DEV Community: Amit Dulariya

Resume Analyzer

Amit Dulariya — Mon, 19 Oct 2026 06:20:22 +0000

ResumeAI – AI-Powered Resume Analyzer & Builder

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

What I Built

I built ResumeAI, an AI-powered resume analyzer and builder designed to help students, freshers, and job seekers improve their resumes and prepare for job opportunities.

Many students struggle to create professional resumes, optimize them for Applicant Tracking Systems (ATS), and understand which skills they need to develop for a particular job role. ResumeAI aims to make this process easier by combining AI-powered resume analysis with practical career guidance.

Key Features

AI Resume Analysis: Analyze resumes and identify strengths, weaknesses, and areas for improvement.
ATS Optimization: Evaluate resume content and provide suggestions to improve its compatibility with Applicant Tracking Systems.
Resume Building: Help users create and improve professional resumes.
Skill Gap Analysis: Identify missing or underrepresented skills based on career goals and job requirements.
Improvement Suggestions: Provide actionable recommendations for improving resume content and presentation.
Career Roadmap: Help users understand what skills and learning steps can improve their career readiness.
Job and Company Targeting: Support resume customization for different job roles and companies.
User Authentication: Support secure sign-in using Firebase Authentication.
Resume History and Dashboard: Organize resume-related activities and analysis results.

The goal is to make professional resume guidance more accessible, especially for students and freshers who may not have access to paid career counseling.

Who could benefit from it?

College students preparing for internships and placements.
Freshers applying for their first jobs.
Job seekers looking to improve their existing resumes.
Career coaches and placement teams helping candidates prepare for recruitment.

ResumeAI could be offered as a freemium product, with essential resume analysis available to users and advanced features provided through a paid plan.

Demo

Live Demo: https://github.com/amit-dulariya/AI-Resume

Code

GitHub Repository: https://github.com/amit-dulariya/AI-Resume

The project includes a web-based frontend, AI-powered resume analysis functionality, authentication, and supporting backend services.

How I Built It

I developed ResumeAI as a web-based AI application using modern web technologies and Google's Gemini AI.

Technology Stack

Frontend: Web technologies for an interactive resume analysis and building experience.
AI Model: Google Gemini API for understanding resume content and generating structured analysis and recommendations.
Backend: Node.js and TypeScript-based server functionality for handling resume analysis requests.
AI Integration: The @google/genai SDK connects the application to Gemini.
Authentication: Firebase Authentication with Google Sign-In.
Database: Firebase Firestore for application data and user-related information.
Resume Processing: PDF text extraction to process resume content before analysis.
Structured Analysis: JSON-based outputs organized into multiple sections to make the results easier to display and use.

The resume analysis workflow processes the uploaded resume, extracts its text, sends the relevant content to the AI model, and organizes the response into structured sections covering resume quality, ATS-related suggestions, skills, and potential improvements.

I also worked on features such as user profiles, resume history, company and job-role targeting, templates, and career improvement roadmaps.

My focus was not just on generating AI responses, but on turning them into practical recommendations that users can act on while preparing for internships and placements.

Why Does Open Innovation Matter?

Open innovation makes it possible for developers and students to learn from existing tools, build on shared knowledge, and create useful products without having to develop every component from scratch.

While building ResumeAI, I explored how AI models, open-source libraries, PDF-processing tools, web frameworks, and authentication services can work together to solve a real-world problem.

Open-source tools make experimentation more accessible, help developers understand how different components work, and encourage community contributions. They also provide flexibility when choosing or replacing individual components as a project grows.

For an AI-powered career tool like ResumeAI, this flexibility is valuable because resume analysis, document processing, and career guidance can continue to evolve as better tools and models become available.

My long-term vision is to make ResumeAI more transparent, accessible, and useful for students who want to improve their career opportunities.

My Agent Sesission

I used AI-assisted development to explore implementation approaches, integrate application features, and improve the overall project workflow.

Built by Amit Dulariya
B.Tech Computer Science Engineering Student | JECRC Foundation

Building practical AI applications to solve real-world problems and make technology more accessible.

Trail - Mate

Amit Dulariya — Wed, 07 Oct 2026 20:02:21 +0000

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

What I Built

🌿 TrailMate AI — Your AI Companion for Touching Grass

TrailMate AI is a simple AI-powered outdoor activity planner designed to help people spend less time in front of screens and more time outdoors.

