System design, AWS, microservices, AI/ML deployment — practical posts for engineers who want to build production-grade systems.
The Kubernetes concepts that actually matter for a Spring Boot service — probes, resource limits sized for the JVM, graceful shutdown, rolling updates, and debugging CrashLoopBackOff without guessing.
A practical decision framework for choosing between REST, GraphQL, and gRPC. Covers HTTP semantics, N+1 problems, schema enforcement, streaming, API versioning, rate limiting, and gateway patterns — with real trade-offs senior engineers face.
How to build an LLM agent that actually finishes multi-step tasks — tool-calling mechanics, the ReAct planning loop, state management across steps, and the guardrails that keep it from hallucinating its way into a bad action.
A practical walkthrough of how real-time updates actually reach a client — the TCP handshake underneath every connection, why Layer 4 load balancers exist, and a working WebSocket architecture like the one behind live location tracking.
Master the art of defining service boundaries using domain-driven design principles. Explore event-driven architecture, async patterns, saga implementations, and how to decouple services while maintaining consistency and system observability.
FastAPI has become the go-to Python framework for serving ML models in production. Here's how to build async inference endpoints, stream LLM responses, and deploy them reliably on AWS.
A practical, production-minded guide to building a CI/CD pipeline for Spring Boot microservices, covering tests, quality gates, Docker image tagging, environment promotion, blue-green and canary deployments, rollbacks, and secrets.
A pragmatic analysis of monolith and microservices architectures. Explores deployment models, team scaling, operational complexity, and the actual costs of distributed systems for senior engineers making architectural decisions.
A practical system design walkthrough for scaling a product from a single server to 1 million requests per day, covering load balancing, caching, database bottlenecks, queues, observability, and operational trade-offs.
A complete guide to building retrieval-augmented generation (RAG) systems in Java using LangChain4j. Learn chunking strategies, embedding pipelines, vector store integration, and how to ship RAG to production.