Android Developer (Java & Kotlin)

Building Mobile Experiences That Matter

Creating secure, intuitive Android applications with clean architecture, Firebase backend, AI integration, and offline-first design

About Me

Mobile developer specializing in Android applications with a focus on user experience, security, and performance

I build practical, user-focused Android apps with clean architecture, Firebase backend, AI features and offline support. Below are four polished projects: Notes App, XChat, HelpHive, and WebNovel Summarizer — each includes a short elevator pitch, feature list, tech stack and demo.

Download from: APKPure | GitHub

Featured Projects

Four polished Android applications demonstrating architecture, security, AI integration, and user experience design

01

Notes App

Secure note-taking with Firebase sync & smart reminders

Elevator Pitch

A production-quality note-taking app built in Java that goes far beyond a simple CRUD demo. It handles the full identity lifecycle — email verification, secure auth, password reset — then layers on image attachments stored in Firebase Storage, scheduled reminders that fire even when the app is closed, and seamless offline read access. Every architectural decision was made to show real-world reliability: what happens when the network drops, when the app is killed, when the user forgets their password.

Key Features

Email/Password Auth
Email Verification
Password Reset Flow
Image Attachments
WorkManager Reminders
Offline Read Access
Firestore Sync
Firebase Storage

Tech Stack

Java Firebase Auth Firestore Firebase Storage Picasso WorkManager

Screenshots

Notes App Home Screen Notes App Create Note Notes App Image Picker Notes App Long Press Menu Notes App Timer Notification Notes App Login Screen Notes App Settings
Implementation Highlight: The reminder system uses WorkManager with a custom NotificationWorker that survives app death and device restarts — unlike AlarmManager, it respects Doze mode and battery optimization constraints. Firestore offline persistence is enabled with multi-tab synchronization so reads are instant from local cache, while writes queue and sync automatically the moment connectivity returns. The full auth lifecycle (register → email verify → login → password reset) is handled without any third-party auth UI library.

Future Improvements

  • Full-text search, tags & filters
  • Export/import notes as JSON backup
  • End-to-end encryption for sensitive notes
02

XChat

E2E encrypted peer chat with WebRTC calls & Gemini AI

Elevator Pitch

XChat is a full-stack, one-to-one messaging app with three layers of real-time technology working simultaneously — Firebase for presence and message sync, Socket.IO for WebRTC signaling, and WebRTC itself for peer audio/video calls. All messages are encrypted end-to-end with AES keys derived from user credentials, so even Firebase cannot read the content. A built-in Gemini AI assistant lets users switch from human chat to AI chat in the same interface, with API keys protected from reverse engineering.

Key Features

AES E2E Encryption
WebRTC Video Calls
WebRTC Audio Calls
Socket.IO Signaling
Gemini AI Assistant
Friend Requests
Image Sharing
Call History Logs
Online Presence
Profile Management

Tech Stack

Kotlin WebRTC Firebase Realtime DB Firestore Socket.IO Gemini AI AES Encryption Glide

Screenshots

Profile Setup User Search Chat Screen Media Sharing Video Call Voice Call AI Assistant Call Logs Settings
Implementation Highlight: WebRTC peer connection setup required a custom Socket.IO signaling server to exchange SDP offers/answers and ICE candidates between devices behind NAT. AES encryption keys are derived per-user from a combination of credentials — never stored in plaintext or transmitted over the wire, meaning message content is unreadable even if the Firebase database is compromised. The Gemini AI integration maintains a fully isolated conversation thread so AI context never bleeds into the human chat history.

Future Improvements

  • Group chat & group calls
  • Signal-protocol-like double-ratchet key management
  • TURN server deployment for better NAT traversal
03

HelpHive

Community care ecosystem — mood tracking, kindness logging & practical help network

Elevator Pitch

HelpHive is a three-app ecosystem designed to combat modern social isolation. HelpHive Core matches neighbors for practical assistance. MoodMosaic lets users log emotions, visualize aggregated community mood patterns on a neighborhood heat map, and receive clinically-informed AI support messages when negative emotions are detected. KindConnect captures everyday acts of kindness and surfaces them in a positive-only community feed — with no leaderboards, only intrinsic recognition. tested with 12+ users. It has offline sync reliability, built on clean architecture with Hilt DI and a layered Room/Firestore repository.

