on screen. It also means debugging, testing real model output, considering device compatibility, and thinking carefully about privacy.

🛠️ Tools & Technologies

React + TypeScript
Vite and Tailwind CSS
Lovable for AI-assisted development
Qwen3-0.6B with Transformers.js for local AI inference
GitHub for source-code management

💡 What I Learned

As an engineering student exploring AI development, LUNORA taught me to turn an idea into a working application, test features instead of assuming they work, and learn from unexpected errors.

I also learned that AI doesn't always need to give people an answer. Sometimes, asking a thoughtful question is a better way to help.

🦥 How LUNORA Fits “Slow and Steady”

The Week 3 Hacktoberfest Open-Source AI Challenge is about building something with open-source AI that makes life easier or saves effort.

LUNORA approaches this through everyday decisions and reflection. Its Reflection Companion uses an open-weight model locally to help users think through their choices without handing control over to AI.

Open innovation matters here because it gives developers the freedom to explore models and local inference, while offering opportunities to build more privacy-conscious experiences.

🚀 What's Next?

I'd like to improve model-loading reliability, make reflections less repetitive, test across more devices and browsers, and continue improving accessibility and the overall user experience.

LUNORA is still evolving, and that's part of the journey.

💜 Why This Project Matters to Me

I didn't want to build just another productivity app. I wanted to create a little universe that feels calm, personal, and playful—a place where people can pause without feeling that everything needs an immediate answer.

LUNORA is a reminder that we don't always need someone to choose for us. Sometimes, we just need a little space to understand ourselves. 🌙

🔗 Live Demo: https://lunora-shra-cosmic-universe.lovable.app

LUNORA — My project name.
SHRA — Represents my personal identity.
COSMIC UNIVERSE — Reflects the app’s space-themed concept and the idea that everyone has their own universe.

💻 GitHub Repository: https://github.com/shraddhakolate30-maker/lunora-your-universe

I'd love to know: Would you use an AI companion that helps you reflect instead of making decisions for you?

devchallenge #hf26challenge #Hacktoberfest #OpenSourceAI #Qwen #TransformersJS

SAATHI AI: What If We Could Recognize the Warning Signs Before It's Too Late?

Shraddha Kolate — Fri, 16 Oct 2026 07:13:32 +0000

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

SAATHI AI: What If We Could Recognize the Warning Signs Before It's Too Late?
**
**AI Detects. Humans Understand. You Decide.

Sometimes, an unsafe situation doesn't begin with an obvious threat.

It can begin with someone constantly asking where you are. Someone getting angry when you spend time with friends. Repeated messages from different accounts. Or a person showing up at places you visit and making you feel watched.

One incident may be difficult to understand on its own. But when these behaviours become patterns, a person may start feeling uncomfortable, controlled, or unsafe.

I started building SAATHI AI around one question: Can AI help people recognize concerning behaviour earlier, understand possible warning signs, and explore safer next steps without taking away their choices?

That question became the starting point for my project.

💜 What Is SAATHI AI?

SAATHI AI is a women's safety and early-intervention prototype designed to help people recognize potential warning signs of controlling behaviour, harassment, stalking, and intimidation.

The name SAATHI represents the idea of having a companion—a tool that helps someone think through a difficult situation without judging them or making decisions for them.

The goal is not to make AI act like a police officer, judge, or therapist.

The goal is to make safety awareness more approachable.

Recognize Early. Reach Help. Protect Dignity.

🚨 The Problem I Wanted to Explore

When someone experiences controlling behaviour or repeated unwanted contact, they may not immediately know how to interpret what is happening.

They might ask themselves:

Am I overthinking this?
Is this behaviour becoming a pattern?
What can I do without making the situation worse?
Where can I look for reliable support?

A website cannot answer every question, and AI cannot know the complete truth about someone's situation.

However, a carefully designed tool may help someone organize their thoughts, recognize possible warning signs, and explore options they might not have considered.

That is the problem SAATHI AI is designed to explore.

✨ What I Built

AI Safety Check

This is the central feature of SAATHI AI.

A user can describe a concerning situation and receive an AI-generated analysis of potential warning signs, along with practical suggestions.

For example, imagine someone says:

A person repeatedly asks for my location, gets angry when I spend time with friends, and expects me to reply immediately.

