Author: JAMIUL ISLAM

Modern OCR powered by multimodal AI can extract structured data from images with 90%+ accuracy, turning messy documents into clean, usable information. Learn how Google, AWS, and Microsoft are changing document processing-and what you need to know before adopting it.

Autonomous agents built on large language models can plan, act, and adapt without constant human input-but they still make mistakes, lack true self-improvement, and struggle with edge cases. Here’s what they can do today, and where they fall short.

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Structured and unstructured pruning help shrink large language models for real-world use. Structured pruning keeps hardware compatibility; unstructured gives higher compression but needs special chips. Learn which one fits your needs.

Vocabulary size in large language models directly impacts accuracy, efficiency, and multilingual performance. Learn how tokenization choices affect real-world AI behavior and what size works best for your use case.

AI-generated UI components can improve accessibility, but only if they properly support keyboard navigation and screen readers. Learn how current tools work, where they fail, and how to ensure real accessibility-not just automated checks.

Transformer layers in production LLMs consume massive memory and compute, with KV cache now outgrowing model weights. Learn how to identify memory-bound vs. compute-bound workloads and apply proven optimizations like FlashAttention, INT8 quantization, and SwiftKV to cut costs and latency.

Latency and cost are now as critical as accuracy in LLM evaluation. Learn how top companies measure response time, reduce token costs, and avoid hidden infrastructure traps in production deployments.