The Architectural Elegance of Model Context Protocol (MCP)
The Model Context Protocol (MCP) architecture simplifies AI integration complexity using clear architectural patterns, including Facade/API Gateway, Adapter, Sidecar, and Orchestrator. By isolating services, standardizing interfaces, and reducing orchestration overhead, MCP makes complex AI systems more maintainable, scalable, and robust. This is ideal for building reliable, production-ready agentic AI applications.
DeskRAG: Create an Offline AI Assistant in One Afternoon
Learn how I built DeskRAG, an offline GPT-style assistant to privately query and summarize personal documents directly on your laptop. Leveraging Vibe coding with Cursor, quantized LLMs, CLIP embeddings, and FAISS vector storage, I quickly created a powerful personal AI tool—transforming days of coding into a single afternoon’s work.
How Vibe Coding is Transforming Software Development
I’ve been watching a fascinating shift in software development lately. The rise of what’s ... Read more
How AI is Transforming Astronomy: A Deep Dive
Explore how AI, including deep learning, generative AI, and LLMs, is transforming astrophysics—from exoplanet discovery and galaxy classification to gravitational wave detection and cosmological simulations.
Understanding Communication Patterns in Distributed ML: A Deep Dive into NCCL, MPI, and Gloo
Understand key communication patterns (Broadcast, Scatter/Gather, All-Reduce, All-Gather, All-to-All) and core protocols (NCCL, MPI, Gloo) crucial for efficiently scaling distributed ML workloads, including LLMs, computer vision, and multimodal models.
3 Key Parallel Computing Concepts for Distributed ML
Understand foundational parallel computing concepts such as SIMD, multithreading, and GPU kernels that underpin distributed machine learning. Learn how mastering these fundamentals helps optimize your ML systems before scaling to multi-node setups.
Scaling AI: The Essentials of Distributed Machine Learning
Distributed ML powers today's massive AI models like GPT-4 and Stable Diffusion. This article introduces key concepts in Distributed ML, explains parallel computing clearly, and highlights real-world challenges and strategies.
Land Your Dream AI/ML Job: A Practical Guide
Want to land your dream AI/ML job? This practical guide breaks down key strategies, ... Read more
AI for Software Engineers: Evolve, Don’t Restart
Explore how software engineers can naturally evolve into AI and ML roles. Learn practical ... Read more