Creating Production-Ready MCP Servers: A Practical Engineering Handbook
The Model Context Protocol gives AI systems a standardized way to connect with tools, files, databases, and business services.
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System Design Under Real Load: Scaling from One to a Million Users
A system that performs well for a handful of users can behave very differently once real traffic, operational failures, and growing infrastructure complexity begin to appear. This handbook follows the architectural decisions involved in scaling applications from early deployment to millions of users, examining load balancing, database scalability, caching, asynchronous processing, fault tolerance, observability, and deployment strategies while focusing on when each pattern should be introduced and the trade-offs engineers must consider.
Evaluating LLM Apps When There’s No Ground Truth
Traditional software tests depend on expected outputs, but production LLM applications often operate in environments where multiple answers can be valid and exact string matching provides little meaningful evidence of quality. This handbook presents a practical framework for evaluation-driven AI development, covering golden datasets, product-specific rubrics, LLM-as-judge systems, human calibration, RAG evaluation, regression testing, production monitoring, cost and latency measurement, and the feedback loops required to replace subjective “vibe testing” with a repeatable engineering system.
Cutting Your LLM Bill 10x — Caching, Model Routing, Quantization, and Local Inference with vLLM
LLM costs can grow rapidly when a simple prototype evolves into a production system with long prompts, RAG, personalization, tool calls, retries, agents, and increasingly complex model workflows. This handbook examines how teams can systematically reduce AI infrastructure costs through measurement, prompt optimization, caching, model routing, output controls, agent budgets, quantization, evaluation, and efficient local inference with technologies such as vLLM, treating cost optimization as a layered engineering discipline rather than a single technical trick.
Building Real-Time AI Apps: Streaming Responses, SSE, and Websockets
Modern AI applications cannot rely on static request-response patterns when users expect models to respond, stream, and interact in real time. This handbook explores the engineering behind streaming AI experiences, from token-level responses and time-to-first-token to Server-Sent Events, WebSockets, frontend rendering, connection management, scaling, observability, and security, providing a practical foundation for building responsive AI systems that work under production conditions.
Writing Good Code with AI: The Discipline of Reviewing What the Machine Writes
AI has made generating code dramatically easier, but the real engineering discipline is increasingly shifting toward understanding, reviewing, testing, and taking ownership of what the machine produces. This handbook examines why AI-generated code can fail silently and introduces practical review disciplines covering correctness, security, maintainability, tooling, team guardrails, and the fundamental principle that engineers should never ship code they cannot fully explain and defend.
Beyond OCR: How to Extract Tables, Forms, Images, and Meaning from Complex Documents
Extracting characters from a document is only the beginning of understanding what that document actually contains. This handbook moves beyond traditional OCR into document intelligence, exploring layout analysis, table detection, paragraph reconstruction, formulas, images, forms, spatial relationships, and semantic term extraction to show how production systems can transform complex documents without losing the structure and meaning embedded in their pages.
Building AI-Powered Document Translation Systems: Layout Preservation, LLMs, and Production Pipelines
Production-grade document translation is far more complex than sending extracted paragraphs to an LLM and placing the translated text back into a PDF. This engineering handbook examines translation architecture, protected content, terminology consistency, document-level quality validation, model-provider management, observability, recoverability, and production pipelines, focusing on the difficult gap between the text a model sees and the final translated document a user actually receives.
Architecting AI-Native Document Processing Systems: From Raw PDFs to Structured and Reconstructed Documents
Documents remain one of the most technically difficult interfaces between artificial intelligence and real-world information because a visually simple page may internally consist of thousands of positioned glyphs, drawing operations, images, vectors, and font references. This handbook explores the architecture required to turn raw PDFs into structured representations and reconstruct them after AI processing, covering spatial intelligence, multimodal ingestion, semantic reconstruction, constrained typesetting, orchestration, and document-level validation.
Database Performance Engineering: Indexing, Query Plans, and Caching That Actually Helps
Database performance problems are rarely solved by simply adding more hardware, especially when inefficient queries, poor indexing strategies, and unnecessary database operations remain unresolved. This practical handbook explores how databases execute queries, how indexes affect retrieval, how execution plans expose bottlenecks, and where caching can genuinely reduce repeated work, providing engineers with a systematic approach to building database-backed applications that remain responsive as data and traffic grow.
Modern Full-Stack with Next.js: Server Components, Streaming, and the Vercel AI SDK
Modern full-stack development is increasingly moving toward server-first architectures, progressive interfaces, and native integration with artificial intelligence. This handbook explores the Next.js App Router, React Server Components, streaming UI, Server Actions, Route Handlers, and the Vercel AI SDK, showing how developers can build high-performance applications with streaming AI responses, function calling, RAG, production deployment strategies, and the security and performance practices required for modern intelligent web systems.