When hiring, top firms like McKinsey value a candidate's ability to articulate a deliberate, logical problem-solving process as much as their past successes. Having a structured method shows you can reliably tackle novel challenges, whereas simply pointing to past wins might suggest luck or context-specific success.

Candidates complete an exhaustive "friction logging" exercise, documenting pain points and user experience issues within a product. This practical test is a primary tool for evaluating a candidate's product sense and problem-identification skills, valued almost as much as the interview itself.

In an era where AI can assist with coding challenges, 10X's solution is to make their take-home assignments exceptionally difficult. This approach immediately filters out 50% of candidates who don't even respond, allowing for a much faster and more focused interview process for the elite few who pass.

With LLMs making remote coding tests unreliable, the new standard is face-to-face interviews focused on practical problems. Instead of abstract algorithms, candidates are asked to fix failing tests or debug code, assessing their real-world problem-solving skills which are much harder to fake.

A common hiring mistake is prioritizing a conversational 'vibe check' over assessing actual skills. A much better approach is to give candidates a project that simulates the job's core responsibilities, providing a direct and clean signal of their capabilities.

Instead of asking hypothetical questions, present senior candidates with a real, complex problem your business is currently facing. The worst case is free consulting; the best case is finding someone who can implement the solution they devise.

The most promising junior candidates are those who demonstrate self-learning by creating things they weren't asked to do, like a weekend app project. This signal of intrinsic motivation is more valuable than perfectly completed assignments.

For high-level leadership roles, skip hypothetical case studies. Instead, present candidates with your company's actual, current problems. The worst-case scenario is free, high-quality consulting. The best case is finding someone who can not only devise a solution but also implement it, making the interview process far more valuable.

Since AI assistants make it easy for candidates to complete take-home coding exercises, simply evaluating the final product is no longer an effective screening method. The new best practice is to require candidates to build with AI and then explain their thought process, revealing their true engineering and problem-solving skills.