The AI may identify possible patterns involving digital monitoring, isolation, or boundary violations.

In another scenario, someone repeatedly appears at places the user visits, contacts them through different accounts, and makes intimidating statements.

The AI can highlight possible concerns such as repeated unwanted contact, surveillance, and intimidation.

I tested the AI Safety Check using example scenarios like these.

The purpose is not to label another person as guilty or claim that a crime has occurred. It is to help users reflect on the behaviour described and consider possible next steps.

Practical Safety Suggestions

Recognizing a warning sign is only one part of the problem.

SAATHI AI can also suggest practical options, such as reviewing account privacy settings, keeping records of unwanted contact when safe to do so, contacting a trusted person, or exploring professional support.

The suggestions are advisory. What is appropriate depends on the person's circumstances, and an AI-generated recommendation may not always be safe or suitable.

Confidential Help Request Interface

The prototype includes a Help Request interface that demonstrates how someone could seek additional support.

The idea is to make asking for help feel more approachable and respectful.

However, the current interface does not guarantee that a request reaches a real support team. A secure submission system and a real support workflow remain areas for further development.

Human Review Dashboard Demonstration

Sensitive situations deserve more than automated decisions.

SAATHI AI includes a demonstration of a Human Review Dashboard to explore how a human-centred review process might work alongside AI.

The intended approach is for AI to assist with organizing information while people remain responsible for sensitive decisions.

At present, the dashboard is a demonstration. It is not connected to a verified team of advocates.

Safety Resources and Quick Safety Exit

The website also includes a safety resources section and a Quick Safety Exit feature.

These features are intended to make safety information easier to access and help users leave the current page quickly when needed.

A quick exit cannot guarantee that browser history or other activity traces are erased. The accuracy and regional coverage of safety resources also need further verification.

🛠️ How I Built It

I used Google AI Studio to help develop the SAATHI AI web application.

The project repository includes technologies such as:

React for the user interface
TypeScript for application development
Vite for development and build tooling
Node.js for server-side functionality
Google AI Studio as the development environment

The project explores the use of open-weight AI, including Google's Gemma model family, for safety-related analysis.

The exact model configuration and integration should be confirmed from the source code. The important part of this project is exploring how AI-generated analysis can be presented in a way that encourages awareness without pretending to provide certainty.

I tested the AI Safety Check with example situations involving controlling behaviour and stalking. These tests helped me see how the system could identify possible warning signs and present suggestions.

The other components are at prototype or demonstration stage and require additional implementation and testing.

🌍 Why Does Open Innovation Matter?

Women's safety is not a problem that can be solved by one application or one developer.

It requires thoughtful design, careful testing, reliable information, and an understanding of the different circumstances people may face.

Open innovation creates opportunities for developers, researchers, designers, and communities to contribute different perspectives to a shared problem.

For a project like SAATHI AI, this matters in several ways.

First, transparency matters. Developers can inspect the code, understand implementation choices, identify weaknesses, and suggest improvements.

Second, experimentation becomes more accessible. Open-weight AI models give developers opportunities to explore how AI can be adapted for specific applications, subject to the model's license and technical requirements.

Third, collaboration can improve the project. Contributors could help with Marathi and Hindi support, accessibility, AI evaluation, privacy engineering, and the verification of safety resources.

Finally, responsible innovation matters as much as technical innovation. A safety-related application should not simply produce convincing answers. It should communicate uncertainty, respect personal choice, and avoid promises it cannot keep.

Open innovation does not automatically make a system safe or accurate. It creates opportunities to examine, challenge, and improve the work.

That is the kind of development community I hope SAATHI AI can benefit from.

🔮 What's Next?

SAATHI AI is a prototype, and there is still important work to do.

My future plans include:

Adding Marathi and Hindi language support.
Improving AI evaluation across different scenarios.
Developing a secure help-request workflow.
Exploring a real human-review process with appropriate safeguards.
Verifying emergency contacts and regional support resources.
Improving accessibility across devices.
Strengthening privacy and security.
Testing the application more thoroughly before any real-world use.

I especially want to explore how a safety-focused AI tool can remain useful without collecting unnecessary personal information or creating a false sense of security.

💭 What I Learned

Building SAATHI AI made me think beyond the question of whether AI can generate an answer.

