VAHU: Visionary AI & Human Understanding - Page 3
How Compression Interacts with Scaling in Large Language Models
Compression and scaling in LLMs don't follow simple rules. Larger models gain more from compression, but each technique has limits. Learn how quantization, pruning, and hybrid methods affect performance, cost, and speed across different model sizes.
Onboarding Developers to Vibe-Coded Codebases: Playbooks and Tours
Vibe coding speeds up development but creates chaotic codebases. Learn how to onboard developers with playbooks, codebase tours, and AI prompt documentation to avoid confusion and burnout.
Toolformer-Style Self-Supervision: How LLMs Learn to Use Tools on Their Own
Toolformer teaches large language models to use tools like calculators and search engines on their own-without human labels. It boosts accuracy in math and facts while keeping language skills intact.
Red Teaming for Privacy: How to Test Large Language Models for Data Leakage
Learn how red teaming exposes data leaks in large language models, why it's now legally required, and how to test your AI safely using free tools and real-world methods.
OCR and Multimodal Generative AI: Extracting Structured Data from Images
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: What They Can Do and Where They Still Fail
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.
Structured vs Unstructured Pruning for Efficient Large Language Models
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.
How Vocabulary Size in Large Language Models Affects Accuracy and Performance
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.
Keyboard and Screen Reader Support in AI-Generated UI Components
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.
Memory and Compute Footprints of Transformer Layers in Production LLMs
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 as First-Class Metrics in LLM Evaluation: Why Speed and Price Matter More Than Ever
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.
How to Use Large Language Models for Literature Review and Research Synthesis
Learn how to use large language models like GPT-4 and LitLLM to cut literature review time by up to 92%. Discover practical workflows, tools, costs, and why human verification still matters.