Key Features

Standard Authentication system
Mood Logging (8 emotions)
Neighborhood Mood Map
AI Support Messages
Crisis Resource Access
Kindness Feed
Group Challenges
Mood Statistics
Community Feed
Offline-First Sync
Hilt Dependency Injection

Tech Stack

Kotlin Clean Architecture Room Database Firestore Hilt DI Gemini AI MPAndroidChart Coroutines & Flow

Screenshots

Home Screen Mood Tracking Mood Statistics Kindness Log Help Requests Chat Interface AI Messages Profile Setup Settings Analytics Notifications Community Feed Privacy Settings Offline Mode Theme Customization
Implementation Highlight: The repository layer merges Firestore (online) and Room (offline) data sources transparently — the UI layer never knows which source it is reading from, giving 99.7% offline reliability in beta. Gemini AI is prompted with clinically-informed context when users log negative emotions, with crisis hotline resources surfaced directly in-app rather than redirecting externally. All community mood visualizations apply a minimum-15-user aggregation threshold so no individual's emotional state is ever identifiable — privacy by design, not policy.

Future Improvements

  • Verified volunteers & human moderation tools
  • Anonymous mode for sensitive posts
  • Integration with municipal emergency response systems
04

WebNovel Summarizer

AI-powered reading assistant for web novel enthusiasts

Elevator Pitch

A specialized Android app that embeds a full-featured web browser — pre-configured for 6+ major novel platforms with a 15+ domain ad blocker and dark-mode CSS injection — and can summarize any chapter with a single tap. Three summary depths (5-second scan, 15-seconds standard, 25-seconds deep dive) are generated by Cerebras GPT-OSS-120B which is very fast and has excellent quality. For fall back, have Groq's Llama 3.3 70B integrated with custom system prompts that preserve honorifics, cultivation terminology, skill names, and character names exactly as written. An offline library organizes saved summaries into a Novels → Volumes → Chapters hierarchy for permanent access — no account, no cloud sync, zero data collection. Text-to-Speech with intelligent sentence-boundary chunking lets users listen to any summary hands-free.

Key Features

Smart WebView Browser
6+ Pre-configured Novel Sites
3-Level AI Summaries
15+ Domain Ad Blocker
Light/Dark Mode CSS Injection
Novel Library (3-tier)
Text-to-Speech (chunked)
Browsing History
Bookmarks
Manual CSS Selector
Offline Access
Cerebras + Groq Fallback

Tech Stack

Kotlin MVVM Room Database Groq AI (Llama 3.3 70B) Cerebras AI(GPT-OSS-120B) Retrofit + OkHttp Coroutines & StateFlow WebView + JS Injection

Screenshots

Home Screen Browser View Novel Site Grid Summarize Button Summary Screen Summary Detail Library Screen Volume List Chapter List Bookmarks History
Implementation Highlight: Content extraction runs a multi-stage JavaScript pipeline injected into the WebView: it strips scripts, styles, ads, navbars, and iframes; then tries 15+ CSS selectors ranked by text yield; if all fail, it falls back to paragraph-density scoring; and finally exposes a manual CSS selector dialog for edge cases. A ContentHolder singleton bypasses Android's Intent 1MB transaction limit when passing large chapter text to the summary screen. The TTS engine splits summaries at sentence boundaries into 4,000-character chunks with sequential playback and full pause/resume state preserved across chunks.

Future Improvements

  • Batch summarization across multiple chapters at once
  • Export library to PDF / EPUB formats
  • Multi-model AI support (Claude, Gemini, GPT-4)

Technical Expertise

Core competencies and technologies I work with daily

Languages & Frameworks

  • Java (Android SDK)
  • Kotlin (Coroutines, Flow)
  • XML Layouts
  • Jetpack Components
  • Room Database

Backend & APIs

  • Firebase (Auth / Firestore / Storage / FCM)
  • Supabase
  • WebRTC (Real-time communication)
  • REST APIs (Retrofit / OkHttp)
  • Socket.IO (Real-time updates)

Security & Architecture

  • AES Encryption
  • Clean Architecture / MVVM
  • Hilt Dependency Injection
  • Offline-First Design
  • Secure Key Management

AI & Specialized Tech

  • Generative AI (Gemini, Groq, Cerebras)
  • WebView & JavaScript Injection
  • MPAndroidChart (Data Visualization)
  • WorkManager (Background Tasks)
  • Glide / Picasso (Image Loading)

Get In Touch

Let's connect and discuss how I can contribute to your next Android project

Let's Build Something Amazing

Location

Dholaipar, Dhaka 1204