Take-home vs live coding vs GitHub review: which should you use first?
Take-home vs live coding vs GitHub review compared fairly: cost, candidate time, AI exposure and fit. Includes a table and when to pick each method.
Updated · 6 min read
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Short answer: use a GitHub review first when the candidate has public work, because it costs the candidate nothing and tells you where to dig. Use live coding when you need to see someone reason in real time and have an engineer available. Use a take-home when you need a consistent, comparable output from many candidates. Most good processes combine two of the three.
The comparison of take-home vs live coding vs GitHub review below is meant to be fair. Each method measures something different and each has a blind spot.
The three methods in one paragraph each
Take-home project. The candidate gets a task to complete alone, usually in two to four hours, and submits code. You or an engineer review it. Vendors such as CodeSubmit sell this as a service.
Live coding. The candidate solves a problem in real time while an interviewer watches, usually in a shared editor such as CoderPad. It lasts 45 to 90 minutes.
GitHub review. You or a tool reads code the candidate has already written and published: merged pull requests, reviews given, and how long they kept working on a project. There is no task and no candidate time.
Comparison table
| Take-home | Live coding | GitHub review | |
|---|---|---|---|
| What it measures | Ability to produce a working solution alone | Reasoning, communication and problem solving under observation | Real past work, collaboration, ownership |
| Candidate time | 2 to 4+ hours, unpaid unless you pay | 1 to 1.5 hours, scheduled | None, unless they are asked to explain it |
| Your time | Review each submission (30 to 60 min) | An engineer present for the whole session | 5 to 15 minutes per profile |
| Needs an engineer | For reviewing | Yes, in the room | No, but useful for interpreting |
| Consistency across candidates | High, if the same task is set | Medium, depends on the interviewer | Low, every profile differs |
| Exposure to AI assistance | High when done at home | Lower in person, depends on rules | Low for history; AI can still help write any code |
| Fairness concerns | Disadvantages people with caring duties or other jobs | Anxiety hurts some strong candidates | Disadvantages people who work mostly in private |
| Cost | Time, or platform subscription | Engineer time, or platform subscription | Minutes, or a per-report fee |
| Best for | Many candidates, a comparable output | Mid and senior roles, pair-work cultures | A first look, and for finding questions to ask |
Pros and cons of each
Take-home
Pros
- Closest to real work when the task resembles your product
- Candidates work in their own environment, at their own pace
- Easy to compare candidates on the same task
- No engineer needed during the work
Cons
- Costs the candidate hours. Candidates with other offers may drop out
- Completable with AI assistance in minutes, so the submission may show the assistant's skill, not theirs. One tech blog makes the same point
- Review is slow: every submission needs reading
- Tasks leak; the same task floats around the internet
Live coding
Pros
- You see how someone thinks, asks questions and recovers from errors
- Hard to outsource when done in person or with the camera on and screen shared
- Short and bounded for the candidate
- Good signal on communication
Cons
- Performance anxiety. Some excellent engineers freeze under observation
- Puzzles often don't resemble daily work
- Needs an engineer for each session
- Interviewer variation makes comparison uneven
GitHub review
Pros
- Costs the candidate nothing
- Shows work that outsiders accepted
- Reveals sustained effort, collaboration and judgment
- Cheap and fast, and a good source of specific interview questions
Cons
- Private work is invisible. Many strong engineers have an empty profile
- Not every developer publishes. Public work favours people with spare time
- Commits show who typed, not who designed
- Does not give a uniform score across candidates
AI and the three methods
AI assistants write plausible code in all three settings. They affect each differently.
- Take-home: the finished artefact tells you little unless you also discuss it.
- Live coding: AI is harder to use unnoticed when the session is supervised, but rules vary.
- GitHub review: AI can write code in public repositories too, so no tool can honestly claim to detect it. What AI cannot easily fake is a long record of changes that other people reviewed and accepted, and the candidate's ability to explain them.
The fix in all cases is the same: ask the candidate to explain their own work.
Which should you use?
| Your situation | Suggested start | Add next |
|---|---|---|
| Hiring your first engineer, no technical team | GitHub review | A conversation with a borrowed engineer |
| Recruiter sending shortlists to a client | GitHub review | Client's own live or take-home step |
| High-volume junior hiring | Take-home or an automated test | A live conversation about the submission |
| Senior role, strong public profile | GitHub review | Live design discussion, no puzzle |
| Senior role, no public work | Structured conversation about past projects | Short paid exercise |
| Regulated or compliance-heavy role | Documented standard test | Interview with a panel |
A reasonable default sequence: GitHub review (5 minutes) → screening call about their work (30 minutes) → technical conversation or live session with an engineer (45 to 60 minutes) → paid exercise for finalists.
How to combine them
- Use GitHub review to prepare, not to decide. It tells you what to ask, and the later steps confirm it.
- Keep tests short and relevant. If you use a take-home, cap it at two to three hours and offer payment.
- Debrief on the submission. Ask the candidate to walk you through their take-home and change it live. This also counters AI assistance.
- Offer alternatives. A candidate without public work can do a conversation or a short exercise instead.
DevEval covers the GitHub review part. It reads only code the candidate wrote, says what it could not see, and suggests interview questions about it. It does not replace a take-home or a live session, and the methodology page lists its limits. Pricing starts at a free basic report; see pricing.
Being fair to candidates
Tell candidates which methods you use and how long each takes. Don't make any one method mandatory for everyone, since each excludes some people. Make the final decision a human one, with the results as inputs. For the no-test route in detail, see how to screen developers without a coding test.
Frequently asked questions
Which method is most predictive of job performance? This guide doesn't claim one, and we have no source we trust to rank them. Pick the method that best resembles the real job and combine two.
Can I skip all three? For some roles, a structured conversation with a reference check works. But most hiring benefits from at least one piece of direct evidence.
What if a candidate refuses a take-home? Ask why. If it is the time, offer a shorter task or pay for it, or switch to a review of existing work.
Related guides
- What is code review, and why it matters when you screen developers
Code review is when developers read each other's changes before they ship. See why reviews a candidate has given are a strong hiring signal, and how to read them.
- How to hire developers when everyone uses AI
How to hire developers when everyone uses AI: stop trying to detect it, look for evidence AI can't fake, and verify understanding by asking about their own code.
- How to Evaluate a Developer's GitHub Profile (A Guide for Recruiters)
What to look at on a candidate's GitHub, what to ignore, and how to turn it into interview questions, even if you have never written code.