AI / MLarticles
Practical AI engineering — from RAG pipelines and agent architectures to deploying models that actually work in production. 99 articles and counting.
How Mixture of Experts Works: Sparse Gating, Expert Routing, and the Architecture Behind Efficient Large Language Models
A deep dive into MoE architecture: how the gating network routes tokens to experts, top-k selection, load balancing losses, capacity factor, token dropping, expert parallelism for serving, and the real production tradeoffs between dense transformers and sparse MoE models.
AI Agent Frameworks Compared: CrewAI, LangGraph, AutoGen, and Mastra for Production Systems
A practical comparison of CrewAI, LangGraph, AutoGen, and Mastra for building production AI agent systems. Covers architecture philosophy, state management, tool integration, observability, and deployment patterns with TypeScript code examples.
Google's Agent2Agent Protocol: How A2A Enables Cross-Framework Agent Communication in Production Systems
A deep dive into Google's Agent2Agent (A2A) protocol covering agent cards, task lifecycle, message parts, streaming via SSE, push notifications, and how A2A complements MCP. Includes TypeScript implementation examples, comparison with MCP and direct API integration, and production deployment patterns for multi-vendor agent ecosystems.
How Transformer Models Work: Self-Attention, Positional Encoding, and the Architecture Behind Modern LLMs
A technical deep dive into the Transformer architecture: tokenization, positional encoding, self-attention with Q/K/V matrices, multi-head attention, the encoder-decoder split, training dynamics, and what it all means for engineers building on top of LLMs.
How LLM Inference Engines Work: KV Caches, PagedAttention, and Continuous Batching From Prompt to Token
A deep dive into the internal architecture of LLM inference engines: autoregressive generation, KV cache memory management, PagedAttention, continuous batching, speculative decoding, tensor and pipeline parallelism, quantization formats, and how to choose between vLLM, TGI, and Ollama in production.
How LLM Tokenizers Work: BPE, Vocabulary Construction, and Why Token Economics Matter for Production AI
A deep dive into how BPE tokenization works, how tiktoken and SentencePiece build vocabularies, why tokens don't map 1:1 to words, and what token economics mean for cost, RAG chunk sizing, and multilingual efficiency in production.
LLM Cost Engineering in Production: Token Budgets, Model Routing, and Unit Economics for AI-Powered Applications
A production guide to LLM cost engineering covering token budget enforcement with per-request circuit breakers, intelligent model routing, semantic caching, agent loop containment, Cost per Successful Task as a unit economics KPI, and organizational governance for shadow AI spend.
Designing an AI Agent Cost Governance System: Token Budgets, Spend Caps, and Automated Circuit Breakers for Production LLM Deployments
How to prevent AI cost overruns in production: per-agent token budgets with hard enforcement, team-level spend allocation, circuit breakers that halt runaway agent loops, cost attribution, model routing by tier, and the critical difference between monitoring and enforcement.
Building Runtime Governance for AI Agents: Pre-Execution Validation, Permission Boundaries, and Behavioral Drift Detection in Production
How to build a governance middleware layer that intercepts every agent action before execution, validates it against policy, and either allows, denies, or escalates to a human. Covers five production failure modes with TypeScript implementation for each governance control.
The 5 Runtime Failure Modes That Kill AI Agent Deployments Before the Model Is the Problem
88% of AI agent projects never reach production, and model quality is rarely the reason. Covers 5 named runtime failure modes that kill deployed agents, with detection signals, real incident examples, and TypeScript countermeasures for each.
AI Supply Chain Security: How Open-Source Dependencies Become Attack Vectors in LLM Applications
How the AI toolchain layer (LiteLLM, LangChain, LlamaIndex, etc.) introduces supply chain attack vectors distinct from traditional OSS risk, with a TypeScript implementation for dependency validation and monitoring, multi-agent cascade risk analysis, and a production lockdown checklist.
Context Engineering for Production AI Applications: System Prompts, Memory Architecture, and Retrieval Strategies That Actually Scale
Context engineering is the discipline of deciding what information reaches your LLM and when. This guide covers system prompt versioning, memory hierarchies, retrieval-augmented context assembly, token budget allocation, and the architectural patterns that separate prototype AI apps from production-grade ones.