Thoughts on Cloud Computing, DevOps, and Software Engineering.
Sharing lessons learned from building scalable systems.
Step-by-step guide to building your first Model Context Protocol server with the official Python SDK: the three primitives, in-memory tests, and connecting it to Claude Desktop, Claude Code, Cursor, or VS Code.
Install n8n with Docker, connect a chat model, give the agent tools, and run automations without writing orchestration code. A 20-minute build: a GitHub stats agent with memory, plus when n8n beats Zapier, LangChain code, or Dify.
Three tools that replace grep -R, cat, and history scrolling for good. Real commands, real outputs, and the gotchas the READMEs warn about but most tutorials skip.
Install Caddy, point it at a domain, and TLS certificates appear on their own. Hands-on steps for static hosting, reverse proxying, load balancing with health checks, and Basic Auth, all from one small Caddyfile.
One model response can carry several tool calls. The parallel tool calling pattern for the OpenAI and Anthropic APIs, with copy-paste Python loops, plus when sequential execution is the right call.
A hands-on tutorial using the current MCP Python SDK (v2). You will build a weather server, test it with an in-memory client, connect over stdio, and wire it into a real host like Claude Desktop.
Chat demos do not catch the day your agent starts refunding orders it never looked up. Here is a pytest eval harness you can build in 30 minutes: golden dataset, deterministic tool-call checks, an LLM-as-judge for open-ended replies, and a CI gate.
Three frameworks, one task: an order-support agent that looks up orders, issues refunds, and escalates to a human. Same spec built three ways, with real code and a decision guide for when each one wins.
Prompting for JSON is not the same as getting JSON. A copy-paste guide to OpenAI and Anthropic structured outputs with Pydantic: raw JSON Schema, refusal handling, schema limits, and a full invoice extractor.
Hands-on semantic search with Chroma: real queries, metadata filters, full-text search, persistence, and a retrieval pipeline for RAG. Every snippet run for real, with the exact output printed.
MCP lets Claude Desktop, Cursor, and other AI hosts use your tools without custom glue. Build a real weather server with the v2 Python SDK, test it in memory, over stdio, and over HTTP, then wire it into a host. Every snippet verified against mcp 2.0.0.
Build a support bot that routes itself: a triage agent with handoffs, an input guardrail that blocks prompt injection before the main model runs, and escalations that carry structured metadata. Full working example on the OpenAI Agents SDK.
An agent that misbehaves is a black box until you trace it. Self-host Langfuse with Docker Compose, instrument a tool-calling agent with the Python SDK, and see every LLM call, tool execution, token count, and cost in one trace tree.
A hands-on tutorial on building multi-agent systems with LangGraph. Covers the supervisor pattern, tool isolation, structured output routing, and the Functional API. With copy-paste Python code.
Step-by-step tutorial to build a custom MCP server in Python with SQLite-backed task management tools. Test it with the MCP Inspector, then connect it to Claude Desktop or VS Code.
Give each AI agent a specific role, hand them real tools, and let them collaborate. Build a research-and-writing crew that researches a topic and writes a report — in under 30 minutes, all in Python.
Give your LLM real tools. Build an MCP server in Python that exposes custom functions and resources — connect it to Claude Desktop in under 15 minutes.
Stop paying full price for the same system prompt on every API call. A hands-on guide to prompt caching with Anthropic (automatic + explicit) and OpenAI — with copy-paste Python code and real cost comparisons.
A hands-on tutorial for building a stateful AI agent with LangGraph. We build a research assistant that searches the web, runs calculations, and routes between tools based on context — with copy-paste code and zero fluff.
A hands-on guide to building a Model Context Protocol server that lets Claude read your files, browse directories, and fetch URLs. Includes copy-paste Python code, MCP Inspector testing, and Claude Desktop integration.
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.