In-depth, opinionated guides on shipping production AI systems — Retrieval-Augmented Generation (RAG), LangChain and LangGraph agent workflows, vector search, Next.js performance, TypeScript architecture, and the DevOps that holds it all together. Every post is written for engineers who have to make the trade-offs, not just describe them.
· Written by Niraj Kumar
Practical breakdowns of AI systems, scalable backends, modern web stacks, and production-ready engineering.
38 articles found
A comprehensive guide to designing clean, scalable, and maintainable RESTful APIs — covering resource naming conventions, versioning strategies, and pagination patterns with real-world examples and modern best practices.
Read ArticleA comprehensive guide to building production-ready REST APIs with Node.js 22+ in 2026. Covers folder structure, error handling, validation, logging, and performance tips for Express and Fastify.
Read ArticleA practical guide for developers building LLM-powered applications in B2B and regulated domains — covering sensitive data handling, compliance frameworks, latency trade-offs, and when to choose local models over cloud APIs.
Read ArticleA comprehensive guide to evaluating and monitoring fine-tuned LLMs and traditional ML models in production — covering key metrics, drift detection strategies, A/B testing frameworks, and real-world monitoring dashboard setups.
Read ArticleA comprehensive guide to designing AI agents in 2026 — comparing single-step vs. multi-agent workflows, tool calling patterns, memory architectures, and how to avoid the most common reliability and safety pitfalls.
Read ArticleA complete developer guide to installing Ollama, pulling and managing open-source LLMs, running in-terminal chat sessions, and integrating local models with Node.js and Python backends — all without sending a single token to the cloud.
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