DEV Community: Raushani Kumari

Cyborg-Landing -page

Raushani Kumari — Mon, 19 Oct 2026 11:49:58 +0000

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

What I Built

I built NEON-X, a futuristic, responsive cyborg-themed landing page that represents a fictional next-generation technology platform where humans and intelligent machines work together.

The project features a cyberpunk-inspired interface with neon cyan and magenta colors, an animated cybernetic core, interactive technology cards, responsive navigation, and smooth CSS animations.

NEON-X demonstrates how basic frontend technologies can be used to create an engaging and modern technology-focused user experience.

The project could serve as a starting point for a futuristic technology brand or an interactive product landing page.

Demo

GitHub Repository:
https://raushanichaudhary77-create.github.io/Cyborg-Landing-Page

Code

Explore the complete source code on GitHub:

https://github.com/raushanichaudhary77-create/Cyborg-Landing-Page

The project uses HTML5, CSS3, and Vanilla JavaScript.

How I Built It

I built NEON-X using three core web technologies:

HTML5: Structured the navigation bar, hero section, technology cards, about section, call-to-action section, and footer.
CSS3: Created the cyberpunk theme, neon glow effects, responsive layouts, animated rings, background grid, and hover effects.
Vanilla JavaScript: Added mobile navigation, interactive components, and the connection success message.

The project is organized into separate HTML, CSS, and JavaScript files, making it easy to understand and maintain.

Currently, NEON-X does not integrate an open-weight AI model or local AI inference. Integrating an open-source AI capability is a potential future improvement.

Why Does Open Innovation Matter?

Open innovation makes it easier for developers to share ideas, learn from existing work, experiment with new technologies, and collaborate with the community.

By publishing NEON-X on GitHub, I make my source code accessible to other developers who can explore the implementation, learn from it, and build upon the project.

In the future, I would like to explore integrating a locally running open-weight AI model to introduce intelligent features while giving users greater control over their data and model choices.

Prize Categories

Not specified.

Touch-grass-ai

Raushani Kumari — Sat, 10 Oct 2026 18:27:32 +0000

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

What I Built

Touch Grass AI is an outdoor mission planner that gets people off their screens and into the world.

Most of us know we should go outside more, but "go for a walk" is easy to ignore. Touch Grass AI turns that vague intention into a small, concrete mission: you tell it how much time you have, what your surroundings are like and how you feel, and a local AI model plans a short outdoor mission you can start right now.

It is for anyone who spends too much of the day at a screen: students, developers, remote workers, and anyone who keeps saying "I'll go out later".

Key ideas:

Missions, not generic advice: short, specific outdoor tasks instead of a vague nudge.
Private by design: the AI runs on the same machine as the app. Nothing you type is sent to a closed AI service.
Simple and fast: a clean interface that takes seconds to use, so the app does not become one more reason to stay on the screen.

Demo

raushanichaudhary77-create / Challenge

Touch Grass AI

A calm, local-first outdoor activity planner. Pick the time, place, energy level, weather, and kind of activity that fits today to get three small, practical ideas. No account, subscription, API key, or external AI service is required.

Requirements

Node.js 18 or newer
Optional: Ollama with a model downloaded locally

The app has no npm dependencies. Its built-in planner works without Ollama or an internet connection once the page has been opened and its static files cached.

Start the app

From this folder, run:

npm start

Open http://127.0.0.1:3000. The server binds to loopback by default and serves the site only on your computer. Use Ctrl+C in the terminal to stop it.

Optional: enable local AI

Install Ollama and download a model, for example:
```
ollama pull llama3.2
```
Start Ollama. Its default local API is http://127.0.0.1:11434.
Copy .env.example to .env if you want to change the model…

View on GitHub

Code

raushanichaudhary77-create / Challenge

Touch Grass AI

Requirements

Node.js 18 or newer
Optional: Ollama with a model downloaded locally

The app has no npm dependencies. Its built-in planner works without Ollama or an internet connection once the page has been opened and its static files cached.

Start the app

From this folder, run:

npm start

Open http://127.0.0.1:3000. The server binds to loopback by default and serves the site only on your computer. Use Ctrl+C in the terminal to stop it.

Optional: enable local AI

Install Ollama and download a model, for example:
```
ollama pull llama3.2
```
Start Ollama. Its default local API is http://127.0.0.1:11434.
Copy .env.example to .env if you want to change the model…

View on GitHub

How I Built It

The project is a full-stack web app built around a locally running open-weight model, with no closed AI APIs anywhere in it.

Frontend: React + Vite + Tailwind CSS, with Lucide icons.
Backend: Node.js + Express. The server receives the user's input, builds the prompt and returns the generated mission.
AI engine: node-llama-cpp runs an open-weight model (Llama family, GGUF format) directly inside the Node.js server. There is no separate AI service to install or manage.

The path to this setup took a few steps. I started with Ollama as the AI engine, then tried running a model in the browser with WebLLM, and finally moved everything to Node.js so the whole app runs from one server with one npm workflow. That keeps setup simple for contributors, and open-source AI stays at the core of the project.

About AI tools: the app's runtime AI is open-weight and runs locally. I also used an AI assistant (Claude, which is not open-weight) to help generate the first version of the project code.

Why Does Open Innovation Matter?

A mission planner only works if people actually use it, and people are more willing to share how they feel and where they are when the data stays with them.