In a sensitive application, the more important questions are:

Is the answer understandable?
Could it create a false sense of certainty?
Does it respect the user's choices?
Are the limitations clear?
What happens when the system gets something wrong?

These questions should influence the design from the beginning, not be added as an afterthought.

SAATHI AI is an early step in exploring these challenges. It is not a finished safety service, but it represents an idea I want to keep developing.

🔗 Explore SAATHI AI

Live Demo: https://saathi-ai-women-s-safety-platform.ai.studio

GitHub Repository: https://github.com/shraddhakolate30-maker/saathi-ai

I welcome constructive feedback, especially on responsible AI design, accessibility, privacy, and ways to make the project more useful.

If you have suggestions for improving SAATHI AI, I would love to hear them.

SAATHI AI is an educational prototype. AI-generated analysis may be inaccurate and does not replace emergency services, trained professionals, or individualized support. The project does not determine guilt or guarantee safety. Do not enter identifying or highly sensitive information into an unverified demo.

SeeMore AI: What If AI Made Us Look Up Instead of Stare at Screens?

Shraddha Kolate — Fri, 09 Oct 2026 05:34:48 +0000

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

🌿 SeeMore AI — Look Closer. See More.

What if AI didn't give you the answer, but gave you a reason to discover it yourself?

We look at the world every day.

We 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.

But what if we could turn these ordinary moments into opportunities to observe, think, and discover something new?

That question inspired me to build SeeMore AI.

👀 What I Built

SeeMore AI is an AI-powered observation training tool that transforms photos of everyday surroundings into interactive observation challenges.

The 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.

Imagine uploading a photograph containing a house, a tree, windows, and a streetlight.

Instead of saying:

“Find the tree.”

SeeMore could challenge you with:

“Find something in the scene that provides natural shade for pedestrians.”

The user must connect the clue with the environment and identify the answer independently.

AI gives the clue. Human does the observing.

The goal is to encourage attention to detail, pattern recognition, and visual reasoning through everyday surroundings.

SeeMore is designed for students, curious learners, and anyone who wants to become more observant.

💡 Where Did the Idea Come From?

When we think about AI, we often think about getting answers faster, automating tasks, and spending more time interacting with technology.

I wanted to explore a different possibility.

What if we used AI to encourage people to interact more with the world outside their screens?

I 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.

The AI would not simply point to an object or reveal the answer immediately. It would create a small challenge that makes the user think.

That became the central idea behind SeeMore AI.

I 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.

And that connects directly with the Touch Grass challenge.

I 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.

🌱 How SeeMore Connects With Touch Grass

SeeMore follows a different interaction pattern:

AI gives a clue → You look around → You observe → You think → You answer.

The 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.

For 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.

The intention is to make familiar surroundings feel interesting again.

Of 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.

The goal isn't to keep people staring at a screen. It's to give them a reason to look up.

⚙️ How It Works

The intended experience follows these steps:

Upload a photo

Choose a photograph of everyday surroundings.

Analyse the scene

The AI examines visual information and looks for useful objects, relationships, patterns, or functions.

Receive an indirect clue

Instead of immediately revealing the target, the system presents an observation challenge.

Observe and think

The user studies the scene and works out the answer.

Submit the answer

The user enters their observation.

Receive feedback and a score

The application is designed to evaluate the response and provide an observation score, with the aim of making each challenge an opportunity to learn.

The project also includes a dashboard concept for tracking progress and encouraging users to continue practising.

🛠️ How I Built It

I built SeeMore AI as a full-stack web application using AI-assisted development.

Here are the main technologies used in the project:

Google AI Studio: The environment I used to build and develop the application with AI assistance.
Gemma Open Models: The intended core AI model for image understanding and generating indirect observation clues.
React: For the interactive frontend.
TypeScript: For application logic and type safety.
Vite: For frontend development and building.
Node.js and Express: For the backend and API layer.
Tailwind CSS: For the user interface styling.
Google Gen AI SDK (@google/genai): For integrating the Google AI model API.
GitHub: For source code management and sharing the project.

I designed the interface around a dark visual theme with cyan and violet accents, keeping the focus on the observation experience.

One 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.

The challenge is not simply about identifying an object. It is about making the user think about what they see.

Note: The model name and runtime configuration should match the actual working configuration of the deployed application.

