Archive: 2025/09
Self-Attention and Positional Encoding: How Transformers Power Generative AI
Self-attention and positional encoding are the core innovations behind Transformer models that power modern generative AI. They enable models to understand context, maintain word order, and generate coherent text at scale.
Vibe Coding vs AI Pair Programming: When to Use Each Approach
Vibe coding speeds up simple tasks with AI-generated code, while AI pair programming offers real-time collaboration for complex problems. Learn when to use each to boost productivity without sacrificing security or quality.
Designing Trustworthy Generative AI UX: Transparency, Feedback, and Control
Trust in generative AI comes from transparency, feedback, and control-not flashy interfaces. Learn how leading platforms like Microsoft Copilot and Salesforce Einstein build user trust with proven design principles.
Prompt Compression: Cut Token Costs Without Losing LLM Accuracy
Prompt compression cuts LLM input costs by up to 80% without sacrificing answer quality. Learn how to reduce tokens using hard and soft methods, real-world savings, and when to avoid it.
Knowledge Sharing for Vibe-Coded Projects: Internal Wikis and Demos That Actually Work
Learn how vibe-coded internal wikis and short video demos preserve team culture, cut onboarding time by 70%, and reduce burnout - without adding more work. Real tools, real results.
Can Smaller LLMs Learn to Reason Like Big Ones? The Truth About Chain-of-Thought Distillation
Smaller LLMs can learn to reason like big ones through chain-of-thought distillation - cutting costs by 90% while keeping 90%+ accuracy. Here's how it works, what fails, and why it's changing AI deployment.