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Select a Focus Area
Pick a specific interview format, from system design to behavioral.
LoopPass Interview AI Prep
Practice FAANG style interviews with real time AI coaching.
Stop memorizing answers. Start training your interview muscle with dynamic, high pressure mock sessions.
- Voice and text modes for realistic knock out interviews
- Actionable round by round coaching on your specific answers
Practice Tracks
Choose the interview format you want to improve.
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System Design
Master large scale architecture, tradeoffs, and API design.
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Coding & Algorithms
Sharpen problem solving and code clarity under pressure.
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Behavioral
Craft compelling STAR stories that highlight your true impact.
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Low Level Design
Nail object oriented principles and clean component boundaries.
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Core Tech Depth
Demonstrate deep expertise in your language and runtime.
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Career Strategy
Navigate offers, levels, and career roadmaps. (Coming soon)
How It Works
Short loop, fast feedback, steady improvement.
02
Simulate Pressure
Use voice or text to answer questions in a realistic, timed environment.
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Instant AI Feedback
Get actionable, round by round coaching to refine your delivery immediately.
Why Candidates Use LoopPass
Structured practice you can actually stick with.
- Voice first interface for realistic verbal practice
- Hyper specific feedback you can apply to the very next round
- 15 minute daily practice loops to build lasting consistency
- Curated question banks modeled after top tier tech companies
Screens
Inside the LoopPass practice experience.
Live Demo
Imagine you have an interview this week.
Wouldn't it be great to practice under real pressure? Watch this short demo to see the actual pace of the interview flow and coaching experience.
Latest Updates
Interview strategies & tech insights.
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Why persistent connections like WebSockets change the rules for load balancing and how to pick the right strategy for your scale.
How to Actually use Python's heapq for Kth Largest Problems
Learn how to efficiently find the Kth largest element using Python's heapq module with the Min heap of size K strategy.