Thoughts on Cloud Computing, DevOps, and Software Engineering.
Sharing lessons learned from building scalable systems.
Most MCP tutorials stop at one client, one server. Real agents need many. This guide builds a Python client that connects to two MCP servers in parallel, prefixes their tools to avoid collisions, and hands them to Claude as Anthropic-format tool definitions.
Use Anthropic's official Claude Agent SDK to build a Python agent with custom tools, in-process MCP servers, PreToolUse hooks, and streaming output. Real code, no hand-waving.
Run PostgreSQL in Docker with production-ready config. Covers initialization, persistent storage, automated backups with pg_dump, connection pooling, and performance tuning.
Step-by-step guide to installing the NGINX Ingress Controller on Kubernetes, configuring path-based routing, and setting up automatic TLS certificates with cert-manager.
A complete GitHub Actions workflow that builds a Docker image, caches layers between runs, and pushes to Docker Hub or GHCR. With copy-paste YAML and a comparison of cache strategies.
A hands-on guide to building a production-like local dev stack with Docker Compose. Covers service setup, volumes, health checks, environment variables, and hot reload with real working code.
Model Context Protocol is becoming the standard way to give LLMs access to tools and data. This guide walks you through building a real MCP server in Python, connecting it to Claude Code, and shipping it in under an hour.
A practical setup for a private, offline code reviewer. Pull a coding model in Ollama, wire it to VS Code through Continue, and review diffs without your code ever leaving the machine.
Build a TypeScript MCP server with the official SDK, expose it over streamable HTTP, add OAuth 2.1, test it with the MCP Inspector, and publish it to the official MCP Registry. Production-grade setup, no hand-waving.
A working RAG pipeline in 200 lines of Python: Postgres with pgvector, Voyage embeddings, Claude for generation, and an evaluation loop. Real commands, real costs, no hand-waving.
Run open-source LLMs on your own GPU box and get an OpenAI-compatible API in one command. Covers install, PagedAttention, OpenAI server, performance tuning, and production deployment.
A hands-on guide to Ollama: install, pull a model, run it from the CLI, call it from Python and Node, build a custom Modelfile, and know when to use it instead of vLLM or a hosted API.
Six practical techniques to shrink Docker images by 90%, cut deployment time, and reduce your attack surface. With copy-paste examples for Go, Node.js, and Python.
Best practices for building reliable deployment pipelines for engineering teams — from concept to production-grade pipeline.
Migration journey from monolith to microservices using Kubernetes and AWS. Lessons learned during the transition process.
Practical tutorial on using k6.io for load testing. Includes example scripts and result interpretation for production environments.
Complete guide to setting up monitoring for production infrastructure. From installation to creating meaningful dashboards.