N-Gram House - Page 3
Why Transformers Replaced RNNs in Large Language Models
Transformers replaced RNNs because they process language faster and understand long-range connections better. With parallel computation and self-attention, models like GPT-4 and Llama 3 now handle entire documents in seconds.
Measuring Developer Productivity with AI Coding Assistants: Throughput and Quality
AI coding assistants promise faster development, but real-world results show trade-offs between speed and code quality. Learn how top companies measure true productivity using throughput and quality metrics-not vanity stats.
Bernard Xavier Philippe de Marigny: Louisiana's Forgotten Nobleman and Cultural Icon
Bernard Xavier Philippe de Marigny was a French Creole nobleman who shaped New Orleans by developing the Marigny neighborhood, allowing diverse communities to thrive together - laying the groundwork for jazz and Creole culture.
Infrastructure Requirements for Serving Large Language Models in Production
Serving large language models in production requires specialized hardware, optimized software, and smart architecture. Learn the real costs, GPU needs, and optimization strategies that separate successful deployments from costly failures.
Hybrid Search for RAG: Boost LLM Accuracy with Semantic and Keyword Retrieval
Hybrid search combines semantic and keyword retrieval to fix RAG's biggest flaw: missing exact terms. Learn how it boosts accuracy for code, medical terms, and legal docs-and when to use it.
Trademark and Generative AI: How Synthetic Content Is Risking Your Brand
Generative AI is copying trademarks without permission, confusing customers and putting brands at risk. Learn how AI-generated content threatens your brand and what steps you can take now to protect it.
Positional Encoding in Transformers: Sinusoidal vs Learned for LLMs
Sinusoidal and learned positional encodings were early solutions for transformers, but modern LLMs now use RoPE and ALiBi for better long-context performance. Learn why and how these techniques evolved.
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 without needing bigger models.
Generative AI in Healthcare: How AI Is Transforming Drug Discovery, Medical Imaging, and Clinical Support
Generative AI is transforming healthcare by speeding up drug discovery, improving medical imaging accuracy, and reducing clinician burnout through automated clinical support. By 2025, it's already saving time, cutting costs, and saving lives.
Architectural Innovations Powering Modern Generative AI Systems
Modern generative AI isn't about bigger models-it's about smarter architecture. Discover how MoE, verifiable reasoning, and hybrid systems are replacing monolithic designs and enabling practical AI at scale.
How to Build a Coding Center of Excellence: Charter, Staffing, and Realistic Goals
A Coding Center of Excellence brings order to chaotic development teams by setting standards, tools, and practices that reduce bugs, speed up delivery, and cut costs. Learn how to build one with the right charter, staffing, and measurable goals.
Productivity Uplift with Vibe Coding: What 74% of Developers Report
74% of developers say vibe coding boosts productivity, but the reality is more complex. AI tools help experienced coders ship faster - but can slow down juniors and create hidden technical debt. Learn how to use them right.