The idea is simple: instead of endlessly scrolling or wondering what to do outside, TrailMate helps you turn your free time into an outdoor activity.

Users can choose their interests, activity type, difficulty, duration, and other preferences. TrailMate then uses a local open-weight AI model to generate a personalized outdoor plan.

It can be useful for:

🌳 People who want to spend more time outdoors
🚶 People looking for simple walking or outdoor activities
🧑‍💻 Developers and students who spend long hours in front of a screen
🏃 Beginners who want easy outdoor activity ideas
🌱 Anyone looking for a simple way to "touch grass"

The main goal of TrailMate isn't to keep users inside an AI application.

It's to use AI as a bridge that helps people get outside.

Demo

🎥 Video Demo:

Trail_Ai.mp4 - Google Drive

drive.google.com

🔗 GitHub Repository:

amit-dulariya / Trailmate

a sort outdoor visiting plan app

<<<<<<< HEAD

TrailMate AI — Ollama local AI integration

TrailMate AI uses a React + Vite frontend and a Node.js + Express backend. The backend sends requests to Ollama's local /api/chat endpoint using llama3.2:3b. The browser never calls Ollama directly.

Requirements

Windows 10/11
Node.js 18 or newer (Node 20+ recommended; native fetch is used)
Ollama for Windows
The llama3.2:3b model downloaded locally

1. Install / verify Ollama

Open PowerShell:

ollama --version
ollama pull llama3.2:3b
ollama list

Make sure llama3.2:3b appears in the list. Ollama normally listens on http://localhost:11434. Start the Ollama app if it is not already running.

Optional direct model test:

ollama run llama3.2:3b

Type a short prompt, then enter /bye to exit.

2. Install project dependencies

Extract the project ZIP, open PowerShell in the extracted trailmate-ai-ollama folder (the folder containing package.json), then run:

npm install

3. Start the backend

In…

View on GitHub

Code

The complete source code is available on GitHub:

https://github.com/amit-dulariya/Trailmate

The project is structured as a React/Vite frontend with an Express backend.

Some important parts of the project include:

src/App.jsx — Main application interface
src/components/ActivityCard.jsx — Activity presentation
src/components/OptionPill.jsx — User preference selection
src/components/PlanResults.jsx — Generated plan results
src/components/OutdoorMap.jsx — Outdoor map component
src/data.js — Application data
server.js — Express backend and AI integration
src/styles.css — Application styling

How I Built It

TrailMate AI is built around open-source/local AI rather than relying completely on a closed AI API.

Tech Stack

⚛️ React
⚡ Vite
🟢 Node.js
🚂 Express.js
🦙 Ollama
🤖 Llama 3.2 3B
🗺️ Leaflet
🌐 OpenStreetMap
💻 JavaScript
🎨 CSS

AI

The AI part of TrailMate runs locally through Ollama using the llama3.2:3b model.

The backend exposes an API endpoint:

POST /api/plan

The user's selected preferences are sent to the backend, which creates a prompt for the local Llama model. The generated response is then returned to the frontend and presented as an outdoor activity plan.

This allows TrailMate to use AI without sending the user's activity preferences to a paid external AI API.

Application Flow

The basic flow is:

User opens TrailMate.
User selects their outdoor preferences.
The frontend sends the preferences to the Express backend.
Express creates the AI prompt.
Ollama runs the local Llama 3.2 model.
The generated plan is returned to the frontend.
TrailMate presents the activity plan to the user.
The user can take the plan offline and actually go outside. 🌳

Why Does Open Innovation Matter?

Open innovation was especially important for this project because I wanted AI to be something that developers can actually experiment with and understand rather than simply calling a closed API.

Using an open-weight model through Ollama allowed me to experiment with local inference and build the AI part of TrailMate around a model running on my own machine.

This has several advantages:

🔓 More control over the AI workflow
💻 Local inference
💰 No dependency on a paid AI API for the core generation
🧪 Easier experimentation during development
🛠️ More freedom to modify and improve the application
🔐 User preferences can remain local during AI generation

A closed API could certainly generate an activity plan, but using open/local AI made the project more interesting from a developer perspective because I could actually build around the model and understand how the AI fits into the application.

For me, open innovation means being able to take existing open technologies, combine them creatively, and turn an idea into something useful.

