In-depth analysis of AI architectures, deployment patterns, and the research shaping the field.
Explore how AI agents, open protocols like MCP and A2A, and computer-use models are transforming the internet from a document-retrieval system into an agentic web where software reasons, acts, and collaborates autonomously.
Qwen-Scope and Anthropic's Natural Language Autoencoders are reshaping LLM interpretability in 2026. Inside the two releases, what they ship, and where each breaks.
Inside DeepSeek V4: hybrid attention (CSA + HCA), 1.6T MoE, 1M context, and the lineage from MLA to NSA to DSA that made it possible.
Learn the architecture, frameworks, and reliability patterns needed to deploy AI agents in production. Covers LangGraph, CrewAI, multi-agent systems, and more.
Explore RAG in 2026: from naive vector search to GraphRAG, agentic retrieval, ColPali, and context engines. A deep technical guide for AI practitioners.
Learn how Model Context Protocol (MCP) became the universal standard for connecting AI models to tools and data, reshaping the entire AI ecosystem.
Mechanistic interpretability lets researchers reverse-engineer neural networks to understand how AI thinks. Learn about sparse autoencoders, circuits, and safety.
Explore how open-source LLMs like Qwen, DeepSeek, Mistral, and Nemotron closed the gap with proprietary models in 2025-2026, reshaping AI's competitive landscape.
Explore DeepSeek's architecture breakthroughs: Multi-Head Latent Attention, auxiliary-loss-free MoE, FP8 training, and GRPO: frontier AI for $5.5M.
Explore how reasoning models like o1, o3, and DeepSeek-R1 use inference-time compute scaling and chain-of-thought to solve problems standard LLMs cannot.
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.
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.
Master LLM inference optimization: speculative decoding, KV-cache compression, quantization, FlashAttention, and serving frameworks compared for fast, cost-effective AI.
Explore vibe coding: the AI development paradigm coined by Karpathy. Compare Cursor, Claude Code, Google Antigravity & Copilot — with honest takes on which tools actually deliver.