Building Production-Grade Microservices with Python and Flask

A practical guide to designing, implementing, and scaling microservices architecture using Python and Flask — based on real experience building 20+ production services in a production SaaS platform.

Getting Started with RAG: Retrieval-Augmented Generation Explained

RAG is one of the most practical patterns for building AI applications with LLMs. This post explains how it works, when to use it, and how to build a basic RAG pipeline from scratch.

Redis Caching Strategies That Actually Work in Production Flask APIs

Caching is one of the highest-leverage performance optimizations you can make. This post covers the five Redis patterns I use in production Flask services — and the mistakes that make caching hurt instead of help.

Docker Best Practices for Python Microservices in Production

Running Python microservices in Docker is easy. Running them well in production is not. Here are the 8 practices I wish I had followed from day one in a production environment.

LLM Engineering in Production: What Nobody Tells You Until It Breaks

Building LLM-powered features is the exciting part. Keeping them reliable, cost-efficient, and observable in production is the hard part. Here are the lessons I learned building AI features at scale.

MySQL Optimization Techniques Every Python Backend Engineer Should Know

A slow query can kill an otherwise well-designed service. This post covers the MySQL optimization patterns I use most in production Python backends — from indexing strategies to query rewriting.

Prompt Engineering for Developers: Practical Patterns That Work in Production

Prompt engineering is not magic — it is structured communication with a probabilistic system. This guide covers the patterns that consistently improve LLM output quality in production applications.

Deploying Flask Microservices to AWS ECS: A Production Setup Guide

AWS ECS is a powerful platform for running containerized Python services, but the setup involves a lot of moving parts. This guide walks through a complete production deployment setup.

Building AI Agents with Tool Use: From Zero to a Working Research Agent

AI agents that can call tools, browse the web, and execute code are no longer research demos — they are production features. This post walks through building a real tool-use agent from scratch.

Celery and Redis for Background Tasks: Patterns for Reliable Async Workflows

Background task queues are essential for any serious backend. This post covers how to build reliable, observable, and maintainable async workflows with Celery and Redis.

Designing REST APIs That Scale: Principles from 20+ Production Services

A well-designed REST API is easy to use, hard to misuse, and holds up under production load. These are the design principles I converged on after building and operating 20+ microservice APIs.

20 Production Microservices Later: What I Learned Building the CertifyMe Backend

A candid retrospective on building and scaling a production SaaS backend — the decisions that paid off, the ones that cost us, and what I would do differently starting from scratch today.

Real-time Emotion Detection with OpenCV: How DJoz Works

DJoz is an AI-powered music and video recommendation system that reads your facial emotions in real time. Here is a deep dive into the computer vision pipeline behind it.

Vibe Coding: How AI Agents Are Changing the Way We Build Software

Vibe coding is the hottest trend in software development — write in plain English, let AI write the code. But is it a revolution or a shortcut? A developer's honest take on building production features with Claude, Cursor, and GitHub Copilot.

Model Context Protocol (MCP): The Missing Piece for AI Agent Integration

Anthropic's Model Context Protocol is quietly becoming the standard interface for connecting AI agents to real-world tools and data sources. Here's what it is, why it matters, and how to build your first MCP server in Python.