Writing about development and technology.
Solving server waste at dawn and crashes at lunch. Understanding Auto Scaling vs Serverless through 'Taxi Dispatch' and 'Pizza Delivery' analogies. Plus, cost-saving tips using Spot Instances.
Deleting 500 lines of loading states with React Suspense. How to handle async UI declaratively.
Understanding load balancing principles and practical applications through project experience
Anatomy of the GCC pipeline. Preprocessor, Compiler, Assembler, and Linker. What happens when you type `gcc main.c`.
Both are children of Transformer, so why the difference? Using 'Fill-in-the-blank' vs 'Write-next-word' analogies to explain BERT vs GPT. Practical guide based on trial and error.
Overcoming RNN's 'dementia' and how Google flipped the world with 'Attention Is All You Need'. From Query-Key-Value library analogies to Multi-Head Attention and Vision Transformers.
I share my experience with overfitting in machine learning. I was fooled by 99% training accuracy, only to fail in production. Learn how I used Dropout, Regularization, and Data Augmentation to build 'real intelligence' instead of a memorization machine.
Understanding vector database principles and practical applications through project experience
How do computers understand that 'King' - 'Man' + 'Woman' = 'Queen'? We dive deep into the evolution of NLP embeddings, from One-Hot Encoding to Word2Vec and Transformer-based models. Learn about Vector Databases, Cosine Similarity math, and how RAG (Retrieval-Augmented Generation) is reshaping modern AI applications.
Understanding differences and selection criteria between fine-tuning and prompt engineering for LLM customization
My AI chatbot was hallucinating wild answers to customers. Here's how I implemented RAG (Retrieval-Augmented Generation) to fix it, covering Vector DBs, Embeddings, and Hybrid Search.
Understanding Transformer architecture through practical experience
Understanding RNN and LSTM principles through practical project experience
A strategic guide for Architects. Covers Conway's Law, Distributed Patterns (Saga, CQRS), Modular Monolith, Migration Strategies, Glossary, and FAQ.
Understanding neural network principles through practical project experience. From factory line analogies to backpropagation and hyperparameter tuning.
A practical guide to choosing between Supervised, Unsupervised, and Semi-Supervised Learning when you don't have labeled data.
Understanding AI, Machine Learning, Deep Learning, and Generative AI. Deep dive into Transformer architecture, RAG vs Fine-tuning, Ethical AI, and a practical roadmap for developers transitioning to AI Engineering.
How I lost my data due to a simple SQL Injection. Why Prepared Statements are the only silver bullet and if ORMs are truly safe.
A Masterclass in API Design. Covers Constraints, Safe/Idempotent Methods, Content Negotiation, Versioning, Security, Glossary, and FAQ.
My admin account was hijacked because of a single comment on the board. I dive deep into the 3 types of XSS (Stored, Reflected, DOM) and concrete defense strategies in React/Next.js environments, including HTML Escaping, CSP, and Cookie Security.
