Building Scalable AI systems with LLMs, SLMs, Semantic Search, NLP, and AI Agents.
Notes on LLM systems, semantic search, embeddings, retrieval, attention, PyTorch, and NLP engineering.
Concise explanations for specific technical problems, with examples and implementation details.
Understanding why we use negative infinity instead of zero for masking in transformer attention mechanisms. Learn about causal masking, softmax behavior, and practical implementation in PyTorch.
Learn about Unicode normalization, non-breaking spaces (\xa0), and how to handle text encoding issues in Python. Understand NFC, NFD, NFKD, and NFKC normalization forms for better text processing....
Broader technical essays published externally, including Medium and Towards AI.