All Articles

The Problem No One Wants to Talk About Large language models have quietly become competitive machine translation systems. GPT-4, Claude, Gemini — they all translate between dozens of language pairs with fluency that often matches or exceeds dedicated MT systems like Google Translate or DeepL. For...

Large pre-trained word embeddings are a cornerstone of modern NLP, but their memory footprint is a real deployment bottleneck. A standard 300-dimensional GloVe vocabulary of 2 million tokens consumes ~2.4 GB in float32. On mobile devices, edge hardware, or in multi-model serving environments, this...

Practical lessons from building production RAG systems, covering chunking strategies, hybrid search, reranking, and evaluation.