What is InterviewSkool?

InterviewSkool is an AI-powered mock interview platform that simulates real FAANG-style coding and system design interviews. An AI interviewer named Alex walks you through every stage: problem clarification, approach design, complexity analysis, coding, and hidden test suite evaluation for coding; requirements, architecture, and deep dives on a whiteboard for system design, and issues a hiring signal at the end.

Does InterviewSkool support system design interviews?

Yes. Pick a problem (or go random) and a level: SDE-2, SDE-3, or Staff Engineer. You design on an interactive whiteboard while talking through requirements, architecture, and deep dives with Alex, and get a 0–100 score with per-parameter feedback and level readiness.

How does the AI interviewer work?

Alex is an AI interviewer. It asks probing clarifying questions, validates your approach, challenges your complexity analysis, observes your code against hidden test cases in real time, and adapts its follow-up questions based on your answers, just like a real FAANG engineer would.

Which companies does InterviewSkool prepare you for?

InterviewSkool currently supports Google, Meta, Amazon, Apple, Microsoft, and many more companies. Problems are sourced from real interviews at those companies and calibrated to the difficulty bar for SDE-1, SDE-2, and SDE-3+ levels.

What programming languages can I use?

You can code in JavaScript, Python, Java, C++, or TypeScript. Pick the language you are most comfortable with.

How is InterviewSkool different from LeetCode?

LeetCode is a problem bank, you solve problems alone and get a pass/fail. InterviewSkool simulates the actual interview conversation: Alex asks questions, challenges your reasoning, and evaluates how you communicate, not just whether your code passes. You get a hiring signal and 12-dimension feedback, not just test results.

What is a hiring signal?

A hiring signal is the recommendation a real interviewer would make after your session: Strong Yes, Yes, Lean Yes, or No. InterviewSkool computes yours from 12 dimensions including code correctness, communication, DSA knowledge, and optimization mindset.

How much does InterviewSkool cost?

InterviewSkool offers paid plans. Sign in with Google to see current pricing and get started.

Is it Interview School or InterviewSkool?

InterviewSkool (one word, no space) is a distinct AI-powered mock interview platform at interviewskool.com. People sometimes search for it as 'Interview Skool' (two words), that's the same platform. InterviewSkool is NOT Interview School (interviews.school), which is a different career platform. InterviewSkool focuses on coding execution and technical system design with real-time voice AI.

See the full Push Notification Service case study for detailed architecture, API design, and scale estimates.

Push Notification Service — System Design Case Study | InterviewSkool

# Push Notification Service \

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0) Problem Restatement\

Design a scalable push notification service that can send notifications to millions of users across multiple platforms (iOS, Android, Web) with high throughput, low latency, and guaranteed delivery. Core challenges include handling massive fan-out (one message to millions of devices), respecting rate limits from platform providers (APNs, FCM), and ensuring reliability despite failures.\

1) Requirements\

1.1 Functional\

Send push notifications to users on iOS, Android, and Web.\
Support multiple notification types: transactional (order updates), promotional (offers), social (friend requests).\
Target individual users, user segments, or broadcast to all users.\
Schedule notifications for future delivery.\
Track delivery status (sent, delivered, clicked, failed).\
Support rich notifications (images, actions, deep links).\
Allow users to manage notification preferences.\
Provide analytics (delivery rate, click-through rate).\

1.2 Non-Functional\

High Throughput: Send millions of notifications per second.\
Low Latency: Deliver critical notifications within seconds.\
Reliability: Guaranteed delivery with retries and fallbacks.\
Scalability: Handle 1 billion+ registered devices.\
Rate Limiting: Respect provider limits (APNs, FCM).\
Availability: 99.9% uptime.\
Deduplication: Prevent duplicate notifications.\
Prioritization: Critical alerts delivered before promotional content.\

1.3 Scale Estimates\

Registered devices: 1 billion devices (500M iOS, 400M Android, 100M Web).\
Daily notifications: 10 billion notifications/day.\
Peak throughput: 200K notifications/second (major events, flash sales).\
Provider rate limits:\

- APNs: ~5000 connections, ~500 notifications/sec per connection.\

- FCM: No published limit, but rate limiting exists.\

Avg notification size: 1 KB (including payload).\
Storage: 10B notifications/day × 1 KB × 30 days retention = 300 TB/month.\

1.4 API Design\

The core APIs required for the service:\

Send Notification: POST /notify - Send to specific user(s).\
Broadcast Notification: POST /broadcast - Send to all users.\
Notify Segment: POST /notify-segment - Send to specific segment.\

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2) High-Level Architecture\

2.1 Overview\

Client Apps → API Gateway → Notification Service → Fan-Out Service → Provider Workers (APNs, FCM) → Platform Providers → User Devices.\
Key components: Fan-out for segment/broadcast, rate limiter per provider, retry queue, analytics pipeline.\

2.2 Architecture Diagram\

```mermaid\

---\

config:\

layout: elk\

---\

flowchart TB\

%% Sender initiates notification\

Sender[\

Push Notification Service

0) Problem Restatement\

1) Requirements\

1.1 Functional\

1.2 Non-Functional\

1.3 Scale Estimates\

1.4 API Design\

2) High-Level Architecture\

2.1 Overview\

2.2 Architecture Diagram\

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