CodeSubmit CodePair

JavaScript CodePair Interviews That Start From Real Code

JavaScript interviews work better when the live session starts from code the candidate already wrote, not a disconnected prompt on a blank editor. CodeSubmit CodePair lets you review the original submission together, walk through async flow and state choices in context, and see how a candidate explains tradeoffs while they keep making progress. That gives hiring teams a clearer read on real JavaScript judgment than another round of toy exercises.

JavaScript CodePair Interviews That Start From Real Code

Turn the Submission Into the Conversation

Review the original take-home together

The best JavaScript live interviews start from a feature, bug fix, or service change the candidate already touched. Use the original repository as the agenda so you can talk through async data flow, state boundaries, module decisions, and testing choices without making the session feel synthetic. If you want the live round to build on prior work, pair it with the JavaScript coding assessment so the conversation begins from a real submission instead of a blank slate.

Use live time for follow-up changes

CodePair is where you pressure-test judgment, not memorize trivia. Ask the candidate to simplify a state flow, extend a test, move business logic out of a component, or handle a loading edge case they did not see in the take-home. For frontend-heavy roles, tie the discussion back to the React interview guide. For backend-leaning JavaScript work, connect it to the Node interview guide and keep the prompt anchored in real tradeoffs.

Keep the session collaborative

CodePair gives both sides a shared environment, built-in communication, and room to talk while the code changes in real time. Hiring teams get a calmer, more reviewable signal on how someone works with feedback, and candidates get an interview that feels closer to the job. Explore the full CodePair product, review the wider take-home workflow, or schedule a demo to see how the loop works end to end.

Turn the Submission Into the Conversation

I like how the library challenges are structured around on-the-job skills. The experience for candidates is excellent. They work locally with the IDE and tools they are most comfortable with.

Kevin Sahin
Kevin Sahin
Co-Founder @ ScrapingBee
Kevin Sahin

Real-time collaboration with AI-powered assistance.

CodePair™ Live Coding

CodePair gives candidates real-world tools and challenges so they can show what they actually know. Collaboration feels near-native, with an AI assistant and a full development environment built in.
Real-time multi-cursor collaboration with blazing-fast performance: Reduced lag and better synchronization, optimized for near-native coding experiences with no delays—just smooth, responsive interactions.
AI agent built in: ChatGPT-like AI agent that gives candidates a natural working environment while revealing their prompting skills and problem-solving approach.
AI Readiness Score: Review prompt quality, critical evaluation, task ownership, and iterative troubleshooting with reviewer-visible evidence instead of guesswork.
Full application builds & instant previews: Build and run entire applications from React frontends to Node.js APIs with real-time previews and automatic port detection.
Complete browser-based shell access: Full terminal capabilities enabling commands, package management, build tools, and more—exactly like a local environment.
24+ programming languages supported: CodePair supports 24+ programming languages out of the box. From initialization to 'Hello, World!' in under 5 seconds.
Project import capabilities: Import existing codebases or completed take-home challenges—perfect for follow-up technical interviews and code reviews.
CodePair
Pair in a real dev environment
TScomponents/UserDashboard.tsx
interface UserProps {
id: string;
name: string;
email?: string;
}
AI usage analyzedATS
AI readiness score
92 / 100 · Strong context, clear trust boundaries
Prompts
23
Accepted
73%
Reviewed
17
Interview notes + AI insights pushed to ATS
AI Governance
AI Readiness Score
Assess how candidates work with AI, not around it. The score starts with prompt-visible evidence and stays focused on judgment, not volume.
Sample
79
/ 100
Critical Evaluation
8.2
Requests validation, tradeoffs, tests, and sanity checks instead of blind acceptance.
Prompt Quality
8.0
Specific asks with relevant repo context, constraints, and acceptance criteria.
Task Ownership
7.8
Keeps the engineer in charge by using AI for bounded steps rather than whole-task delegation.
Iterative Troubleshooting
7.6
Follows up, narrows scope, and builds on previous output when the first answer is not enough.
Measured in AI-enabled CodePair sessions
Review whether the candidate scoped requests well, challenged output, and kept the work moving in bounded steps.
Architecture ownership
Debugging independence
Integration quality