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DeepSeek V4 and the Hybrid Attention Bet
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Attention Mechanisms
How transformers decide what to focus on. From the original scaled dot-product attention to multi-head attention, grouped query attention, and multi-head latent attention. The mechanism at the heart of every modern AI system.
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Understanding Transformer Architectures from Scratch
Master the transformer architecture from first principles: self-attention, multi-head attention, positional encodings, encoder-decoder design, and modern innovations like RoPE, GQA, and SwiGLU, with code.
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LLM architectureattention mechanismsdeep learningscalinginference optimization22 min read
Mixture of Experts Demystified: Why Every Frontier Model Uses MoE Now
Learn how Mixture of Experts (MoE) powers frontier AI models like DeepSeek-V3 and Mixtral: sparse routing, load balancing, and why MoE beat dense scaling.
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LLM architectureattention mechanismsmodel traininginference optimization20 min read
Inside DeepSeek: The Architecture Innovations That Shook the AI Industry
Explore DeepSeek's architecture breakthroughs: Multi-Head Latent Attention, auxiliary-loss-free MoE, FP8 training, and GRPO: frontier AI for $5.5M.
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