My Agent Session

I used AI-assisted development during the development of TrailMate to help with implementation, debugging, architecture decisions, and improving the application.

Prize Categories

I am submitting TrailMate AI for the following category:

🌿 Touch Grass

TrailMate is specifically designed around the idea of using technology to encourage people to step away from their screens and spend time outdoors.

Instead of building another application that keeps users engaged on their devices, TrailMate uses AI to help users decide what they can actually do outside.

The AI is the starting point.

The destination is outdoors. 🌳

Future Improvements

There are several features I would like to add to TrailMate:

📍 Real-time nearby outdoor location discovery
🗺️ Interactive outdoor maps
🌳 Nearby parks, gardens, walking and hiking locations
📏 Distance-based location filtering
📍 Browser-based current location detection
🤖 Personalized plans based on the selected real-world location
📱 Better mobile experience
💾 Saved outdoor plans
🏆 Outdoor activity streaks and challenges

The goal is to gradually turn TrailMate from an AI activity generator into a complete outdoor companion.

Conclusion

TrailMate AI started with a simple question:

What if AI could help us spend less time using technology instead of helping us spend more time using it?

That question led me to build TrailMate.

AI generates the plan.

You go outside.

That's the whole idea behind TrailMate. 🌿

Thanks for checking out my project!

GitHub: https://github.com/amit-dulariya/Trailmate

StudyChamp

Amit Dulariya — Sun, 04 Oct 2026 06:15:46 +0000

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

What I Built

StudyChamp: The Ultimate Study Companion
I built this application for a friend who is currently a student and struggles with context-switching between multiple productivity apps. They needed a unified solution to manage their focus time and study materials without getting distracted.

StudyChamp solves this by combining four essential tools into one sleek, gamified dashboard:

Pomodoro Timer: For structured, focused study sessions with break intervals.
Task Manager: To track assignments (integrated directly with the Pomodoro timer so you can mark a task as "Active").
Flashcard System: For interactive memorization with a 3D flip effect.
Quiz Builder: To create and take custom quizzes for self-assessment.

By adding a gamified XP system, visual statistics tracking (via Chart.js), and celebratory confetti, it keeps studying engaging and keeps everything confined to a single browser tab.

Demo

Demo.mp4 - Google Drive

drive.google.com

Code

amit-dulariya / study_champ

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

What I Built

StudyChamp: The Ultimate Study Companion I built this application for a friend who is currently a student and struggles with context-switching between multiple productivity apps. They needed a unified solution to manage their focus time and study materials without getting distracted.

StudyChamp solves this by combining four essential tools into one sleek, gamified dashboard:

Pomodoro Timer: For structured, focused study sessions with break intervals.
Task Manager: To track assignments (integrated directly with the Pomodoro timer so you can mark a task as "Active").
Flashcard System: For interactive memorization with a 3D flip effect.
Quiz Builder: To create and take custom quizzes for self-assessment.

By adding a gamified XP system, visual statistics tracking (via Chart.js), and celebratory confetti, it keeps studying engaging and keeps everything confined to a single browser tab.

…

View on GitHub

How I Built It

StudyChamp was built using Vanilla HTML, CSS, and JavaScript to ensure it remains lightweight, blazingly fast, and completely free of heavy build dependencies.

I collaborated with the Antigravity AI Agent to rapid-prototype and architect the application. The agent assisted in:

Designing the modern glassmorphism UI and responsive side-navigation layout.
Writing the core vanilla JavaScript logic for the integrated Pomodoro and Task system.
Implementing LocalStorage data persistence so no external backend was required.
Integrating open-source libraries like Chart.js for the focus history dashboard and Canvas-Confetti for visual rewards.

Why Does Open Innovation Matter?

Open innovation is crucial because it democratizes access to powerful tools and education. By leveraging open-source web technologies and AI coding assistants, I was able to quickly build a highly customized, premium-feeling application at zero cost. It removes the barrier to entry, allowing anyone in the community to clone this repository, learn from the codebase, and extend the tool to fit their unique study needs without being restricted by closed APIs or paywalls.

My Agent Session

(Insert your DevRelay agent session link or embed tag here if you have one)

Prize Categories

AI-Assisted Innovation (Adjust based on the specific partner categories you are entering)

New to community

Amit Dulariya — Fri, 02 Oct 2026 19:37:14 +0000

hello Everyone!