What I know — honestly
Skills
Every skill carries a real level, from Learning to Advanced. Click any skill to see the projects that use it.
- C Familiar
- C++ Familiar
- Java Learning
- Python Familiar
- JavaScript Familiar
- Go Learning
- Rust Learning
- HTML Familiar
- CSS Familiar
- Django Familiar
- REST APIs Familiar
- MySQL Familiar
- Backend Development Familiar
- Responsive Web Development Familiar
- Machine Learning Learning
- Deep Learning Learning
- Natural Language Processing Learning
- Computer Vision Learning
- LLM Concepts Learning
- AI Engineering Learning
- Embeddings Learning
- Vector Search Learning
- RAG Concepts Learning
- Local AI Models Learning
- LangChain Learning
- Haystack Learning
- FAISS Learning
- Pinecone (concepts) Learning
- Weaviate (concepts) Learning
- Ollama Learning
- Llama-based local models Learning
- Document Embeddings Learning
- PDF Processing Learning
- Git Familiar
- GitHub Familiar
- VS Code Familiar
- Linux Familiar
- Windows Familiar
- Docker Learning
- Virtual Machines Familiar
Ecosystem
Technologies I build with
The stack orbiting my work right now.
- Python
- Java
- C++
- JavaScript
- Django
- MySQL
- Git
- GitHub
- Linux
- AI
- ML
- NLP
- Computer Vision
- LLMs
Knowledge map
How my knowledge connects
Click a node to see the skills, projects and writing connected to it.
DSA
Problem solving
My data structures & algorithms roadmap.
Foundations
- Complexity Analysis — In Progress
- Arrays — In Progress
- Strings — In Progress
- Recursion — In Progress
- Searching — In Progress
- Sorting — In Progress
Core structures
- Linked Lists — Planned
- Stacks — Planned
- Queues — Planned
- Hashing — Planned
Non-linear
- Trees — Planned
- Heaps — Planned
- Graphs — Planned
Paradigms
- Greedy Algorithms — Planned
- Backtracking — Planned
- Dynamic Programming — Planned
AI / ML
Exploring intelligence
My AI journey spans machine learning, deep learning, NLP, computer vision, LLMs, embeddings and RAG — and I learn best by building: assistants, document intelligence and AI-powered education.
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Foundations In Progress
Machine Learning
Supervised & unsupervised learning, evaluation, feature engineering.
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Foundations In Progress
Deep Learning
Neural networks, backpropagation, training dynamics.
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Language In Progress
Natural Language Processing
Tokenisation, text representations, classic NLP pipelines.
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Perception Planned
Computer Vision
Image processing, CNNs and recognition tasks.
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Language In Progress
Large Language Models
How transformers and LLMs work under the hood.
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Retrieval In Progress
Embeddings
Representing documents and meaning as vectors.
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Retrieval In Progress
RAG
Retrieval-augmented generation over my own documents.
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Applications In Progress
AI Assistants
Voice + chat assistants that act on real systems (AISHA).
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Applications In Progress
Local AI
Running open models locally with tools like Ollama.
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Applications In Progress
Document Intelligence
OCR, extraction and understanding of PDFs.
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Applications In Progress
AI-powered Education
Tutoring, revision and study planning (AI Study Hub).
My AI philosophy
AI should be built, not just consumed.
Calling an API is a starting point, not the destination. I want to understand how AI systems actually work — data, architectures, training, evaluation and deployment.
My long-term direction is to train and build my own models rather than depending entirely on external AI APIs. Today that means learning the foundations and experimenting with local, open models. It's an ongoing path, and I'm documenting it honestly as I go.