Interview Decoder AI

Your Invisible Interview Assistant.

Don't Let a Tough Question Cost You Your Dream Job

On which Platform we Work?

Here's an up-to-date list of how well we work with each platform.

In addition to this list, Interview Decoder AI works with any web-based interview platform.

Before you sign up for a subscription, make sure you pass our basic checks.

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Our Features

We are fully customisable and packed with feature app. Have a look below.

Customise AI Models

Tailor AI models to suit your specific interviews and questions.

Useful & Intuitive Shortcuts

Navigate faster with thoughtfully designed keyboard shortcuts.

Screen Sharing Invisibility

Your tools stay hidden during screen shares for full privacy.

Active Tab – Always There

Keep your assistant accessible no matter where you are.

Fast & Accurate Responses

Experience lightning-fast answers with high precision.

Multi-Language Support

Select coding language of your choice

How to Use

We are fully customisable and packed with feature app. Have a look below.

Get Started

Login to Interview Decoder AI

Create an account and login to Interview Decoder AI. Get instant access to our AI-powered interview solution generator.

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Keyboard shortcuts
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Q
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Capture the Problem

Start taking screenshots

Use ⌘ + H to capture the problem. Up to 2 screenshots will be saved and shown on the application.

Background screenshot
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Screenshot:1
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Delete Screenshot:
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AI Solve:
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Solve

Get your solutions

Once you've captured your screenshots, press ⌘ + ↵ to generate solutions. We'll analyze the problem and provide a solution with detailed explanations.

Problem Statement

Generating solution...

Debug and Optimize

Debug your solutions

If the solutions are incorrect or you need an optimization, take extra screenshots of your code with ⌘ + H. Press ⌘ + ↵ again and we'll debug and optimize your code, with before and after comparisons.

What I Changed (Read these aloud)

The current solution uses nested loops, resulting in O(n²) time complexity.
We can optimize this by using a hash map to store previously seen numbers.
This reduces time complexity to O(n) with O(n) space trade-off.

Solution

def twoSum(nums: List[int], target: int) -> List[int]:
    seen = {}  # Value -> Index mapping
    for i, num in enumerate(nums):
        complement = target - num
        if complement in seen:
            return [seen[complement], i]
        seen[num] = i
    return []  # No solution found

Complexity

Time Complexity: O(n)
Space Complexity: O(n)

Common Questions

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