Watch candidates build real features on your codebase. AI captures every decision, scores every line, and tells you who can actually ship.
Technical interviews no longer predict job performance.
Engineers use AI daily, but interviews ban it
Every engineer ships with AI. Your interview should reflect how they actually work.
Vetting candidates takes valuable time
Engineers want to build products, not spend hours reviewing take-homes for AI-generated code.
Take-homes are unstructured and unscalable
No standardization, no visibility into process, no way to compare candidates fairly.
Work trials are the gold standard, but don’t scale
The best signal is watching someone do the work. We make that possible for every candidate.
“We don't just check if the code works. We measure how they think.”
What We Measure
Verification Depth
Do they look past surface-level correctness? We track if candidates test edge cases, check for scale issues, and validate AI-generated code.
Architectural Reasoning
Do they understand the system as a whole? We measure how candidates reason about code organization, dependencies, and maintainability.
Spec-Driven Approach
Do they plan before coding? We detect if candidates write specs, define requirements, or break down problems before prompting AI.
AI Interrogation Skill
Do they treat AI as an oracle or an intern? We score prompt quality, context-setting, output verification, and iteration on AI suggestions.
Debugging Methodology
Systematic or random? We track how candidates diagnose issues—do they read errors, isolate variables, or just re-prompt AI hoping for a fix?
Quality Gate Awareness
Do they set up linting, type checking, error handling, and testing? Senior engineers establish guardrails before writing code.
Automated
Scoring
Custom criteria scored by AI agents against your rubric. Consistent, unbiased results in minutes, not days.
Cloud
Sandboxes
Live Video
Calls
Every Interaction Captured
Every keystroke, AI prompt, terminal command, and file change is recorded. See not just what candidates build\u2014but how they think.
Watch live.
Replay later.
Drop into any active session with a live video call — observe and talk to candidates in real time. After they submit, replay the full session: every code edit, AI prompt, terminal command, and file change, scored against your rubric.
RUBRIC-BASED EVALUATION
Three Steps to Clarity
01. Connect & Configure
Link your GitHub repo or choose from templates. Define custom evaluation criteria. Invite candidates with a single link.
repo: 'your-org/your-repo',
rubric: customCriteria,
timeLimit: 120
};
02. Candidates Build
A browser-based IDE with AI assistant, terminal, and live preview. No local setup required. They build, debug, and refactor real code.
// Recording every interaction...
// AI assistant available...
03. Review & Score
AI scores every submission against your criteria. See the full code diff, recording timeline, and behavioral signals at a glance.
score: 87,
diff: baselineVsSubmission,
timeline: recordingEvents
};
Your Current Process
- close
Phone screen, take-home, on-site interviews, team debrief
- close
3–4 weeks, 20+ hours of engineering time
- close
No visibility into how candidates actually work with AI
With Litmus
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Candidates build on your actual codebase with AI
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Every keystroke and AI interaction captured
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AI scores against your custom criteria — results in under 2 hours
Get in before
launch.
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