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.

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

A retry can turn a temporary failure into a permanent outage. One request fails. 1,000 clients retry. Now the system has a bigger problem.

AI didn't make software engineering easier. It made bad software much cheaper to produce.

10 things that can break a distributed system 1. Network failures 2. Timeouts 3. Retries 4. Duplicate messages 5. Clock differences 6. Partial failures 7. Stale caches 8. Queue backlogs 9. Schema changes 10. Dependency outages

Your database rarely complains when your schema is bad. It just gets slower as your data grows.

AI-generated code still needs 1. Context 2. Tests 3. Review 4. Security checks 5. Error handling 6. Profiling 7. Observability 8. Documentation 9. Edge-case testing 10. Production validation

Your service receives: 10,000 events/sec Your consumer processes: 8,000 events/sec Everything looks healthy. Until the queue keeps growing. What metric tells you the system is heading toward failure?

10 database questions before going to production 1. What are the indexes? 2. What are the slow queries? 3. What happens at 10× data? 4. Are transactions small? 5. Are constraints enforced? 6. Is pagination efficient? 7. What happens during locks? 8. Are backups tested? 9. Can migrations roll back? 10. What happens when the DB is unavailable?

This code looks fine. Two requests arrive when the token is expired. Both refresh it. What kind of race condition is hiding here?

10 DevOps things developers underestimate: 1. Backups 2. Rollbacks 3. Secrets 4. Logs 5. Alerts 6. DNS 7. Certificates 8. Rate limits 9. Disk space 10. Disaster recovery

10 things that look simple until you scale: 1. Notifications 2. Search 3. Payments 4. File uploads 5. Counters 6. Chat 7. Analytics 8. Leaderboards 9. Feeds 10. Scheduling

Project Manager: "It is just a small change"

10 backend mistakes that cause outages: 1. No timeouts 2. Blind retries 3. Missing indexes 4. N+1 queries 5. No rate limits 6. Unbounded queues 7. No circuit breakers 8. Huge payloads 9. No monitoring 10. No rollback

A senior engineer is not someone who knows every tool. It is someone who knows when not to use one.