Thinking about hiring, out loud.
Ideas on technical assessment, AI in hiring, and building better evaluation systems.
Do coding assessments predict job performance?
Google measured its own interviews and found zero relationship with job performance. The algorithm interview has never been isolated and validated in the peer-reviewed record — not once. Here is what the research hierarchy actually shows, why AI finished off whatever was left, and what work-sample assessment replaces it with.
Why algorithm interviews fail modern engineering teams
Google's own research found essentially zero correlation between algorithm interview scores and on-the-job performance. Work-sample tests outperform every other hiring method. So why is the industry still clinging to LeetCode? We dig into the data, the hidden costs, and the alternative that actually predicts engineering success.
AI in technical assessments: ban it, ignore it, or evaluate it?
89% of engineers use AI tools daily. Assessment scores have inflated 34% while job performance stayed flat. Banning AI is unenforceable. Ignoring it is negligent. We make the case for the harder third option: designing assessments where AI usage itself becomes a signal of engineering judgment.
What makes a coding challenge 'real-world'?
Every platform claims their assessments are realistic. Almost none deliver. We introduce a five-dimension fidelity framework for evaluating assessment quality—environment, codebase, task, tools, and evaluation—and show why fidelity matters more than difficulty.