🔓 Why Does Open Innovation Matter?

For SeeMore AI, the AI model is central to the experience.

An open-model approach creates opportunities to experiment with how visual information is interpreted and how observation challenges are generated.

It 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.

In future versions, I would like to investigate model customisation, different difficulty levels, educational use cases, and privacy-conscious deployment options.

Open innovation matters because it gives developers room to learn, experiment, adapt, and build on shared technology.

For a project like SeeMore, that flexibility could help make observation challenges more useful for different users and environments.

I 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.

🚀 What's Next for SeeMore AI?

This project began as a Hacktoberfest challenge, but I would love to develop it further.

Some possibilities I want to explore include:

More diverse observation challenges.
Better difficulty adaptation.
More detailed progress tracking.
Outdoor exploration activities.
Educational modes for students.
Improved answer evaluation.
Additional open-model deployment options. My long-term goal is to turn everyday environments into opportunities for curiosity and learning.

Because sometimes, we don't need a new place to explore.

We just need to learn how to look at the world we're already in. 🌿

🎥 Demo

Live Demo: https://seemore-aibyshara.ai.studio

Explore SeeMore AI and discover how an ordinary photograph can inspire an observation challenge.

💻 Code

GitHub Repository: https://github.com/shraddhakolate30-maker/seemore-ai

The repository contains the application's source code, project structure, configuration example, and documentation.

CalmSpace - Pause. Talk. Reset.

Shraddha Kolate — Sun, 04 Oct 2026 14:58:48 +0000

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

What I Built

I built CalmSpace, a small AI-powered reflection tool for a friend or anyone who needs a moment to pause before reacting.

Sometimes we feel angry, stressed, hurt, or overwhelmed and want to react immediately. CalmSpace gives a simple space to write what is bothering you. It listens to the situation, helps you pause, suggests one practical next step, and asks one question to understand what you need.

It also has a “Calm my message” feature that rewrites an emotional message into a calmer version while keeping the original complaint and main point.

I wanted to build something simple, private, and easy to use when emotions are running high.

Demo

https://youtu.be/xGp40oXi5jU

Code

https://github.com/shraddhakolate30-maker/CalmSpace

How I Built It

CalmSpace is built with Python and Streamlit. For the AI part, I used Gemma 3:1B, an open-weight model, running locally through Ollama.

The app has two simple features:

Talk to me — the user writes what is bothering them, and Gemma helps them understand the situation, pause before reacting, choose one practical next step, and think about what they need.
Calm my message — the user can paste an emotional message, and Gemma rewrites it in a calmer way while keeping the main complaint and request.

I chose local inference with Ollama so the AI runs on my own computer instead of depending on a paid cloud AI API.

Why Does Open Innovation Matter?

Open innovation made it possible for me to build CalmSpace using an open-weight AI model that I could run locally.

Using Gemma 3:1B with Ollama meant I could experiment with AI without depending on a paid cloud API. It also helped me understand how a local AI model can become part of a real application, rather than just being something I use through a website.

For a small student project like CalmSpace, this made AI development more accessible and gave me the freedom to experiment, learn, and build with technology that I can run and understand myself.

My Agent Session

Prize Categories

Best Use of Gemma — $200

CalmSpace uses Gemma 3:1B locally through Ollama as the core AI model.

Exploring Microsoft Fabric: From Data to Insights 🚀

Shraddha Kolate — Thu, 01 Oct 2026 14:38:01 +0000

👀 Quick question for students & tech enthusiasts:

If you had to manage a large amount of data, what would you want first?

🔹 A place to store everything
🔹 A way to clean & transform it
🔹 Tools to analyze it
🔹 Dashboards to visualize it

What if you could connect all of these in one platform? 🤔

I recently explored Microsoft Fabric through Microsoft Learn and discovered how it brings different stages of the data lifecycle together — from getting data to analyzing, visualizing, and governing it.

At the center of this is OneLake, a centralized data lake that connects different Fabric workloads. 🚀

📖 Want to explore how it works?

👉 https://learn.microsoft.com/en-us/fabric/fundamentals/data-lifecycle?wt.mc_id=studentamb_657273

💬 Which stage would you explore first — Store, Transform, Analyze, or Visualize?

MicrosoftFabric #MicrosoftLearn #DataAnalytics #CloudComputing