Demystifying Artificial Intelligence through simple, hands-on lessons and bite-sized daily paper breakdowns. Designed for anyone to learn, build, and share.
A beginner-friendly, step-by-step 30-day guide to understanding Artificial Intelligence. From NLP fundamentals to AGI — explained in plain language, no programming required.
AI From Scratch
Master retrieval-augmented generation with hybrid search, re-ranking, multi-hop reasoning, and production-grade RAG pipelines.
Advanced Retrieval
Build autonomous AI agents that plan, reason, and collaborate. Multi-agent orchestration, tool use, and real-world deployment.
Autonomous Systems
Learn to fine-tune foundation models, build evaluation pipelines, and deploy custom LLMs at scale with modern MLOps practices.
Model Customization
Explore image generation, video understanding, spatial intelligence, and building applications that see, hear, and understand the world.
Beyond Text
One AI research paper explained clearly every day. Stay at the frontier of AI research — no jargon, just insight. Designed for curious beginners and builders alike.
Read Today's Paper →AI Engineer at JPMC • NLP Researcher (IIT Gandhinagar)
I specialize in building fiduciary-grade hybrid RAG solutions and scalable AI systems for institutional finance. As a pioneer of Hybrid RAG at JPMC and published researcher at EMNLP 2024 (LEGOBench), I bridge the gap between cutting-edge AI research and production-ready enterprise applications.
View Full Portfolio →Start with Python — it's the essential programming language for AI. Then build your math intuition (linear algebra, calculus, probability). Move to classical machine learning, then deep learning, and finally NLP and large language models. Our free AI Learning Roadmap breaks each step down for complete beginners.
Yes, Python is practically required. It's the primary language for every major AI framework (PyTorch, TensorFlow, Hugging Face). You don't need to be an expert — basic proficiency in variables, loops, functions, and working with libraries like NumPy and Pandas is enough to get started.
A Hybrid RAG (Retrieval-Augmented Generation) system combines both dense vector search (semantic similarity) and sparse keyword search (like BM25) to retrieve the most relevant information for a Large Language Model. This ensures the AI gets both the broad conceptual context and exact-match keywords, making it much more accurate for enterprise use cases.