CodeSubmit CodePair

CodePair Live Coding Interviews in Ruby

Looking for the best way to conduct a Ruby pair programming interview? Invite your candidates to a realistic Ruby pair-programming session with CodeSubmit. CodePair makes it easy to set up a powerful shared coding environment and work through Ruby coding problems with your candidates. Uncover your candidates' real programming competencies, and identify the best Ruby developers for your team!

CodePair Live Coding Interviews in Ruby

Conduct Great Ruby Pair Programming Interviews

Evaluate Ruby coding skills

CodeSubmit makes it easy to create, conduct, and evaluate Ruby pair programming interviews. Save time with templates, keep your notes in one place, and quickly and accurately identify qualified candidates. CodePair is ultra-flexible to accommodate even the most creative and complex pair-programming challenges! Test for Ruby coding skills using real-world tasks, and hire your next dev with confidence.

Related: The Best Ruby Developer Interview Questions to ask your next Ruby candidate!

Provide a great candidate experience

CodePair makes it easy to set up a robust shared coding environment and work through coding problems with your candidates.

We built our pair programming environment with the candidate in mind while also providing a superior evaluation experience for hiring managers. CodePair features include Dolby™ Video & Audio calls, beautiful custom branding, custom files & databases, and tons of powerful add-ons.

How it works

It's easy to get started with CodeSubmit! Simply create an account, set up your first CodePair template, and start inviting candidates. Our integrations make it easy to keep track of candidates and hire the best one for your open role!

Conduct Great Ruby Pair Programming Interviews

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