Real environments. Real tasks. Real signal.
Algorithm puzzles test a narrow, artificial skill. Coding Simulations put candidates in the actual tools and environments the job requires, and evaluate whether they can do the work.
Coding ability you can't fake in a real environment.
Whiteboard-style questions don't predict real coding performance.
Algorithm questions measure a specific kind of problem-solving ability that may have little to do with the actual job, and a developer who can recite time complexity but struggles to implement a REST endpoint isn't ready for the role.
Portfolio reviews are slow, inconsistent, and easy to game.
Portfolio reviews put the burden on hiring teams to manually evaluate work that may be polished for presentation rather than representative of actual skill. They're time-consuming, hard to standardize, and easy to misrepresent across candidates.
Most tests cover developers but ignore QA and DevOps.
Engineering hiring spans more than frontend and backend development, and QA engineers who can write test automation scripts or DevOps engineers who can debug configuration issues are equally hard to evaluate without a realistic environment, leaving those roles without a reliable signal.
Test candidates in the job, not on a puzzle.
Run complete projects in a browser-based development environment.
WebIDE Simulations let candidates build complete frontend, backend, or full-stack projects in any programming stack, directly in the browser with no setup required. Frontend tasks involve implementing CSS, building Angular functionality, or debugging a React application. Backend tasks cover building a FIFO cache, optimizing query execution plans, or implementing REST endpoints. Each task maps to what the job requires, and auto-grading scores the output against defined criteria.
Simulate a full Linux environment, configured to your stack.
Virtual Desktop provides a complete simulated Linux machine configurable to the exact tools, frameworks, and conditions of the role, so data science candidates work with Spark and Hadoop on real data tasks, DevOps candidates debug configuration issues and write deployment scripts in Docker or Jenkins, and QA candidates write and debug automation scripts in Selenium or Pytest. The environment is the evaluation, and there's no way to perform well without being able to do the work.
Cover the full engineering org, not just developers.
Coding Simulations cover the full technical hiring footprint: data science, frontend, backend, quality assurance, and DevOps. Each domain includes tasks specific to the tools and workflows of that function. Environment realism and auto-grading apply consistently across all of them. AI proctoring runs throughout every session, so results come with a verified integrity record alongside the technical score.
Frequently asked questions.
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