A structured learning path for AI architects, covering ML fundamentals, Transformers, LLM inference, RAG, vector search, multi-agent systems, model deployment, cost optimization, safety governance, and production observability for real-world AI platforms.
Learn Transformer architecture through self-attention, masking, positional signals, encoder-only, decoder-only, and encoder-decoder variants. Covers complexity, KV cache, reproducible evaluation, and deployment trade-offs without universal model rankings.
A practical guide to attention mechanisms: Query-Key-Value projections, scaled dot products, masking, multi-head attention, complexity, diagnostics, and Transformer integration. Includes NumPy and PyTorch examples, shape checks, implementation limits, long-context trade-offs, and cautions against treating heatmaps as explanations.
A rigorous introduction to neural networks, automatic differentiation, optimization, CNNs, sequence models, Transformers, generative models, data leakage, regularization, and reproducible evaluation. The guide separates illustrative equations from production decisions and avoids unsupported performance claims.
Build a precise mental model of neural networks from affine layers and nonlinearities to backpropagation, losses, initialization, optimization, regularization, CNNs, RNNs, and Transformers. Includes a shape-safe PyTorch example, evaluation and reproducibility practices, and the limits of biological analogies and benchmark claims.
Learn how vector embeddings work, choose models and dimensions, measure similarity, and build semantic search, recommendations, and RAG retrieval with Python examples.
Comprehensive guide to Generative AI covering core principles, key technologies (LLM, Diffusion Models, GAN, VAE), and applications. Includes GPT, Claude, Midjourney comparisons with practical tips and tool recommendations.
A comprehensive guide to NLP natural language processing, covering tokenization, named entity recognition, sentiment analysis, machine translation, and mainstream models like BERT and GPT.
Understand diffusion models from the forward noising process to learned denoising, DDPM/DDIM sampling, latent diffusion, conditioning, and deployment trade-offs. This guide separates equations from version-sensitive Diffusers code and covers reproducibility, safety, licensing, quality, latency, and cost evaluation.
Learn how Large Language Models generate text. A deep dive into the LLM inference process, covering tokenization, Prefill vs. Decode phases, KV Cache optimization, and latency metrics.
Discover how Mixture of Experts (MoE) architecture works. Learn how routing mechanisms in GPT-4 and DeepSeek reduce compute costs while massively scaling LLM parameters.
Explore the groundbreaking architecture behind OpenAI o1 and DeepSeek R1. Learn how Reasoning Models use test-time compute, reinforcement learning, and hidden Chain of Thought to solve complex problems.
A deep technical analysis of Mamba and State Space Models (SSM). Covers the evolution from S4 to Mamba-2 to Mamba-3, the math behind selective state spaces, linear complexity advantages, and Transformer + Mamba hybrid architectures in production. Includes code examples and benchmark comparisons.
A version-aware guide to reasoning controls in hybrid language-model APIs. Learn how to distinguish a product switch from an internal architecture, measure quality, latency, and cost under a fixed protocol, route requests with bounded budgets, and treat returned reasoning text as non-authoritative.
A comprehensive technical analysis of Claude 4 (Opus 4, Sonnet 4). Covers Extended Thinking hybrid reasoning, 7-hour autonomous execution, SWE-bench 72.5% record, Claude Code, Agent SDK, MCP Connector, and ASL-3 safety, with full code examples and benchmark comparisons.
A practical, version-aware four-layer model for AI context: instructions, knowledge, memory, and orchestration. Learn how to set budgets, route retrieval, compact memory, validate tool output, and measure quality without treating token ratios or model behavior as universal facts.
Deep dive into Together AI's Mixture of Agents (MoA) architecture: layered LLM collaboration design, Proposer-Aggregator pipeline, production Python/TypeScript implementations, and GPT-4o + Claude + Gemini joint inference with performance benchmarks and cost optimization strategies.
A comprehensive engineering guide to Test-Time Compute (TTC)—from Chain-of-Thought and Self-Consistency to Tree-of-Thought and MCTS reasoning search. Covers OpenAI o1, DeepSeek R1 internals with production-ready Python and TypeScript implementations.
A comprehensive architecture guide for building an LLM Gateway with intelligent model routing, token-based rate limiting, real-time cost tracking, semantic caching, and automatic fallback chains. Includes production-ready Python and TypeScript implementations.