Built for take-homes, not for invite-metered timed tests

They lead on timed screens, question libraries, and webcam/device proctoring — and on huge shared plagiarism databases. DevParser is built for real take-homes: hidden tests + Claude review, paste-blocking editor telemetry, optional public-git process evidence, and same-company peer similarity. We are intentionally not a proctoring clone. Their libraries are also their weak spot: a shared bank of thousands of questions is a bank that leaks, which is why they run leak-monitoring services against it — DevParser generates the task per candidate, so there is nothing to leak. On published entry plans we are about a sixth of HackerRank’s cost per screen.

Every plan includes the paste-blocking editor and server-scored session telemetry. Candidates can also submit a public git repo so commit history becomes process evidence for take-homes. Growth, Professional, and Enterprise add peer similarity, deep web/repository search, and an AI-pattern review pass that is capped when uncorroborated — style alone never decides. We never auto-reject; reviewers confirm or override and the disposition is logged.

We run the code against your hidden test cases, then Claude analyses the outcome. You get objective results (pass/fail, runtime errors) plus scoring on quality, efficiency and best practices. That beats reading the code alone.

DevParser pairs hidden-test execution with AI-assisted review. You get hard test outcomes plus a summary of correctness, quality, efficiency and risks. Your team can always review and adjust the score — the AI supports your judgement, it doesn't replace it.

Yes. We integrate with 11 ATS platforms including Greenhouse, Lever, Workable, BambooHR, Ashby, Teamtailor, Recruitee, eRecruiter, Traffit, Recruit CRM, and more. On Growth and Pro plans, agentic screening auto-syncs candidates, generates personalised coding tasks, and queues AI evaluations. You set daily limits; DevParser handles the rest.

Both. A pair session opens a shared editor and gives you one link to send — the candidate joins with no account and no install, you both type in the same document in real time, and the session is saved when it ends. That is on every plan, including the trial. On paid plans the live interview co-pilot builds the question plan beforehand, listens during the call, flags evasive or contradictory answers, and hands you a scorecard when you hang up. The take-home is what we screen with; the live round is where you decide.

Deliberately small — 66 curated templates — because the library is not where the work happens. DevParser generates a fresh task per candidate from the role, stack and seniority you specify, so there is nothing to leak: the exact problem your candidate sees has never been posted, sold, or indexed. Platforms with 2,000+ shared questions have to run leak-monitoring services against Stack Overflow, LeetCode and GitHub for exactly this reason. And you do not have to give up the take-home you already use: paste it in — the README, the brief, whatever you send candidates today — and we pull out the description, requirements, test cases and starter code for you to check before anything is saved. We extract, we never invent: a field the brief did not contain comes back empty and says so.

We execute code in 35+ languages, including Python, TypeScript/JavaScript, Java, C, C++, C#, Go, Rust, Ruby, PHP, Swift, Kotlin, Scala, Elixir and Haskell. Framework code — React, Django, Rails, Spring — can be submitted and goes into the AI review, but hidden tests run against the language runtime rather than a framework test harness. New languages land regularly.

You pick a plan and enter your card to start, but nothing is charged until day 15 — cancel before then and there is no charge to reverse. For those 14 days you get the full plan you chose, with the same limits and features a paying customer gets. After that, plans are month-to-month with no long-term contract.

Candidate data is encrypted in transit and at rest and processed with GDPR-aligned and EU AI Act-ready controls (transparency notice, explanations, human oversight tools). Submissions are used only to run your evaluations — they are not used to train foundation AI models, and you can delete a candidate and their data at any time.