The Signal Recovery Protocol

We filter for high-motivation, company-specific intent before we touch code. The engine looks for how precisely the candidate understands your domain, why they want this role, and whether they've done real homework on your stack and constraints.

We test whether candidates are awake to the ecosystem they're building in—from React 19 shifts to security incidents like Log4j to AI's impact on their tools. High-signal architects continuously integrate new constraints into their mental model of the stack.

We bypass "Green Squares." Instead, GitHub Trajectory maps language growth, commit quality, and architectural stewardship over time. This stage can be toggled on for candidates with public work, giving you a longitudinal view of how they actually evolved.

Resumes are now AI-hallucinated. We cross-reference every claim—from "Kubernetes Expert" to "Low-Latency Guru"—against real-time performance in our probes. We flag the Integrity Gap immediately.

Before any coding, we test whether candidates interrogate the spec. High-signal architects clarify ambiguous milestones, dependencies, and failure modes instead of sprinting into code that solves the wrong problem.

We inject "Logical Drift"—a fundamental rule change in a standard problem that breaks LLM training data. AI-cheaters follow the training; Engineers follow the logic.

We present code with a hidden dependency mapped to dummy logs. A junior cleans the code and breaks the system. A Senior stops and asks: "What owns these orphaned keys?"

Modern 10x Engineers leverage AI as a force multiplier. We measure the "Token-to-Productivity Ratio." Can they guide an LLM to a complex fix in 2 prompts, or 20?

We analyze non-linear coding telemetry. Real developers delete, pause, and refactor. LLMs dump perfect blocks of code. Our Signal Confidence Score guarantees a human brain is in control.

We generate a high-speed filmstrip of the candidate’s entire thought process—every edit, pause, and branch—compressed into a 2x replay for your final decision review. Your team sees the real problem-solving arc, not just the end state.

// Initial problem: sort by custom rule
function sort(arr) {
  return arr.sort((a, b) => a - b);
}