Recently I interviewed a Senior Engineer.
Strong candidate.
Kafka? โ
Redis? โ
Microservices? โ
Distributed systems? โ
Then I asked:
"Your payment service receives 100,000 payment requests per minute."
A customer clicks Pay.
The request reaches your payment service.
Payment succeeds.
But before your service sends the response, the network connection drops.
The customer sees:
"Payment failed."
They click Pay again.
Now you have two requests for the same order.
Both can reach different servers.
Both can be processed concurrently.
How do you guarantee the customer is charged exactly once?
"Just use a database transaction."
Doesn't solve it.
"Use Redis."
Still doesn't solve the distributed failure scenario.
"Check the order status first."
What happens when two requests check it at exactly the same time?
Your turn:
How would you design this payment API so retries don't create duplicate charges?
There's a specific distributed-systems concept behind this.
And you'll encounter it everywhere payments are involved.๐
BREAKING: These 15 careers will quietly dominate the next 10 years.
Most people wonโt noticeโฆ
until the money, leverage, and opportunities are gone.
Use Claude to learn these early. ๐
Your AI stack is too complicated.
You do not need 20 tools. You need 5 that work.
1. Ollama for local development
2. LangGraph for agent orchestration
3. SQLite-vec for vector search
4. FastAPI for backend services
5. Vercel for frontend deployment
6. LangSmith for tracing and evals
7. GitHub Actions for CI/CD
8. Docker for containerization
9. Prometheus for metrics
10. Grafana for dashboards
11. Redis for caching
12. PostgreSQL for structured data
13. Sentry for error tracking
14. Stripe for payments
15. Resend for transactional email
16. Cloudflare for edge security
17. Terraform for infrastructure
18. Your brain for architecture decisions
Remove everything else. Ship faster. Break less.
Bookmark & Repost.