40 hours of YouTube and you still freeze when the interviewer asks you to design something. Aurora teaches through dialogue, then you practice — functional requirements, non-functional requirements, and capacity estimation across six progressive sets — before the canvas ever opens.
Aurora is Levelop's AI system design mentor built for FAANG interview preparation. Instead of giving you a blank canvas and a problem statement, Aurora starts with a voice-narrated knowledge session that walks you through each concept with checkpoint quizzes. Then you practice — working through functional requirements, non-functional requirements, and capacity estimation in a dedicated workspace. Each problem is spread across six progressive sets that deepen your understanding from different angles. Only after you've built a complete mental model does the interactive canvas unlock, where you design real architectures with load balancers, databases, caches, and message queues. This approach builds the ability to reason about system design from first principles — exactly what interviewers at Google, Meta, and Amazon are looking for.
Each system design problem starts with an Aurora knowledge session. Aurora walks you through the core concepts via live voice narration — not a pre-recorded video, but an adaptive dialogue with checkpoint quizzes that verify your understanding at every stage.
After the knowledge session, you practice in the system design workspace. Each problem is broken into three phases — functional requirements, non-functional requirements, and capacity estimation — so you build a complete mental model, not just surface familiarity.
Each problem is spread across six progressive sets. You revisit the same system from different angles — each set pushing you deeper into the trade-offs, failure modes, and scaling decisions that interviewers actually ask about.
Once you've completed the practice phases, the interactive canvas opens. Build real system architectures — load balancers, caches, databases, message queues — on a node-based editor. The same problems that appear at Google, Meta, and Amazon.
The same problems that appear in FAANG interviews. Each starts with an Aurora knowledge session, progresses through requirements and capacity practice across six sets, then opens the canvas.
Hash functions, distributed ID generation, read-heavy optimization.
Fan-out strategies, caching layers, ranking algorithms.
WebSocket connections, message queues, delivery guarantees.
Consistent hashing, eviction policies, cache invalidation.
CDN architecture, adaptive bitrate, content delivery.
Token bucket, sliding window, distributed rate limiting.
And 24 more patterns covering search engines, payment systems, notification services, and beyond.
Aurora AI is included from day one. No credit card required.