Technical Guides

DeepSeek V4 hits 1M context at 27% of V3.2's per-token compute. How Compressed Sparse Attention and Heavily Compressed Attention combine to do it.

Why long context is expensive: the math of the KV cache, the architectural moves that shrink it (MQA, GQA, MLA, paged attention), and why 1M-context models are an engineering problem before they're a research problem.

How Mixture of Experts lets small models punch above their weight. Architectural deep-dive into Mixtral, DeepSeek-MoE, Phi-MoE, and the efficiency math behind sparse routing.

How AI video generation works: diffusion foundations, temporal modeling, audio sync, and the multimodal architectures behind Seedance 2.0.

The benchmarks behind AI model claims: SWE-bench, ARC-AGI-2, GPQA Diamond, and more. What they measure, how they work, and what they miss.

A guide to agentic AI patterns: ReAct loops, tool-use protocols, multi-step planning, memory, and multi-agent coordination in production.

MoE architectures explained: gating mechanisms, expert routing, load balancing, and why sparse models deliver frontier AI at fraction cost.

A technical deep-dive into transformer architectures, attention mechanisms, scaling laws, and emerging techniques for reliable AI reasoning.

A map of AI systems — machine learning, deep learning, LLMs, multimodal models, and world models — with clear definitions and comparisons.

Learn 7 battle-tested prompt engineering patterns that reduce failures and improve reliability in production AI systems. Includes code examples.

Discover how LLMs process text through tokenization and inference. Essential knowledge for optimizing AI costs and prompt performance.

Architecture patterns and implementation considerations for building retrieval-augmented generation systems that work reliably at scale.