Writing about development and technology.
Three core patterns that show up repeatedly in AI agent systems—Tool Use, ReAct, and Chain of Thought—explained with real TypeScript code. Understanding these patterns makes agent design significantly clearer.
When ChatGPT first came out, developers were terrified. 'Coding is dead.' I was scared too. But after integrating LLMs into production for a year, I realized: AI is not a God, but an incredibly smart intern who sometimes hallucinates confidently.
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
Understanding neural network principles through practical project experience. From factory line analogies to backpropagation and hyperparameter tuning.