Privacy: because the model is open-weight and runs locally through node-llama-cpp, your mood, schedule and surroundings never leave your machine. A closed API would require sending them to someone else's servers.
No keys, no bills, no rate limits: anyone can clone the repo and run it for free, including students without a credit card.
Works offline: once the model is downloaded, the app does not need an internet connection to plan a walk.
Swap the model: because the engine reads standard GGUF files, contributors can try a smaller or larger open model, or one fine-tuned for a different region or language, with a small config change.
Hackable by anyone: the prompts, the server and the UI are all open, so the community can make missions friendlier, funnier or more local.

kitchen-allergy-planner

Raushani Kumari — Sun, 04 Oct 2026 19:34:17 +0000

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

What I Built

Kitchen Allergy Planner is a dark, futuristic food-tech web app that helps people plan meals around their food allergies.

I built it for a close friend who has to avoid certain foods because of allergies. Every week they repeat the same routine: reading ingredient lists, double-checking recipes, and worrying that one planned meal might hide an ingredient they must avoid. Most recipe sites and meal planners treat allergies as an afterthought, so I wanted a planner that starts with the allergy profile instead.

What it does:

Allergy profile: pick from 16 allergens across 6 categories (Dairy, Eggs, Nuts, Gluten, Seafood, Other).
Recipe discovery: search by name, ingredient or cuisine, filter (Safe, Contains Allergens, Vegetarian, High Protein, Quick Meals) and sort.
"Why?" explanations: an unsafe recipe doesn't just say "unsafe". It shows exactly which ingredients matched your profile, for example "Contains 2 ingredients from your allergy profile: cream (Milk), parmesan cheese (Cheese)."
Weekly meal planner: 7 days x 4 meals, with a live Weekly Safety Check showing how many meals are compatible and which ones need attention.
Ingredient Safety Scanner: type milk, rice, tomato, peanuts and see which ingredients match your profile.
Saved recipes, toast notifications, and a responsive layout with a sidebar on desktop and bottom navigation on mobile.
Local-first: everything is saved in the browser with localStorage, so there is no account and no data leaves the device.

The app avoids medical claims and shows this disclaimer: "Always verify ingredient labels and cross-contamination information. This application is for meal organization and educational purposes only and does not replace professional medical advice."

Demo

Live demo: https://raushanichaudhary77-create.github.io/kitchen_allergy_planner/

Code

raushanichaudhary77-create / kitchen_allergy_planner

🥗 A modern allergy-aware meal planning web app to manage allergies, discover compatible recipes, plan weekly meals, and generate smart shopping lists — built with a futuristic Food-Tech UI

🥗 Kitchen Allergy Planner

A dark, futuristic food-tech web app that helps you plan meals around your food allergies Built with plain HTML, CSS and JavaScript (Tailwind via CDN). No backend, no build step.

Always verify ingredient labels and cross-contamination information. This application is for meal organization and educational purposes only and does not replace professional medical advice.

Features

Allergy profile with 16 allergens in 6 categories
Recipe search, filters (Safe, Vegetarian, High Protein, Quick Meals) and sorting
"Why?" panel that shows exactly which ingredients match your allergies
Weekly meal planner (7 days x 4 meals) with a Weekly Safety Check
Ingredient Safety Scanner
Saved recipes, toast notifications, responsive layout
Everything saved in your browser with localStorage

Project structure

kitchen-allergy-planner/
├── index.html   # page layout
├── style.css    # custom styles (glass, glow, animations)
├── script.js    # data, state, views and events
└── README.md

How to run

Download or clone this…

View on GitHub

Project structure:

kitchen_allergy_planner/
├── index.html   # page layout
├── style.css    # glass, glow and animation styles
├── script.js    # data, state, views and events
└── README.md

How I Built It

The app is plain HTML, CSS and JavaScript with Tailwind CSS (via CDN). There is no backend and no build step, so a beginner can open index.html and start contributing right away.

Data: allergens and recipes live in two simple arrays in script.js. Each allergen has a list of keywords (for example, Wheat matches pasta, bread, flour, tortilla).
Matching: each recipe ingredient is compared against the keywords of every allergen the user selected. The matches power the "Why?" panel, the planner warnings and the dashboard stats.
State: one small state object (allergies, favorites, meal plan) is saved to localStorage, and the UI re-renders from it.
Real stats: dashboard numbers such as Safe Recipes and Meals Planned are computed from the current state, not hard-coded.

About AI: the allergen matching is simple keyword logic that runs in the browser. The app uses no AI model at runtime. I used an AI assistant (Claude, which is not open-weight) to help generate the first version of the code, and I then set up the project and repository myself.

Because matching is keyword-based, it can miss unusual ingredient names. That is why the app always tells users to verify labels.

Why Does Open Innovation Matter?

Food allergies are personal, and the data about them is sensitive. Building this as an open-source, local-first project means:

Privacy by default: the allergy profile never leaves the user's browser. There is no API, no account and no tracking.
Anyone can improve it: a recipe, an allergen keyword or a translation is a one-line change in a plain file. People in different countries can add the foods and ingredient names that matter in their own kitchens.
Transparent logic: anyone can read exactly how a recipe is flagged, instead of trusting a black box with something that affects their health.
Free to fork: a friend, a family or a community can adapt it to their own needs without asking anyone's permission.

NEW TO COMMUNITY😊😊.....

Raushani Kumari — Fri, 02 Oct 2026 17:14:19 +0000