Our Mission
Algorithmic trivia, whiteboard performance, weekend take-home projects — none of it maps to what makes someone effective on the job. The signal was always noisy. Most teams just learned to live with it.
AI didn’t create this problem. It made it impossible to ignore. When anyone can produce working code with a prompt, the gap between “can produce code” and “can engineer reliably” is the only gap that matters — and nothing in the traditional hiring toolkit measures it.
Our mission is to create interview formats that match the way engineers actually work.
Our Approach
Identify what separates a critically thinking engineer from a vibe coder. These differentiators are enumerable — we’ve identified seven.
Determine the real-world conditions where these competencies surface: reviewing code you didn't write, navigating confident-but-wrong advice, reasoning about systems you can't fully hold in your head.
Ask: why not just ask the AI and trust the output? Whatever survives that question is what’s actually worth measuring.
Once you've found what's hard, make it harder. Plant flaws the AI defends. Poison the context with plausible-but-wrong guidance. Create conditions where uncritical trust fails visibly and critical thinking succeeds measurably.
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