Brain Series Current: Cocktail Party Effect Change Blindness All Posts Earworms You’re in a loud, crowded room. Dozens of conversations happening simultaneously. You’re focused on talking to someone right in front of you, filtering out all the background noise. ...
Brain Series Current: Meditation for Skeptics Digital Detox All Posts Social Connection “Meditation is for hippies. I don’t have time to sit cross-legged chanting ‘om’ for an hour.” ...
Introduction: The Memory Wall Modern LLMs can process context windows of 100K+ tokens. But there’s a hidden cost: the KV cache. As context grows, the memory required to store key-value pairs in attention explodes quadratically. This creates a bottleneck: Memory: KV cache can consume 10-100× more memory than model weights Bandwidth: Moving KV cache data becomes the primary latency source Cost: Serving long-context models requires expensive high-memory GPUs Two innovations address this: Grouped Query Attention (GQA) and Multi-Head Latent Attention (MLA). They reduce KV cache size by 4-8× while maintaining quality. ...
Brain Series Current: The 20-20-20 Rule Blue Light and Melatonin All Posts Morning Routines You’re staring at a screen. Your eyes burn. Your head aches. You can’t focus anymore. You’ve been working for 4 hours straight without looking away. ...
Introduction: The Paper That Changed Everything In 2017, Google researchers published “Attention is All You Need”, introducing the Transformer architecture. This single paper: Eliminated recurrence in sequence modeling Introduced pure attention mechanisms Enabled massive parallelization Became the foundation for GPT, BERT, and all modern LLMs Let’s visualize and demystify this revolutionary architecture, piece by piece. The Problem: Sequential Processing is Slow Before Transformers: RNNs and LSTMs graph LR A[Word 1The] --> B[Hidden h1] B --> C[Word 2cat] C --> D[Hidden h2] D --> E[Word 3sat] E --> F[Hidden h3] style B fill:#e74c3c style D fill:#e74c3c style F fill:#e74c3c Problem: Sequential processing—each step depends on the previous. Can’t parallelize! ...