Take-home challenges
Let candidates shine in projects shaped like the role
Give candidates a real repo and the room to work in their own tools. Your team gets better signal, faster review, and a cleaner path into live follow-up.
4.9
4.9
5
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
Co-Founder @ ScrapingBee
Candidate experience
Familiar tools in a repo shaped like the role
Candidates work in their own editor, terminal, and Git workflow. Your team gets a reviewable submission that looks like engineering work, not a browser recording or a toy prompt.
Own environment
Candidates can clone the repo, run the app locally, and work with the tools they already know.
Reviewable diffs
You review commits and code changes your team can actually discuss, not snapshots detached from the workflow.
Calmer signal
Removing the browser toy makes it easier to see how someone will work on day one, not just how they perform in an artificial interface.
Take-home challenge
Frontend Engineer Challenge
Terminal
$ git clone git.codesubmit.io/acme/frontend-challenge
$ cd frontend-challenge && npm install
$ git push origin main
→ Solution submitted
AI review ready
12 files reviewed · 4 follow-up prompts
Fully automated handoffATS
Warden security check
Passed
AI review
12 files · 4 prompts
Routed to Senior Engineer pipeline
Submission -> Review -> Follow-Up
Turn submissions into better interviews
The strongest loop combines a realistic assignment, faster AI-supported review, and a live follow-up in the same project. Reviewers skip the boilerplate, and candidates get a fair shot to explain real decisions.
Real repo assignments
Choose from hundreds of take-home assignments that feel closer to real feature work than a throwaway browser prompt.
Git-based Workflow
Candidates clone the assignment, work locally with their own tools, and push back a reviewable diff your team can actually discuss.
Faster review with AI
Use AI to summarize structure, testing gaps, and likely follow-up questions without handing it the final hiring decision.
60+ Languages & Frameworks
From JavaScript to Rust, comprehensive support for multiple programming languages and frameworks.
Test-driven Development
Screen candidates early with our TDD approach where candidates write code against predefined tests.
Challenge remixing with AI
Paste a job post, describe the role, or start from an existing assignment and let AI draft a version your team can tune before you send it.
AI-readiness follow-up
Continue shortlisted submissions into AI-enabled CodePair and score how candidates prompt, validate, and steer AI in the same repo they just completed.
AI follow-up
Pick up where the take-home left off, live in a full dev setup
Use AI-supported review to walk in with the right context, then use CodePair to see how candidates explain decisions, validate AI output, and move the work forward in the same project.
Same submission, deeper signal
Start from the submitted work instead of resetting the interview around a new prompt.
Visible AI usage
See when candidates reach for AI, what they ask, and how they validate the result.
Human review still leads
AI can speed reviewer preparation, but the hiring call stays grounded in the candidate’s actual work and follow-up decisions.
AI Governance
AI review summary
AI highlights repo structure, likely gaps, and follow-up topics so reviewers can start deeper before opening the same submission in CodePair.
Measured, not banned
Prompt-visible reviewer context
Code evidence layers in when available
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.
Sample reviewer output
Strong AI judgment
Score
79
/ 100
Start with context, then use the live follow-up to see how the candidate explains, adapts, and validates the work.
Measured in CodePair follow-up sessions created from take-home submissions
Starts with prompt evidence: prompt quality, critical evaluation, task ownership, and iterative troubleshooting.
Adds code evidence when available
Architecture ownership
Debugging independence
Integration quality
When repo, diff, terminal, or test-run evidence exists, reviewers can extend the prompt-only score with stronger signals about how AI output was integrated into the actual work.