System Design
Senior engineers are judged on judgment. Learn the primitives — estimation, databases, replication, caching, queues — and how they compose into AI-powered systems that stay fast, reliable, and affordable under load.
Start the first lesson →Scaling Fundamentals
Latency vs. throughput, vertical vs. horizontal scaling, and the numbers every engineer should know by heart.
Not started2Back-of-the-Envelope Estimation
Turn 'a lot of users' into QPS, storage, and bandwidth in your head. The math that sizes a design in two minutes.
Not started3Databases, Indexing & Transactions
SQL vs. NoSQL for real, why an index is a B-tree, and what ACID actually guarantees when things go wrong.
Not started4Replication, Sharding & Consistency
Copy data for reads, split it for writes, and confront the CAP trade-off. Where consistency models come from.
Not started5Caching & the CDN
The cheapest request is the one you never make. Cache layers, invalidation, and why it is genuinely hard.
Not started6Queues & Async Workflows
Decouple producers from consumers to absorb spikes and survive failures. Backpressure, retries, and idempotency.
Not started7Designing a Distributed Payments System
A full consistency-critical design: idempotent APIs, payment state machines, double-entry ledgers, processor failures, webhooks, and reconciliation.
Not started8APIs, Load Balancing & Rate Limiting
The front door: REST vs. gRPC, how a load balancer spreads traffic, and rate limiters that protect you from spikes.
Not started9Observability & Reliability
Metrics, logs, and traces; SLOs and error budgets; and the failure patterns (timeouts, retries, circuit breakers) that keep systems up.
Not started10Designing AI-Powered Systems
Put it together: a production RAG/inference architecture with cost, latency, and failure modes made explicit.
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