An API Gateway is not just a reverse proxy. It’s the control plane in front of your services. If you’re building microservices and don’t have one, your architecture will eventually get messy. Here’s what it actually does 👇 What is an API Gateway? It’s a single entry point that sits between clients and your backend services. Instead of: Client → Service A Client → Service B Client → Service C You have: Client → API Gateway → Internal Services The gateway handles cross-cutting concerns so your services don’t have to. What does it do? An API Gateway typically handles authentication, authorization, rate limiting, request routing, response aggregation, logging, monitoring, and sometimes caching. It can: - Validate JWT tokens - Enforce rate limits - Route /users to User Service - Route /payments to Payment Service - Combine multiple service responses into one Your microservices stay focused on business logic. When do you need it? You likely need an API Gateway when: - You have multiple microservices - You want centralized authentication - You need rate limiting at the edge - You want to hide internal service structure - You are exposing public APIs If you have a simple monolith, you probably don’t need one yet. Common real-world examples: Netflix, Amazon, and most SaaS platforms use API gateways to manage traffic at scale. Popular solutions include: - NGINX - Kong - AWS API Gateway - Envoy Without an API Gateway: Every service reimplements auth, logging, and rate limiting. With an API Gateway: Infrastructure concerns are centralized and standardized. It’s not just traffic routing. It’s architecture discipline. Building microservices? Repost. Follow. Bookmark this.

What is your preferred way to handle API rate limiting? Redis, API gateway, application middleware, or something else?

Which backend task do you think AI still struggles with the most?

Which is more important for a junior developer? Writing clean code or solving problems quickly?

With AI writing more code than ever, what is one technical skill that has actually become more valuable?

10 hidden costs of AI-generated code 1. Reviewing larger diffs 2. Debugging unfamiliar logic 3. Maintaining duplicate abstractions 4. Fixing subtle edge cases 5. Discovering security flaws 6. Understanding generated SQL 7. Updating outdated assumptions 8. Managing unnecessary dependencies 9. Testing unexpected interactions 10. Owning the code in production

Your database has a unique constraint. Two requests try to insert the same record at the same time. Which request wins? And more importantly: What happens to the other one?

Your application runs on 20 replicas. Each replica has: pool: { min: 5, max: 50 } Your PostgreSQL server allows 500 connections. What is the maximum number of connections your application could request? And what happens when every replica reaches its limit?

Why do some websites become slower when you open DevTools? Is the website detecting DevTools… or is something else happening?

10 questions every developer should ask about caching 1. What exactly are we caching? 2. How long is it valid? 3. How do we invalidate it? 4. What happens on a cache miss? 5. What happens when Redis goes down? 6. Can stale data cause problems? 7. Can one key become too popular? 8. Can concurrent misses overload the database? 9. Are we caching errors? 10. Do we actually need a cache?

What is the first thing you would remove from a backend if you had to cut infrastructure costs by 50%? No new optimization. Something has to go. What would you remove?

AI agents do not need your password if they can operate inside your authenticated browser session. Is the browser becoming the new security perimeter?

A human clicks Buy. An AI agent clicks Buy. The server receives the same HTTP request. Can the server actually tell the difference?

A bug that happens once is annoying. A bug that happens once every 10,000 requests is dangerous. Intermittent bugs are still bugs.

If an AI agent can write the code, deploy the code, monitor the code and fix production issues… What is the developer’s most important job left?

AI agents are getting better at browser automation. If an AI can perform every task a human can perform in a browser, what should CAPTCHA actually verify?

What’s the most underrated part of backend engineering? Caching? Observability? Database design? Error handling? Idempotency?

AI coding agents are getting scary good. But what is the one task you still would not trust AI to do without supervision?

If you had to build a backend from scratch today: Would you use an AI agent to design the architecture first, or start coding and let the agent evolve it?

Sometimes the architecture problem is wanting more architecture.

10 backend lessons that only production teaches 1. Everything fails 2. Traffic is unpredictable 3. Retries can hurt 4. Caches become stale 5. Queries get slower 6. Logs become expensive 7. Dependencies go down 8. Queues pile up 9. Data gets duplicated 10. “Works locally” means nothing