Fahim Shahriar Anik

Haus of Axion — Overview

Haus of Axion is a premium fitness and lifestyle platform that combines elite in-home personal training, hybrid athletic programming, and tech-enabled client experiences.
It is designed for high-performing professionals, bridal clients, and lifestyle-focused individuals who want luxury, accountability, and results—without the friction of traditional gyms.

The brand positions itself at the intersection of high-touch concierge services and scalable digital fitness infrastructure.

Haus of Axion App — Key Features

1. Client Experience
   Secure user onboarding & profiles
   Fitness goals, injuries, preferences tracking
   Personalized training plans
   Workout history & progress tracking
   Media uploads (progress photos, form videos)
   In-app messaging with trainers
   Session booking & rescheduling
   Notifications & reminders

Differentiation
Luxury positioning (not mass-market fitness)
High-touch concierge model
Hybrid delivery (offline + online)
Bridal & lifestyle specialization
Founder-led quality control
Scalable tech backbone from day one

Tech Stack (Current & Planned)
Frontend
Web App
Angular (latest stable)
TypeScript
Responsive UI (mobile-first)

Backend
API Layer
Node.js / NestJS
GraphQL (interactive, client-driven queries)

Authentication
Secure token-based auth
Role-based access (client / trainer / admin)

Database & Storage
Primary Database
PostgreSQL (hosted via Supabase)
File Storage
AWS S3 (user-uploaded media, progress photos, videos)

Cloud & Infrastructure
AWS
Lambda (serverless backend)
API Gateway (GraphQL & REST where needed)
CloudWatch (logging & monitoring)
Deployment
CI/CD pipelines
Frontend hosted via AWS Amplify
Backend deployed via Serverless Framework
Analytics & Observability
Usage & engagement tracking
Error monitoring
Performance logging

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Description:
The Food Detection Gradle Project is a Java-based application that identifies and classifies food items using machine learning models. Built with a focus on modularity and scalability, this project integrates Gradle as the build automation tool and employs image recognition techniques to detect various food items. It showcases the combination of Java and AI in real-world applications.

Key Features:

Food Item Detection: Recognizes and classifies food items from images using trained models.

Scalable Build System: Utilizes Gradle for dependency management and project organization.

Seamless Integration: Designed for easy integration with existing Java projects.

Customizable ML Model: Supports the use of custom-trained models for unique datasets.

Cross-Platform Support: Runs efficiently on different platforms with Java runtime support.

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Description:
Fruit and Vegetable Classification is a machine learning-based project that classifies fruits and vegetables using image recognition. It demonstrates the use of convolutional neural networks (CNNs) for accurate identification and categorization. Designed as a practical implementation of deep learning techniques, the project provides an engaging exploration of AI in the food industry.

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