I’m a developer focused on building practical AI systems using Machine Learning, Computer Vision, Large Language Models, and agent-based architectures.
My goal is simple: build technology that turns complex data and problems into useful, intelligent solutions.
- 🤖 Interested in Artificial Intelligence, Machine Learning, and Generative AI
- 🧠 Exploring LLMs, RAG pipelines, and Multi-Agent Systems
- 📊 Building intelligent systems for data analysis and automation
- 👁️ Experience working with Computer Vision and object detection models
- 🚀 Always looking to build projects that go beyond tutorials and solve real problems
Lumora is a multi-agent platform designed to make data analysis more intelligent and accessible.
Instead of treating data analysis as a single workflow, Lumora uses specialized agents that handle different stages of the process.
┌─────────────────┐
│ Dataset │
└────────┬────────┘
│
▼
┌─────────────────┐
│ Atlas │
│ Data Cleaning │
└────────┬────────┘
│
▼
┌─────────────────┐
│ Scout │
│ EDA │
└────────┬────────┘
│
▼
┌─────────────────┐
│ Canvas │
│ Visualization │
└────────┬────────┘
│
▼
┌─────────────────┐
│ Oracle │
│ Insights │
└─────────────────┘
The vision is to create an AI-powered environment where users can upload data, explore it, generate insights, visualize patterns, and interact with their data using natural language.
Core areas:
Multi-Agent AI • LLMs • LangGraph • Data Analysis • RAG • Visualization
Python • Java • JavaScript • SQL • C++
PyTorch • TensorFlow • Scikit-learn • OpenCV • YOLO • Pandas • NumPy
LangChain • LangGraph • LLMs • RAG • AI Agents
React • Node.js • HTML • CSS • REST APIs
Git • GitHub • Docker • Jupyter • VS Code • MySQL • MongoDB
Artificial Intelligence
│
├── Machine Learning
├── Deep Learning
├── Computer Vision
├── Generative AI
├── Large Language Models
├── Retrieval-Augmented Generation
└── Multi-Agent Systems
Projects involving machine learning models, deep learning, and intelligent prediction systems.
Building object detection and computer vision applications using modern deep learning architectures such as YOLO.
Experimenting with LLMs, Retrieval-Augmented Generation, AI agents, and intelligent workflows.
Developing systems that transform raw datasets into insights, visualizations, and actionable information.
Building intelligent systems and learning something new along the way.