How Topalupu Works: AI Mock Interviews for FAANG — A Complete Case Study

What Is Topalupu?

The Problem Topalupu Solves

Traditional Prep Is Broken

What LeetCode TeachesWhat Interviews Actually Test Solve the problem correctlyCommunicate your thinking process Submit and check test casesHandle follow-up questions in real time Work at your own pacePerform under time pressure Read editorial solutionsExplain trade-offs and alternatives Practice aloneCollaborate with an interviewer

Mock Interviews Are Expensive and Hard to Schedule

How Topalupu Works

Step 1: Choose Your Target Company and Role

• Google: Heavy focus on algorithms, system design at scale, and the "Core Competencies" behavioral round
• Amazon: Leadership Principles drive every behavioral question; coding focuses on practical, service-oriented problems
• Meta: Speed-focused coding (2 problems per round), product sense for senior roles, billion-user scale system design
• Apple: Deep technical interviews with team-matching; secretive process with emphasis on craftsmanship
• Microsoft: Growth mindset culture; "As Appropriate" final round with hiring manager
• NVIDIA: Systems-level depth; GPU architecture, parallel computing, CUDA-specific questions

Step 2: Start an Interview Session

• The AI asks you to clarify the problem
• You explain your approach — the AI responds with follow-up questions
• You write your solution in Python, JavaScript, Java, or C++
• Your code runs on real Judge0 infrastructure — actual execution, not simulated
• The AI evaluates your solution for correctness, efficiency, edge cases, and code quality

• Requirements gathering — the AI plays the role of product manager
• High-level architecture — you propose components, the AI asks why
• Deep dive — database schema, caching strategy, failure modes
• Scale and trade-offs — the AI pushes you on bottlenecks and alternatives
• Evaluation — scored on completeness, scalability, trade-off awareness, and communication

• Google's "Core Competencies" (leadership, collaboration, impact)
• Amazon's Leadership Principles (Customer Obsession, Ownership, Bias for Action)
• Meta's "Move Fast" culture (speed, boldness, impact)

Step 3: Get Detailed Feedback

DimensionWhat It Measures CorrectnessDoes your solution work? Does it handle edge cases? EfficiencyIs your solution optimal in time and space complexity? CommunicationDid you explain your thinking clearly? Problem-SolvingHow did you approach the problem? Did you consider alternatives? Code QualityIs your code clean, readable, and well-structured?

The Technology Behind Topalupu

AI Interviewer: Google Gemini

• Context-aware follow-up questions based on your specific code and explanations
• Adaptive difficulty — the AI adjusts its probing based on your skill level
• Real-time evaluation — instant scoring with specific, actionable feedback
• Company-specific personas — the AI adapts its interview style to match each company's culture

Code Execution: Judge0

• Actual runtime in milliseconds
• Memory consumption
• Test case pass/fail results
• Edge case coverage

Question Bank: Curated and Company-Specific

Content TypeCountSource Coding problems73 unique problemsReal interview reports, company-verified patterns System design problems107 scenariosScaled infrastructure challenges from FAANG Behavioral questions50+ per companyCompany-specific values and culture fit

Full-Stack Architecture

• Frontend: Next.js 15 with React 19, Tailwind CSS
• Backend: Next.js API routes with server actions
• Database: PostgreSQL via Supabase with Prisma ORM
• Authentication: NextAuth.js with Google OAuth
• Payments: Stripe for subscription management
• AI: Google Gemini API for interview simulation
• Code Execution: Judge0 for real-time code running
• Hosting: Vercel for edge-optimised deployment

Pricing Model

TierPriceWhat You Get Free$0 forever3 full interview sessions, Microsoft & Apple roles, real code execution, session reports Pro$29/monthUnlimited sessions, Google, Amazon, Meta & NVIDIA, full behavioral rounds, priority support Premium$49/monthEverything in Pro + Full Loop Simulation mode, custom study schedule, advanced analytics

Case Study: From LeetCode Grinding to Interview Confidence

The Problem

• Panicked when the interviewer asked clarifying questions she hadn't prepared for
• Coded silently for 10 minutes before the interviewer interrupted to ask about her approach
• Finished with a working solution but couldn't explain the time complexity when asked
• Received a rejection email 3 days later

The Root Cause

• Thinking aloud while coding
• Responding to follow-up questions in real time
• Explaining trade-offs between different approaches
• Testing her code systematically while narrating

The Solution

• Communicate her approach before writing code (the AI won't let you start coding without explaining)
• Handle interruptions — the AI asks follow-up questions mid-solution
• Explain complexity — the AI specifically probes time/space analysis
• Test systematically — the AI asks "what edge cases might break this?"

The Result

Who Is Topalupu For?

Perfect Fit

• Engineers preparing for FAANG interviews who want realistic practice
• Career switchers who need to learn the interview format, not just the algorithms
• International candidates who want to practice English-language technical communication
• Anyone who's failed a big tech interview and wants to understand why

Not a Replacement For

• Learning data structures from scratch — Topalupu assumes basic CS knowledge
• Getting your first referral — we help you pass the interview, not get one
• Compensation negotiation — though our blog covers this extensively

What Makes Topalupu Different

Start Practising